Low Number Of Samples Research Articles

P–531 hCFTR p.G970D mutation causes Sertoli Cell-only Syndrome (SCOS) and Congenital bilateral absence of the vas deferens (CBAVD)

Abstract Study question Whether CFTR is a pathogenic gene for azoospermia? Summary answer CFTR p.G970D affects spermatogenesis and leads to male infertility by affecting the proliferation and survival of Germ Cell. What is known already Male infertility is a multifactorial and heterogeneous pathological condition affecting 7% of the general male population. However, up to now, only a relatively low number of genic factors have a clear relationship with spermatogenesis. Although, increased frequency of CFTR mutations or impaired CFTR expression in men with non-obstructive azoospermia or oligospermia as compared to the fertile men has been reported, but there is no direct evidence CFTR mutations cause azoospermia. Compared to F508Del mutations in Caucasians, p.G970D mutation is the most frequent CFTR mutation identified in Chinese CF patients. However, p.G970D has not been reported involved with male infertility. Study design, size, duration In this study, began in an infertile man suffering CBAVD and SCOS with no CF-like phenotype related symptoms up to now. By identifying the patient with CFTR p.G970D mutation, we further verified the function of the mutation in spermatogenesis in spermatogonia cell lines. Control testicular tissue sample was obtained from fertility man donors. Participants/materials, setting, methods WES was performed for probands and relatives and the mutation was confirmed by Sanger sequencing. Hematoxylin-eosin (HE) staining and immune fluorescence (IF) was performed on seminiferous tubules from the patient and control to characterize the structural anomalies present in the patient. GC2 mCFTRG965D cells was knocked in by the CRISPR/Cas9 gene editing system. The effects of mutations on the growth and proliferation of GC2 cells were detected by CCK8, IF, WB, BCECF staining and RT-PCR. Main results and the role of chance First, we identified the CBAVD and SCOS patient with homozygous missense mutations p.G970D in the CFTR gene, and his mutation inherited from both parents. The patient has normal general parameters and fertility parameters except for smaller testes, lower semen volume and pH. His testicular histopathology and co-location of CFTR and DDX4 which is the marker of spermatogonia likewise showed SCOS. Second. given that the amino acid sequence is conserved and the same expression and localization patterns of CFTR between human and mouse, we generated mouse derived cell lines model (mCFTRG965D) that carried a homozygous mutation equivalent to the CFTR variant in patients, using CRISPR/Cas9-mediated genome editing. mCFTRG965D affects the proliferation of Germ Cell, but has less effect on Sertoli cells, which is similar to the SCOS patient’s phenotype. Third, lower mature CFTR were observed in the GC2 mCFTRG965D groups cells compared to those in wild type groups, and CFTR protein is not evident in the GC2 mCFTRG965D groups’ cell membrane, which demonstrated the mutation affecting the anchoring of CFTR to the cell membrane. What’s more, the missense mutation will affect the function of CFTR in regulating pH, thus affecting cell homeostasis. Limitations, reasons for caution The low number of biological samples, we need more patients to confirm this mutation and azoospermia. We only validated at the cellular level, not in an animal model. It is noteworthy that, the CFTRF508del mice are fertility. Wider implications of the findings: Our study reveals that CFTR has a broader indication than just the absence of the vas deferens. We recommend to take further understanding of CFTR playing important role in spermatogenesis by affecting germ cell survival not just regulating cell volume during spermiogenesis. Trial registration number Not applicable

Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

Both sampling seasonality and geographic origin contribute significantly to variations in raw milk microbiota, but sampling seasonality is the more determining factor

