I can see that Ms. Sian-Siden, and I hope I'm saying it close to the right pronunciation. She's already there, so I'm going to give you the general introduction comments on this panel. So first of all, I would like you to welcome, I'd like to welcome you all to the Parallel Section 1 of this 2021 Global Symposium on Soil Biodiversity. It's a great honor to be here with you today. My name is Lucia Angus. I'm from Brazil and I'll be moderating this to our session. So during the first hour, we are going to be listening to four presentations of 10 minutes each. And I really ask the people responsible for the presentation to stick to that limit of time. And so we have some time at the end for question and answer sessions. If by any chance there is a delay by the nine minutes of your intervention, I'm going to let you know that you have only one minute left. It is important, so we have at least 20 minutes or two days to that at the end. And after the first section, we are going to have a second one following the same format, the same procedure. There will be just a question and answer between those two sessions. And before starting, I ask you to check the Zoom chat as some additional rules and information on this session will be posted, mainly overall information address internet connection information about the meeting itself. And please use the chat also to post your questions and include at the beginning of the question, the name of the presenter to whom your questions addressed. We will choose a few questions to be answered live if we have time, and the rest will be answered via chat. So I also ask the participants presenting their speech to pay attention to the shot and try to answer it in the same way. So, without further delay, I'd like now to give the floor. And I still don't see Mr. somewhere. Lucy, if I may, it just brought to me had issues in entering the meeting. I'm trying to liaise with him maybe you can swap with the second presentation while. So please just tell him that we are going to do that. Today, we start the presentation with our second. This is about so pretty to do a diversity at the co post mining area at a different age of reclamation so miss. Taty, you have the floor. Please open your presentation. Thank you. She may need some help with that is about because they don't have time to prepare. Can you share your screen please. Yes. Yes, just completely. Thank you. Can I start. Yes. Okay. Thank you. Good morning. Good afternoon. Good evening to all of you. Thank you. I present our work in the whole post mining area. So, I'm from Indonesia. And I come from Institute of Technology Banu. This is a new university. And the study site is in another island. And here is the study site. The study site in East Kalimantan. In this area is already have a post mining and the authority has already made a reclamation or refrigeration. So, we know already this mining activity can interfere the soil structure and function. Open cast mining method will change the soil surface and its environment. So, the reclamation, so this is the process of the mining and then the reclamation process started moving the overburden from the mining activity. And we set the overburden following the contour and spread the soil, the top soil, about 30 centimetre. And then this area is ready for refrigeration. This is the worker starting by planting the first growing tree. So, this reclamation area you can see this is the mining area. And here is the reclamation area. You can see different performance of the forest or of the tree because they have different age of the reclamation. So, we can find the area that already refrigerated, already planting and then we can follow for different age of reclamation. So, the reclamation process in this area carried out in several stages. So that in the field, there are land with different ages of reclamation from one year to 16 years old. And they are planting with Acacia maniung, Parasyantes parcateria, salmon and salmon, and also the lefart tree. There is elder pylon, sorya, and geobalanus. But in this study, I will concentrate to the first growing tree species. And the determination of study site based on refrigeration and the age of reclamation. So, the objective of this study is to assess the diversity of say protozoa in early period of reclamation at post for mining area. So, we have seven area we compare between the area. So, and we hope that the age of reclamation or the component in the area increase and the soil by the component will increase including the protozoan community. So, the methods in this study for example were collected from six different age of reclamation, sorry, seven. At each site, so the sample was collected from three different area with the different tree. And identification of soil protozoa was conducted following the references until more for spacious level. Some of the feature how to collect the sample, how to treat, and then we make, we bring to the laboratory and make identification. For Amuba and Pesti Amuba, we followed the reference from Smirnov and Brown. And then from flood of floodgillets, we followed the reference from Adair. And also we use other references for identification like Lewis and Banford. Near the result of our study, we found four groups of the protozoa, that is Amuba, Pesti, Amuba, floodgillets, and sliata. And for each group, we have different phylum, we have for Amuba, we have three different phylum that's circundinal with four different, with different number of species we found. And for Pesti Amuba, we found seven species for Amubizoa. And then for Ceresozoa, we found five species. For floodgillets, we found an ill-blended protozoa, we found seven species. For Cerecozoa, we found two more for species because we cannot arrive to the spacious level. And then also for Coanozoa, we found only Sipinacea species. And for Cereata, this is the most, we found 13 species for Cereopora. So, for all of these, we found 14 species, 14 three more for these species. Here are some examples of the protozoa that we found from the area, from the post-mining area. This is the Aquinochris, Petrochris, Panela, so some of it, of the protozoa. And when we see the occurrence of protozoa at different area of trees and age of reclamation, here the blue is for the tree of, we planted by Parasyantes falcataria, and then the red one is replanted with Makakasia mahmio. So we can see the, like the succession of protozoa community in different age of the reclamation. So this is the number of species we can find in each age. And we found that the number of species of protozoa still increase in this area. And when we see for each group, it seems that the flagellates is the highest in all of the different tree area, and also in different age of reclamation. We can see that the flagellates is the green one. We can see and then it is very interesting because of flagellates in Acacia mahmio. This is the reclamation at five years old. And also we found in Samania's other tree, in Samania's salmon. That's the reclamation, the highest at the age of five years old after plantation. So this is very interesting because we can see the colonization of the protozoa mostly tend to increase. However, we can see that the flagellates is the lowest compared to other group of the protozoa. And Amuba and Teste Amuba, it's between the flagellates and the serials. So in this research, so we concluded that after six years of reclamation, we found all these species of protozoa. And then in the first growing tree, the number of protozoa, especially flagellates, tend to increase by the increasing of age reclamation. And Paracera and Tespacataria facilitated better than Acacia mahmio in Samania's salmon on the developing of protozoa at mining area. I think that's all my presentation here. Thank you very much for your timing and presentation. And if I think Mr. Samuel is still not connected, so I'm going to ask the next presenter to go ahead and share with us the paper. So it's Miss Simona de Gregorio. And the paper is unexpected microbial functions in agricultural soil. The contamination from PCB by SMS spent mushrooms substrate. So please, Miss Simona, you have the floor. Thank you. Thank you very much. Can you see my presentation? Yes, it's perfect. Just go ahead. You have the floor. Okay. Thank you. Good evening to everyone. And I'm to the University of Pisa, really. And my story. I should remove the southern of this way. No, sorry. Okay. So the subject of my talk is the contamination by PCB in agricultural soil where some spots in Italy, and it is important to say that actually the contamination by PCB in agricultural soil is quite low it's funds from two to 10 ppm but the problem is very urgent and very important because of the extension of the contamination and also, of course, the matter that is contaminated and it is made mainly do it is mainly by the activity of some industrial sites and also dismisses industrial sites but the contamination is very persistent so it's the heads for the case. And for these reasons. So in the last case, most of the attention is miss focus of the metabolic activity of fungi, and especially white raw fungi, but because they are capable to produce in the battery of enzymes that are very specific to words barrier constant to either the finalic and non-finalic moieties of lignin as perhaps all of you knows, and so they produce polyphenol oxides at high red exponential that can be exploited also for the transformation of PCBs in an oxidative environment, instead of the transmission in an aerobic environment. So they can be exploited for the treatment of soils. The subject of our work was also the recovery of the waste is that the rise from the production of the edible mushroom that lot of social status and as you know, perhaps as you know, for when close up mushrooms on the market, we have five kilos of spent mushroom substrate that is a waste is and has to be disposed of. Actually, these waste is an way of resources because it's source of these kinds of enzymes the polyphenol oxides is produced by fungi. And you can see in this picture that if you look at which kind of enzymes that can produce either manganese peroxidases and like cases but also versatile and lignin peroxidases in the lowest levels, but very, very important. And this can be exploited by a very high red exponential and that can be exploited also for transformation of PCBs. Moreover, the spent mushroom substrates are also a source of inoculum in the mattresses that is amended with the substrates because they have a high load either like fungal load up to 10 to the seven calling for new units for gram of dry weights or material load 10 to the eight gram kind of community for gram of dry weights of spent mushroom substrates. So what we did we did an experimentation at pilot scale on this kind of pilots that is called the one over. We can monitor and control many parameters and it is used to simulate processes like long farming or biopilot but especially long farming so the Norman management of agricultural soils and we were dealing with a side that was contaminated by 9.3 PM of PCBs. These are limits that you have to spare to reach in you, we nearly at least for the introduction of the soils in the public area or in industrial area. And so we amended the soil to percentage of spent mushroom substrates. One is was 0.1% and one was 1%. The results have been a very clear we have no depletion of PCAs after 90 days of incubation in absence of the spent mushroom substrates and here you can see the depletion of PCBs. That is quite interesting in if we amend 0.1% of SMS it's even better if we amend 1% because the kinetic is quicker. It's more favorable. And we have a 90 days more or less the depletion of all the contamination