Agaricus bisporus var. bisporus, the button mushroom, has a predominantly pseudohomothallic life cycle. Most of its spores are heterokaryotic and give rise to fertile heterokaryons. However, previous studies have suggested that outcrossing should not be rare in wild populations. In order to discover how outcrossing occurs, we experimentally favored it between aerial propagules of a fruiting donor mycelium and a delayed receiver mycelium that only invaded culture trays. Two donor/receiver pairs were studied, and potentially hybrid basidiomata collected on the receiver trays were analyzed with a mitochondrial marker, two unlinked nuclear CAPS markers, then haplotype markers based on DNA sequences obtained after PCR cloning of the rDNA ITS region and the fruk gene. For one of the two pairs, most basidiomata were hybrids between the donor and the receiver. Genotyping of the hybrids revealed only two genotypes consistent with outcrossing involving airborne mycelium fragments rather than basidiospores. The resident receiver heterokaryon that provided its mitochondria to the hybrid basidiomata is suspected to have had a trophic contribution to their growth and successful fruiting. The high level of heterozygosity and the cultivar introgression previously revealed in wild populations of this pseudohomothallic species may result from outcrossing involving airborne pieces of mycelium. Agaricus bisporus (J.E.Lange) Imbach, is a secondary decomposer ) [fruk gene are haplotypes characterized by their nucleotides at all the SNPs found in these sequences.Different patterns of DNA fragments result from SNP in the recognition site of (Tetr)]) , using t(Tetr)]) . The difiAbi11 intron of the cox1 gene and electrophoresis as indicated above, the lengths of amplified fragments for strains Bs243, Bs177, and Bs256 were 0 (lacking intron), 987 , and 2230 nt, respectively. In the latter case, the resident heg of the iAbi11 intron would itself be invaded by a mobile ORF.The design of the mitochondrial marker is based on previous data of Jalalzadeh et al.  for the Basidiomata of the three strains Bs177, Bs256 and Bs243 used in this study exhibited different cap colors in cultivation. They are dark brown, medium brown, and cream, respectively A.fruk DNA segments were 721 and 840 bp in length, respectively. As reported in a previous study [fruk gene revealed 7 and 22 single nucleotide polymorphism (SNP) positions, respectively.The sequenced ITS and us study  a high lfruk and detailed in fruk sequence, respectively (SNPs were numbered according to their position in the 5′3′sequenced fragments of ITS and ectively . These hits:541) and the other is at position 655 of the fruk sequence (locus fruk:655). As explained in detail in HaeIII is only available when nucleotide C is present at these loci. Therefore, after digestion by HaeIII and electrophoresis different patterns of DNA fragments can be observed. These patterns and the genotypes are reported in detail in its:541 and fruk:655 are its:541-2/2 fruk:655-1/1, its:541-1/2 fruk:655-1/2, and its:541-1/1 fruk:655-1/2, respectively. Once developed for parental strains, CAPS markers can be used to quickly screen genotypes among a large number of samples, offspring or hybrids by simply digesting PCR products and running electrophoreses.CAPS makers were based on two SNPs (C/T). One is at positions 541 of the ITS sequence  were found and reported in fruk haplotype profiles are useful markers for our purpose because, following a single meiotic generation, they should remain intact in the descendants of the three strains or the hybrids occurring between them. Indeed, crossovers between SNPs in ITS or fruk sequenced DNA segments is unlikely not only because of the short genetic distance between them but also because intrachromosomal recombination (crossovers) is suppressed in A. bisporus var. bisporus [fruk markers which are found in Chromosome IX [MAT-linked on Chromosome I [its and fruk haplotype markers are its-1/2 fruk-1/1, its-3/4 fruk-2/3, and its-5/5 fruk-4/5, respectively.Haplotype nuclear markers were based on the haploid DNA sequences obtained after PCR cloning of ITS, which varied at 7 SNPs, or those of  strains . Throughbisporus . Howeverosome IX  and are mosome I , respectits and fruk gene of the three wild strains have been previously deposited in GenBank [fruk gene have been deposited in GenBank and their accession number are also in Sequences of  GenBank  are recaiAbi11 intron of cox1 gene as previously reported by Jalalzadeh et al. [iAbi11-S , iAbi11-0 (0 = no intron), and iAbi11-L (L = large intron), to strains Bs177, Bs243, and Bs256, respectively.The mitochondrial marker is based on the length of the PCR products of the h et al.  and in tIn total, 50 basidiomata (25 per tray) were collected for the receiving strain Bs177. Basidiomata were harvested every day for one month and as much as possible over the entire compost surface. All the basidiomata were dark brown, as was the receiving strain Bs177.its:541-2/2 fruk:655-1/1, the same mitochondrial haplotype iAbi11-S, and the same dark brown color of the cap. Despite the fact that we did not observe any hybridization events, after PCR amplification we sequenced 6 of these 26 samples and confirmed that their genotypes at the two haplotype markers were its:541-1/2 and fruk:655-1/1 as in Bs177. We conclude that hybridization did not spontaneously occur in this experiment. In this experiment the donor was Bs243 and 26 of the 50 basidiomata collected on the trays of the receiver Bs177 were analyzed. For the CAPS markers, all had the same nuclear genotype as the receiver Bs177 In this experiment the donor was also Bs243. A total of 50 basidiomata (25 per tray) were collected for receiving strain Bs256. Trays were divided in small plots as shown in iAbi11-L of the receiver Bs256. The CAPS markers revealed two genotypic classes. One class includes 18 basidiomata with the genotypes its:541-1/2 fruk:655-2/2 and a cream cap color. The two constituent nuclei of these fruiting bodies had the genotypes its:541-1 fruk:655-2 and its:541-2 fruk:655-2, respectively. its:541-2 fruk:655-2 came from spores or mycelium of the donor Bs243 because allele ITS:541-2 is not present in Bs256. In contrast, nucleus its:541-1 fruk:655-2, which could theoretically come from both parents likely came from Bs256 because fruk is tightly linked to MAT and these heterokaryons should not have received two times the same fruk allele and thus the same mating type allele from the same parent Bs243. In conclusion, these 18 basidiomata are hybrids bearing the mitochondria and one nucleus of the receiver, whereas the second nucleus came from the mycelium or the spores of the donor. In the other genotypic class, the 14 remaining basidiomata had the genotype its:541-1/1 fruk:655-1/2 with the nuclear constituent genotypes its:541-1 fruk:655-2 and its:541-1 fruk:655-1. Despite they had a cap color similar to the receiver and also its mitochondrial haplotype iAbi11-L, All 32 basidiomata inherited the mitochondrial haplotype its-4/5 fruk-3/4. In its4 fruk3 and another nucleus bearing Bs256 receiver-specific haplotypes its5 fruk4. One of these hybrid basidiomata is shown in its-3/5 fruk-2/4. One of these hybrid basidiomata is shown in its3 fruk2 and nuclei bearing Bs256 receiver-specific haplotypes its-5 fruk-4. We note that the latter nucleus was present in all hybrids of both groups. The two remaining medium brown samples had the genotype of the receiver its-5/5 fruk-4/5 (nuclei its-5 fruk-4 + its-5 fruk-5). Since both nuclear and mitochondrial haplotypes as well as the cap color were consistent with the receiver, these two basidiomata were likely fruiting bodies of the receiver Bs256. One of them is shown in iAbi11-L and the same nuclear haplotype its-5 fruk-4 of the receiver Bs256. However, 6 of the 10 hybrids received the haplotypes its-4 fruk-3 from Bs243, whereas the remaining 4 hybrids received the haplotypes its-3 fruk-2 from Bs243. As indicated in its-4 fruk-3-and its-3 fruk-2 could simply be the genotypes of the two constituent nuclei of the donor Bs243. In this scenario hybridizations occurred between pieces of mycelium from Bs243 and the mycelium of the receiver Bs256.Twelve basidiomata, six of each two genotypic classes described above using the CAPS markers, were analyzed with haplotype nuclear markers using sequencing after PCR amplification. The six samples which had hybrid genotypes with CAPS markers and a cream cap all had the same haplotypes fruk sequences of two hybrid basidiomata H10 and H17 belonging to the two genotypic classes with the haplotypes its4/5 fruk3/4 and its3/5 fruk 2/4, respectively, were deposited in GenBank. The accession numbers are in ITS and Using haplotype markers, we concluded above that hybrid basidiomata collected on the Bs256 trays likely resulted from crosses between Bs256 and airborne pieces of mycelia from Bs243. In an alternative interpretation, hybridizations would have occurred between Bs256 mycelium and spores or mycelia from spores of Bs243. However, this latter scenario is unlikely for the four following reasons.fruk gene and the MAT locus are tightly linked, the fact that all hybrids possessed the fruk-4 nuclear haplotype of the Bs256 receiver and received either fruk-2 or fruk-3 from the Bs243 donor implies that fruk-2, fruk-3, and fruk-4 are linked to three different mating type alleles. Since there is no sexual incompatibility, if the crosses had occurred via basidiospores, we should have observed four genotypic classes of hybrids that would have received nuclei with genotypes its-3 fruk-2 or its-4 fruk-3  as well as genotypes its-3 fruk-3, or its-4 fruk-2 (presumably recombinant types) from donor Bs243  in each of two plastic tunnels. The length of the pieces of mycelium varied from 7µm, which is also the mean length of the spores, up to 45 µm.In conclusion, under the conditions of our experiment, among the airborne propagules dispersed on the receiver trays, mycelium fragments were surprisingly as numerous as spores. We tested outcrossing for two pairs of donor and receiver strains. For one pair, all basidiomata growing on the receiver trays were fruiting bodies of the receiver. For the other pair, most basidiomata were hybrids with only two types of genotypes likely resulting from outcrossing involving airborne mycelium fragments. All the analyzed