Accurately profiling and characterizing factors shaping raw milk microbiota would provide practical information for detecting microbial contamination and unusual changes in milk. The current work was an observational study aiming to profile the microbiota of raw milk collected across wide geographic regions in China in different seasons and to investigate the contribution of geographical, seasonal, and environmental factors in shaping the raw milk microbiota. A total of 355 raw cow milk samples from healthy Holsteins and 41 environmental samples (farm soil and surface of milking room floor) were collected from 5 dairy farms in 5 Chinese provinces (namely, Daqing in Heilongjiang province, Jiaozuo in Henan province, Qingyuan in Guangdong province, Suqian in Jiangsu province, and Yinchuan in Ningxia Hui Autonomous Region) in January, May, and September 2018. The microbial communities in raw milk and farm environmental samples were determined using the PacBio small-molecule real-time circular consensus sequencing, which generated high-fidelity microbiota profiles based on full-length 16S rRNA genes; such technology was advantageous in producing accurate species-level information. Our results showed that both seasonality and sampling region were significant factors influencing the milk microbiota; however, the raw milk microbiota was highly diverse according to seasonality, and sampling region was the less determining factor. The wide variation in raw milk microbial communities between samples made it difficult to define a representative species-level core milk microbiota. Nevertheless, 3 most universal milk-associated species were identified: Lactococcus lactis, Enhydrobacter aerosaccus, and Acinetobacter lwoffii, which were consistently detected in 99%, 95%, and 94% of all analyzed milk samples, respectively (n = 355). The top taxa accounting for the overall seasonal microbiota variation were Bacillus (Bacillus cereus, Bacillus flexus, Bacillus safensis), Lactococcus (Lactococcus lactis, Lactococcus piscium, Lactococcus raffinolactis), Lactobacillus (Lactobacillus helveticus, Lactobacillus delbrueckii), Lactiplantibacillus plantarum, Streptococcus agalactiae, Enhydrobacter aerosaccus, Pseudomonas fragi, and Psychrobacter cibarius. Unlike the milk microbiota, the environmental microbiota did not exhibit obvious pattern of seasonal or geographic variation. However, this study was limited by the relatively low number and types of environmental samples, making it statistically not meaningful to perform further correlation analysis between the milk and environmental microbiota. Nevertheless, this study generated novel information on raw milk microbiota across wide geographic regions of China and found that seasonality was more significant in shaping the raw milk microbiota compared with geographic origin.

Presence and Levels of Galactosyllactoses and Other Oligosaccharides in Human Milk and Their Variation during Lactation and According to Maternal Phenotype.

Among the human milk oligosaccharides (HMOS), the galactosyllactoses (GLs) are only limitedly studied. This study aims to describe the presence and relative levels of HMOS, including GLs, in human milk (HM) according to maternal Secretor and Lewis (SeLe) phenotype and lactation stage. Relative levels of 19 HMOS were measured in 715 HM samples collected in the first 4 months postpartum from 371 donors participating in the PreventCD study. From a subset of 24 Dutch women (171 HM samples), samples were collected monthly up to 12 months postpartum and were additionally analyzed for relative and absolute levels of β6′-GL, β3′-GL and α3′-GL. Maternal SeLe phenotype or HM group was assigned based on the presence of specific fucosylated HMOS. Most HMOS, including β6′- and β3′-GL, were present in the vast majority (≥75%) of HM samples, whereas others (e.g., LNDFH II, 2′-F-LNH and α3′-GL) only occurred in a low number (<25%) of samples. Clear differences were observed between the presence and relative levels of the HMOS according to the maternal phenotype and lactation stage. Absolute concentrations of β6′-GL and β3′-GL were higher in HM group IV samples compared to samples of the other three HM groups. β3′-GL was also higher in HM group II samples compared to HM group I samples. β3′-GL and β6′-GL were stable over lactation stages. In conclusion, presence and levels of HMOS vary according to HM group and lactation stage. Not all HMOS behave similarly: some HMOS depend strongly on maternal phenotype and/or lactation stage, whereas others do not. β3′-GL and β6′-GL were present in low concentrations in over 75% of the analyzed HM samples and showed differences between HM groups, but not between the lactation stages.

Random Acquisition in Compressive Sensing

Compressive sensing has the ability of reconstruction of signal/image from the compressive measurements which are sensed with a much lower number of samples than a minimum requirement by Nyquist sampling theorem. The random acquisition is widely suggested and used for compressive sensing. In the random acquisition, the randomness of the sparsity structure has been deployed for compressive sampling of the signal/image. The article goes through all the literature up to date and collects the main methods, and simply described the way each of them randomly applies the compressive sensing. This article is a comprehensive review of random acquisition techniques in compressive sensing. Theses techniques have reviews under the main categories of (1) random demodulator, (2) random convolution, (3) modulated wideband converter model, (4) compressive multiplexer diagram, (5) random equivalent sampling, (6) random modulation pre-integration, (7) quadrature analog-to-information converter, (8) randomly triggered modulated-wideband compressive sensing (RT-MWCS).