at the same time it serves an unification of the organic matter because we have a significant increment in the content of you may come for because it's in the soil so we have a net real improvement of the quality of the soil that has been treated with the amendment of the spent mushroom substrates and in relation to the contamination of the older containers of PCBs that were recovered in these soils. We had with 1% of amendment to the spent mushroom substrates actually quite complete depletion of all the isomers. But we, well, what we've done at that time was a lot of metal barcoding on the fungal specimen, and on the material specimen. So metal barcoding on the ITS for fungi and metal barcoding of 16S4 bacteria. And what we found with a certain level of surprise that there was any significant change in the fungal ecology in the process of PCB depletion you can see from these three indexes of biodiversity that are not changing either in the control or in the two treatment control F1 or F7 so 0.1% or 1% of amendment of the SMS. We have a significant changes in the material culture during the process of depletion here you can see that in the pre different condition we have a sort of specification of the material community, a loss of biodiversity and a recover to, well, to higher value at successive levels that is more accelerated in a way by the amendment of 1% of SMS because here we have a specification that takes over in 30 days instead of 60 and then we have recovered to values of biodiversity of the, of the initial time of incubation. There was a correlation between the changes in the bacterial ecology and the process of the PCB depletion. And actually this correlation was confirmed by this kind of analysis where we can see that the amended soils with 0.1% or 1% of SMS were positively correlating with the humidification, the increase in concentration of humicant food, the acids, and the depletion of PCB use them in the soil. So the bacteria seems to participate to the process of PCB depletion in presence of fungi that have been located by the amendment of the SMS. As you can see, well, to be a lot of try to study it to to have a look to what's what's going on with the bacterial functions in these processes. We did a lot of predictive methods on a functional profiling to quantify the contribution different material taxa to abundances of functional features that might be of interest in the processes. And we found that the bacteria that are characterizing this mattresses has a functional features that can be involved, of course, in the degradation of PCBs because they show activity like the allogeneity, the transformation of allogeneity compound comprising dioxins. In conjunction with other capabilities that goes on to the semi biotic biodegradation metabolism module of the of the bacterial community. And also that the dysfunction functional features are related to different bacterial facts. So during the process you will see the, the times of incubation related to the initial time of the experimentation to the final time where we have observed PCB depletion. And here the times of analysis that are related to the initiation of the process of PCB depletion. And you can see that every orange or yellow or even red square little square is indicating an increment in the in the counts for the functional features of that particular bacterial genus. And you can see that the material general that are involved in the three different phases are very diverse. So what we search income in parallel to the decreasing biodiversity we observe so so a functional segregation of bacterial taxa that are participating to the PCB depletion that is observed only in presence of 0.1% of 1% of 1% spends mushroom substrates while in the control we do not have you do not assist to the speciation of the functional. Moyers that are in broadly involved in PCB depletion. And what is quite interesting that that the, the philomaxenobacterium that is participating in the in the process of in the transitional times of analysis in the process of PCB depletion are harboring dye the colorizing for oxidases. So these are polyphenol oxidases, bacterial polyphenol, polyphenol oxidases that are properties that are quite similar to the one of fungi that we mentioned before. So quite high rate of potential that is capable to transform either the phenolic or non phenolic gaming models compounds. Okay, so what happened when we look at PCB depletion mediated by spent mushroom subsets we have a speciation of the bacterial community and the loss of some bacterial philom that of course in all the test in all the experimental condition that we tested that only where we observed the PCB depletion we observed also the increasing or the increase in the functional features related to the actinobacteria that harbour the IP. So the polyphenol oxidases, and only these, if these increment is occurring, we have the blooming of bacteria that are putatively associated with the traditional PCB that correlates positively with the division of PCB. But at the same time the enzyme is not correlated to the depletion of the, the contamination does these enzymes might seem to be associated more to the saprophytic metabolism of the phenobacteria than to the depletion, the direct depletion or intervention of the depletion. And this is the production of these kind of enzymes by fungi or bacteria are related to that capability to participate to the carbon transformation in the soil that might be intended as carbon stabilization but also has an activity that smooth release carbon for the other bacteria in the environment. And in the case of an historical contamination by recalcitrant might be involved also in the mobilization of the slaughter and creating the condition for the blooming of the specific species that actually are might be directed to the depletion of the contamination. So quickly to the conclusion, we thought that the management of this kind of source, and this kind of contamination really diffuse contamination at a low level might encourage the exploitation of the spent mushroom species, but as resources. That's when we look at the functional features that might be involved in the process of the contamination. Usually, we look for specialists, the specimen so microbial species that are involved in the mineralization of the contamination, but that that might be a little bit, we're taking our perspective perspective we should have a look to what is defined as a general species that is involved actually in the carbon cycle, and perhaps also in the mobilization of the contamination when it is actually not available and also for the reason really recalls the transfer to biodegradation. Thanks for your attention. Thank you. I'm going to proceed with the next presentation, and so I invite Miss Maria Fernandez Bravo to present her paper, Diverse in Abundance of Entomo Patrogenic Fungi Metahesium SPP at High Sampling Resolution and TIPEC Swiss Permanent Grassland. This is Maria, you have the floor. Thank you. Thank you. Can you see my presentation? Yes, perfect. Thank you. So I'm Maria Fernandez, I'm actually involved in the molecular ecology group in AgroScope in Switzerland. And the main topic is focused on the entomopathogenic fungi and arthropods soil communities. And but today I will focus on only on the entomopathogenic fungi. So, so entomopathogenic fungi are natural regulators of arthropod communities and also it has been discovered that they can also be antagonists of some plant pathogens. Some of them have been associated with some plants, has endophytes or working on the risk sphere, and it has been discovered or described more than 700 species, which are a great alternative to reduce the pesticide use. And in fact, we can find now many products on the market, developed with some of these entomopathogenic fungi species. And one of the most interesting groups is the genus metharysium, which harbour around 30 species identified, distributed worldwide. And in Switzerland is one of the most abundant entomopathogenic fungi and their main reservoir is the soil. But in previous studies we were analyzing the metharysium abundance and diversity in different land use types across Switzerland. We took three composite soil samples from 30 sites belonging to three land use types, arrabalans, grasslands and forests. These sites belongs to the National Soil Monitoring Network. And then we analyze the diversity and abundance. And we discovered that there are huge differences between the site of the three land use types, but not within the land use types. And so in grassland it was the most abundant and diverse. We didn't detect strong differences between the sites. And then we were wondering if we increase the sampling resolution, we could detect more differences. And then it's because the objective of this work was investigate abundance, diversity and population structure of metharysium species within increased sampling resolution in grassland soils. And also to assess the spatial and temporal distribution of metharysium species and compare the accuracy of sampling density. For that we select three permanent grassland plots in a radio within a radius of 400 meters. Within each plot 10 by 10 meters, we divided in 25 subplots and then we took a soil core in the centre of each subplot. We repeated this sampling four times in a year in autumn and spring in early summer and late summer to increase the spatial and temporal resolution of the sampling. And then in the lab, each sample soil was sheathed and homogenized and suspended in the still water and plated in a selected medium. And then with the abundance per cream of soils we count the colony forming units, and then some colonies were selected and isolated for Montesbury cultures. The DNA was extracted and then we determined the diversity and population structure using 15 micro satellite markers. And so we sequenced the elongation factor for region to identify the species. And we use this information to analyze the population structure of metharysium communities. And then as a result we observe that the abundance of metharysium was highest in the blood number one compared to the two and three in all the sampling times but particularly the sampling time for was the most significantly abandoned in all the plots. And if we go a little bit more in detail how the fungal was distributed across the plots and the sampling times, we observe a high spatial and temporal variability between the plots, particularly between the plot number one and the two and three and also a high distribution in sampling time for which suggests that the sampling time for the late summer could be the best moment for the sampling in Swiss grasslands. And then we were wondering also if which could be the minimum number of samples to have a good representation of the abundance of metharysium in each plot and sampling time. And for that we calculate in this graph we can see for each plot and sampling time we calculate the differences between the plot sampling time mean abundance and the mean abundance is we take only one sample or two samples or three samples and so on. And then what we all said was for example for the plot number one and the sampling time one, we have this color lines to have like an arbitrary accuracy to reach the sampling. For for example, if we want to obtain 500 colony forming units per gram sum is the line green for the plot number one and the sampling one, we have to take around 14 samples, but in the case is the plot number two and the sampling one, we only need six samples, which means that the sampling density accuracy differ among the plots and also across the time. And it could also influence the outcome of the analysis. And then finally, let's take a look at