hybrids had the mitochondrion and one nucleus  from the receiver, whereas its other nucleus was one of the two presumed constituent nuclei of the donor. All data can be simply explained by crossing with mycelium fragments without any evidence of spore involvement.However, half of the spores could have potentially produced hybrids with similar genotypes as those produced by mycelium fragments. Therefore, we cannot exclude that some of the evidenced hybrids resulted from outcrossing between spores of the donor and mycelium of receiver.A. bisporus var. bisporus, knowing that the preferred life cycle of this variety is mainly pseudohomothallic and thus theoretically does not favor outcrossing. To do so, we simulated a situation in which crosses were promoted between airborne propagules from a fruiting donor mycelium and a receiver mycelium that invaded the culture substrate but had not yet fruited. Indeed, when the temperature and humidity remain relatively high after substrate invasion, there is a phase during which an aerial mycelium covers its surface. This aerial vegetative phase that is known in culture but that we have also observed in the wild has no nutritional interest for a secondary decomposer species such as A. bisporus. Thus, we suspected that it could be a phase naturally favoring the emission of mycelium fragments or the reception of propagules and consequently outcrossing. It was the challenge of our experiment to use this vegetative phase and the time lag between the fruiting of two strains to promote spontaneous hybridization between them. At the time of this phase, a cloud of propagules consisting of spores and mycelium fragments of the donor mycelium was aerially disseminated onto the receiver. The conditions for mycelium growth, fruiting, propagule dissemination and isolation were appropriate since we successfully collected hybrid fruiting bodies and they did not possess any unexpected alleles not belonging to the parental strains. This indicates that our experiment was somewhat secure, without crosses with unexpected propagules and without interaction between the two couples of parents which were isolated in two plastic tunnels. Similar and simultaneous processes were applied for both donor/receiver pairs Bs243/Bs177 and Bs243/Bs256 but hybrids were detected only among basidiomata collected on the receiver Bs256 and they only had two different genotypes. These experiments prove that outcrossing spontaneously occurs between strains growing in close proximity to each other. Given the predominantly pseudohomothallic life cycle of A. bisporus var. bisporus, outcrossing could be considered infrequent, but studies of local populations have shown a high level of diversity and heterozygosity with infrequent clonal or pseudoclonal lineages, suggesting that outcrossing may not be so rare [A. bisporus generally extend to a small area in nature. However, in light of the present study strains isolated from specimens collected in the wild could have themselves resulted from hybridization between strains of the same collection site and thus the area of the resident mycelium could be larger than estimated from the genotypes of collected specimens. In addition, different genotypes and even different varieties or even species of Agaricus are frequently found close to each other probably because a resident mycelium can stimulate spore germination [Our general goal was to understand how outcrossing naturally does occur in  so rare . In thismination  even betmination . In addimination . These ciAbi11-L) and one of its two constituent nuclei that possesses the its-5 and fruk-4 haplotypes. The remaining constituent nucleus of the hybrids was one of the two constituent nuclei of the donor Bs243 that likely possess either the haplotypes its-4 and fruk-3 or its-3 and fruk-2. In addition, hybrids with the former haplotypes exhibited a cream cap like the donor parent, whereas those receiving the latter haplotypes exhibited a medium brown cap like the receiver parent Bs256. Therefore, the cap color of the later hybrids was not distinguishable from those of the basidiomata of their parent Bs256 fructifying on the same culture tray at the same time. Although data remained insufficient to hypothesize the genetics of cap color, this trait did identify one of the two classes of hybrids. The two hybrid genotypes and the absence of recombinants completely agree with the hypothesis that hybrid basidiomata resulted from crosses between the receiver BS 256 and only mycelium fragments of the donor Bs243 and not its spores.Genotype analysis using a mitochondrial marker based on intron presence/absence and two nuclear haplotypes markers based on multiple SNPs revealed that most basidiomata collected on the receiver trays invaded by Bs256 were hybrids that all inherited both its mitochondrial haplotype . Outcrossing was evidenced by analyzing the basidiomata. How this occurred remains unknown but we can propose some hypotheses. The fact that both mycelia of the donor and the receiver were involved simply suggests that crosses may have occurred between heterokaryons. It is the first time that outcrossing between heterokaryons is reported in situ. Crosses between heterokaryons of A. bisporus were first evidenced by Raper et al. [The question arises as to why no hybrid or only hybrids with two genotypes were found among the basidiomata collected on the receiver trays. In first, the absence of hybrid basidiomata in the case of the couple Bs243/Bs177 suggests either a total incompatibility between these two strains and that outcrossing never occurred or hybridization occurred but the hybrid was not able to fruit when the receiver Bs177 was installed in the substrate. In this case, a problem of incompatibility might exist between the new hybrids and the resident mycelium. Genetics of vegetative incompatibility remain unknown in r et al.  who obseIt could be hypothesized that deheterokaryotisation would have occurred through mycelium fragmentation and that, in this case, crosses might occur between homokaryotic and heterokaryotic mycelia following a process called the Buller phenomenon . We noteA. bisporus) may be favorable. Therefore, it could exist different seasons favorable for outcrossing or for fruiting.Another question is when does outcrossing occur? If mycelium fragments are involved, it should not necessarily occur during the fruiting process as in our experiment, but at any time when the aerial mycelium rises to the soil surface in warm, humid weather as indicated above in the conditions for outcrossing. In temperate north hemisphere, the period following the summer thunderstorms  and proceeding the fruiting season , and this for seven days A,B. PiecIn the future, the respective role of spore and mycelium fragments should be evaluated. Based on the new information provided by this first approach to understanding outcrossing processes, it may make sense to use parents with better known constituent nuclei for their mating types, as well as additional independent markers to analyze outcrossing events. Vegetative incompatibility between parental heterokaryons should be assessed in vitro.We did not observe recombination between the two markers located on different chromosomes in agreement with the fact that meiotic spores were not involved in the crosses. However, this is not a strict rule since Xu et al.  evidenceOur objective was to experiment outcrossing as it presumably occurs in the wild. If the objective would have simply been to obtain hybrid basidiomata, our data suggest that a simple inoculation of the donor mycelium in the casing layer of the receiver trays could produce same hybrids as in our experiment. This could be a simpler but less efficient method than the one proposed by Callac et al.  who obtaA. bisporus, nuclear migration rarely occurs through the septa of either homokaryotic or heterokaryotic hyphae, which are both multinucleate and without clamp connection [In nnection ,41,42. Innection  with itsnnection  such as nnection , or in cnnection . The bioOur experiment can be compared to a previous study in which a parental homokaryotic strain was co-inoculated as grain spawn in the culture substrate simultaneously with spores (mainly heterokaryotic) from another parental heterokaryotic strain. All the fruiting hybrids possessed the nucleus and the mitochondria of the inoculated homokaryotic strain and resulted from crosses with heterokaryotic spores or mycelium from the latter ,40. AlthWhen we designed the experiments, we expected to collect basidiomata on the receiver trays which were produced either by the receiver mycelium or by hybrid mycelia between the receiver and propagules from the donor. We tested two pairs of parents in the same conditions. In both cases, about half of the propagules were mycelium fragments and half were spores but in one case none of the basidiomata was hybrid, whereas in the other case, most basidiomata were hybrids. Among the hybrids only two genotypes were found. All these hybrids shared the mitochondia and one nucleus of the receiver and one of the two presumed constituent nuclei of the donor. The absence of recombinant nuclei from the donor revealed that airborne mycelium fragments rather than spores were involved in outcrossing. Therefore, outcrossing can easily occur in the wild between heterokaryotic mycelia growing in close proximity to each other. The success of the mycelium fragments is possibly due to their resistance to dryness and their activation probably faster than that of the spores when these propagules are disseminated on a resident mycelium. The comparison with previous experiments suggests that the inheritance of the mitochondria of the receiver resident strain should favor its biotrophic contribution to the growth of the new hybrid mycelium and its fruiting.A. bisporus var. bisporus was considered as a pseudohomothallic species in which outcrossing should unfrequently occur. Our data show that outcrossing involving mycelium fragments could easily occur and thus contribute to the high level of heterozygosity and the cultivar introgression previously reported in wild populations. We do not claim to have elucidated all the allogamous processes that occur in A. bisporus var. bisporus, but we believe we have uncovered an effective and unexpected process that could impact population dynamics and structure.