Comparative study of Legiolert with ISO 11731-1998 standard method-conclusions from a Public Health Laboratory

BackgroundLegionella pneumophila (L. pneumophila) is responsible for 96% of Legionnaires' disease (LD) and 10% of all worldwide pneumonia cases. Legiolert™, a liquid culture method for most probable number (MPN) enumeration of L. pneumophila, was developed by IDEXX Laboratories. The method detects all serogroups of L. pneumophila in potable and non-potable water samples. ObjectiveThe goal of this study is to establish that Legiolert is a suitable alternative method to meet testing requirements in Spain for the enumeration of Legionella in water samples. MethodologyThe laboratory analyzed 118 environmental water samples from the Barcelona region (56 potable and 62 non-potable) in parallel by the Standard method for detection and enumeration of Legionella (ISO 11731:1998) and by Legiolert. Comparison of the recovery of the alternative method (Legiolert) and the Standard was made using ISO 17994:2014 and McNemar's binomial test statistical methods. Results44 samples were positive for Legionella (36 potable and 8 non-potable). Legiolert and the Standard method detected a similar percentage of positive samples, with Legiolert being slightly higher (31 vs 30%) and detecting higher concentrations of Legionella within the samples. ISO 17994:2014 analysis of the potable water samples found Legiolert was more sensitive than the Standard at detecting Legionella, even when complete Legionella species (L. spp.) results were considered for both methods. The two methods also demonstrated equivalent detection of L. spp. according to the McNemar's test. The comparison is significantly more in favor of Legiolert when only L. pneumophila results are considered. Each confirmation run with material extracted from positive Legiolert wells contained L. pneumophila, giving the method a specificity of 100%. Although statistical results for non-potable waters are not included because of the low number of samples, the two methods trended towards equivalence. ConclusionsRelative to the Standard method, Legiolert has a greater sensitivity and selectivity, and appears to have higher recovery for L. pneumophila, and equivalent recovery when L. spp. is included in the comparison. Legiolert also has high specificity. The procedural advantages of Legiolert allow laboratories to save on resources, costs, and time and consequently to test more frequently. In conclusion, the study finds IDEXX Legiolert a suitable alternative to ISO 11731:1998.

Penultimate Glacial Cycle glacier extent in the Iberian Peninsula: New evidence from the Serra da Estrela (Central System, Portugal)

The objective of this work is to present a first assessment on the age of the glacial features of the Serra da Estrela, in the central Portugal, Iberian Peninsula (40°19′ N, 7°37′ W, 1993 m), using Cosmic-Ray Exposure dating (in situ cosmogenic 36Cl). A total of 6 samples were dated, 4 extracted from exposed moraine boulders and 2 from glacially polished bedrock surfaces. Despite the low number of samples, the results are consistent, reinforcing previous dating obtained by other methods and geomorphological and paleoclimatic information. The maximum extension of the glaciers occurred at the end of the Penultimate Glacial Cycle (Marine Isotope Stage 6), during Heinrich Stadial 11, around 140 ka. At the end of the Last Glacial Cycle, slightly before the Last Glacial Maximum, at around 30 ka, the Estrela glaciers reached again a similar extent. Finally, the glaciers disappeared from the Serra da Estrela at the beginning of the Bølling-Allerød Interstadial, at around 14.2 ka. These data confirm a certain synchronicity in the major glacial phases in most the Mediterranean and also European mountains, although there are notable differences in the maximum extent in the two cycles.

Advanced molecular pathology for rare tumours: A national feasibility study and model for centralised medulloblastoma diagnostics.

AimsApplication of advanced molecular pathology in rare tumours is hindered by low sample numbers, access to specialised expertise/technologies and tissue/assay QC and rapid reporting requirements. We assessed the feasibility of co‐ordinated real‐time centralised pathology review (CPR), encompassing molecular diagnostics and contemporary genomics (RNA‐seq/DNA methylation‐array).MethodsThis nationwide trial in medulloblastoma (<80 UK diagnoses/year) introduced a national reference centre (NRC) and assessed its performance and reporting to World Health Organisation standards. Paired frozen/formalin‐fixed, paraffin‐embedded tumour material were co‐submitted from 135 patients (16 referral centres).ResultsComplete CPR diagnostics were successful for 88% (120/135). Inadequate sampling was the most common cause of failure; biomaterials were typically suitable for methylation‐array (129/135, 94%), but frozen tissues commonly fell below RNA‐seq QC requirements (53/135, 39%). Late reporting was most often due to delayed submission. CPR assigned or altered histological variant (vs local diagnosis) for 40/135 tumours (30%). Benchmarking/QC of specific biomarker assays impacted test results; fluorescent in‐situ hybridisation most accurately identified high‐risk MYC/MYCN amplification (20/135, 15%), while combined methods (CTNNB1/chr6 status, methylation‐array subgrouping) best defined favourable‐risk WNT tumours (14/135; 10%). Engagement of a specialist pathologist panel was essential for consensus assessment of histological variants and immunohistochemistry. Overall, CPR altered clinical risk‐status for 29% of patients.ConclusionNational real‐time CPR is feasible, delivering robust diagnostics to WHO criteria and assignment of clinical risk‐status, significantly altering clinical management. Recommendations and experience from our study are applicable to advanced molecular diagnostics systems, both local and centralised, across rare tumour types, enabling their application in biomarker‐driven routine diagnostics and clinical/research studies.