the diversity and population structure from the 607, 670 meter ism isolates we detected 22 genotypes belonging to three meter ism species, a meter ism robertsi meter ism Brune and meter ism and we observed differences between the diversity of the three plots and also the four sampling times where one more time we observed the highest diversity in the sampling time for which refers the idea that the sampling time for is the best moment to sample in Swiss crosslands. And finally, here in this unconstructed ordination, we represent is the population each dot represent the population of each sample for each plot and sampling time. And then with the permanent analysis we observed that the plot significantly explain more variation than sampling time among the meter ism populations. And then we can finally summarize that all grassland plots harbor a different materials in abundance within plots among plots and across time. A high spatial and temporal sampling resolution show with a high abundance diversity and population structure, variability among grassland plots. So, how we were discussing before the sampling time for seems this the best moment for show get the highest meta recent abundance and diversity in all plots. And also the sampling density can influence the outcome analysis so concluding we can also say that in order to increase the biocontrapotation efficient protocols are needed to monitor metareasian populations in the field. The summary is the best sampling time to claim the biggest differences in abundance and diversity in Swiss crowds and sites sites. And it is recommended to design a specific sampling densities for each proposed you to the variability of service among the sites, but the methodology describe it here cool helps to easily calculate those something densities adapted to each experiment. So, I would like to thanks to my research group molecular ecology group all my colleagues my supervisors and also the financial support for learning to make reactions. And also thanks to you for your attention. Thank you very much. I mean, all the presentations also the beautiful images those that they are lovely. Thank you. Well, as I think we still did not hear from Mr somewhere. So I'm going to start with the question and answer presentation. And I'm going to ask my dear please to stop sharing. Thank you much. I'm ready. So happens in my group and then you don't so. Okay, so without further delay we are going to have the presentation from Dr somewhere. Thank you from the ITPS please tomorrow you have the floor. Thank you. We can see the presentation just says yes. Go ahead. Thank you. You can see the present. Hi, everyone. Sorry, I cannot connect in with our system. Thank you for your attention. And presentation is comparative evaluation of bacterial biodiversity. This is the first phase of vegetative cover of exotic species of Australia. And that is free for us from Chile. Patlons eroded soil of Chile. General work is a microbiological analyze of the evolution by diversity and quantification of bacteria strain. We developed in the resource very of eroded soil, exotic coverage, like eucalyptus and nothing for us. We want to evaluate the effect of comprehensive soil restoration treatment. So the application of soil and water conservation. And also the microbiological properties of the biodiversity of bacteria in resource fairs of vegetative cover of exotic and native species in eroded soils. This project is located located in the semi-arid zone of the inland coastal dry land of the central Pacific zone of Chile in the Mipo River basin. Our territory are eroded soils. In this case, we have a galley adoption. Like methodology, we will take samples. We make a microbiological analyze laboratory analyze. We have analysis with software premium. To identify the similarity or the diversity of. For example, this is our survey. We have. We have an area with. Not it for us. And. With infiltration teachers. And also we have the forested area with eucalyptus command lenses. With infiltration teachers.  And you can see here the red eucalyptus from Victoria, like Australia. Eucalyptus command lenses. The results is the evolution of pilot area in the last three decades has been a process characteristic by recovery in terms of vegetative mass index and better soil condition. We are using soil loss at ocean in the forested areas and the soil conservation tree. The eucalyptus command lensy a tree from Australia of Lake Albuquia. And key eye. Is an enemy tree of Chile had evolved for 1000 of years. Adapting to various sound climatic assault. Condition and genetic memory. Which has led to parallel evolution of the species of bacteria and soil. Another element for bacteria adaptation has been the road exudate. Released by key eye, which allow to establish that the rose exudate is different from the eucalyptus space. Because they belong to a different ecological and soil origin. According to the non metric. Multidimensional is calling the bacterial communities of the resource fair of Eucalyptus key eye. And bar soils are significantly different. That's mean that's depending bacterias. Propulsion. Depending off the vegetation. Soil does the sustainable management or soil after ecosystem approach. Have demonstrated high efficacy. To recover the greatest soil and melody the soil of the biodiversity. In this context. They contribute to the recovery and recognition of another soil. The integration of potential recovery process. And the generation of pedogeneity support and provision service. In the interaction and synergy with soil biodiversity and soil conservation practice at the level of micro waters. We, we begin, we began to obtain a soil samples approach. We had low level of organic matter. They have a, they have this weekly acid, acid range, low electrical conductivity, low detergent, low forceful. And, and high level of potassium. That's your exchange capacity also is very low. In the evolution of the vegetation. In the watershed pilot areas in the last two decades. Have been a process characterized by recovery and term of the kitty view mass and soil condition. With reducing soil erosion. And the soil conserving conservation treatment. Also, be a mass show a substantial increase in. In the watershed. According the non metric. A scaling of bacteria's community exhibit high similarity when they grow in the same environment. In this case. The communities of the reasons for the of Eucalyptus GI and bar eroded soil are significantly different depending of vegetation. You have he you have here not the soil you have here Eucalyptus when you have here. Or bacteria's group of bacteria's from is dependent of the cover vegetation. We have also a sample is to similar bacteria communities. The nice samples have high biodiversity diversity, which is the solid state in the vegetation of the tree then the ground. This is very interesting. To establish the non similarity in this case between the look like sequence. Of section is a acid. At the beginning of the project. We have a. A very high galley and ocean. We try with modern control dams in granitic soil. To control the ocean. At the second year, we make successive border control dams with current stacks treatment to cover the galley erosion area. In the third year, we integrate treatment with acacia street grass spaces. And not it for us constake. And wouldn't control them on is all. The area. Cover be with vegetation. After 15 year, we have controlled the galley erosion. No, we have a guy tree and acacia wood also. We, the soil carbonic carbon are increment and also sold by university. The system now is in process of restoration to obtain soil service and ameliorate the soil by university. With that forest of key eye and acacia, like site indicator. We also we are working now. But I'm going to need you to speed up a little bit. Sorry. Yeah, it's the last picture. The last life. Thank you. We are working now integrating soil restoration of the galley erosion. To integrate many kinds of technique. We have also developed a erosion handbook of control erosion with 25 techniques. The conclusion. The my conclusion. We don't have time. The my conclusion is that the sustainable manager of soil. After ecosystem approach have demonstrated high efficacy to recover the graduate soil and to measure the soil by university. In this context, they contribute to restoration and recalibration of another soil. So the induction of potential recovery process and the generation of pedo kinetic support and provision service in the interaction and synergy with soil by the diversity and soil conservation practice at the level of what she on the last conclusion is in is the integration of soil conservation technique and biodiversity with ecosystem approach. Many times. Sorry. With my delay. I just don't want to go too much on the next section. We have a few few minutes for questions for the first presenter. If you look in the chat, they were both already answered. I'll just comment briefly. So if anybody else is interested in the question and answer, they could also read in the chat. The questions would were about the interaction for microphone or protozoa in also how close the protozoan community in the sorry us on the reclamation to the original. So anybody else interested in those answers, you can read the chat because the study already answered it. I ask a question and actually it's also in the chat, but I want to just point for you because I think it's important contamination from PCB is still a major problem in many regions of the world. So I asked Miss Simona how she think that her results could contribute as indicators of strategies for the contamination of this site. And she also mentioned here a long answer in the chat, but I would like to just give you a few minutes if you want to add some comments on your answer. I would like to ask also, Mr. Samuel to stop the sharing of the screen. Well, I think that we diffuse. Can you hear me? Okay, I think that we diffuse contamination. The only strategy that we especially they are related to contaminate to agricultural soils. I mean only sustainability of the intervention can be adopted because of the extension the contamination and also the problem of the low level of contamination so there's no high availability of the contaminants actually so that that's one the main reason why they are not biodegraded actually. So the importance of this piece of work that we realize that actually we are always looking for bacteria or microorganisms or fungi that are capable to mineralize the contamination and it is very far from being reached and it's difficult to release it or difficult to take the to create a condition for while I would say that would be better to have a look to other class of microorganisms that are more related to the carbon cycles. That is the main factor at the base of the resilience but also the ecosystemic values of the soils that might be actually the one that are involved in improving the viability in certain condition of historical contamination. Improve the viability of the contamination and create the condition for the blooming of the specialist that perhaps we are not able to to increase in the activity with our intervention, but it's only the natural you know, in the natural installation of the function of activities of interest in our processes that is mobilizing the carbon sources and then create the condition for a specialist to bloom and to exploit their really well the nature of the metabolic activity. To prevent mushroom substrates only make many other waste is can be waste is in brackets, there are resources that can be exploited for for this is this processes. Thank you much for complimenting that information I have one quick question for Miss Maria