Early Infant Diagnosis was launched in India in 2010 and its effect on the diagnosis of HIV-exposed infants needs to be assessed. The present study was done to find out the median age at DBS sample collection for early infant diagnosis and its trend over years, the median age at diagnosis of HIV among the HIV-exposed infants with DNA PCR tests, and the proportion of infants who completed testing cascades after detection of HIV-1 in a sample.DNA PCR data (from 2013 to 2017) maintained at all regional reference laboratories in India was collated with each infant identified by a unique code. Cohort analysis of the infant data was used to find the median age at sample collection and diagnosis. The outcomes of testing in each cascade and the overall outcomes of testing for infants were prepared.The median age at sample collection for the four years combined at all India level was 60 days (48–110 days). The median age at diagnosis of HIV was 285 days (174–418 days). HIV-1 was detected in samples of 1897 (6.3%) infants out of 30,216 infants who had a DNA PCR test, out of whom 1070 (56.4%) completed the testing cascade and the rest were lost to follow-up.The data highlights delay in diagnosis; both due to delay in sample collection and turn-around-times. Loss to follow-up of HIV-exposed infants with virus detection is a significant concern to the Early Infant Diagnosis and tracking systems need to be strengthened.The online version contains supplementary material available at 10.1186/s12887-022-03656-w. Perinatally acquired Human Immunodeficiency Virus (HIV) infection has posed considerable challenges to concerning its prevention, diagnosis, and treatment in program settings globally. Mortality can be reduced by three-fourths with early initiation of Antiretroviral Treatment (ART) among such infants . More thIndia continues to be a low prevalence epidemic country and the National AIDS Control Programme (NACP) is able to detect about 15,000 HIV-positive pregnant women annually out of the estimated 22,000 . Early IWe conducted a retrospective analysis of data collected under the NACP about the EID services. The DNA PCR testing for detection of HIV-1 among samples of HIV-exposed infants is made available at seven RRLs that have digitized and stored data for each test. The DNA PCR tests at these RRLs were conducted using the Amplicor ® HIV-1 DNA Test, version 1.5 by Roche, USA on the DBS specimens. Each HIV-exposed infant is given a unique ID when the sample is collected first time for the DNA PCR test. All the subsequent DNA PCR samples from that infant are tagged with the same unique ID. Data from all seven laboratories from the year 2013 to 2017 was pooled. This dataset included some samples of infants born before 2013 whose samples were collected in the evaluation period and such data was removed before analysis. Thus, the final dataset included DNA PCR test data of HIV-exposed infants born from 2013 to 2016.Under NACP, periodic samples are to be collected at age of 6 weeks, 6 months and 12 months at the stand-alone ICTC (SA-ICTC). If the child is less than 6 months old, then a dried blood spot (DBS) sample is collected for a DNA PCR test. For infants aged 6 to 18 months, initially, antibody tests are performed at the SA-ICTC, and samples for DNA PCR tests are collected only among those who had reactive antibody tests. If HIV-1 is detected on the initial DBS sample at any period, the second DBS sample is collected for a confirmatory test. HIV diagnosis is confirmed as positive if the second sample is also detected as positive. A third sample is needed if there is a discordant result in the first and second sample. Operationally a testing cascade was considered to be completed only if the second (repeat) DBS sample is found to be positive or if the second DBS sample is found to be negative and the third DBS sample is available for testing  had a higher prevalence of HIV compared to other states and these were termed as ‘high prevalence’ states by NACP . Five ofA total of 30,216 HIV-exposed infants’ samples were received by the seven regional reference laboratories during the study period. Out of these, birth dates were available only for 23,602 for the birth cohorts of 2013 to 2016. Under the 1st testing cascade, only 10,774 (45%) of the HIV-exposed infants were brought to the SA-ICTCs for sample collection within 8 weeks (56 days) of birth and another 5382 (23%) between 8 weeks to 3 months (57–89 days)  were from the seven Indian states. The median age at initial DBS sample collection under 1st testing cascade was declined over the period of four years 2013–2016) for both the high prevalence and low prevalence states  infants’ initial samples entering the first cascade but out of which only 906 (57.5%) completed the first cascade with next sample/s  had HIV-1 detected in their initial samples but only 130 (52.4%) completed the second cascade (confirmatory DBS sample was collected and tested); 9916 (96.9%) had no HIV-detected and 69 (0.7%) had inconclusive results. Out of the 9916, 2330 (23.5%) entered the third cascade and the rest had no more DNA PCR tests.In the third cascade, out of 2330 infants, 73 (3.1%) had HIV-1 detected in their initial samples but only 34 (46.6%) reported further for confirmatory test and completed the 3rd cascade.Overall, 1897  infants had HIV-1 detected in the initial DNA PCR sample; 1576, 248 and 73 in the first, second and third cascade respectively. Of these 1897 infants, 1070 (56.4%) completed the respective cascade and the rest 827 (43.6%) did not complete the cascades.Of these 1070 infants who completed the cascades for confirming the diagnosis, 960 (89.7%) were diagnosed with HIV infection; 949 with the second sample whereas only 11 (1.14%) needed a third sample (because the second sample did not detect HIV-1). The rates of confirmation of HIV diagnosis after initial detection were 91.4% (828/906) in first, 80.8% (105/130) in second and 79.4% (27/34) in the third cascade. Overall HIV-1 was not detected among 110/1070 confirmatory samples leading to a discordance of 10.3%. It is important to note that out of the final 960 HIV positive infants diagnosed, 828 (86.3%) were diagnosed during first cascade, while 105 (10.9%) and 27 (2.8%) were diagnosed during second and third cascade respectively.The overall results of the 30,216 infants can be summarized into five categories Fig. ; infantsThis is first Indian report highlighting the national level data on how early HIV infection is being diagnosed among HIV-exposed infants (HEI) under EID program. It also reflects the status of HEI who were able to complete the cascade in order to confirm their HIV status. The data of all HIV-exposed infants born in India from 2013 to 2016 and had at least one DNA PCR test been presented in the paper. Study has highlighted that overall, 43.6% HEI who detected positive on first sample did not have confirmatory tests (cascade completion). Though, there is ample of evidence  on loss http://naco.gov.in/sites/default/files/Annual%20Report%202015-16.pdf) reflect that, only 70% infants who had a HIV test under the Early Infant diagnosis program had a DNA PCR test while rest (30%) presented after 6 months of age and were subjected to antibody test. This highlight the delay in collection of first sample. Although, the reporting time for first EID test reduced over the four-year period, it still remains high. Late reporting for giving the first sample delays the whole cascade and strategies are needed to follow-up the mother-baby pair and decentralise sample collection. Optimal decentralisation of sample collection is crucial to achieve this [The findings show that less than half of the infants who had undergone a DNA PCR test had samples collected by 8 weeks of age. The NACP annual report 2015-16  of the infants with confirmed HIV-1 detection were diagnosed in second or third cascade; their initial DNA PCRs were negative. This highlights the third possibility of ARV drug exposure discussed above. It seems that for these infants the viral loads were too low for detection during the first DNA PCR test or they might have acquired HIV infection through breastfeeding later on. The viral load would have increased to detectable levels during second/third cascade. Even after stopping ARV to the infant, some amount of anti-retroviral drugs would pass through breast milk to the baby. Hence, the strategy to test at 6 months, 12 months and 6 weeks after stopping breast feeding has relevance due to the possibility of false negative results and needs to be continued .This study found that nearly 10% of the infants with HIV-1 detected in first sample, no virus was detected in the second and third DBS samples. False positive results are also likely with lower prevalence. In India, the EID testing is performed using the Amplicor ® HIV-1 DNA Test, version 1.5 by Roche, USA which involved the manual sample processing followed by PCR. The samples are handled separately to reduce any cross contamination. However, the sample integrity and less amount of blood collected on the DBS cards are some of the factors that can contribute to negative results whereas mixing of samples with positive samples as well as mislabeling of samples at the collection site could be possible factors for false positivity of the first sample. However, the discordant samples are being further confirmed during the next follow-up visits. This phenomenon has been reported also in newer interventions with sample collection in first week . IndeterOption B plus was initiated in India in 2013 but only in three states and it expanded to the rest of the states over time. This could explain relatively high rate of HIV-1 detection in first DBS sample of 6.3%. India adopted ‘test and treat’ approach in 2017 and expanded access to HIV testing for the pregnant women which is likely to reduce the transmission rates.The median age of HIV diagnosis was 285 days among HEI in India and only 26.5% children were able to diagnose within first six month of age. Although by 2016, the age at sample collection and subsequent turn-around-time for confirmatory test improved, the median age at first sample was within 2 months whereas the median time at diagnosis remained around 6 months. This time lag between sample collection and sample testing is the crux for delayed HIV diagnosis. Decentralisation of DNA PCR testing at state levels is crucial to reduce the turn-around time and decentralisation of the DBS sample collection shall help in samples collected earlier. Point-of-care testing is being under consideration but the low prevalence setting in India shall pose an eminent challenge. The prevalence of HIV among pregnant women is very low, hence the number of HIV-exposed infants at peripheral facilities are very few. Due to better ART, the mother-to-child transmission is likely to decline significantly and that may pose further challenges to POC strategy. Even a highly specific test may struggle given poor positive predictive value due to very low prevalence. However, POC offers value in cutting down the turn-around-times and need to be tested in an Indian context, given their potential to diagnose early. The HIV epidemic in India is concentrated at few districts. This data from 2013 to 2016 shows that 40% of the cases were from 35 districts out of 700 plus districts. These districts could be focused for better implementation of existing strategies as well as implementation research for POC and similar strategies. The model of testing may be different in districts with high case load and those with low caseload .The study had certain limitations. Since the dataset at RRL level did not include antibody tests done among infants, infants who reported back at 6, 12 and 18 months cannot be confirmed from this study finding. Hence, we are unable to comment on overall retention within EID cascades. Birthdate details were not available for many infants due to poor data quality within the program settings which need to improve. Currently, infant tracking data is being maintained in the program including both antibody and DNA PCR results for HIV-exposed infants which was not the case in 2013-16. Nevertheless, this study is the first one which calculated median age at sample collection and at HIV diagnosis in India for HIV-exposed infants from birth cohorts 2013 to 2016. It also highlights significant attrition from the testing cascades resulting in missed opportunity for diagnosis and treatment of HIV infected infants.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3