Population Pharmacokinetic Model of Iohexol in Dogs to Estimate Glomerular Filtration Rate and Optimize Sampling Time.

Monitoring iohexol plasma clearance is considered a useful, reliable, and sensitive tool to establish glomerular filtration rate (GFR) and early stages of kidney disease in both humans and veterinary medicine. The assessment of GFR based on iohexol plasma clearance needs repeated blood sampling over hours, which is not easily attainable in a clinical setting. The study aimed to build a population pharmacokinetic (Pop PK) model to estimate iohexol plasma clearance in a population of dogs and based on this model, to indicate the best sampling times that enable a precise clearance estimation using a low number of samples. A Pop PK model was developed based on 5 iohexol plasma samples taken from 5 to 180 minutes (min) after an intravenous iohexol nominal dose of 64.7 mg/kg from 49 client-owned dogs of different breeds, sexes, ages, body weights, and clinical conditions (healthy or presenting chronic kidney disease CKD). The design of the best sampling times could contain either 1 or 2 or 3 sampling times. These were discretized with a step of 30 min between 30 and 180 min. A two-compartment Pop PK model best fitted the data; creatinine and kidney status were the covariates included in the model to explain a part of clearance variability. When 1 sample was available, 90 or 120 min were the best sampling times to assess clearance for healthy dogs with a low creatinine value. Whereas for dogs with CKD and medium creatinine value, the best sampling time was 150 or 180 min, for CKD dogs with a high creatinine value, it was 180 min. If 2 or 3 samples were available, several sampling times were possible. The method to define the best sampling times could be used with other Pop PK models as long as it is representative of the patient population and once the model is built, the use of individualized sampling times for each patient allows to precisely estimate the GFR.

Real-Time Geometric Glint Anti-Aliasing with Normal Map Filtering

Real-time geometric specular anti-aliasing is required when using a low number of pixel samples and high-frequency specular lobes. Several methods have been proposed for mono-lobe bidirectional reflection distribution functions (BRDFs), but none for multi-lobe BRDFs, e.g., a glinty BRDF. We present the first method for real-time geometric glint anti-aliasing (GGAA). It eliminates most of the inconsistent appearing and disappearing of glints on surfaces with significant curvatures during animations. The technique uses the glinty BRDF of Chermain et al. [2020] and leverages hardware GPU-filtering of textures to filter slope distributions on the fly. We also improve this glinty BRDF by adding a correlation factor of slope. This BRDF parameter allows convergence to normal distribution functions that are not aligned on the surface's axes. Above all, this parameter makes glint rendering compatible with normal map filtering using LEAN mapping. Using GGAA increases the rendering time from 0.6 % to 4.2 % and it requires 1/3 more memory due to MIP mapping of tabulated slope distributions. The results are compared with references using a thousand samples per pixel.

An evaluation of patients with a previous endemic coronavirus infection during the COVID-19 pandemic.