Fernandez. If she could at her final conclusion she was talking about protocols for monitoring the fungi matter reason. If she could just quick mention a little bit what would be her suggestion in terms of this protocols. And I'm going to also go ahead and ask Dr. somewhere to read in the chat. There are some questions. One of them is how sensitive are the primers that we use the in a PCR. It's also mentioned the question is because the presence of some inhibitors, for example, have metals. So I'm going to ask you Dr. somehow if you could answer in the chat. So we don't have all the delay but please address this and Maria back to you about the protocols. Thank you. Okay, so I have to answer now or if you could quick mention that. What I suggest in my presentation for the monitoring was, for example to design a specific protocols for the densities. So because how we have seen is very variable so and then it's needed to at the beginning maybe like a pilot project to reach which number of samples could be the most interesting for your project and then start from this point so if somebody says more interesting maybe we can continue discussing in the chat or some. Thank you very much. So just ending then this section that is somewhere already answered one of the questions about the trees. I just want to point to the book to this first question that was way off. Just repeat quick. If you want to mention it online. It's about the primers that were used. If not, you can answer in the chat. And that way we could go to the next section. Somehow it's why I contact the other presenters in the second sector. If you want to answer it quick, you have the floor. We begin to work with species like Acacias. Acacias need very, very low nutrients and very low water also. And later we begin to establish also Graminion and in the serious phase phase we establish rapid forest but in this consideration we make the forest restoration together with soil and water conservation at the beginning is very important the soil and water conservation practices and we have to begin with species with low demand of nutrients and low demand of water. Later we can for example, later we can for example use trees but at first we have to begin with soil and water conservation technique later with species like for example Acacias. Acacias tree has the properties to fix it nutrient has many possibilities to obtain a several kind of productive for the community. In our country it's very important to to apply soil and water conservation technique especially for conservation of the water in the watershed. I will say the combination between vegetation soil and water conservation and the participation of the people in the project with a consistent approach is very important to recovery for the restoration of the granite. Thank you much. Thanks for all the presenters in this section just to give a general explanation we had a slight delay to the beginning between the general session and the beginning of this with result in the delay of this section followed out 15 minutes. I'm sorry if that resulted in some problems for any of the participants trying to reach two different panels. So without further delay I would like them to give the floor Alexander Krank for his presentation in the subject is assessment of agricultural conditions of terrain and soil cover using remote sensing data. So please Mr. Alexander. Hello, am I heard? You have the floor you can share your presentation. Yeah. One second. Do we see it now? Yes, I can see it. Just open entirely the screen, please. Thank you. Thank you. I'm working in the Institute of Geography in Russia and alongside in a company which participates in in the agricultural relation of land. Sorry. In our work we often come to a situation when we need to do original scale assessment of agricultural conditions which are of course associated with soil biodiversity and the general conditions general condition of soil. Most importantly the condition of first 30-40 cm of soil cover. And our goal was to assess the conditions using non-direct methods which mean the usage of remote sensing instruments. And often the task is required to be done over the big time series because we need to estimate the condition of soils not just in present but the dynamics of soil condition of 10-20 or more years. It is important especially for countries which develop it after the end of the Soviet Union because they often have very limited amount of soil data and very enclosed or non-existent data banks on soil conditions. And several government structures and commercial land users are interested in re-evaluation of soil conditions and specifically with the ability to make backtrack of changes over a decade or something. So, our group which were previously led by Professor Puzochenko and now we continue to develop his ideas about remote sensing and we adopt several main strategies in usage of such an instrument. Firstly, it must be understood that remote sensing is a sensor which measures the reflected soil radiation and therefore it can be used to estimate the work of the surface or soil color in our particular case as a dynamical machine. And it is absolutely paramount and in the case of agro-ventscapes it's even most important to use detailed time series of information because the soil color changes rapidly under human activities and to stabilize some conditions you must conduct a large amount of measurements separated in time. The methods we used we can call a hierarchical factor analysis the idea is such that yes, the reflectance of the soil color different greatly over time it is differentiation is based on local weather condition some condition of agro-technology which is used on spot and on time and the current species which grown over the soil color and so on, but if you monitor the landscape over sufficient time series for example 3, 4, 5 years you can derive from the large amount of data stable stable factors using the dimensionality reduction procedure you can derive stable factors which are the part of information which remain stable over time and we use a hierarchical model which derive such factors from each of our time series, for example time series within one year, then we obtain stable conditions in all such factor groups integrate them together and from such an integrated data set we derive another stable factor another stable factors the residual information is considered a dynamic components and the stable factors we call invariance sorry yes then we can classify our stable conditions and dynamic conditions and I will call it a stable course of such classes which reside over the changing conditions throughout the time series such course can be physically interpretable through remote sensing indexes and through relations with a field data such as measurements of basic nutrients or cell chemistry when we going to the interpretation of factor obtained on this slide it is shown as a result of this technique supplied to region of Uzbekistan it's a Jezak region of Uzbekistan we through relation with thematical data like salinity mineralization of soils productivity productivity data we can rely on invariance obtained through previously discussed dimensionality reduction procedures with semantic information and while our environment invariance were previously classified we can estimate which class can be given which agroecological states on the screen you see that we drive main factor for salinity mineralization I'm sorry but I'm going to need you to speed up a little bit thank you and also we calculated the differences over time so as you can see on this image the system the water system of this region is it's function in such manner that it speed up the soil degradation from from north to south do the inclination of the relief the derived classes are well separated in time if we observe them through dynamics of spectral indices as you can see the derived group are well saturated over the course of several years and they can be they can be a good there's a good machine there's a good machine learning coincidence between groups so we can see that the most most of the land are in a relatively stable condition while we can detect a special dynamical elements also my report is not entirely on the matter of biodiversity the shown which also can be interpreted in other areas obviously related such instruments can be coupled with biodiversity field data which can be also interpreted in such manner over a large period of time for example 20-30 years thank you okay one phrase if anyone interested in performing such analysis on their data regarding the biodiversity I'm glad to hear it out thank you thank you again I would like to ask if you could stop sharing your screen I'm going to invite the next presentation the next one is going to be Mrs. Antonietta Latessa if you could start to share the presentation right away okay good afternoon can you see the presentation yes I just ask you to expand completely the screen since we still don't know okay so good afternoon to everyone from Italy I'm Antonietta Latessa and I am basically a proto-zoologist even if I currently use also other soil tax as indicator of soil health such as micro-articles today I would like to provide you an overview of the main outcomes in obtaining the using affiliated products as indicator of soil health in different types of agro-ecosystem with different level of soil disturbances from this point of view I would like to present the results obtained in the framework of three projects conducted in Italy the first project were realized in March region where it was my university and the community structure of soil was investigated just in cropland and arable lands and natural sites such as the forest in the second project the community structure has been investigated in vineyards in the third case of studies that I would like to present to you the community structure of ciliate has been investigated in arable land in the close adhesion to industrial sites you are just at the glance an example of the great morphological diversity of ciliate the protest and one aim of Father Aime of this presentation is just to raise awareness on these often overlooked components of the soil biodiversity which is readily used in the bio monitoring and this in spite of the fact that ciliate the protest are play key roles in the soil microbial loops and just shaping the bacterial biomass in the rhizospera and those they are deeply involved in the recycling of the nutrients and those they are able to promote soil fertility and productivity that is soil health so the question that is addressed by all the three projects that I am going to introduce was to what extent and how do ciliate communities contribute to soil bio-indication. Here a quick look to the mythological approach for the qualitative and quantitative analysis of the community structure from sample collection to sample processing and regarding the processing of the sample they were treated using the no-fluted petition methods that imply simply to wet the soil sample in order to further the ciliate existence followed by the collection of the soil renown at the regular interval of time and fixing the slide preparation the protocol staining and in final the identification genus species level and direct counting on slide. So the aims of the first case studies was to evaluate the capacity of ciliate to discriminate between different land uses forest and agro-system problems I mean in this case but also to discriminate between different farming management practices in particular organic versus conventional Here there is a map of the different of the ten investigated sites and the dark green dot highlight the forest site and disturb the soil and the yellow dot