Estimating accurate 3D human poses from 2D images remains a challenge due to the lack of explicit depth information in 2D data. This paper proposes an improved mixture density network for 3D human pose estimation called the Locally Connected Mixture Density Network (LCMDN). Instead of conducting direct coordinate regression or providing unimodal estimates per joint, our approach predicts multiple possible hypotheses by the Mixture Density Network (MDN). Our network can be divided into two steps: the 2D joint points are estimated from the input images first; then, the information of human joints correlation is extracted by a feature extractor. After the human pose feature is extracted, multiple pose hypotheses are generated via the hypotheses generator. In addition, to make better use of the relationship between human joints, we introduce the Locally Connected Network (LCN) as a generic formulation to replace the traditional Fully Connected Network (FCN), which is applied to a feature extraction module. Finally, to select the most appropriate 3D pose result, a 3D pose selector based on the ordinal ranking of joints is adopted to score the predicted pose. The LCMDN improves the representation capability and robustness of the original MDN method notably. Experiments are conducted on the Human3.6M and MPII dataset. The average Mean Per Joint Position Error (MPJPE) of our proposed LCMDN reaches 50 mm on the Human3.6M dataset, which is on par or better than the state-of-the-art works. The qualitative results on the MPII dataset show that our network has a strong generalization ability. With the popularity of virtual reality devices, the task of 3D human pose estimation from a monocular image is gaining importance and becoming one of the challenging tasks in the field of computer vision. A general and effective method for 3D human pose estimation is a two-stage approach, in which the 2D joints are detected from a monocular input first , and theWhile deep learning brings remarkable progress in many computer vision tasks, 3D human pose estimation from a monocular image still faces the problem of depth ambiguity. To be specific, different 3D human poses may have the same or similar 2D projections under different conditions, such as lighting or viewing angle, making the task of estimating 3D human poses based on deep learning from a single RGB image uncertain. Some methods use various geometric constraints between joints, such as joint limits , etc., tIn contrast to single estimation, it can effectively eliminate model over-fitting and enhance the generalization capability. Based on it, Li et al.  proposesIn order to solve the problems above, we propose the Locally Connected Mixture Density Network (LCMDN) for 3D human pose estimation. The LCMDN proposes improvements for the MDN  and is i(1)We propose an LCN-based human pose estimation network that learns a Gaussian mixture model matching the distribution of human joints to output multiple hypotheses.(2)LCN is applied to a 3D human pose estimation task with multiple pose outputs, which improves the accuracy of the estimation task by learning the structural relationships of human joints.(3)A 3D pose selector is design to select the best predicted 3D human pose. In the selector, an ordinal matrix containing joints relationship is learned from the input RGB images via an hourglass network.(4)Our network achieves comparable or better results than the state-of-the-art in terms of accuracy and visualization with better robustness, and experimental results on the MPII human dataset validate the generalization ability of our method.We test the proposed LCMDN on public datasets. Qualitative experiments show that our method can effectively obtain accurate 3D human poses from 2D images. Quantitative experiments show that our network achieves outstanding estimation accuracy. In addition, we conduct robustness tests and ablation studies for the proposed LCMDN for further analyses of the network’s ability. The main contributions of this paper are listed as follows:Graph convolution network (GCN) is commonly visible in computer vision and achieves state-of-the-art performance by leveraging GCN to model the relations such as temporal sequences ,17 and vCurrently, several works attempt to apply GCN to human pose estimation tasks. Zhao et al.  proposedIn this paper, we apply the idea of graph convolution to the MDN-based human pose estimation task to replace FCN. Our proposed LCMDN learns the semantic relationships between body joints by introducing a unique weight matrix for each joint point.Existing methods for estimating 3D human pose are divided into two categories according to their inferring process. One is an end-to-end approach ,30,31,32The other method ,6,7,8,9 Considering the special characteristics of human joints, most of the multi-pose estimation hypothesis methods do not exploit the structural relationships between joints. In view of this, we apply the LCN to the 3D human pose estimation task with multi-pose output and design a 3D human pose estimation network that can exploit the structural connectivity relationships of human joints effectively based on a step-by-step training model.Moreover, the methods using a mixed density model ,34 have In this section, we first introduce the modeling of our proposed LCMDN. The architecture of LCMDN is shown in M is the number of Gaussian kernels, mth Gaussian distribution, and mth Gaussian distribution, which can be denoted asmth kernel, respectively.Different from directly training CNNs to estimate the 3D human poses from images or 2D poses, our network can estimate multiple diverse 3D pose hypotheses by learning the Gaussian distribution of human body poses. More specifically, the probability density of 3D pose joints The whole training process of our network can be described as follows: given the 2D joint detections, it outputs the parameters of the Gaussian mixture model. The multiple hypotheses for 3D human pose estimation can be generated from the Gaussian mixture model.Furthermore, in our proposed LCMDN, to select the best 3D pose from the multiple hypotheses, a joints ordinal ranking matrix is generated from the input image, which indicates the depth position relationship between joints. The estimated pose that matches the relationship best is considered as the final estimated 3D human pose.To extract 2D pose joints, the state-of-the-art stacked hourglass network  is adoptS is shared for all LCN layers and is constructed based on the specified joints dependence. Weight matrix W is learned end-to-end, and it varies with different LCN layers.For existing classical MDN-based 3D pose estimation methods , the fulK. For instance, the distance between Joint2 and Joint1 is 1 because they are directly connected. The distance from Joint2 to Joint4 is 2 because they are connected indirectly through Joint3.Joint dependence is determined by ensuring whether the distance between two joints is less than a hyper-parameter Specifically, as shown in M denotes the number of Gaussian kernels.Unlike the regression of 3D human joint point coordinates directly from human pose features, the role of the hypotheses generator is to learn the Gaussian mixture distribution model of human 3D joint points from human pose features. As shown in x. Specifically, suppose the human features learned in the feature generator are The mixing coefficients To ensure the validity of the variance In recent work ,37,38,39Similar to Sharma , we use The whole training process is divided into two parts. Firstly, multiple 3D poses are output from the Hypothesis Generator. Then, the pose selector is used to obtain the ordinal matrix from the input images. The obtained matrix is compared with the ordinal matrix generated from the predicted poses. The one that matches best is used for the backpropagation in the training phase.During the testing phase, multiple human pose hypotheses are generated, and corresponding ordinal matrices are outputted. Then, the pose selector selects the most realistic 3D pose by comparing ordinal matrices and finding the best match.In this section, qualitative and quantitative experiments are conducted to evaluate our proposed LCMDN. At first, implementation details, datasets and metrics are presented. Then, we test the network on the Human3.6M  and MPIIFor the optimizer, we choose the Adam  with explization  is appliThe hardware for experiments includes the following: CPU: Intel i5-10500, GPU: RTX 3060, RAM: 32 GB. The developing environment is Ubuntu16.04, CUDA 10.1, cuDNN 7.4, and TensorFlow.Human3.6M dataset : The Human3.6M dataset is a large public human pose dataset with about 3.6 million indoor images. These images are captured to show 15 different human actions performed by seven people. The dataset offers both 2D and 3D human pose ground truth by labels of human joints’ positions. Different subjects are selected for training  and testing . We follow [MPII dataset : The MPII dataset is a small 2D human pose dataset with image data mainly from major video sites, such as YouTube, and it provides before and after unannotated frames. The MPII dataset includes about 25,000 images with human behavioral actions, including over 40,000 annotated human joints, covering 410 human activities, including walking, sitting, skiing, and hiking. It is one of the mainstream human pose datasets. Since the MPII dataset does not provide 3D pose information, we only report qualitative visualization results for the MPII dataset.Evaluation Metric: For Human3.6M, there are two common evaluation protocols. One standard protocol computes the Mean Per Joint Position Error (MPJPE) in millimeters between the ground truth and predictions after being aligned with the root joint. This metric is called Protocol #1. The other protocol further aligns the predictions with the ground truth, leveraging a rigid transformation. This protocol is called Protocol #2. We use the more challenging Protocol #1 for evaluation in all the experiments.The experiment results of our method and several latest methods for 3D pose estimation on the Human3.6M dataset are shown in From the results, although the LCMDN is without any multi-view or video information, it still outperforms most other spatial or temporal constraints-based methods due to the extraction of human joints correlation, which indicates the improvement of our model.Considering some joints are often occluded or missing due to various kinds of interference in realistic scenarios, we simulate the situation of missing joins by randomly dropping one or two limb joints to test our network. The results are reported in To demonstrate the effectiveness of our method more intuitively, we give some visualization results in Different step of neighbors. To verify the effect of the neighborhood distance K , for 3D human pose estimation. Our network introduces the Locally Connected Network to the Mixture Density Network to integrate the representation and optimization ability of these two networks.The LCMDN generates multiple 3D pose hypotheses at first. Then, the most realistic pose is selected based on the positional relationship of joints via the pose selector of the LCMDN. It solves the result selection problem when there is no ground truth label to evaluate the best result of multiple hypotheses.Experimental results show that the average MPJPE of our proposed network reaches 50 mm on the Human3.6M dataset, which is on par or better than the state-of-the-art works. Moreover, it is robust in the occlusion or undetected joints scenario and generalizes well for the in the wild scenario. In the future work, we will optimize our network ,46 for b


