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This study aimed to address the gap in knowledge regarding the fate of foodborne pathogens within agro-ecosystems. It specifically focused on the surrogate microorganism Clostridium sporogenes , which was introduced into lettuce-producing environments via surface and spray irrigation methods, respectively. The concentration of C. sporogenes in the rhizosphere, phyllosphere, and non-rhizosphere soil was quantified by quantitative polymerase chain reaction (qPCR) over a 42-day trial. The surface irrigation method exhibited a more noticeable contamination effect on the soil environments, compared to the phyllosphere. The results indicated a noticeable increase in C. sporogenes concentrations during the initial 22 days, with a 10.4-fold rise (0.39-4.05 log copy numbers/g soil) in the rhizosphere and 1.9-fold increase (2.97-5.59 log copy numbers/g soil) in the non-rhizosphere. However, concentrations in both soil environments subsequently decreased, falling below the initial inoculum concentration by the end of the trial. In contrast, the spray irrigation method resulted in most of the contamination being localised on the lettuce phyllosphere, with a high C. sporogenes concentration of 9.09 log copy numbers/g leaves on day 0. This concentration exponentially decreased to a minimal 0.019 log copy numbers/g leaves by day 32. Although concentrations in both soil environments decreased over time, trace concentrations of C. sporogenes were detectable at the end of the trial, posing a potential hazard to the microbiological safety of postharvest produce. These findings shed light on the dynamics of C. sporogenes in agro-ecosystems and underscore the importance of irrigation practices that ensure the safety of those who consume fresh produce., Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-04069-5., Competing Interests: Conflict of interestThe corresponding author is an associate editor for this journal. The authors declare that they have no other competing interests., (© The Author(s) 2024.)

Priestia is a genus that was renamed from the genus Bacillus based on the conserved signature indels (CSIs) in protein sequences that separate Priestia species from Bacillus, with the latter only including species closely related to B. subtilis and B. cereus. Diagnosis of anthrax, a zoonotic disease, is implicated by tripartite anthrax virulence genes (lef, pagA, and cya) and poly-γ-D-glutamic acid capsular genes cap-ABCDE of Bacillus anthracis. Due to the amplification of anthrax virulence genes in Priestia isolates, the search for homologous anthrax virulence genes within the Priestia genomes (n = 9) isolated from animal blood smears was embarked upon through whole genome sequencing. In silico taxonomic identification of the isolates was conducted using genome taxonomy database (GTDB), average nucleotide identity (ANI), and multi-locus sequence typing (MLST), which identified the genomes as P. aryabhattai (n = 5), P. endophytica (n = 2) and P. megaterium (n = 2). A pan-genome analysis was further conducted on the Priestia genomes, including the screening of virulence, antibiotic resistance genes and mobile genetic elements on the sequenced genomes. The oligoribonuclease NrnB protein sequences showed that Priestia spp. possess a unique CSI that is absent in other Bacillus species. Furthermore, the CSI in P. endophytica is unique from other Priestia spp. Pan-genomic analysis indicates that P. endophytica clusters separately from P. aryabhattai and P. megaterium. In silico BLASTn genome analysis using the SYBR primers, Taqman probes and primers that target the chromosomal marker (Ba-1), protective antigen (pagA), and lethal factor (lef) on B. anthracis, showed partial binding to Priestia regions encoding for hypothetical proteins, pyridoxine biosynthesis, hydrolase, and inhibitory proteins. The antibiotic resistance genes (ARG) profile of Priestia spp. showed that the genomes contained no more than two ARGs. This included genes conferring resistance to rifamycin and fosfomycin on P. endophytica, as well as clindamycin on P. aryabhattai and P. megaterium. Priestia genomes lacked B. anthracis plasmids and consisted of plasmid replicon types with unknown functions. Furthermore, the amplification of Priestia strains may result in false positives when qPCR is used to detect the virulence genes of B. anthracis in soil, blood smears, and/or environmental samples., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition., (© 2024. The Author(s).)

Forty-five accessions of the genus Phaseolus from the orthodox seed collection of the National Center for Genetic Resources (CNRG) of the National Institute of Forestry, Agricultural, and Livestock Research (INIFAP) of Mexico were sequenced using RADseq. The species utilized were: P. acutifolius (14), P. coccineus (12), P. lunatus (8), P. dumosus (6), P. leptostachyus (2), P. filiformis (2), and P. vulgaris (1). A variant call file (VCF) was generated using GATK with the P. vulgaris reference genome GCF_000499845.1, identifying 97,103 shared SNPs among the species. These data have the potential to be used for studies of genetic diversity intra and interspecies, phylogeny, evolution, genetic resource conservation, and agricultural improvement., (© 2024 The Author(s).)