represent the organic farm with minimum tillage and the yellow dot just show the conventional farm with subsiding and chemical with control that means glyphosate. So a sample were collected twice in spring and autumn and the next slide show substantially the results that were obtained in this project we can see the different land uses of the different ciliate protist community we can see a separation between the forest the forest system with respect to the cropland and also we can see how the organic farm are very well separated from the conventional the conventional farm these results were also supported by another analysis indicator species analysis that show that each of the exploded sites are also characterized by different set of indicator species here you can see the topscored ciliate species with respect to the three investigated sites moving from cropland to vineyards the aims of the second project was to assess the long-term effect of organic floor management on soil health and in a commercial vineyard that was located in the terroir of the verdicchio matelica which is a worldwide famous wine and in this commercial vineyard we selected three vineyards of different age that were organically managed at the time of sampling for 19 years so this was the oldest vineyard and in final the youngest vineyard for two years with respect to the soil floor management this is quite common in this area they just imply the tillage and no tillage of the interrow with annual cover crops cilates so it's fine for ciliate investigation were collected in both the no tillage and the interrow the slide just shows the result and you can appreciate that the oldest vineyards just host more stable ciliate community as you can see in the three different assembly events the oldest vineyard, the ciliate community and the oldest vineyard experience less fluctuation in terms of species and abundances and this is different from what was recorded for the youngest vineyard in which as you can see there is a larger fluctuation of the ciliate community across the three independent events maybe due to the greater soil resilience possible achieved during the long-term organic management of the vineyards and I mean similar results was also obtained using different soil bioindicator, microarthropod communities and so for those that could be eventually interested in this other part of the work can have a look to the poster session with the poster of Dr. Aldo D'Alessandro in final the third case of studies the main aims was to evaluate the potential of the ciliate communities to discriminate between different levels of soil contamination in four industrial areas and as you can see I have organized the main old sites industrial sites according to a decreasing level of soil contamination from this point of view the most polluted sites was Bershakafro and as you can see here there is a long list of inorganic and organic pollutants that I mean widely exceed the allowed legal needs the second position there is from cement factory and then the latter two industrial sites were less contaminated or not contaminated at all the slide is shown here and is a generalized analysis which I just used all the data from the agronomic factor means pH, texture macro micronutrient and obviously the biological that matters is about ciliate and what we can appreciate here is that the most polluted sites are different I mean are separated each other but also are separated from the less polluted sites to summing up all the results and just to recall the question that I just posed at the beginning so to what extent and how do ciliate community contribute to soil biondication we can say that according to the first case studies ciliate community would discriminate between natural forest natural and the agrocosystem as well as discriminating between different management system providing a specific land use set of indicator species and in the second case studies acting as a proxy of soil resilience in agrocosystem and thus as an indicator of sustainable land management and in final the third case studies discriminating between different level of soil contamination in polluted sites overall these outcomes add new knowledge towards a more informed use of ciliate as a bio indicator of soil health and broaden our understanding on how land use intensity agricultural management and contamination level can shape ciliate protist communities as we learn from the beginning session protist are groups that are missing and are in general over under studies in final just this is the last slide these types of analysis allow has to investigate and read the details diversity and just allow has to discover several different news pieces for the science. A great thanks to all of you for the attention. Thank you very much. I'd like them to without further delay to invite Mrs. Maria to stop sharing the screen. Thank you. Yes, I'm here. Can you hear me well? Yes, great. And can you see my screen? Okay, great. So good morning or good afternoon everybody depending on where you're connected from. So indeed my name is Maria Rosa and I'm from the University of Italy. This work was actually done during my master of research at University and was part of agro cycle which was in horizon 2020 project addressing the recycling and valorization of ways coming from the agri food sector. So let's get immediately started. Thank you. I've heard many times about soil quality so you all know what I'm talking about and soil quality is that are mined by the physical, chemical and biological components of the soil. I would like to spend a couple of words on the use of biological indicators in particular. Biological processes are intimately linked with the maintenance of the soil. Biological indicators may provide early warnings of system collapse and allow us to react before irreversible damage occurs. That said, let's remember that this work was performed in the context of agro cycle so from an agricultural perspective in order to conserve and enhance soil health it is often necessary to use organic material, organic matter production and for example through the use of organic fertilizers. Digestate derived from farm and agro industrial residues can be used as fertilizers in agricultural fields. The effects on soil of different digestates have been used in terms of physical and chemical impacts mainly on the soil. The aim of our study was to investigate the impacts of digestate on soil microarthropods communities as quantified by the soil biological quality index and soil respiration tests. One of our main aims was also to compare these different two metrics that are used to evaluate soil health because we were interested in seeing how the effect of digestate would have been reflected by the two different soil health metrics. I show you the methods of our project. We collected soil samples from an already established field experiment in 2018. The field had a previous history of amendments. It was treated with compost, chicken manure and some plots were left untreated. In 2018, the digestate was applied in half of the plots. We collected the soil samples soon after the application. In 2018, the digestate was applied in the field of soil respiration tests. If you had a chance to follow yesterday, you probably know about the QBS but I will tell you briefly what I'm talking about. QBS is an index that is based on the concept that the degree of adaptation to soil of an organism is adapted to live in the soil, the more vulnerable it is. Which means that the presence or absence of the most adapted organisms is a good indication of the level of soil disturbance. Another interesting thing about QBS is that it is not based on difficult taxonomic classification but you basically classify the organisms depending on their biological form. For example, imagine I'm asking you which is the one that is most adapted to live in the soil. I think all of you could easily reply. Let's have a look to the one that is shown in the left side of my slide. It is very dark. It has long appendixes. When you live above ground because you need to walk, you need eyes to see, you need a dark skin because you need to be protected from the solar radiation. While moving to the left, the organisms are more and more adapted to live below ground. There is no color anymore. The appendixes are shorter. You can classify the organisms like this. For example, on the left side, we have a higher score and we will contribute on a higher level to the QBS final score. The Solvita instead, which is the second metric that we used is a commercial test that measures soil health. How? Measuring basically the carbon dioxide flux that can be used for the Solvita. Basically, you use a digital color reader to measure the carbon dioxide that has been respired in the soil. Quite easy to apply. The soil respiration rate can be influenced by a wide range of parameters, including temperature, humidity, incubation conditions. We can also consider that there are in the soil there can be organisms respiring that are not good. There can be pathogenic organisms respiring in the soil. We were considering all of these parameters when applying and when comparing the two metrics. Let's have a look at the results that we obtained. These bars that I'm showing you in this slide show the QBS result. It is very clear that the plots that were amended with the digested state despite the previous amendments, despite the farmer manual application, chicken manual application or the others had higher soil biological quality index scores. I show you now the results that we obtained with the Solvita soil respiration tests. They look very different from those that we obtained with the QBS. They are very different from those that we obtained with the Solvita soil respiration tests. The difference is between the different plots and between the different treatments and most of all between the digested amended plots and the control. What's the answer? Does the digested really increase soil health or not? We correlated our metrics. Solvita was showing no significant differences between digested state application and control while the QBS showed higher values in digested amended plots. In our opinion, we considered all the parameters that I mentioned before and as the respiration rate can be influenced by a wide range of these parameters, it wasn't clear to us that the digested state application can have a positive impact on the biological community. The soil biological quality index is an easily applicable index and it has been widely used in the literature by several authors. It seems actually that digested state application can have a positive impact on the biological community. Why? Because maybe it increases organic matter, but the higher moisture levels are more suitable for organisms to live in. While on the other end with Solvita we found not such a result. We need to consider that conclusions can be method dependent, meaning that when we choose a method to evaluate soil health we need to be careful in selecting the method and maybe use different indicators to conclude correctly on the real state of soil health. I thank you very much for listening and I would like to point your attention once again on the beautiful sentence that we used last year from FAO, Keep Soil Alive, Protect Soil Biodiversity. If you have any questions feel free to contact me as this presentation was quite slow. Thank you very much. I would like to invite to the floor our last presentation of this session. Mr. Alan Brahman, please correct me, probably I said your name wrong. Yes, this is Alan Brahman, yeah. Thank you very much, you have the floor. Yes, I'm okay. Great, thank you very much. Okay, so okay, so first, yes, I will begin by the first one. So thank you for inviting me to provide my result on biofone tools. So I am Alan Brahman, a soil ecologist, and I belong to IRD, a French National Research Institute for Sustainable Development. And today I will present biofone tool which is a multifunctional approach of soil health related to soil biota activity. And you see that this multifunctional approach is interesting compared to just the Solvita one. And so I present this work with Alexi Tomazzo from IRD because he is a co-conceptor of biofone tool and we use this tool with a lot of partners in Thailand and Ivory Coast in other countries. So it's really a partnership construction. As you may know, soil health is defined by the ability of the soil to function as a living ecosystem. The problem is when you want to monitor soil health, what kind of function you will monitor, you will have say. So keep an eye on the seminal paper in 2008 and make these following assessments that agriculture, for agriculture we need to focus on four major functions, carbon transformation, nutrient cycling, soil structure and regulation of pests and disease. And each function relies to the biota assemblage to soil biodiversity. Soil structure is an habitat for this biodiversity, carbon transformation is an energy for the soil biota, nutrient cycling is a result of the activity of the soil biota. So that we have, we go to the literature, we have a little problem with soil health assessment. No, because soil health is considering as a sum of independent property.  