Few studies exist on the clinical manifestation of coronavirus disease 2019 (COVID‐19) in patients who previously had a common cold due to an endemic coronavirus (eCoV). In a retrospective scan of the data obtained in our microbiology laboratory, 64 patients who were diagnosed with an eCoV infection between 2016 and 2020 were identified. National COVID‐19 surveillance data showed that four (6.2%) of 64 patients were infected with severe acute respiratory syndrome coronavirus 2 by the end of 2020, while, simultaneously, the COVID‐19 prevalence in the city of Malatya ranged from 7.8% (polymerase chain reaction‐based diagnosis) to 9.2% (total diagnosis). The differences were found statistically significant (6.2% vs. 7.8%, p < .01; 6.2% vs. 9.2%, p < .001). Patient interviews and evaluation of medical records revealed that these four patients did not manifest any severe COVID‐19 symptoms despite their substantial comorbidities, and they did not require hospitalization. Consequently, despite a low number of samples, we determined a lower frequency of COVID‐19 among the patients who had a prior eCoV infection, and the results of this study support the previous findings that people with a prior eCoV infection develop a milder case of COVID‐19. Our results may provide some insights for future studies aiming at vaccine development, but detailed investigations are still required.

LAMA: automated image analysis for the developmental phenotyping of mouse embryos.

ABSTRACTAdvanced 3D imaging modalities, such as micro-computed tomography (micro-CT), have been incorporated into the high-throughput embryo pipeline of the International Mouse Phenotyping Consortium (IMPC). This project generates large volumes of raw data that cannot be immediately exploited without significant resources of personnel and expertise. Thus, rapid automated annotation is crucial to ensure that 3D imaging data can be integrated with other multi-dimensional phenotyping data. We present an automated computational mouse embryo phenotyping pipeline that harnesses the large amount of wild-type control data available in the IMPC embryo pipeline in order to address issues of low mutant sample number as well as incomplete penetrance and variable expressivity. We also investigate the effect of developmental substage on automated phenotyping results. Designed primarily for developmental biologists, our software performs image pre-processing, registration, statistical analysis and segmentation of embryo images. We also present a novel anatomical E14.5 embryo atlas average and, using it with LAMA, show that we can uncover known and novel dysmorphology from two IMPC knockout lines.

Occurrence of faecal endoparasites in reindeer (Rangifer tarandus) in two grazing areas in northern Norway

BackgroundSemi-domesticated reindeer represent an important livestock industry and livelihood for a proportion of the human population in northern Fennoscandia. Reindeer husbandry is considered an extensive animal husbandry, where the animals are kept mostly on natural pastures, although sometimes kept in fenced areas for shorter periods. These reindeer may harbour a variety of parasites that may affect animal health and production. The relatively limited close contact between herds and owners gives limited opportunities for diagnosis and treatment of diseases in general. Furthermore, the effects of subclinical parasitism in livestock are commonly expressed as a reduction in productivity rather than clinical disease and mortality. Thus, specific knowledge of endoparasites and parasitic infections in these herds is scarce. This study investigated the occurrence of various endoparasites in reindeer by analysis of a total of 114 faecal samples from winter-slaughtered reindeer from two different grazing areas in Troms and Finnmark, Norway.ResultsUsing a McMaster method, a Baermann technique, and a direct immunofluorescent antibody test, the following parasites were identified in the faecal samples with the occurrence data given as percentages: Strongylid eggs (62%), Nematodirinae spp. eggs (24%), Capillaria sp. eggs (42%) and Moniezia spp. eggs (17%), Dictyocaulus spp. larvae (14%) protostrongylid larvae (40%), Eimera spp. oocysts (23%), and Giardia duodenalis cysts (5%). Cryptosporidium oocysts were not detected. Parasite eggs, oocysts, and cysts were quantified.Molecular analysis revealed G. duodenalis sub-assemblage AI, a potentially zoonotic genotype not previously reported in reindeer from this region. Morphological analyses of Eimeria oocysts identified two species, Eimeria mayeri and Eimeria rangiferis, and molecular analyses of the cytochrome C oxidase I (coi) gene and 18 s rRNA (18SSU) gene of Eimeria confirmed the presence of Eimeria species that are specific to reindeer.ConclusionsA high prevalence, but low burden, of endoparasites was detected in samples from these semi-domesticated reindeer. The samples were collected during winter, when adult gastrointestinal parasites usually produce only low numbers of transmission stages. Therefore, together with the low number of samples, detailed and definitive conclusions regarding parasite status of semi-domesticated reindeer are avoided. Nevertheless, these data provide a snapshot overview of parasite occurrence in a semi-domesticated animal group vulnerable to the various environmental changes to which they are exposed.

Workflow for the Supervised Learning of Chemical Data: Efficient Data Reduction-Multivariate Curve Resolution (EDR-MCR).