There is a rapid spread of antibiotic resistance in the environment. However, the impact of antibiotic resistance in drinking water is relatively underexplored. Thus, this study aimed to quantify antibiotic resistance genes (ARGs) and antibiotic residues in two drinking water production facilities (NW-E and NW-C) in North West Province, South Africa and link these parameters to bacterial communities. Physicochemical and ARG levels were determined using standard procedures. Residues (antibiotics and fluconazole) and ARGs were quantified using ultra-high performance liquid chromatography (UHPLC) chemical analysis and real-time PCR, respectively. Bacterial community compositions were determined by high-throughput 16S rRNA sequencing. Data were analysed using redundancy analysis and pairwise correlation. Although some physicochemical levels were higher in treated than in raw water, drinking water in NW-E and NW-C was safe for human consumption using the South African Water Quality Guideline (SAWQG). ARGs were detected in raw and treated water. In NW-E, the concentrations of ARGs (sul1, intl1, EBC, FOX, ACC and DHA) were higher in treated water than in raw water. Regarding antimicrobial agents, antibiotic and fluconazole concentrations were higher in raw than in treated water. However, in NW-C, trimethoprim concentrations were higher in raw than in treated water. Redundancy analysis showed that bacterial communities were not significantly correlated (Monte Carlo simulations, p-value >0.05) with environmental factors. However, pairwise correlation showed significant differences (p-value <0.05) for Armatimonas, CL500-29 marine group, Clade III, Dickeya and Zymomonas genera with environmental factors. The presence of ARGs and antibiotic residues in the current study indicated that antibiotic resistance is not only a clinical phenomenon but also in environmental settings, particularly in drinking water niches. Consumption of NW-E and NW-C treated water may facilitate the spread of antibiotic resistance among consumers. Thus, regulating and monitoring ARGs and antibiotic residues in drinking water production facilities should be regarded as paramount., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tsholo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Initial dementia prevalence estimates have revealed a significant burden of the disease in Indigenous communities in Amazonas, Brazil. However, the need for culturally adapted cognitive tools poses a critical challenge when assessing cognitive performance in these communities. This study addressed this issue by culturally adapting and providing validity indicators for the Brazilian Indigenous Cognitive Assessment (BRICA) tool in Manaus, Brazil's urban multiethnic Indigenous community., Methods: Using a three-stage process and a stakeholder-engaged approach, the BRICA tool was culturally adapted in an urban multiethnic Indigenous community from Manaus, Brazil. The content validity index (CVI) examined inter-rater concordance between experts, while criterion and concurrent validity were performed using diagnostic consensus criteria in 141 Indigenous participants aged ≥ 50 years., Results: Findings showed evidence of content validity in terms of equivalence aspects (scale CVI [S-CVI] 0.93) and relevance ratings (S-CVI 0.85) between expert panels. The identified cut-off score of ≤ 33/39 on the BRICA demonstrated a sensitivity of 94.4%, specificity of 99.2%, positive predictive value of 94.4%, and negative predictive value of 99.2% for dementia diagnosis., Discussion: Using a stakeholder-engaged approach, we culturally adapted the BRICA tool for a Brazilian urban multiethnic Indigenous community. This comprehensive adaptation process resulted in favorable indicators of content, construct, and criteria validity for the BRICA tool. By addressing the existing bias in cognitive assessment within Indigenous communities, the BRICA tool represents a noteworthy breakthrough. Its implementation exhibits potential for improving the early detection and management of dementia among Indigenous groups., Highlights: Culturally sensitive tools are essential to assess cognition in Indigenous populations.An expert panel and stakeholders' perspectives were incorporated to design the Brazilian Indigenous Cognitive Assessment (BRICA) tool.A cognitive screening tool was adapted and validated using a stakeholder approach.BRICA is the first culturally sensitive cognitive tool for urban Brazilian Indigenous individuals., Competing Interests: The authors declare no conflicts of interest. Author disclosures are available in the supporting information., (© 2024 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Escherichia coli, a ubiquitous inhabitant of the gut microbiota, has been recognized as an indicator of fecal contamination and a potential reservoir for antibiotic resistance genes. Its prevalence in drinking water sources raises concerns about the potential dissemination of antibiotic resistance within aquatic ecosystems and the subsequent impact on public health. The ability of E. coli to acquire and transfer resistance genes, coupled with the constant exposure to low levels of antibiotics in the environment, underscores the need for comprehensive surveillance and rigorous antimicrobial stewardship strategies to safeguard the quality and safety of drinking water supplies, ultimately mitigating the escalation of antibiotic resistance and its implications for human well-being., Methods: WG5D strain, isolated from a drinking water distribution source in North-West Province, South Africa, underwent genomic analysis following isolation on nutrient agar, anaerobic cultivation, and DNA extraction. Paired-end Illumina sequencing with a Nextera XT Library Preparation kit was performed. The assembly, annotation, and subsequent genomic analyses, including phylogenetic analysis using TYGS, pairwise comparisons, and determination of genes related to antimicrobial resistance and virulence, were carried out following standard protocols and tools, ensuring comprehensive insights into the strain's genomic features., Results: This study explores the notable characteristics of E. coli strain WG5D. This strain stands out because it possesses multiple antibiotic resistance genes, encompassing tetracycline, cephalosporin, vancomycin, and aminoglycoside resistances. Additionally, virulence-associated genes indicate potential heightened pathogenicity, complemented by the identification of mobile genetic elements that underscore its adaptability. The intriguing possibility of bacteriophage involvement and factors contributing to pathogenicity further enriches our understanding. We identified E. coli WG5D as a potential human pathogen associated with a drinking water source in South Africa. The analysis provided several antibiotic resistance-associated genes/mutations and mobile genetic elements. It further identified WG5D as a potential human pathogen. The occurrence of E. coli WG5D raised the awareness of the potential pathogens and the carrying of antibiotic resistance in drinking water., Conclusions: The findings of this study have highlighted the advantages of the genomic approach in identifying the bacterial species and antibiotic resistance genes of E. coli and its potential as a human pathogen., (© 2024. The Author(s).)

Profiling the variability related to the estrous cycle is essential for assessing depressive-like behavior and screening drugs. This study compares circulating plasma corticosterone levels [CORT] and behavioral alterations in mice exposed to sucrose preference, forced swimming, and tail suspension tests (SPT, FST, and TST, respectively). While SPT exposure did not significantly alter [CORT], FST and TST showed notable changes. Mice in the TST exhibited increased movement and decreased immobility time compared to FST, suggesting a lower likelihood of depressive-like behavior in male mice. Notably, during the proestrus phase, female mice displayed the highest tendency for depressive-like behavior and elevated [CORT], but similar response to antidepressants (imipramine and fluoxetine). The inherent stress of the FST and TST tasks appears to influence [CORT] as well as depressant and antidepressant effects. These comparisons provide valuable insights for further behavioral phenotyping, model sensitivity assessment, and deepen our neurobiological understanding of depression in the context of drug screening., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Rodrigues et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Because of its wide distribution, high yield potential, and short cycle, the potato has become essential for global food security. However, the complexity of tetrasomic inheritance, the high level of heterozygosity of the parents, the low multiplication rate of tubers, and the genotype-by-environment interactions impose severe challenges on tetraploid potato-breeding programs. The initial stages of selection take place in experiments with low selection accuracy for many of the quantitative traits of interest, for example, tuber yield. The goal of this study was to investigate the contribution of incorporating a family effect in the estimation of the total genotypic effect and selection of clones in the initial stage of a potato-breeding program. The evaluation included single trials (STs) and multi-environment trials (METs). A total of 1,280 clones from 67 full-sib families from the potato-breeding program at Universidade Federal de Lavras were evaluated for the traits total tuber yield and specific gravity. These clones were distributed in six evaluated trials that varied according to the heat stress level: without heat stress, moderate heat stress, and high heat stress. To verify the importance of the family effect, models with and without the family effect were compared for the analysis of ST and MET data for both traits. The models that included the family effect were better adjusted in the ST and MET data analyses for both traits, except when the family effect was not significant. Furthermore, the inclusion of the family effect increased the selective efficiency of clones in both ST and MET analyses via an increase in the accuracy of the total genotypic value. These same models also allowed the prediction of clone effects more realistically, as the variance components associated with family and clone effects within a family were not confounded. Thus, clonal selection based on the total genotypic value, combining the effects of family and clones within a family, proved to be a good alternative for potato-breeding programs that can accommodate the logistic and data tracking required in the breeding program., Competing Interests: 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., (Copyright © 2023 Martins, Andrade, Padua, Miguel, Fernandes Filho, Guedes, Nunes, Hoffmann, Zotarelli, Resende, Carneiro and Marçal.)