So this is not for us a real assumption of soil health. And so our suggestion proposition is having this reductionic approach based on the assessment of independent properties. We suggest to have a more dynamic integrative approach based on the assessment of function, function result of interaction. This is, I've been nicely provided by my, the talk before me. So for example, soil respiration is due to the fact that biomass, the microbial biomass is used in the interaction with the edific component of the soil. So this vision is based on three main components. First, the non-manager are the main actors. So if you want to select tools, they must be easy to use tools because non-manager should be able to evaluate by themselves the efficiency of their practice. So biodiversity, we are in this Congress, as you may know, or as a main driver of some functioning, so I will not go into detail on that. Everybody knows that now. And an important issue too is to provide the scoring an easy way to score the method, to score the data we have in order to be easily understandable by the non-manager. And so we provide this scoring method based on the three main functions, stricter maintenance, nutrient cycling, and carbon transformation. And so this is bio-function tools. So we have a set of nine indicators. Each indicator is linked to a defined function. For example, for stricter maintenance, we have three indicators. For carbon transformation, we have four. And for nutrient cycling, we have three. So how we select these indicators? All the indicators were select on the basis of their low-tech and cost-effective. Why? Because we want to transfer it to non-manager, especially in developing countries where we work. There must be infill indicators. It's not because we love a field by itself. It's because we want to be more linked to the reality of the function. If you take a soil, you sieve it, you dry it, and you send it to the lab, you will not really respect the physical integrity of the soil. All the measurements are done in the field. And they also provide immediate results in the field. So that's a good way to interact with the non-manager too. So bio-function tool was validated. We have more than six papers now in a different kind of pedo-climatic condition with different actors and geo or technical assistants and different agricultural practice from agroforestry to annual crop or an agroecological, like agroforestry site. So just, I will give you two examples. This first, if you want, here we compare two kinds of practice, conservation agriculture compared to conventional tillage. And you see that whatever the site, Cambodia and New Caledonia, which are completely different in terms of pedo-climatic conditions, we show that conservation agriculture practice deeply improves soil health. And you can see easily too here that you can monitor each, the score of each function. For example, if you compare here, you have more nutrient cycling, excuse me, here than here. So if you want to store carbon, you will perhaps choose this monoculture. This is associated culture. So another issue concerning the agroecological transition is to monitor soil health over time. So here, for example, this is a work we have done in Ivory Coast that after logging, this was a main disturbance, tree logging. We monitor the evolution of soil health in different treatments. Here is our link to the adding of soil organic matter residue. And what we see here is after six months, we don't see any difference after logging, but after 18 months, we do see that the adding of soil organic matter increase soil health quality. Excuse me, soil health, soil quality index, exactly. So we claim that biofung tool is linked to soil biodiversity. In the same experiment, we monitor soil for another biodiversity and soil functioning using biofung tool before a T0, before tree logging, after six months after tree logging and 12 months after tree logging. And if we do a statistical analysis, this is what we call a correlation, we can see here, this is a co-inertial analysis, sorry. So we can see that there is a good overlap between macrophona biodiversity and biofung tool analysis. So soil function reflect here the evolution of soil biodiversity, but the advantage of biofung tool is that you don't need so much expertise to monitor soil function. If you want to monitor soil macrophona biodiversity, you need a good expertise. So what is our, I try to respect the time, so I think I have some time now, it's biofung tool provide to be sensitive to various aggregator practice. This is what I showed you before. And so it's a good way to compile non-manager to adopt management practice that really improves soil health and perhaps the most important thing for us, it's to be able to strengthen the capacity and autonomy of non-manager to monitor by themselves. This is not exactly, we are not at this stage now, but this is exactly the objective for biofung tool. Thank you for your attention and I will be really pleased to answer all the questions you have. Thank you very much indeed. Your time was valuable, precious and perfect for us. We did have some questions for the previous presentation and some of them were already answered in the chat, but since yours was the last presentation, I still want to add at least one of them and I will select here the question from Mrs. Lydia Nicola. She poses a very interesting one. How does biofung tool actually work and what do you do in the field? And since there is a second portion and third one, so if you could just use a few minutes for that I would appreciate it. Thank you. I don't know if I have the time to describe how I use biofung tool on the field because I use nine parameters, nine tools, so I will not have the time to describe all the tools, so perhaps I can send the basic papers describing how we use it. The thing is, it's a really simple tool. For example, I will take one or two we use for soil respiration, for example, we don't use solvita, we use another one, but what we do, we measure carbon labile and we do, and in fact the respiration we have, we will take it in correspondence with the soil carbon available. That's like that, perhaps you will not have the problem you saw Maya, perhaps with solvita because this respiration is really linked to the carbon content of the soil. So by that, what I want just to highlight the fact that one indicator is not really a good way to measure soil health. You need to measure soil health using different kind of indicators and after you can really rely them between, and I think this is, I don't know if I really answer the question, I'm sorry for that, but I will really be pleased to send the methodology of biofung tool to anyone who wants it. Yes, I think indeed it would be just another presentation to be able to answer those in details. So if you could please add to the chat now to everybody how to contact and how to get more information and some based papers, that would be very interesting for everybody. Okay, I will try my best perhaps my colleague to Alexi Tumazo perhaps can send to her. Thank you very much. So finally we are at the end of the presentation so of the session I'd like to thank you again all the invited presenters for the magnificent works. We had a variety of materials very interesting here since biological assessment which is so much needed nowadays we have like a protocol for voluntary guideline for soil management in one of the main questions last year in the Planner Assembly was in the aspects of biodiversity how to measure biodiversity and how to use the concepts in this voluntary guidelines so I presume that after the end of this meeting we are going to have some very good ideas to ask that questions and to increase some information that protocol so I'd like to express also thanks to all the people that are helping in the background the FAO personnel and Isabel for all the support and thanks a lot for all the 107 participants that are still here with us I suggest all of you check in the chat you can copy it because there is nice information being presented and posted by everybody that was here with us today my last question is going to be for the presenters because I know that may be important for you if you want to open the camera so that way you can highlight yourselves and make a picture for your records of this meeting I think it would be interesting for yourselves so we have some of the presenters here it would be nice to have a first screen with pictures and people that way each one of you can save that as a memory so just three more to go Mr. Alexander if you could open your camera then we have all the presenters thanks a lot Isabel maybe she is busy already with something else but anyway thanks a lot it's a beautiful image to see and thanks a lot and continue with our next sections tomorrow thank you thank you have a lovely day everyone thank you so