Patients with mental illness have an increased risk of cardiovascular morbidity. The Swedish-developed Health Dialogue is a pedagogical tool to individualize lifestyle counselling, used in specific age-groups to improve lifestyle habits and decrease mortality, but not tested specifically for patients with mental illness. Patients > 18 years old seeking primary care due to symptoms related to mental illness and diagnosed with depression, sleeping disorders, stress and anxiety, were included. A nurse-led health dialogue was conducted, focusing on lifestyle habits, anthropometric measurements, and blood samples, resulting in tailored advice regarding the individual’s risk profile. All 64 participants had lifestyle areas with increased risk level. Approximately 20% had elevated fasting glucose, blood pressure or cholesterol levels, and over 40% had highest risk level in Waist–Hip-Ratio. 30% were overweight, or physical inactive. The results suggest the need of a larger cohort study with long-term follow up, to establish potentially positive effects on wellbeing, and decreased cardiovascular risk in patients with mental illness.Clinical trial registration: The study was registered at ClinicalTrials.gov January 6th, 2022, registration number NCT05181254. Patients with mental illness in Sweden are managed on different levels, depending on the condition’s severity. Depression, anxiety, stress, or sleeping disorders are common conditions handled by the primary care, while patients with more severe psychiatric diagnosis are treated in psychiatric clinics. It is of great importance to identify high-risk patients early in order to be able to prevent and treat high blood pressure, diabetes, hypercholesterolemia and obesity and to reduce the risk of metabolic and cardiovascular complications4.Both patients with severe mental illness such as bipolarity or schizophrenia and patients with depression and anxiety, have an increased risk of cardiovascular morbidity and mortality compared to the rest of the population, partly related to a higher risk of obesity and diabetes5. Meanwhile, the patients want to be encouraged by the health care to change their habits into a healthier lifestyle6. Positive life changes and a functioning social network can contribute to a faster recovery from mental illness7. There are good examples of lifestyle interventions with physical activity and diet in primary health care for this group of patients, with significant improvements on stress, anxiety and depression9. There are also indications that individualized programs provide better effects on health and cardiovascular risk factors compared with general lifestyle screening in the population10. Targeted health conversations can reduce the risk of developing type 2 diabetes mellitus and cardiovascular disease11. To individualize lifestyle counselling in primary care, the Swedish-developed Health Dialogue  showing a risk assessment from green to yellow, orange and red Fig.  to be us13. A following study showed that the Health Dialogue is a prognostic tool for assessing the risk of developing diabetes, cardiovascular disease, and cancer14. A more recent study with a long-term follow-up showed even reduced mortality after a Health Dialogue15.In a population-based cohort of 35-year-olds, with an intervention consisting of a health survey based on the Health Dialogue, the participants reported improvements in several lifestyle habits such as less smoking, lower fat intake and higher physical activityHowever, the Health Dialogue is not aimed specifically at patients with mental illness, despite a potentially higher expected benefit of the intervention of this group with an increased risk for metabolic and cardiovascular complications. There are no previous studies on the effect of a systematic approach with the Health Dialogue in patients with mental illness in primary care.The main aim of this first part of the HEAD-MIP project is to make a base-line description of the patient population included in the project at a primary health care center.Patients > 18 years old seeking one primary health care center due to symptoms of mental illness and diagnosed with depression, anxiety, sleep disorders or stress by a general practitioner were offered to participate in the study at the same time with initiation of treatment as usual (psychotherapy or/and medication). At baseline the patients filled out a questionnaire regarding lifestyle habits before the Health Dialogue at the health care center. They were also appointed for fasting blood sampling, measurement of blood pressure, and BMI. A nurse with special training in the Health Dialogue met the patient and provided individually tailored advice based on the patient's unique conditions and the risk profile on the Health Dialogue, such as help with smoking cessation, Swedish version of physical activity on prescription (S-PaP), contact with a dietitian or a physiotherapist. A continued contact with a psychologist or physician was planned if necessary. Patient recruitment took place opportunistically  and chronologically from the start of the project in February 2020.Assessment of the risk profile on the Health Dialogue, self-reported answers about lifestyle  and metabolic markers  from the Health Curve.2 test. A p-value < 0.05 was considered significant. SPSS version 27 (IBM Corporation®) was used for all statistical analyses.Baseline data were analyzed with descriptive statistics. Group comparisons were analyzed with Student’s t-test and χThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Swedish Ethical Review Authority, reference number 2019-04990.Informed consent was obtained from all individual participants included in the study.A total of 64 patients were included in the study. All patients had at least one psychiatric diagnosis, 27 patients (42%) had two diagnoses and 8 (12.5%) even a third psychiatric diagnosis. At baseline there were 24 (37.5%) patients diagnosed with depression, 19 (29.7%) diagnosed with anxiety disorder, 22 (34.4%) diagnosed with stress-related disorder (burnout) and 23 (35.9%) diagnosed with sleeping disorder. Seven patients (11%) were already at baseline diagnosed with hypertension, 3 (4.7%) with diabetes, 1 (1.6%) with cardiovascular disease and 3 (4.7%) with hyperlipidemia.Twenty-one percent of the patients had abnormal fasting glucose values or elevated blood pressure and almost thirty percent had elevated cholesterol levels. Twenty-three percent were very physical inactive and even three quarters of the patients were overweight or obese. Further characteristics are showed in Table Risk levels were measured based on the reports on the Health Dialogue formulary, and the results are showed in Table No participant had the lowest level (1/green) in all measured domains. Forty-nine of the 64 individuals (76.6%) had an elevated risk of at least 2  for three or more lifestyle areas  had the highest risk level (3/orange or 4/red) on at least one of the studied lifestyle areas (Table 2) was found in 31% of the women and 41% of the men. There was a slightly higher proportion of overweight (BMI 25–29.9 kg/m2) among men with 47% compared to women 40%. The distribution of WHR is showed in Fig. Almost a quarter of both women 23.4%) and men (23.5%) had the highest level (4) for physical inactivity with less than 500 kcal/week. Obesity (BMI ≥ 30 kg/m.4% and m2 test and Student’s t-test) of higher-risk individuals (defined as having a level of 2 or more in at least 2 lifestyle areas) with lower-risk individuals showed no differences regarding sex (p = 0.30) or age (p = 0.72).Group comparison analyses , abnormal fasting glucose values  or physical inactivity . The cohorts might not be entirely comparable, since the population in the present study was older (mean age 51.9 years) and all participants were included in the study due to psychiatric diagnoses. However, self-reported data in the screening project of the 40-year-old individuals, showed that 41% of the women and 34% of the men had felt depressed, and roughly half of the participants had sleeping problems during the last year. Many of our study participants in the HEAD-MIP-study, were physically inactive. We believe that a targeted intervention with focus on lifestyle improvement in the group patients with psychiatric diseases might be even more cost effective, as positive life changes as improved diet and increased physical activity have been showed to increase remission rates of non-psychotic mental illness18.Our results are comparable with findings in a screening project using the same method, performed at the same time among 40-year-old individuals in the general population in Southern Sweden, with 411 included participants19. The accumulation of several bad habits, smoking, high alcohol consumption, inactivity and poor nutrition, increases the risk of depression. It has also been shown that psychological wellbeing is influenced by lifestyle habits, with reported higher grade of psychological wellbeing among those who had a healthy diet, were physical active and nonsmokers20.In older adults it has been revealed that lifestyle habits such as smoking or low levels of physical activity can indicate mental illness such as depressionDespite the small sample size of this study, it confirms the hypothesis that this group of patients needs closer attention regarding lifestyle areas that might affect the psychiatric wellbeing as well as increase the risk to develop diabetes or cardiovascular disease. Detecting patients with the highest risks allows targeted interventions aiming to improve mental wellbeing but also lowering the cardiovascular risk and complications. We could show that more than 20% had either elevated blood glucose, cholesterol, blood pressure or several altered measurements. Even if some of the participants had previously diagnosed diabetes, hyperlipidemia or hypertonia, the Health Dialogue helped to discover impaired lab results even in other participants without previously known chronic disease.The results indicate that Health Dialogues might be an effective method of detecting elevated blood glucose and blood pressure in patients with mental illness. Early detection enables follow-up combined with treatment using lifestyle interventions and if necessary, even with medication, to avoid or at least postpone both a chronic disease diagnosis and later complications.To our knowledge, the use of a Health Dialogue has not previously been studied in a cohort of patients with mental illness. In the second part of the HEAD-MIP study we are aiming to evaluate the effect of the Health Dialogue on lifestyle habits among patients with mental illness.21. Greater financial stress may also be associated with increased rates of mental illness. Even if patients were listed on the same health care unit, meaning that the socioeconomic status is expected to be approximately the same on group level, individual socioeconomic differences might have existed.The study cohort is limited to a small amount sample size of patients recruited at one primary health care center, therefore with low generalizability. A larger cohort with higher sociodemographic representation and several primary health care centers is planned. The screening project with 40-year-old individuals using the same method in the same primary care context showed good feasibility in the general population as well as cost effectiveness. A major limitation in our study is that we were not able to study the inclusion rate, due to the recruiting method. Several caregivers  included patients opportunistically after the initial contact at the primary health care center due to psychiatric illness. However, we believe that the inclusion method might have included a higher rate than screening with invitation letter in this group of patients, a strength of the study is therefore the opportunistic patient inclusion, with patients being recommended a lifestyle assessment by the caregiver, minimizing the risk for inclusion bias. Severe mental illness was not an exclusion criterion. However, patients with severe mental illness are not usually managed in primary care in Sweden and none were therefore included in this study. Our results are therefore not transferable to this patient group, and this is a limitation of the study. Another limitation of the study is lack of data on socioeconomical status of the participants. Socioeconomic status is associated with increased prevalence of tobacco, physical inactivity, and poor nutritionSeveral participants in our study had increased levels of blood pressure and fasting blood-glucose. Even if these surrogate measurements might indicate possible development of chronic disease as high blood pressure or diabetes, diagnosis needs several repeated valid values. Another limitation is lack of data about medications with potential side-effects as weight gain such antipsychotics or some anti-depressants. Patients with pre-existing conditions as diabetes or hypertension diagnosed prior to the inclusion in the present study have probably received qualified lifestyle counselling by health care personnel at the point of diagnosis. Due to the limited number of patients in the present study, no subgroup analysis was made. Future studies should take this aspect in consideration.The Health Dialogues in primary care patients with mental illness showed that all participants had at least one lifestyle area with increased risk level to develop diabetes or cardiovascular disease. In addition, most of the participants were obese, physically inactive, or both. We suggest that this patient group should receive closer attention and argue that a larger cohort with long-term follow up is highly motivated.Supplementary Information.