The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa cells observed with an electron microscope with dimensions 8192×8192×517. From there, a smaller region of interest (ROI) of 2000×2000×300 was cropped and manually delineated to obtain the ground truth necessary for a quantitative evaluation. A qualitative evaluation was performed on the 8192×8192 slices due to the lack of ground truth. Pairs of patches of data and labels for the classes nucleus, nuclear envelope, cell and background were generated to train U-Net architectures from scratch. Several training strategies were followed, and the results were compared against a traditional image processing algorithm. The correctness of GT, that is, the inclusion of one or more nuclei within the region of interest was also evaluated. The impact of the extent of training data was evaluated by comparing results from 36,000 pairs of data and label patches extracted from the odd slices in the central region, to 135,000 patches obtained from every other slice in the set. Then, 135,000 patches from several cells from the 8192×8192 slices were generated automatically using the image processing algorithm. Finally, the two sets of 135,000 pairs were combined to train once more with 270,000 pairs. As would be expected, the accuracy and Jaccard similarity index improved as the number of pairs increased for the ROI. This was also observed qualitatively for the 8192×8192 slices. When the 8192×8192 slices were segmented with U-Nets trained with 135,000 pairs, the architecture trained with automatically generated pairs provided better results than the architecture trained with the pairs from the manually segmented ground truths. This suggests that the pairs that were extracted automatically from many cells provided a better representation of the four classes of the various cells in the 8192×8192 slice than those pairs that were manually segmented from a single cell. Finally, the two sets of 135,000 pairs were combined, and the U-Net trained with these provided the best results.

In this work, the performance of five deep learning architectures in classifying COVID-19 in a multi-class set-up is evaluated. The classifiers were built on pretrained ResNet-50, ResNet-50r (with kernel size 5×5 in the first convolutional layer), DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transformer. The cross entropy and weighted cross entropy were minimised with Adam and AdamW. In total, 20 experiments were conducted with 10 repetitions and obtained the following metrics: accuracy ( Acc ), balanced accuracy ( BA ), F 1 and F 2 from the general F β macro score, Matthew's Correlation Coefficient ( MCC ), sensitivity ( Sens ) and specificity ( Spec ) followed by bootstrapping. The performance of the classifiers was compared by using the Friedman-Nemenyi test. The results show that less complex architectures such as ResNet-50, ResNet-50r and DenseNet-121 were able to achieve better generalization with rankings of 1.53, 1.71 and 3.05 for the Matthew Correlation Coefficient, respectively, while MobileNet-v3 and CaiT obtained rankings of 3.72 and 5.0, respectively.

Competing Interests: The authors declare that they have no conflict of interest to this work.

This paper investigates the classification of radiographic images with eleven convolutional neural network (CNN) architectures ( GoogleNet, VGG-19, AlexNet, SqueezeNet, ResNet-18, Inception-v3, ResNet-50, VGG-16, ResNet-101, DenseNet-201 and Inception-ResNet-v2 ). The CNNs were used to classify a series of wrist radiographs from the Stanford Musculoskeletal Radiographs (MURA) dataset into two classes-normal and abnormal. The architectures were compared for different hyper-parameters against accuracy and Cohen's kappa coefficient. The best two results were then explored with data augmentation. Without the use of augmentation, the best results were provided by Inception-ResNet-v2 (Mean accuracy = 0.723, Mean kappa = 0.506). These were significantly improved with augmentation to Inception-ResNet-v2 (Mean accuracy = 0.857, Mean kappa = 0.703). Finally, Class Activation Mapping was applied to interpret activation of the network against the location of an anomaly in the radiographs.

The best agro-industrial performance presented by a crop genotype in one environment may not be reproduced in another owing to complex edaphoclimatic variations. Therefore, breeding programs are constantly attempting to obtain, through artificial hybridization, novel genotypes with high adaptability and stability potential. The objective of this study was to analyze genetic divergence in sugarcane based on the genotypic values of adaptability and stability. A total of 11 sugarcane genotypes were analyzed for eight agro-industrial traits. The genotypic values of the traits were determined using mixed model methodology, and the genetic divergence based on phenotypic and genotypic values was measured using the Mahalanobis distance. The distance matrices were correlated using the Mantel test, and the genotypes were grouped using the Tocher method. Genetic divergence is more accurate when based on genotypic values free of genotype-environment interactions and will differ from genetic divergence based on phenotypic data, changing the genotype allocations in the groups. The above methodology can be applied to assess genetic divergence to obtain novel sugarcane genotypes with higher productivity that are adapted to intensive agricultural systems using diverse technologies. This methodology can also be tested in other crops to increase accuracy in selecting the parents to be crossed., Competing Interests: The authors have declared that no competing interests exist.

In this work, an unsupervised volumetric semantic instance segmentation of the plasma membrane of HeLa cells as observed with serial block face scanning electron microscopy is described. The resin background of the images was segmented at different slices of a 3D stack of 518 slices with 8192 × 8192 pixels each. The background was used to create a distance map, which helped identify and rank the cells by their size at each slice. The centroids of the cells detected at different slices were linked to identify them as a single cell that spanned a number of slices. A subset of these cells, i.e., the largest ones and those not close to the edges were selected for further processing. The selected cells were then automatically cropped to smaller regions of interest of 2000 × 2000 × 300 voxels that were treated as cell instances. Then, for each of these volumes, the nucleus was segmented, and the cell was separated from any neighbouring cells through a series of traditional image processing steps that followed the plasma membrane. The segmentation process was repeated for all the regions of interest previously selected. For one cell for which the ground truth was available, the algorithm provided excellent results in Accuracy (AC) and the Jaccard similarity Index (JI): nucleus: JI =0.9665, AC =0.9975, cell including nucleus JI =0.8711, AC =0.9655, cell excluding nucleus JI =0.8094, AC =0.9629. A limitation of the algorithm for the plasma membrane segmentation was the presence of background. In samples with tightly packed cells, this may not be available. When tested for these conditions, the segmentation of the nuclear envelope was still possible. All the code and data were released openly through GitHub, Zenodo and EMPIAR.