usage. There the tools are slightly less efficient and slightly less useful I find in the end than what we have in terms of time but there are a few and so it can be useful to know of them. First there is line by line memory usage there and for this we can use the memory profiler module if you have not installed it you can install it with this little comment and let's see there is a little question. Okay so let's come back then to Ram. So memory profiler so what the way it works that you import it and then you import what we call a decorator. A decorator is a little like well it it decorates the function so you put that before a function with at and then the name of the decorator and what internally it does is that it wraps the function inside like another function to actually tune a little bit the way that it will work all right and that's what it does. So let's try that one and once you have defined it like this you may then outside in the comment line call it with python-memory profiler and you would call like this code there and that would do a memory profiling of this code line by line with Jupyter magic you load x so that loads the extension memory profiler and then you call percent-memit of my function. Sorry I forgot to run this one here and now that one and just like this this is what we see okay so you see here it first begins with a little error and then it gives us info so the first one is fairly specific to the fact that we are in a space in the weird context that is the that is the Jupyter sales context so what happens is that Jupyter here has this little cell and then it will push it to a temporary python file and then try and give that with to to memory profiler but internally something doesn't work there and so it's not really able to go through that what this means is that it's not able to do a line by line monitoring but it's still able to monitor that the peak memory usage there was 1.1 gigabyte okay and that the increment that existed during this like during this execution was 160 megabit you can here see for instance another result that I got earlier so this is basically the current usage that means that it takes into account all of the things that I already have in my RAM at this moment that's why for instance here I have more objects than what I had when I did this first test there that's why this is larger but then the increment is fairly similar although it's not exactly the same it should be said also that this RAM measurement is fairly sensitive to time that is operation that are too fast might be missed by the profiler all right only because it only checks it only checks the the RAM every fraction of a second or so so sometimes small small very fast increment might be missed all right now if we try and put our function into another script so for instance here I've taken the time before the course to write this here the same code but now in a file all right so now I can more easily try and call this and now when I call this because it comes from an external file it's able to do the line by line profiling and so we can kind of see line by line what happens there all right so here I profile when I enter in the function that is what RAM I have used then here you see that I create a which is one million elements so there is an increment of 7.5 megabyte and there now I create something which is much larger with 20 million elements so I have now this much larger increments there times 20 about and then I delete b so I freeze this RAM and then return a so that's just returning it okay so now you see a little bit how this works line by line okay so it's fairly nice oh yes of course so by bush rack question can you explain what ad profile does again so ad profile is what we call a decorator okay so what it basically what it does is that it will wrap this function there inside another another function which will be which is called profile so basically it tunes the way that this will be executed okay such that when you call my function it's not only this code there that you wrote but it's this code but with this profile modification does and profile will just set out the RAM monitoring either just complete RAM monitoring or line by line monitoring depending exactly on what is the execution be executed from where does that clarify or yes okay again do not hesitate if you have any question or if you would like me to like delve a bit deeper in one subject or another right so now we can try and do the same thing in our pairwise distance function so now what I'm going to do is I'm going to write memory profile a profile here I set up the profiler and I set the precision the default precision is fairly low so I increase the precision of the memory profiler so that its precision is higher there because we have small increments and then with this magic I will write it to a file so this will not be executed this will be written and when I executed that it says writing tmp.py okay and now I can time it to check like how long it takes when it's in an external file you can indeed check that now there is a tmp.py file there okay and now what you see is that's okay it took as you can see 13 seconds and you can sort of see what happens there now as you have this and what you can see that we start with fairly small usage okay there and then we increase there and we have there the creation of all our distance on so forth but you see that there is no specific increase and the increase actually happened inside this function there okay that's kind of related to the internal working of python there which tend to create stuff I think on a lazy basis in terms of assignment of memory and so we see here that per increment it will increase by 0.258 megabyte in total and so then we have this final usage so we come from here to that and then at the end we come we have there that left all right so what we can see also here and what I want to convey to you is that it's not super easy with memory profiler at least in all the tests that I've seen to check easily what and what is happening there because first off you need to export stuff to file and then you also sometimes it can be a bit counter intuitive exactly where the increment happen okay there you see it happens on the for loop there whereas it's actually the assignment inside these metrics that cause the increase okay it's just that it is reported on the line that we would not necessarily expect there's a question by Jörg yeah for me it looks different because the the increment is two rows below so it's in the for loop so it's there yeah okay interesting so as I said here I think the main reason is also as I said like the monitoring itself is somewhat time dependent and yeah am I my experience with this with this module is that it's not the most nice it's not the nicest to play with it it exists and I think that for fairly simple case like this it performs quite well but when we start to have several for loops and call to different functions and so on so forth it becomes a bit difficult to play with and I think that this illustrates that fairly well one other thing that I want to point out is if I do the same thing but now without the profile you see that what took 13 seconds with the profile takes only 86 milliseconds there without the profiler you see that also a little bit when you see that all these numbers they are fairly big but now the whole time there is super high so this memory profiler has an extremely high overhead that means that here it kind of worked because we played with super small data but if I were to use any sort of slightly larger data real data this would take hours and for a slightly subbar result as you have seen okay so that's then kind of too carried out and I would say it can work and sometimes it's quite useful sometimes I would I would think twice before before using it all right um okay so sometimes instead you can prefer another alternative which I like to use as well and this one I use it more often is time-based memory usage okay so basically you will just have externally something that monitors what happens not line by line but just based on on a time basis okay again with you always have this sort of limitation that uh the that that that if memories happen too fast if memory change too fast in big peaks and and dips and stuff like this you might have to adapt how often you monitor the memory in order to catch this small modification but most of the time this works fairly well and this is a parameter that is easy to change okay here it's the same code but it's slightly weird because you don't want to import memory profiler in the script okay because otherwise it will default back to the line by line profiling so there is this little trick there but that's how you do that otherwise it's still memory profiler it's just that you use their mprof executable so I just do this now I have tmp.py but now tmp.py doesn't have this import memory profiler there all right you see here I do that with now bigger data than before because I know that the overhead is smaller during my testing I saw that and then I run this script so whenever I start a line there with an exclamation mark this will be sent to the command line so time and prof and then run tmp.py maybe if you have a windows maybe this time there will not work in which case you can just remove it okay and of course you need to have the memory profiler installed so when I run that you see that it tells to me that it samples the memory every 0.1 second you have an argument of mprof to increase or remove that and what you can see also is that it took three seconds to run so here the overhead is actually super super small if I were to run this with 500 vectors the pairwise distance would actually increase by 25 so I would be fairly close to maybe 2.5 second and there now I am somewhere at three seconds so overhead also is not too much of a problem anymore and now it generates a file that has maybe this name something close okay this is a date and hour and time so of course this will change and you have here there are now what was just created for me it's not really made to be seen by yourself so for that we call now mprof again with subprogram plot and then we say okay take this profile by default it takes the last profile generated but you can also manually specify which profile should be run and put it in a file called tmp.png so you run that okay and now it has taken this profile and created this file there this image file and when I show it it kind of looks like this so what you have here is the execution time and the memory used and what is so you can have it like just like this and now the decorator of profile here has flagged this function to be bracketed so you have here the time so you have the pairwise distance profile there it took 1.7 seconds so it's here and you see how it works so you see that you have a slight increase at the beginning and there a small you know increase all throughout the execution time that seems to reflect that as we populate this matrix there it actually gives us and uses memory okay the here the reservation of the of the space is or the usage of the space is not in a big term but step by step okay and we see here all little cross represent one point where the memory was sampled all right so that's the thing there you see that you don't have this line by line thing but if you have a good little idea about what's in your code I find it much nicer to see exactly like how things happen at what speed and is it big peaks or smaller peaks and there are some options to improve to change the sampling if you want to monitor very finely or more coarsely your RAM usage also you have the added benefit of having also the little time that lets you monitor how long it took to go into each of these space and and also should be noted that this works with a Python script but it actually works with all sort of executables so improv can be run on every kind of executable it's just that it's only with Python that you get this nice little brackets there's a question by this asking if more than one function can be monitored and indeed more than one function can be monitored I just would need to have another function and a decorated with the ad profile there and I would then get another bracket so that you can precisely see what happens here okay so personally I would say that this is my tool of choice if I have to if I have to precisely monitor the RAM okay um there we go um tata tata and then as you can see as I said the the overhead there is much much much much much smaller all right it's actually well manageable because that way we see three seconds versus 2.1 seconds so you know it's actually doable to just have 30% more in the execution time where as the line by line is more like a multiplication by 10 or so or even more okay um so that's it one little thing there you can see this big jump at the beginning these are these correspond to the import of the of the libraries so this also tells you a little bit how long it costs and and how much it can be important not to import too many unused libraries sometimes all right