A new method termed efficient data reduction-multivariate curve resolution (EDR-MCR) has been devised for classification of high-dimensional data. The method introduces the coupling of EDR and MCR as a new strategy for data splitting, variable selection, and supervised classification of high dimensionality data. The method reduces data dimensionality and selects the training set using principal component analysis (PCA) and convex geometry prior to data classification. Then, the reduced data are categorized using an MCR model, in which numerical constraints are imposed to resolve the data into classes and readily interpretable pure component signal weights. The performance of the EDR and supervised MCR methods were tested for their ability to enable discrimination between the constituents of two benchmark and two high-dimensional data sets. The results were compared with the output of the application of different data splitting methods including iterative random selection (IRS), Kennard-Stone (KS), and discrimination methods including partial least-squares-discriminant analysis (PLS-DA) and the ensemble-learning frameworks of linear discriminant analysis (LDA), k-nearest neighbors (KNN), classification and regression trees (CART), and support vector machine (SVM). Overall, EDR resulted in comparable results with other data splitting methods despite the small size of the training set samples that it created. The proposed MCR approach, in comparison with other commonly used supervised techniques, has the advantages of speed in implementation, tuning of fewer parameters, flexibility in the analysis of data characterized by low sample numbers and class imbalances, improved accuracy from the inclusion of additional system information in the form of numerical constraints, and the ability to resolve pure components signal weights.

101 lakes - lentic fish community monitoring using eDNA metabarcoding

We have recently developed and deployed methods for environmental DNA (eDNA) based monitoring of lake fish communities in the UK. This approach combines eDNA with modern High-Throughput-Sequencing technology, so-called eDNA metabarcoding. This non-invasive method has proven to be more effective at detecting elusive species than established invasive surveying techniques such as electro fishing or fyke netting and can provide meaningful semi-quantitative abundance estimates. The UK Environment Agencies have funded the collection of an eDNA meta-barcoding data set of vertebrates from 101 UK lakes covering a broad spectrum of environmental conditions Fig. 1. This dataset is based on analysing 20 water samples per lake and has successfully been used to develop a WFD compatible water quality assessment tool. In its current form this tool is suitable for reporting the status of fish in water bodies where eutrophication is the dominant pressure. DNA is not homogeneously distributed in lentic environments and hence the detection of species relies on the collection of an adequate number of samples from a water body to capture the eDNA signal. Previous analyses on a subset of lakes have indicated that the number of samples used for the 101 lake fish data set is more than sufficient to reliably estimate species richness of lakes, but it is unclear how exactly reduced sampling effort affects other biodiversity estimates and inferences made about water quality. As the number of samples determines the cost of monitoring programmes it is essential that the sampling effort is optimised for a specific monitoring objective. The objective of this study was to explore the effect a reduced sampling effort would have on various biological inferences using algorithmic and statistical resampling techniques. with a much lower number of samples. For example, almost 90% of lakes achieved a sample coverage of 95% with only 10 samples. However, rare species are more often missed with fewer samples, with implications for monitoring programs of invasive or endangered species. Estimates of community composition and the ecological quality ratio (EQR) responded slowly to decreasing sampling effort. For example, subsets of 10 samples were in most cases much more closely related to each other than to sample sets from other lakes and showed very similar Ecological Quality Ratios. These results are able to inform the design of eDNA sampling strategies, so that these can be optimised to achieve specific monitoring goals.

KFGRNI: A robust method to inference gene regulatory network from time-course gene data based on ensemble Kalman filter.

A central problem of systems biology is the reconstruction of Gene Regulatory Networks (GRNs) by the use of time series data. Although many attempts have been made to design an efficient method for GRN inference, providing a best solution is still a challenging task. Existing noise, low number of samples, and high number of nodes are the main reasons causing poor performance of existing methods. The present study applies the ensemble Kalman filter algorithm to model a GRN from gene time series data. The inference of a GRN is decomposed with p genes into p subproblems. In each subproblem, the ensemble Kalman filter algorithm identifies the weight of interactions for each target gene. With the use of the ensemble Kalman filter, the expression pattern of the target gene is predicted from the expression patterns of all the remaining genes. The proposed method is compared with several well-known approaches. The results of the evaluation indicate that the proposed method improves inference accuracy and demonstrates better regulatory relations with noisy data.

Hydrogen fuel quality for transport – First sampling and analysis comparison in Europe on hydrogen refuelling station (70 MPa) according to ISO 14687 and EN 17124.