Here, we report high-quality annotated draft genomes of eight coagulase-negative staphylococci (CoNS) isolates obtained from South Africa and Nigeria. We explored the prevalence of antibiotic resistance and virulence genes, their association with mobile genetic elements. The pan-genomic analysis highlighted the environmentally driven heterogeneity of the isolates. Isolates from Nigeria had at least one gene for cadmium resistance/tolerance, these genes were not detected in isolates from South Africa. In contrast, isolates from South Africa had a tetM gene, which was not detected among the isolates from Nigeria. The observed genomic heterogeneity correlates with anthropogenic activities in the area where the isolates were collected. Moreover, the isolates used in this study possess an open pan-genome, which could easily explain the environmentally driven heterogeneity., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Pseudomonas aeruginosa is an opportunistic pathogen that often causes nosocomial infections resistant to treatment. Rates of antimicrobial resistance (AMR) are increasing, as are rates of multidrug-resistant (MDR) and possible extensively drug-resistant (XDR) infections. Our objective was to characterize the molecular epidemiology and AMR mechanisms of this pathogen. We sequenced the whole genome for each of 176 P. aeruginosa isolates collected in the Philippines in 2013-2014; derived the multilocus sequence type (MLST), presence of AMR determinants and relatedness between isolates; and determined concordance between phenotypic and genotypic resistance. Carbapenem resistance was associated with loss of function of the OprD porin and acquisition of the metallo-β-lactamase (MBL) gene bla VIM . Concordance between phenotypic and genotypic resistance was 93.27% overall for six antibiotics in three classes, but varied among aminoglycosides. The population of P. aeruginosa was diverse, with clonal expansions of XDR genomes belonging to MLSTs ST235, ST244, ST309 and ST773. We found evidence of persistence or reintroduction of the predominant clone ST235 in one hospital, and of transfer between hospitals. Most of the ST235 genomes formed a distinct lineage from global genomes, thus raising the possibility that they may be unique to the Philippines. In addition, long-read sequencing of one representative XDR ST235 isolate identified an integron carrying multiple resistance genes (including bla VIM-2 ), with differences in gene composition and synteny from the P. aeruginosa class 1 integrons described previously. The survey bridges the gap in genomic data from the Western Pacific Region and will be useful for ongoing surveillance; it also highlights the importance of curtailing the spread of ST235 within the Philippines., ((c) 2021 The authors; licensee World Health Organization.)

Sceletium tortuosum (L.) N.E.Br. (Mesembryanthemaceae), commonly known as kanna or kougoed, is an effective indigenous medicinal plant in South Africa, specifically to the native San and Khoikhoi tribes. Today, the plant has gained strong global attraction and reputation due to its capabilities to promote a sense of well-being by relieving stress with calming effects. Historically, the plant was used by native San hunter-gatherers and Khoi people to quench their thirst, fight fatigue and for healing, social, and spiritual purposes. Various studies have revealed that extracts of the plant have numerous biological properties and isolated alkaloids of Sceletium tortuosum are currently being used as dietary supplements for medicinal purposes and food. Furthermore, current research has focused on the commercialization of the plant because of its treatment in clinical anxiety and depression, psychological and psychiatric disorders, improving mood, promoting relaxation and happiness. In addition, several studies have focused on the isolation and characterization of various beneficial bioactive compounds including alkaloids from the Sceletium tortuosum plant. Sceletium was reviewed more than a decade ago and new evidence has been published since 2008, substantiating an update on this South African botanical asset. Thus, this review provides an extensive overview of the biological and pharmaceutical properties of Sceletium tortuosum as well as the bioactive compounds with an emphasis on antimicrobial, anti-inflammatory, anti-oxidant, antidepressant, anxiolytic, and other significant biological effects. There is a need to critically evaluate the bioactivities and responsible bioactive compounds, which might assist in reinforcing and confirming the significant role of kanna in the promotion of healthy well-being in these stressful times.

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1-5%) of the aerial images as compared to the background region (95-99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision-recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1-10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

Methicillin-resistant Staphylococcus aureus (MRSA) remains one of the leading causes of both nosocomial and community infections worldwide. In the Philippines, MRSA rates have remained above 50% since 2010, but resistance to other antibiotics, including vancomycin, is low. The MRSA burden can be partially attributed to pathogen-specific characteristics of the circulating clones, but little was known about the S. aureus clones circulating in the Philippines. We sequenced the whole genomes of 116 S. aureus isolates collected in 2013-2014 within the Antimicrobial Resistance Surveillance Program. The multilocus sequence type, spa type, SCC mec type, presence of antimicrobial resistance (AMR) determinants and virulence genes and relatedness between the isolates were all derived from the sequence data. The concordance between phenotypic and genotypic resistance was also determined. The MRSA population in the Philippines comprised a limited number of genetic clones, including several international epidemic clones, such as CC30- spa -t019-SCC mec -IV-PVL+, CC5-SCC mec -typeIV and ST239- spa -t030-SCC mec -typeIII. The CC30 genomes were related to the South-West Pacific clone but formed a distinct, diverse lineage, with evidence of global dissemination. We showed independent acquisition of resistance to sulfamethoxazole/trimethoprim in various locations and genetic clones but mostly in paediatric patients with invasive infections. The concordance between phenotypic and genotypic resistance was 99.68% overall for eight antibiotics in seven classes. We have made the first comprehensive genomic survey of S. aureus in the Philippines, which bridges the gap in genomic data from the Western Pacific Region and will constitute the genetic background for contextualizing prospective surveillance., ((c) 2021 The authors; licensee World Health Organization.)