um so there you go that gives us now two options for the monitoring light by line due to overhead and time based of course if you do very very fast operations you might have to reduce the interval of your monitoring to maybe go every 0.01 second or 0.001 seconds but that comes with of course then slightly increased overhead because you do more monitoring now what remains is what we do when we have sort of let's say okay now you have done a few execution of this and you have an idea about where which are the memory structure which takes a lot of space okay typically or so if we come back to what we had before it's say that when we try to create these matrix there which is lend dissect by lend dissect in size so one billion by one one million by one million it fails because it would require 7.28 terabyte so sometimes it's interesting when you know exactly which are the problematic object who just know what size you can expect one single object will take in memory so that you know then knowing about your RAM or the RAM of the computers that you are going to execute your code on how much how far you can go when you know it it breaks down so for that the first thing you can use is sys dot get size of okay very simple function from the sys module so for example you get this and then you have okay you have size as a single float 24 bytes and oh sorry yeah bytes and then size of a string compensating 1 2 3 4 5 6 letter it's 55 bytes all right to get kilobyte you divide by 1024 megabyte 1024 by 1024 so for example i create now a 1000 by 1000 matrix okay and i created it and now i see that it takes 7.63 megabyte all right now 10 000 by 10 000 takes 100 times that okay so 762.94 megabyte and so on so forth and that's how we can quickly quickly go towards something that is bigger than my RAM now that's simple enough but there is a limitation to get size of and it's that uh well i'm going to demonstrate that briefly let's consider this so size of a float and now i take a list okay of 10 float what do we see there one float is 24 whereas 10 float is certainly 184 all right which is not really what you would expect you would expect that it's at least 240 bytes plus whatever overhead comes for the list all right so something happens there get size of has a limit is that it struggles a lot with containers and in particular lists and dictionaries and stuff like this and even more when they are when they are nested because what it does is that it it only gives you the size of the list itself and not of its contents okay so for that there is if you google a little bit there is a let's say classical function that we always copy and paste that looks like this so total size and then you give it some object and then what it does is that it goes it has like a few a few templates there for different style and then it will go inside all of the level of this object maybe this object is nested with different subsets and so on so forth so it will go through all of this deeply and sum up all the size of all the content that is in there and return all of it to you at the end all right and so now if we execute this function and we just go and execute that now we do get a size that kind of makes sense so this is 10 times the 24 for each of the float in there plus the here 184 was just the size of the container and not of the contained elements and we do get 424 so now we have the actual size of the object in memory with all of its content right so that's a little caveat sometimes be careful be mindful about this because if you don't know that but it can be very counterintuitive not that because NumPy arrays do not access the data by reference but have automatically like their data let's say linearly in memory with them you don't see really this problem with NumPy arrays all right so it's not necessarily something that you need to pay too much attention to if you work only with NumPy arrays but as soon as you have more types different types of containers on so forth be mindful of it okay there you see they return to you the same thing okay now second micro exercise for you so find out which is the largest square matrix that your RAM could reasonably accommodate okay my RAM is 32 gigabytes but maybe yours is different okay so try to find out this number you can do a trial and error approach or you can be a bit more mathematical about it and not crash your computer whatever you know how you want to tackle that is up to you and then if you have a little bit of time with any extra time try to think on how we could modify the main script to make it less memory hungry all right so I will while you solve that I will paste the main script there so that then we can discuss together how we wouldn't do the modification uh like this okay so I'll leave you to it as usual please do ask any question in the chat or by voice or in the google doc and please put a little green tick yes when you are done with the first part there okay find out what is the largest square matrix your RAM can reasonably accommodate okay and also you can all write your the RAM of your computer in the chat and if you don't know the RAM of your computer then you can try and google how can I learn the RAM of my computer it's always useful to know okay all right so we have done the correction for this little part I'm going to just paste in the chat just a little code that we use to to do the monitoring of the thing and so that you can fairly easily replicate that on your own to adapt it to your own system so that's how we can know like how large a matrix will be without having to at least do it ourselves but just by sort of understanding what is the kind of rule that go underneath that and then doing the computation for us for ourselves now the next part was about taking our script and trying to modify it to think about its structure so that we can maybe make it less memory hungry such that even if the file is super large we can still run okay with here we have to remember that this is what causes problems this is what is too big okay so now my question to you if you have had time to think a bit on it and you think you have an idea please write in the chat or raise your hand to explain your idea and we'll see what we can do okay no particular ideas or are you just ah yes you are just typing so Jörg proposed that we could use a smaller data type with less precision okay and not store the duplicated values okay and then the second one by test could you use a generator for example so for the first one with Jörg I kind of see what you mean but for this you use you would use a generator yes but at some point you have to maybe store these results no or how would you how would you do that maybe with a bit more precision now Jörg I think that what you propose is already kind of a good idea in itself okay first you could realize that maybe this square matrix there stores each result twice but maybe we could have some sort of a triangular matrix format where we only store things once so okay so now we would go from uh remember there was like seven terabyte terabyte and then we go from 3.5 terabyte just with that okay so still not tractable on our little laptop but a bit better now then you say okay maybe we can go from a very precise format so double precision would be float represented in a 64 byte and maybe we can go to 32 byte and say okay that's precision enough this is something that you might want to check with exactly like what sort of precision you do need for this computation but that's something we can do indeed of course so then we would again divide it by two maybe so we would go to I don't know how to do division anymore right uh we will go to something like this okay so now okay now a lot of uh a lot of of big HPC computer of big uh a big super computer would be able to uh to service and to help us we would be able to do that but still it would be very like we would need to go there in order to do this computation I would not be able to do that on my laptop but that's two very good very good proposition there so well played now do anyone has another idea on how we could think slightly differently our problem such that we could have this run on a normal laptop right so from David there is a nice proposition we can divide our big big big matrix into smaller sub matrix and then we could store on a hard drive on on the disk basically this smaller sub matrix as we kind of as we move as we go that's a very good idea so that's kind of the idea to say okay when you don't have enough RAM maybe what you want to do is to store temporary result on the disk this will slow down your execution time tremendously because you store stuff on the disk and that takes time to read and write to the disk but at the very least now your problems become extractable as I said remember it's always possible to wait a long a bit longer it's not possible to increase your RAM so that's actually a good solution and in many cases that's maybe what we would do we would start maybe with what Jörg proposed and then from there we would divide it into smaller problems that we would store on the hard drive now for this specific problem my personal proposition would be to say that step four compute the pairwise distance matrix and then we directly write the matrix without doing further computation on it okay so then I would say why do we actually need to store the matrix okay why not write it as we compute it okay which just like we compute one little bit of the matrix and then we write and then we don't need to remember the rest of the matrix there so this is of course a little bit specific to what we do in this specific code but whenever you encounter this sort of problem you always kind of have to go let's say specific in your in your in your solution so the way I would sort of do that here would be to sort of change this to say that you have your main script always and there here you would not allocate that part but you would maybe just here say that you mix the writing with the rest so I would sort of mix it here make that climb back a level and of course then you want to mix both sets of instructions so we start we write a header and then here for each of these we will then print the different element as they come right so maybe I can yeah write that and then I will print yeah I just keep that as just a similarity I just need to keep a single one and then I just keep the similarity there you want to change that the end is nothing okay so that you don't come back to the line then of course the file that you write to is out and then what you want to count for is that if it's not the last element then you should put a little comma between these so how you do that might vary a little what I think I will do is that I will say if j is not zero so if it's zero then there is no comma before but if it's not zero there is a comma before so if j is not zero then I will print a comma okay all right and then that should do it and the last thing that we need is whenever we have finished with one sequence we need to go back to the line so we just print this new line character the end is nothing and then the file is always out and then we have that all right so there you have it I might have made a small mistake because I wrote that very fast but I think that the concept is sound here that I open the file and then as soon as I have computed the sequence similarity I push it I write it to the file there I never have to store it all right okay so now you see a little bit the process does it make sense to you what I just did and how I did it yes for a few maybe for the others you need a bit of time to digest this yes okay cool and so then with this new structure now I don't need to allocate anything anymore and it's just this should be super lean in memory