For Ninapro DB2 exercise B (17 hand movement classes), this improvement was from 81.43 to 82.87%. Moreover, three stages of analysis in task-transfer approach proved that it is possible to classify combination hand movements using the knowledge learned from a set of basic hand movements with zero, few samples and few seconds of data from the target movement classes. First stage takes advantage of shared muscle synergies to classify combined movements, while second and third stages take advantage of novel algorithms using few-shot learning and fine-tuning to use samples from target domain to further train the classifier trained on the source database. The use of information learned from basic hand movements improved classification accuracy of combined hand movements by 10%.Over the past several years, electromyography (EMG) signals have been used as a natural interface to interact with computers and machines. Recently, deep learning algorithms such as Convolutional Neural Networks (CNNs) have gained interest for decoding the hand movement intention from EMG signals. However, deep networks require a large dataset to train appropriately. Creating such a database for a single subject could be very time-consuming. In this study, we addressed this issue from two perspectives: (i) we proposed a subject-transfer framework to use the knowledge learned from other subjects to compensate for a target subject’s limited data; (ii) we proposed a task-transfer framework in which the knowledge learned from a set of basic hand movements is used to classify more complex movements, which include a combination of mentioned basic movements. We introduced two CNN-based architectures for hand movement intention detection and a subject-transfer learning approach. Classifiers are tested on the Nearlab dataset, a sEMG hand/wrist movement dataset including 8 movements and 11 subjects, along with their combination, and on open-source hand sEMG dataset “NinaPro DataBase 2 (DB2).” For the Nearlab database, the subject-transfer learning approach improved the average classification accuracy of the proposed deep classifier from 92.60 to 93.30% when classifier was utilizing 10 other subjects’ data  Over the past several years, electromyography (EMG) signals have been used as a natural interface to interact with several sorts of external devices  such as Traditionally, surface EMG (sEMG) signals were pre-processed and segmented into windows; then, signal features were calculated over each window and fed to a classifier . A signigithub.com/Rahil-Soroush/Nearlab_sEMG_dataset).This work aims to explore data transferability in sEMG hand gesture classification and to design algorithms to be used by myo-controlled upper limb devices. To this aim, the contributions of this paper are the following. (i) The development of a new subject-transfer framework to be used when a limited amount of data is available for every single subject. In this regard, the effect of different database sizes for each subject and the number of subjects which we borrow knowledge from, on the performance of the proposed novel TL algorithm is explored. (ii) The investigation of valuable shared information between two different movement groups and the design of a mechanism to take advantage of this shared information. To the best of authors’ knowledge, sEMG data transferability between complex movements and their associated basic movements have not been explored in a TL study using a Deep Leaning approach. It is important to mention that all the proposed classifiers are designed with the consideration of real-time execution. (iii) The introduction of a new sEMG-based hand gesture database referred to as “Nearlab dataset.” The main reasons to create a dedicated sEMG database for this paper was first to add combined movements, suitable for task-transfer experiments, which are not present in other public databases. Second, to include certain sEMG variabilities present in real-life applications. Indeed, one of the sources of EMG variability is the orientation of hand when performing the hand gestures. By considering 3 hand orientations when executing each hand gesture, this variability was accounted for in the proposed database. The proposed approaches are tested on Nearlab dataset and on an open-source sEMG dataset (Ninapro database 2) . NearlabWe acquired sEMG-based hand/wrist movement dataset to compose the Nearlab Dataset, which we made available to the scientific community, as one of the main contributions of this work. This database includes hand muscles’ electrical activities detected by surface electrodes when performing 8 basic hand movements and 6 combined movements. To the best of our knowledge, this is the only published dataset which includes two sets of basic and combined tasks from same subjects, and it is our hope that it will become a useful tool to compare different task-transfer strategies. The Nearlab dataset comprises sEMG signals of 11 able-body subjects . The only inclusion criterion was the absence of a history of neuromuscular disorders. The ethical research committee of Politecnico di Milano approved the data acquisition protocol on October 16th, 2019. All participants had been briefed about the experiments and gave informed written consent.Acquisition Setup - SEMG signals have been acquired by 10 differential channels using passive Ag/AgCl electrodes with conductive gel and were sampled at the rate of 2,048 Hz with “Porti” polygraph from TMSi . A MatlaExperimental Protocol - The experimental protocol was composed of two phases, with instructions presented to subjects through a video cue. Phase 1—each participant was required to perform 15 repetitions of each of the 8 classes of basic movements . In orde1.Upward starting position, where the palm is faced upward;2.Sideway starting position, where the palm is faced medially;3.Downward starting position, where the hand palm is faced to ground.Movement instructions were presented in random order. Each movement was performed and held for 5 s following 3 s rest and 2 s preparation windows. Phase 2—each participant was required to perform 4 repetitions of each of the 6 classes of combined movements  with sideway starting position. As before, the order of movements was random. Each movement was performed and held for 7 s following 3 s rest and 2 s preparation windows. Incorrect basic or combined movements were discarded based on operators’ observations at the end of each experimental session, before any processing.Atzori in 2014  publisheNearlab raw data were filtered using a 10–500 Hz band-pass filter (Butterworth 4th order) and a 50 Hz Notch filter (Butterworth 2nd order) . FiltereNinapro data was already preprocessed by the creators of the database using notch filter (Hampel) to remove power-line interference (50 Hz and harmonics). In the present study the pre-processed data were segmented into windows of 250 ms (512 samples) length and 62.5 ms (128 samples) stride. Repetitions 1, 3, 4, and 6 were included in the training set, and the two remaining repetitions were considered as the test set, as suggested by the introducers of the Ninapro dataset , 2016.In order to compare the performance of proposed deep networks, classical classifiers with few selected common features were tested.Based on the available literature on feature selection for sEMG classification  and dataWe selected (i) K-Nearest Neighbors (KNN) with 40 neighbors and Euclidean distance metric, (ii) Support Vector Machine (SVM) with linear kernel and regularization parameter equal to one, (iii) Multilayer Perceptron (MLP) with 300 neurons in the single hidden layer, tanh as activation function and 0.0001 learning rate and (iv) LDA as reference classifiers, given that they have shown promising results in many studies in sEMG classification, including . The optTwo baseline CNN s are proposed in this article. Both architectures can be divided into 2 parts. The first part is an inter-connected network of convolutional blocks working as a “feature extractor” and the second part consists of few fully connected layers serving as the “classifier.” The mentioned networks are implemented using Keras (v 2.1.0) , Python Drop out: Batch normalization: Introduced by Early stopping: This regularization technique monitors the validation error in each update during training. When it reaches a minimum, the learner continues training only for a certain number of iterations and then stops the training. This mentioned number of iterations is referred to as Patience and is set by the user.This architecture includes 3 consecutive convolution blocks constructing the feature extractor part, followed by 2 fully connected blocks as the classifier part . Each coThe architecture of Cnet1D is similar to that of Cnet2D. However, the filter’s shape is such that it does not exploit the relations between channels in the feature extraction part. Filter sizes of convolutional layers are , , , respectively. Similar to Cnet2D, Adam optimizer is used. Excluding filter sizes, this network’s architecture is the same as Cnet2D depicted in Two transfer learning frameworks are defined, which are applied exclusively to previously defined deep neural networks.In order to apply the subject-transfer framework to Nearlab subjects, one deep network (Cnet1D or Cnet2D) is trained on the target subject’s database (referred to as “Target Network”). Another network (referred to as “Source Network”) is trained on all subjects except the target subject (10 subjects). The features extracted by the mentioned networks are concatenated. Their classifier parts are disregarded, and a new classifier part is added after the feature layer, completing the architecture of “Final Network.” Parameters of the two parallel feature extractors are then frozen , and the classifier part (fully connected layers) is trained using the target subject’s database . Thus, in the process of the proposed transfer learning method, target subject’s data is used two times at two different training stages. This parallel feature architecture is referred to as “PFCnet.” The task-transfer framework aims to transfer knowledge learned from the database including 8 basic movements, to another classification problem containing 6 combined movements. Each combined movement is associated with two basic movements which are always a wrist rotation (pronation or supination) and a hand function . Task-transfer experiments are introduced in three stages. In the first stage, the following question is targeted: is there any valuable shared information to be transferred between source (8 basic movement) and target domain (6 combination movements)? Here we propose a method which uses the classifiers trained on source domain to classify the target domain movement classes with zero labeled