Milk production phenotypes are the main focus of genetic selection in dairy herds, and although there are many genes identified as related to the biology of these traits in pure breeds, little is known about crossbreed animals. This study aimed to identify potential genes associated with the 305-day milk yield in 337 crossbreed Gir × Holstein (Girolando) animals. Milk production records were genotyped for 45,613 single-nucleotide polymorphisms (SNPs). This dataset was used for a genome-wide association study (GWAS) using the 305-day milk yield adjusted for the fixed effects of herd and year and linear and quadratic effects of age at calving (in days) and calving factor averaged per animal. Genes within the significant SNPs were retrieved from the Bos taurus ARS-UCD1.2 assembly (bosTau9) for gene ontology analysis. In summary, the GWAS identified 52 SNPs associated [ p ≤ 10 -4 , false discovery rate (FDR) = 8.77%] with milk production, including NUB1 and SLC24A2, which were previously described as related to milk production traits in cattle. The results suggest that SNPs associated mainly with NUB1 and SLC24A2 could be useful to understand milk production in Girolando and used as predictive markers for selecting genetic predisposition for milk yield in Girolando., Competing Interests: 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., (Copyright © 2021 da Cruz, Silva, Minasi, de Farias Teixeira, Rodrigues, da Silva, do Carmo, da Silva, Utsunomiya, Garcia and da Cruz.)

Accurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo Here, by using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm, we established a 'ground truth' for muSC behaviour. This revealed that segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user-defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) is a package that runs two existing programs, HK Means and Phagosight within the Icy image analysis suite, to enable user-managed cell tracking from 3D time-lapse datasets to provide measures of cell shape and movement. We demonstrate how CTP can be used to reveal changes to cell behaviour of muSCs in response to manipulation of the cell cytoskeleton by small-molecule inhibitors. CTP and the associated tools we have developed for analysis of outputs thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets that are not amenable to straightforward analysis., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2020. Published by The Company of Biologists Ltd.)

Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature-for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers-specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.

With the increasing spread of antimicrobial resistance, there is growing attention to the contribution made by drinking water systems. The potential health impact of two drinking water treatment and distribution systems (A and B) in the North-West Province of South Africa was determined by investigating the water quality and occurrence of antimicrobial-resistant heterotrophic bacteria and genes in the raw and treated water over four seasons. Most of the physicochemical parameters except for electrical conductivity were within permissible limits. Coliform bacteria reduced from raw to potable water except for counts higher than the threshold recorded in Summer and Winter. A total of 203 heterotrophic bacterial isolates were recovered on chromogenic R2A medium and subjected to susceptibility testing to twelve antibiotics. Most of the isolates were resistant to β-lactam antibiotics and Trimethoprim, whereas they were susceptible to Ciprofloxacin, Erythromycin, and Neomycin. The proportions of Cephalothin and Kanamycin-resistant isolates were significantly higher ( p < 0.05) after treatment for site A, compared to significantly lower β-lactam, Oxytetracycline, and Trimethoprim-resistant isolates for B. Over 50% of isolates were of high risk, indicating their origin from high antibiotic-use sources. Seventy-one (35%) isolates were multidrug-resistant, out of which the majority (53.5%, n = 38) possessed the strA gene, followed by strB 21 (29.6%), dfrB 13 (18.3%), aadA 11 (15.5%), bla CTX-M 5 (7.0%), and tetA 3 (4.2%). The 16S rRNA gene sequences of the isolates revealed strains belonging to eight bacterial families, some of which are clinically important.

We aimed to estimate the rate of germline mutations in the offspring of individuals accidentally exposed to Cesium-137 ionizing radiation. The study included two distinct groups: one of cases, consisting of males and females accidentally exposed to low doses of ionizing radiation of Cs137, and a control group of non-exposed participants. The cases included 37 people representing 11 families and 15 children conceived after the accident. Exposed families incurred radiation absorbed doses in the range of 0.2 to 0.5 Gray. The control group included 15 families and 15 children also conceived after 1987 in Goiânia with no history of radiation exposure. DNA samples from peripheral blood were analyzed with the Affymetrix GeneChip® CytoScanHD™ to estimate point mutations in autosomal SNPs. A set of scripts previously developed was used to detect de novo mutations by comparing parent and offspring genotypes at the level of each SNP marker. Overall numbers of observed Mendelian deviations were statistically significant between the exposed and control groups. Our retrospective transgenerational DNA analysis showed a 44.0% increase in the burden of SNP mutations in the offspring of cases when compared to controls, based on the average of MFMD for the two groups. Parent-of-origin and type of nucleotide substitution were also inferred. This proved useful in a retrospective estimation of the rate of de novo germline mutations in a human population accidentally exposed to low doses of radiation from Cesium-137. Our results suggested that observed burden of germline mutations identified in offspring was a potentially useful biomarker of effect to estimate parental exposure to low doses of IR and could become an important marker suitable for biomonitoring human population exposed to environmental mutagens., Competing Interests: The authors have declared that no competing interests exist.

Practices in intensive animal farming such as the extensive use of antimicrobials have significant impacts on the genetic make-up of bacterial communities, especially on that of human/animal commensals. In this report, whole genome sequencing of two vancomycin-resistant enterococci (VRE) isolates from a cattle feedlot in the North West Province, South Africa, was used to highlight the threats that extensive antimicrobial usage in intensive animal rearing represents for environmental microbiomes and the food chain. The genomic DNA of the studied strains was extracted using a DNA extraction kit. Whole-genome sequencing was performed through next-generation sequencing. The genomes of Enterococcus durans strain NWUTAL1 and Enterococcus gallinarum strain S52016 consisted of 3,279,618 and 2,374,946 bp, respectively with G + C contents of 40.76 and 43.13%, respectively. Antibiotic resistance genes (ARG), plasmids and virulence factors (involved in biofilm formation, colonization and copper/silver efflux system), were detected in the genomes of both strains. The presence of these genetic determinants in the studied strains is a cause for concern as they may disseminate and find their way into the food chain via horizontal gene transfer amongst bacteria of the different ecological niches. Issues of this nature cannot be undermined and are relevant as far as food safety is concerned., (Copyright © 2020 Foka, Mienie, Bezuidenhout and Ateba.)

The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures: VGG16 (93%, 90%), ResNet18 (94%, 88%), Inception-ResNet-v2 (94%, 89%), and U-Net (92%, 56%)., Competing Interests: The authors have declared that no competing interests exist.