Fuel cell electric vehicles are getting deployed exponentially in Europe. Hydrogen fuel quality regulations are getting into place in order to protect customers and ensure end-users satisfactory experiences. It became critical to have the capability to sample and analyse accurately hydrogen fuel delivered by hydrogen refuelling stations in Europe. This study presents two separate comparisons: the first bilateral comparison between two sampling systems (H2 Qualitizer) and (“H2 Sampling System” of Air Liquide) and the interlaboratory comparison between NPL and Air Liquide on hydrogen fuel quality testing according to EN 17124. The two sampling systems showed equivalent results for all contaminants for sampling at 70 MPa hydrogen refuelling stations. The two laboratories showed good agreement at 95% confidence level. Even if the study is limited due to the low number of samples, it demonstrates the equivalence of two sampling strategies and the ability of two laboratories to perform accurate measurement of hydrogen fuel quality.

Evaluation of Host Immune Response in Diabetic Foot Infection Tissues Using an RNA Sequencing-Based Approach.

The normal continuity of skin tissue can be affected by invading pathogens and lead to a series of complicated physiological events. Using an RNA sequencing-based approach, we have captured a metatranscriptomic landscape from diabetic foot infections (DFIs). The hierarchical clustering of the top 2,000 genes showed the expression of four main clusters in DFIs (A, B, C, and D). Clusters A and D were enriched in genes mainly involved in the recruitment of inflammatory cells and immune responses and clusters B and C were enriched in genes related to skin cell development and wound healing processes such as extracellular structure organization and blood vessel development. Differential expression analysis showed more than 500 differentially expressed genes (DEGs) between samples with a low number of virulence factors and samples with a high number of virulence factors. Up-regulated and down-regulated genes were mainly involved in adaptive/native immune responses and transport of mature mRNAs, respectively. Our results demonstrated the importance of inflammatory cytokines of adaptive/native immunity in the progression of DFIs and provided a useful groundwork for capturing gene snapshots in DFIs. In addition, we have provided a general introduction to the challenges and opportunities of RNA sequencing technology in the evaluation of DFIs. Pathways identified in this study such as immune chemokines, Rho GTPases, and corresponding effectors might be important therapeutic targets in the management of DFIs.

Evaluation of I-TAC as a potential early plasma marker to differentiate between critical and non-critical COVID-19.

The COVID-19 pandemic has led to significant global health and economic consequences. There is an unmet need to define a molecular fingerprint of severity of the disease that may guide an early, rational and directed intervention preventing severe illness. We collected plasma from patients with moderate (nine cases), severe (22 cases) and critical (five cases) COVID-19 within three days of hospitalization (approximately one week after symptom onset) and used a cytokine antibody array to screen the 105 cytokines included in the array. We found that I-TAC, IP-10, ST2 and IL-1ra were significantly upregulated in patients with critical disease as compared to the non-critical (moderate and severe combined). ELISA further quantified I-TAC levels as 590.24±410.89, 645.35±517.59 and 1613.53±1010.59 pg/ml in moderate, severe and critical groups, respectively. Statistical analysis showed that I-TAC levels were significantly higher in patients with critical disease when compared with moderate (p = 0.04), severe (p = 0.03) or the combined non-critical (p = 0.02) group. Although limited by the low sample numbers, this study may suggest a role of I-TAC as a potential early marker to discriminate between critical and non-critical COVID-19 cases. Such knowledge is urgently needed for appropriate allocation of resources and to serve as a platform for future research towards early interventions that could mitigate disease severity and save lives.

Drivers of harmful algal blooms in coastal areas of Eastern Mediterranean: a machine learning methodological approach.

Harmful algal species are present in the Mediterranean Sea and are often associated with toxic events affecting the nearby coastal zones. The presence of 18 marine microalgae, at genus level, associated with potentially harmful characteristics was predicted using a number of machine learning techniques based exclusively on a small set of abiotic variables, already identified as drivers of blooms. Random Forest (RF) algorithm achieved the best predictive performance by correctly identifying the presence of most genera with a mean of 89.2% of total samples. Although, RF has shown lower predictive performance for genera present in a low number of samples, its predictive power remains at least "fair' in these cases. The main tree-based advantage of RF was thereafter used to assess the importance of the input variables in predicting the presence of the algal genera. Temperature had the most powerful effect on genera's presences, although this effect varies among genera. Finally, the genera were clustered based on their response to the considered abiotic variables and common trends in an ecological context were identified.