target domain samples. This method is relying on the fact that a combined movement can be reconstructed by a combination of muscle synergies used in basic hand movements. An accuracy higher than chance level would imply that source domain data has valuable shared information with target domain data. In the second stage, a few-shot learning approach  has beenIn the first stage, no training data are used from the combined movement dataset, and the classifiers are only trained with the basic movement dataset. The output layer of the networks is then used to extract more than one intended basic movement. When a combined movement is given to the network, the rotation is first determined by extracting the more probable class between pronation and supination according to associated neurons in the final layer. The hand function is then determined by extracting the most probable class between pinch, lateral pinch, and grip. Thus, six movement classes are recognized based on outputs of 8-neuron final layer. This network is referred to as “Syn0net,” highlighting the fact that combined muscle synergies of basic movements was the main idea behind this network while 0 samples of target domain data was used to train it. An additional experiment is designed to examine the activation function penalization when there is uncertainty in classification. Hence, last layer activation function is replaced with Sigmoid function, trained on the basic movements dataset and tested as described before with combination movements (please see section “Task-transfer experiments” for details). The results from this stage will determine if the information contained in basic movement group is useful by their own to classify the target domain movements (6 combination movement).In the second stage a 2-branch Siamese network  is impleIn the third stage, the network trained with basic movements is modified to adapt to the target domain specification. The last layer which previously had 8 neurons with Sigmoid/Softmax activation function is replaced with a layer with 6-neuron with Sigmoid activation function to meet the number of classes in the target domain (6 combination movement). To train the newly added layer, only 1 repetition of the combination movements (7s of movement per class) is used. Thus, the new layer (which is after a fully connected layer with 50 neurons) with 306 new parameters (50 × 6 = 300 weights and 6 bias parameters) is trained with only approximately 430 samples. It’s important to mention that the parameters from the previous layers are locked (non-trainable). To train, 400 epochs with learning rate 0.01 reduced to 0.000002 and batch size 128 are used. Next, the parameters from the 50-neuron fully connected block before the 6-neuron layer are unlocked. Then 50-neuron block and 6-neuron block are fine-tuned with low learning rate with the same limited training data mentioned before. 15,456 parameters are fine-tuned in this stage. This network is referred to as “FTnet.” To fine-tune, 300 epochs with learning rate 0.0001 and batch size 128 are used. Going further back in the network exposed too many parameters to be trained with small training data and worsened the performance of the network.post hoc pairwise test with the Wilcoxon signed-rank test. Finally, Holm’s method is applied to significance thresholds  to control the group comparisons’ family wise error rate.The most common performance measure in a classification problem is “accuracy,” namely the number of correct classifications over the total number of classifications of testing samples. Moreover, it is necessary to ensure that the proposed methods can satisfy the time constraints of real-time applications. In order to do so, one random subject is selected, and the time needed for producing prediction related to each network is calculated by averaging this time over 1,000 repetitions of the procedure. Pairwise Wilcoxon signed-rank test is used to determine if two methods have significant statistical difference in their classification accuracy. Wilcoxon signed-rank test is a non-parametric statistical hypothesis test suitable for a repeated measure design where two different conditions are applied to the same subjects . It is aAll combinations of 4 selected classical classifiers with 4 feature sets were employed. The average testing classification accuracy and standard deviation over all subjects for Ninapro DB2 exercise B (17 movements) are shown in p < 0.0001) with PFCnet applied to Cnet2D achieving the highest accuracy. The p-values mentioned in p-value of PFCnet applied to Cnet2D vs. KNN is < 0.00001).The PFCnet method was applied only to Cnet2D, which had higher testing classification accuracy. The need for transfer learning is more evident when the training data is limited. We simulated this situation by training the networks with portions  of the training data of the target subject, removing one entire repetition at a time. The average accuracy over all subjects is shown in Combinations of 4 feature sets and 4 classical classifiers were tested on all subjects of the Nearlab database . Also, tp = 0.27, pairwise Wilcoxon test), only the results related to Cnet1D, which produced higher accuracy, are reported. In p-values were 0.00022, < 0.00001, 0.00015, respectively. The p-values mentioned in The PFCnet architecture was applied to Cnet1D and Cnet2D. The average testing accuracy over all subjects using 1/3, 2/3, and 3/3 of the target subject training dataset  is shown in p = 0.0097.p = 0.0097.The study aimed to apply transfer learning approaches for hand movement intention detection based on sEMG signals as a solution for training deep learning algorithms with limited EMG data. These algorithms can be used in EMG-controlled devices for human-computer interaction, with possible repercussions on hand prosthesis. We proposed two deep learning architectures as the base classifiers and two transfer learning algorithms with two different perspectives, taking advantage of similarities found in EMG patterns of different subjects or different tasks.The following discussed results are related to Nearlab and Ninapro DB2 databases. The number of movement classes for Nearlab, and Ninapro databases are 8 and 17, respectively. Also, the number of repetitions per movement class is higher in Nearlab comparing to Ninapro DB2. Consequently, the average accuracy of Nearlab is higher than Ninapro DB2 in all experiments. Another notable difference between the two databases is the addition of combined movements in the Nearlab dataset.The proposed deep network approaches can perform equally or more accurate comparing to reference classical classifiers for both tested databases , 6A. We Based on Between the two proposed CNN networks, Cnet1D for the Nearlab dataset and Cnet2D for the Ninapro dataset achieved the higher average accuracy. Cnet2D classifier is exploiting the relationship among adjacent channels in the feature extraction part. Since the electrode placement is different in Ninapro and Nearlab datasets, Cnet2D is expected to behave differently.In 2016, The idea of leveraging the other subjects’ sEMG data has been previously studied . Cote-AlThe novelty of this study in comparison with the mentioned TL studies, in addition to a new subject-transfer framework, is exploring and designing/testing new mechanisms for task-transferability in different movement groups. A further element is the inclusion of the sEMG variability introduced by different hand orientations in the created dataset. An additional consideration, which makes the results of this paper closer to hardware implementation, is its consideration on execution time in accordance with real-time criteria.According to Task-transfer learning has been previously examined by In this paper, two methods of transfer learning for hand movement classification were investigated. A subject-transfer framework was proposed, which took advantage of shared information among all subject’s sEMG data. A CNN-based network was pre-trained on the target subject while another network was pre-trained on all subjects except the target subject. Feature extractor parts of these two networks were frozen and concatenated at the final layer. Finally, a classifier part was added to complete the final network. The subject-transfer framework was tested on the Ninapro DB2 dataset and a dataset developed on purpose for this paper. Experimental results proved that the proposed framework could enhance the classification accuracy using other subject data and act as a feasible solution when the target subject’s data is not sufficient.Moreover, three task-transfer frameworks were proposed to classify combined hand movements in 3 stages. Initially, the network trained only with basic movement’s sEMG data (Syn0net) was used to classify combined movements. In the later stages, task-transfer mechanisms including few-shot learning (Sia5net) and fine-tuning using minimum training data from target domain (FTnet) were implemented to classify the combined hand movements. According to experimental results, these frameworks were able to transfer knowledge from basic tasks to combined tasks significantly increasing classification accuracy with minimum training data for combined movements. The proposed method could be beneficial when the aim is to increase the number of movement classes without acquiring a big training dataset. Therefore, these three frameworks could be employed to avoid acquiring large databases from each individual.In the future, the performance of proposed methods will be further evaluated using databases containing multi-session recordings and end users. Moreover, the application of the proposed frameworks will be investigated in online experiments. The pre-processing steps and classification method will be modified to meet the conditions of real-time classifications, and the effect of visual feedback will be investigated. Additionally, the effect of cross-subject variability in our database can be further investigated with the methods inspired from recent literature .github.com/Rahil-Soroush/Nearlab_sEMG_dataset.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The studies involving human participants were reviewed and approved by the Ethical Board of Politecnico di Milano (Opinion no. 26/2019). The patients/participants provided their written informed consent to participate in this study.RS and SJ contributed to conceive the study, and experimental protocol definition, acquired, analyzed the data, and drafted the manuscript. MG and AP conceived the study, contributed to the experimental protocol definition, performed data interpretation, supervised the project, and substantially contributed to manuscript writing. All authors read and approved the final version of the manuscript.