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu's binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

Fractures of the wrist are common in Emergency Departments, where some patients are treated with a procedure called Manipulation under Anaesthesia. In some cases, this procedure is unsuccessful and patients need to revisit the hospital where they undergo surgery to treat the fracture. This work describes a geometric semi-automatic image analysis algorithm to analyse and compare the x-rays of healthy controls and patients with dorsally displaced wrist fractures (Colles' fractures) who were treated with Manipulation under Anaesthesia. A series of 161 posterior-anterior radiographs from healthy controls and patients with Colles' fractures were acquired and analysed. The patients' group was further subdivided according to the outcome of the procedure (successful/unsuccessful) and pre- or post-intervention creating five groups in total (healthy, pre-successful, pre-unsuccessful, post-successful, post-unsuccessful). The semi-automatic analysis consisted of manual location of three landmarks (finger, lunate and radial styloid) and automatic processing to generate 32 geometric and texture measurements, which may be related to conditions such as osteoporosis and swelling of the wrist. Statistical differences were found between patients and controls, as well as between pre- and post-intervention, but not between the procedures. The most distinct measurements were those of texture. Although the study includes a relatively low number of cases and measurements, the statistical differences are encouraging., Competing Interests: The authors have declared that no competing interests exist.

The Quilombola communities are mostly isolated and deprived of sources of treated water, garbage collection and sewage, consuming fresh water from wells, streams, lakes, among others. This lack of basic infrastructure can be a relevant factor in exposing residents to substances and factors that are harmful to the integrity of their genetic material that can lead to carcinogenesis. Based on this, the objective of this study was to evaluate the genomic and mutagenic/cytotoxic damage in the adult population of two Quilombola communities (one urban and another rural region), in the state of Goiás, Brazil. For this purpose, the leukocyte of peripheral blood Comet Assay in 68 individuals and Micronucleus Test from exfoliated buccal cells of oral mucosa in 21 volunteers were performed. The results evidenced genomic damage, especially for the community of Aparecida de Goiânia city, which detected significant values (p < 0.05), for the length of the comet's tail and for of the Olive Tail Moment. In the micronucleus test, significant differences were only detected (p < 0.05), when it came to the distribution of nuclear changes among the groups. Therefore, it is essential to perform constant population biomonitoring studies to help guarantee health and, consequently, the quality of life.

National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor resistance trends and disseminate these data to AMR stakeholders. Whole-genome sequencing (WGS) can support surveillance by pinpointing resistance mechanisms and uncovering transmission patterns. However, genomic surveillance is rare in low- and middle-income countries. Here, we implement WGS within the established Antimicrobial Resistance Surveillance Program of the Philippines via a binational collaboration. In parallel, we characterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey. By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying the expansion of specific resistance phenotypes that coincide with the growing carbapenem resistance rates observed since 2010. Our results enhance our understanding of the drivers of carbapenem resistance in the Philippines, while also serving as the genetic background to contextualize ongoing local prospective surveillance.

Centrosome separation in late G2/ early prophase requires precise spatial coordination that is determined by a balance of forces promoting and antagonizing separation. The major effector of centrosome separation is the kinesin Eg5. However, the identity and regulation of Eg5-antagonizing forces is less well characterized. By manipulating candidate components, we find that centrosome separation is reversible and that separated centrosomes congress toward a central position underneath the flat nucleus. This positioning mechanism requires microtubule polymerization, as well as actin polymerization. We identify perinuclear actin structures that form in late G2/early prophase and interact with microtubules emanating from the centrosomes. Disrupting these structures by breaking the interactions of the linker of nucleoskeleton and cytoskeleton (LINC) complex with perinuclear actin filaments abrogates this centrosome positioning mechanism and causes an increase in subsequent chromosome segregation errors. Our results demonstrate how geometrical cues from the cell nucleus coordinate the orientation of the emanating spindle poles before nuclear envelope breakdown., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

In this paper, a novel method for interaction detection is presented to compare the contact dynamics of macrophages in the Drosophila embryo. The study is carried out by a framework called macrosight, which analyses the movement and interaction of migrating macrophages. The framework incorporates a segmentation and tracking algorithm into analysing the motion characteristics of cells after contact. In this particular study, the interactions between cells is characterised in the case of control embryos and Shot mutants, a candidate protein that is hypothesised to regulate contact dynamics between migrating cells. Statistical significance between control and mutant cells was found when comparing the direction of motion after contact in specific conditions. Such discoveries provide insights for future developments in combining biological experiments with computational analysis.

Background: Food contamination with Salmonella enterica and enterohemorrhagic Escherichia coli is among the leading causes of foodborne illnesses worldwide and crop plants are associated with > 50% of the disease outbreaks. However, the mechanisms underlying the interaction of these human pathogens with plants remain elusive. In this study, we have explored plant resistance mechanisms against these enterobacteria and the plant pathogen Pseudomonas syringae pv. tomato (Pst) DC3118, as an opportunity to improve food safety., Results: We found that S. enterica serovar Typhimurium (STm) transcriptionally modulates stress responses in Arabidopsis leaves, including induction of two hallmark processes of plant defense: ROS burst and cell wall modifications. Analyses of plants with a mutation in the potentially STm-induced gene EXO70H4 revealed that its encoded protein is required for stomatal defense against STm and E. coli O157:H7, but not against Pst DC3118. In the apoplast however, EXO70H4 is required for defense against STm and Pst DC3118, but not against E. coli O157:H7. Moreover, EXO70H4 is required for callose deposition, but had no function in ROS burst, triggered by all three bacteria. The salicylic acid (SA) signaling and biosynthesis proteins NPR1 and ICS1, respectively, were involved in stomatal and apoplastic defense, as well as callose deposition, against human and plant pathogens., Conclusions: The results show that EXO70H4 is involved in stomatal and apoplastic defenses in Arabidopsis and suggest that EXO70H4-mediated defense play a distinct role in guard cells and leaf mesophyll cells in a bacteria-dependent manner. Nonetheless, EXO70H4 contributes to callose deposition in response to both human and plant pathogens. NPR1 and ICS1, two proteins involved in the SA signaling pathway, are important to inhibit leaf internalization and apoplastic persistence of enterobacteria and proliferation of phytopathogens. These findings highlight the existence of unique and shared plant genetic components to fight off diverse bacterial pathogens providing specific targets for the prevention of foodborne diseases.

This paper describes an unsupervised algorithm, which segments the nuclear envelope of HeLa cells imaged by Serial Block Face Scanning Electron Microscopy. The algorithm exploits the variations of pixel intensity in different cellular regions by calculating edges, which are then used to generate superpixels. The superpixels are morphologically processed and those that correspond to the nuclear region are selected through the analysis of size, position, and correspondence with regions detected in neighbouring slices. The nuclear envelope is segmented from the nuclear region. The three-dimensional segmented nuclear envelope is then modelled against a spheroid to create a two-dimensional (2D) surface. The 2D surface summarises the complex 3D shape of the nuclear envelope and allows the extraction of metrics that may be relevant to characterise the nature of cells. The algorithm was developed and validated on a single cell and tested in six separate cells, each with 300 slices of 2000 × 2000 pixels. Ground truth was available for two of these cells, i.e., 600 hand-segmented slices. The accuracy of the algorithm was evaluated with two similarity metrics: Jaccard Similarity Index and Mean Hausdorff distance. Jaccard values of the first/second segmentation were 93%/90% for the whole cell, and 98%/94% between slices 75 and 225, as the central slices of the nucleus are more regular than those on the extremes. Mean Hausdorff distances were 9/17 pixels for the whole cells and 4/13 pixels for central slices. One slice was processed in approximately 8 s and a whole cell in 40 min. The algorithm outperformed active contours in both accuracy and time.

The initial host response to fungal pathogen invasion is critical to infection establishment and outcome. However, the diversity of leukocyte-pathogen interactions is only recently being appreciated. We describe a new form of interleukocyte conidial exchange called "shuttling." In Talaromyces marneffei and Aspergillus fumigatus zebrafish in vivo infections, live imaging demonstrated conidia initially phagocytosed by neutrophils were transferred to macrophages. Shuttling is unidirectional, not a chance event, and involves alterations of phagocyte mobility, intercellular tethering, and phagosome transfer. Shuttling kinetics were fungal-species-specific, implicating a fungal determinant. β-glucan serves as a fungal-derived signal sufficient for shuttling. Murine phagocytes also shuttled in vitro. The impact of shuttling for microbiological outcomes of in vivo infections is difficult to specifically assess experimentally, but for these two pathogens, shuttling augments initial conidial redistribution away from fungicidal neutrophils into the favorable macrophage intracellular niche. Shuttling is a frequent host-pathogen interaction contributing to fungal infection establishment patterns., Competing Interests: The authors have declared that no competing interests exist.

AmpC beta-lactamase genes are some of the most common antibiotic resistance genes and require special attention once they have become mobilised. The detection of these genes is well documented in clinical settings. However, there is insufficient knowledge of both plasmid and genomic AmpC genes in aquatic environments. This systematic review aimed to determine the extent of the knowledge gap in the literature regarding the prevalence of AmpC beta-lactamase genes in aquatic systems. Using selected criteria, a total of 27 databases were searched for applicable peer-reviewed journal articles. No date and language restrictions were applied. Journal articles that highlighted the detection of AmpC beta-lactamase genes in environmental aquatic systems, including wastewater treatment plants, were included. Of the 950 literature sources that were identified, 50 were selected for full text analysis based on predetermined criteria. Studies on AmpC genes detection were traced in 23 countries. These studies focused on surface water (24), wastewater (17), sea water (4) and both surface and wastewater (5). Most studies did not specifically aim to detect AmpC genes, but to detect antibiotic resistance genes in general. Presently no surveillance protocols, standardised detection methods or environmental limits exist for these genes and, due to a paucity of research in this field, it is unlikely that such systems will be implemented in the near future. The implications and dynamics of AmpC genes in aquatic systems remain unclear and require intense research to ensure the sustainability of environmental systems and human health., (Copyright © 2019. Published by Elsevier Ltd.)

Chromosome banding techniques were applied and standardized to obtain karyotype characteristics for the first time in Brazil of Nelore cattle - Bos taurus indicus Linnaeus, 1758 - (bovine subspecies most prominent in Brazilian livestock). Blood samples were collected from the animals of the School of Agrarian and Biological Sciences of the Pontifical Catholic University of Goiás, two males and two females of pure breed. These samples were submitted to the cell culture method to study metaphase chromosomes. Chromosome banding techniques (C, G and NOR) revealed the karyotype architecture of Nelore cattle common with that of other breeds of zebu cattle formerly karyotyped. The diploid chromosome number was invariably normal, 2n = 60. C-banding revealed C-positive heterochromatin in centromeric regions almost in all chromosomes. G-banding presented the expected band pattern in the respective chromosome pairs in correspondence with the established chromosomal patterns for the species. Ag-staining for nucleolus organizer regions (AgNOR) was identified on the telomeric end of the long arm in 7 autosomal chromosomes. In this study we found more regions in chromosomes with staining than presented in the literature for the Bos indicus group (BIN). These NOR regions were repeated on the same chromosomes for the 4 animals studied.

Busseola fusca (Fuller) (Lepidoptera: Noctuidae), a major insect pest of maize in sub-Saharan Africa, has developed high levels of non-recessive resistance to Cry1Ab toxin expressed in genetically modified Bt maize. Multiple resistance mechanisms to various Cry toxins have been identified in Lepidoptera, but no study has yet been done to determine the mechanism of Cry1Ab resistance in B. fusca. Therefore, the larval transcriptome of B. fusca was sequenced, de novo assembled and characterized. Differential expression analysis was performed to compare gene expression profiles of Cry toxin challenged and unchallenged neonate larvae to assess the molecular basis of the defence mechanism employed by this insect. Several genes associated with Cry toxin resistance in other lepidopteran pests were detected in B. fusca. Results suggest that differential expression of metabolic and immune-related genes might explain Cry1Ab toxin defence in this pest (supplemental file). Transcript expression profiles of neonates demonstrated that 33.59% and 60.31% of the 131 differentially expressed genes were upregulated and downregulated in the toxin-challenged neonate larvae, respectively. Transcripts were grouped into two subclusters according to the similarity of their expression patterns. Transcripts in subcluster 1 were moderately upregulated in the toxin-challenged neonate larvae, and, conversely, downregulated in the unchallenged neonate larvae. The solute carrier organic anion transporter, which is involved in insecticide detoxification, was upregulated in the toxin-challenged neonate larvae. Conversely, most of the transcripts in subcluster 2 were moderately downregulated in the toxin-challenged neonate larvae, and upregulated for neonates feeding on non-challenged maize. Four unidentified transcripts were extremely down-regulated in the toxin-challenged neonate larvae, and upregulated in the unchallenged neonate larvae. Further studies are recommended to establish if there is a direct correlation between these differentially expressed genes and the observed resistance. Elucidation of such defence mechanisms is crucial for developing insect resistance management strategies to ensure sustainable use of genetically modified maize in Africa. Nevertheless, this is the first study on gene expression profiles of B. fusca strains challenged with Cry toxin. The transcriptome characterized in this study provides a significant resource base for future studies on B. fusca and contributes to understanding some of the gene regulation and signalling networks involved in the defence of B. fusca against Cry1Ab toxin., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Surface water systems in South Africa are experiencing a major decline in quality due to various anthropogenic factors. This poses a possible health risk for humans. Here, we present the draft genome sequences of three Clostridium perfringens isolates obtained from a fecally polluted river system in the North West province of South Africa., (Copyright © 2019 Fourie et al.)