Assessing tidal volume during mechanical ventilation is critical to improving gas exchange while avoiding ventilator-induced lung injury. Conventional flow and volume measurements are usually carried out by built-in pneumotachographs in the ventilator or by stand-alone flowmeters. Such flow/volume measurement devices are expensive and thus usually unaffordable in low-resource settings. Here, we aimed to design and test low-cost and technically-simple calibration and assembly pneumotachographs. The proposed pneumotachographs are made by manual perforation of a plate with a domestic drill. Their pressure-volume relationship is characterized by a quadratic equation with parameters that can be tailored by the number and diameter of the perforations. We show that the calibration parameters of the pneumotachographs can be measured through two maneuvers with a conventional resuscitation bag and by assessing the maneuver volumes with a cheap and straightforward water displacement setting. We assessed the performance of the simplified low-cost pneumotachographs to measure flow/volume during mechanical ventilation as carried out under typical conditions in low-resource settings, i.e., lacking gold standard expensive devices. Under realistic mechanical ventilation settings , inspiratory tidal volume was accurately measured (errors of 2.1% on average and <4% in the worst case). In conclusion, a simple, low-cost procedure facilitates the construction of affordable and accurate pneumotachographs for monitoring mechanical ventilation in low- and middle-income countries. The importance of mechanical ventilation in patients requiring intensive care for acute respiratory failure has been recently underlined as a public health issue worldwide in the context of the COVID-19 pandemic. This major healthcare emergency has highlighted the need for sufficient mechanical ventilators to be readily available even in countries with the most developed health care systems . Indeed,Measuring inspiratory airflow and computing the associated tidal volume is crucial for achieving suitable gas exchange levels while providing protective ventilation  thereby In this context, the work described herein aimed to devise and test a procedure for very easily constructing and calibrating low-cost pneumotachographs that are robust, accurate, and affordable for users in low-resource regions. The devised procedure, which does not require expensive gold standard measuring devices , 15, was1 and K2 represent the linear and non-linear components of the resistor, respectively.The simple and low-cost procedure proposed for constructing an accurate pneumotachograph is based on creating a resistor by drilling a series of parallel narrow cylindrical channels, as illustrated in 1 and K2 can be determined from the pressure signals (P1 and P2) recorded from the pneumotachograph during any two different maneuvers involving known volumes . The proposed setting, which is based on measuring the volume of air from the volume of water displaced by the airflow maneuver, is described in We have devised a simple procedure for accurately calibrating a non-linear pneumotachograph. Indeed, as delineated in greater detail in 2O, LCVR; Celesco, Canoga Park, CA, USA)] from the volumes of the manual maneuver determined by water displacement. To cover a range of flows representative of those commonly used in mechanical ventilation, the maneuvers should include relatively both low and high flows within the clinical ranges. To this end, the resuscitation bag operator  was simply asked to perform two maneuvers: (a) slowly, with an almost constant compression rate to empty the resuscitation bag (≈1 L) in 6–10 s (to target flows in the range 0.1–0.2 L/s), and (b) a fast maneuver to almost empty the bag in 2–3 s to ensure higher flows. As this maneuver offered more possible options to select the rate of bag compression, the operator was asked to carry out four maneuvers of this type.We assessed the practical reproducibility of the procedure for calibrating the low-cost pneumotachograph number 2 in 2O) and a compliance (20 mL/cmH2O) mimicking a patient with severe lung disease. To have the highest possible accuracy reference, a Fleisch-type pneumotachograph  with a pressure transducer  was also included in series in the inspiratory line of the ventilator. The flow signal V' from the pneumotachograph under test was computed from its K1 and K2 obtained by water displacement from manual maneuvers and the pressure signal P recorded from the pneumotachograph. The flow signals from both pneumotachographs were recorded and the associated volumes were computed by flow integration. Comparisons were made for different mechanical ventilation settings (for both pressure and volume control modes) covering a realistic range of tidal volumes in patients (200–600 mL). These inspiratory maneuvers covered the ample range of flow magnitudes (up to >1.2 L/s) and waveforms in routine mechanical ventilation in patients with different diseases . The pneumotachograph calibrated by the resuscitation bag maneuvers  from maneuvers with a resuscitation bag and water displacement measurement, thereby allowing accurate flow and tidal volume measurements when employed to assess mechanical ventilation. In addition to its use for the training for a correct setting of mechanical ventilation or for experimental studies, the simple pneumotachograph described herein can be incorporated into clinical practice after approval by the competent authorities in each region .This study provides a novel approach for simply constructing and calibrating accurate pneumotachographs for measuring flow and volume to be used during mechanical ventilation. The proposed procedure, which does not need laboratory gold standard devices, has been satisfactorily tested showing that a simple manually drill-based pneumotachograph can be calibrated  and a non-linear (K2) component conceptually representing the contribution of capillaries and constrictions, respectively (2O·s/L in Fleisch-II type) presenting maximum resistance well-below 1 cmH2O·s/L at peak spontaneous breathing flows (≈1 L/s), enabling its use not only for mechanical ventilation but also for recording spontaneous ventilation in a wide variety of patients and clinical applications such as monitoring resting ventilation or studying sleep apnea.From a geometrical viewpoint, the type of pneumotachograph proposed and evaluated here can be seen as a modification of the capillary-based approach in conventional Fleisch-type pneumotachographs: capillaries are replaced by parallel cylindrical drilled holes . Howeverectively . In factectively  and is nectively –22. Figu1 and K2 values in 1 and K2 in any given pneumotachograph. Indeed, for flow ranges up to 1 L/s, such error would be very small (<2%) in the pneumotachographs with lower resistance and limited to <4% in the most resistive pneumotachograph. However, this potential error can be easily avoided by using the K1 and K2 coefficients corresponding to each flow direction. Indeed, inspiratory and expiratory flows/volumes in mechanical ventilation are usually measured by two different pneumotachographs, one placed in the inspiratory line and the other one in the expiratory line of the ventilator circuit. Interestingly, this separated-line setting facilitates the detection (and correction if required) of any potential zero-flow offset in each transducer pressure signals. Such zero-flow correction would avoid any potential error in the flow and volume computation from the pneumotachograph pressure, thereby facilitating the use of low-cost differential pressure transducers even in case they are not thermally compensated . In this connection, it is worth noting that the current e-market offers differential pressure transducers with excellent performance characteristics (data not shown), requiring no additional conditioning circuit since the pressure output signal is directly provided . Such ready-to-use transducers connected to the low-cost pneumotachograph allow effective solutions affordable for low resource regions.The K1 and K2, and the computation of flow from pneumotachograph pressure  or mesh screen (Lilly-type), or to alternatives requiring a 3D printer , the proThe requirement for individual calibration of each resistor is not specific to the proposed pneumotachograph but is in common with conventional Lilly- and Fleisch-type devices. The reason is that the resistor P–V' relationship (either linear or non-linear) critically depends on the exact dimensions of the tiny sensing elements  that cannot be precisely reproduced during the construction and assembling of each pneumotachograph.1 and K2 from any two maneuvers  whose volume is measured by a simple water displacement measurement. The water displacement procedure we implemented by using a common plastic bottle  from the difference (ΔV = V12 – V1) between the volume of the air mixture (V12) and the initial air volume in the chamber (V1) depends on the partial pressure of water vapor at room temperature, on volumes V1 and V2 and on the relative humidities of room air and of air in the chamber before and after air mixing. For conditions closely approximating the specific setting we employed  and reasonably assuming that the air enclosed in the chamber is initially saturated with water vapor (achieved after sufficient time equilibrium with the liquid water), the volume error would be <1.1% . Accordingly, given its negligible value, we did not correct this potential error. However, depending on the specific setting employed to implement The underlying assumption is that when ambient air (from a syringe or resuscitation bag) is introduced into the chamber (at constant pressure and temperature), the volume of the total air mixture (and thus the volume of displaced water) is the addition of the air volumes before mixing. The fulfillment of this hypothesis depends on the relative humidities of the air volumes before and after mixing. According to the analysis in Since using a scale is a simple way to assess the volume of displaced water from its weight, we assumed that tap water density is 1 g/L. This provides a sufficiently accurate volume measurement (error <0.5%) since: (a) pure water density at 20°C is 0.9982 g/L and its dependence on laboratory temperature varies by only 0.2% between 15 and 25°C , and (b)1 and K2 derived from manual resuscitation bag maneuvers carried out by an operator unaware of the aim resulted in very small variation in the calibration coefficients . Using repeated syringe maneuvers was already reported for improving the calibration of conventional pneumotachographs , i.e., a 20% amplitude window again much greater than our potential errors in volume measurement ( ≤ 4%; It is worth mentioning that the errors we found in measuring tidal volume  are simibination . Moreovebination  gives a bination  ranges (t  ≤ 4%; . WhereasThis work provides solid laboratory evidence that accurate pneumotachographs can be very easily constructed, and that they can be calibrated with no need for conventional and expensive gold standard reference devices in advanced laboratories. Accordingly, the proposed approach facilitates the low-cost and simple availability of pneumotachographs for accurately controlling mechanical ventilation in low-resource settings, either by incorporating them into the ventilators or as external measuring devices for quality control.The original contributions presented in the study are included in the article/RF conceived the study, analyzed the data, wrote the manuscript, and supervised the work. MR-L carried out the experiments. GT, DN, DG, and JO contributed to discussing the research, interpreting the data, and providing insightful comments. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.