Endophytic fungi have the ability to live inside the host plant tissues without causing neither symptoms of diseases/or harm. Opportunistic infections are accountable for majority of the outbreaks, thereby putting a burden on the health system. To investigate and characterize the bioactive compounds for the control of bacteria of clinical importance, extracts from endophytic fungi were isolated from indigenous South African medicinal plants. Extracts from endophytic fungi were isolated from 133 fungal strains and screened against Gram positive and negative bacteria namely Bacillus cereus, Escherichia coli , Enterococcus faecium, and E. gallinarum using disk diffusion. Furthermore, gas chromatography-mass spectrometry was performed to identify the bioactive compounds. Sixteen out of one hundred and thirty-three (12%) fungi extracts exhibited antibacterial properties against some of the selected bacteria. E. coli was found to be the most susceptible in contrast to E. faecium and E. gallinarum which were the most resistant. The isolate MHE 68, identified as Alternaria sp. displayed the greater spectrum of antibacterial activities by controlling selected clinical bacteria strains including resistant E. faecium and E. gallinarum . The chemical analysis of the extract from MHE 68 indicated that linoleic acid (9,12-octadecadienoic acid (Z,Z)) and cyclodecasiloxane could be accountable for the antibacterial activity. This is the first study conducted on the secondary metabolites produced by endophytic fungal strains isolated from the Pelargonium sidoides DC. possessing antibacterial properties., (© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Korean Society of Mycology.)

The process of speech production, i.e., the compression of air in the lungs, the vibration activity of the larynx, and the movement of the articulators, is of great interest in phonetics, phonology, and psychology. One technique by which speech production is analysed is electropalatography , in which an artificial palate, moulded to the speaker's hard palate, is introduced in the mouth. The palate contains a grid of electrodes, which monitor the spatial and temporal pattern of contact between the tongue and the palate during speech production. The output is a time sequence of images, known as palatograms, which show the 2D distribution of electrode activation. This paper describes a series of tools for the visualisation and analysis of palatograms and their associated sound signals. The tools are developed as Matlab ® routines and released as an open-source toolbox. The particular focus is the analysis of the amount and direction of left-right asymmetry in tongue-palate contact during the production of different speech sounds. Asymmetry in the articulation of speech, as measured by electropalatography, may be related to the language under consideration, the speaker's anatomy, irregularities in the palate manufacture, or speaker handedness (i.e., left or right). In addition, a pipeline for the segmentation and analysis of a three-dimensional computed tomography data set of an artificial palate is described and demonstrated. The segmentation procedure provides quantitative information about asymmetry that is due to a combination of speaker anatomy (the shape of the hard palate) and the positioning of the electrodes during manufacture of the artificial palate. The tools provided here should be useful in future studies of electropalatography.

The propagation of radiation in an absorbing-scattering soil with constant or spatial variation of the refractive index is investigated. The soil consists of a plane parallel with Fresnel reflection at the boundaries and is exposed at one boundary to a diffuse or collimated incident radiation. The discrete spherical harmonics method using Marshak boundary conditions is introduced to approximate the directional hemispherical reflectance and transmittance as well as the bidirectional reflectance. The effect in spatial variation of the refractive index on the reflectance and transmittance predictions is examined. A comparison of the directional transmittance and reflectance with the literature results demonstrates that the present method gives accurate results for optically thin and thick soil with a maximum relative error in all cases less than 1%. The bidirectional radiance for variable refractive index soils also shows excellent agreement as compared to the literature results. The results demonstrated that the anisotropic soil interfaces cause a significant decrease of energy reflected and transmitted as well as the bidirectional reflectance.

Background: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images., Methods and Findings: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows., Conclusions: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images., Competing Interests: The authors have declared that no competing interests exist.

This paper presents a novel software framework, called macrosight, which incorporates routines to detect, track, and analyze the shape and movement of objects, with special emphasis on macrophages. The key feature presented in macrosight consists of an algorithm to assess the changes of direction derived from cell-cell contact, where an interaction is assumed to occur. The main biological motivation is the determination of certain cell interactions influencing cell migration. Thus, the main objective of this work is to provide insights into the notion that interactions between cell structures cause a change in orientation. Macrosight analyzes the change of direction of cells before and after they come in contact with another cell. Interactions are determined when the cells overlap and form clumps of two or more cells. The framework integrates a segmentation technique capable of detecting overlapping cells and a tracking framework into a tool for the analysis of the trajectories of cells before and after they overlap. Preliminary results show promise into the analysis and the hypothesis proposed, and lays the groundwork for further developments. The extensive experimentation and data analysis show, with statistical significance, that under certain conditions, the movement changes before and after an interaction are different from movement in controlled cases.

Background: Supernumerary Marker Chromosomes consist in structurally abnormal chromosomes, considered as an extra chromosome in which around 70% occur as a de novo event and about 30% of the cases are mosaic. Tetrasomy 9p is a rare chromosomal abnormality described as the presence of a supernumerary isochromosome 9p. Clinical features of tetrasomy 9p include a variety of physical and developmental abnormalities., Case Presentation: Herein, we reported a postnatal case of a newborn who died in early infancy with multiple congenital malformations due to a mosaic de novo tetrasomy 9p detected by Chromosomal Microarray Analysis. Conventional cytogenetics analysis of the proband was 47,XY,+mar[45]/46,XY[5]. The parental karyotypes presented no visible numerical or structural alterations. Microarray Analysis of the proband revealed that the marker chromosome corresponded to a mosaic de novo gain at 9p24.3q21.11., Conclusions: Chromosomal Microarray Analysis was helpful to identify the origin of the supernumerary marker chromosome and it was a powerful tool to carry out genetic diagnostic, guiding the medical diagnosis. Furthermore, the CMA allowed observing at the first time in Central Brazil the tetrasomy 9p and partial tetrasomy 9q in mosaic, encompassing a large duplicated region with several morbid genes, in an infant with multiple congenital malformations.