Metabolic and mitochondria alterations induced by SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10.

Antiviral signaling, immune response and cell metabolism are dysregulated by SARS-CoV-2, the causative agent of COVID-19. Here, we show that SARS-CoV-2 accessory proteins ORF3a, ORF9b, ORF9c and ORF10 induce a significant mitochondrial and metabolic reprogramming in A549 lung epithelial cells. While ORF9b, ORF9c and ORF10 induced largely overlapping transcriptomes, ORF3a induced a distinct transcriptome, including the downregulation of numerous genes with critical roles in mitochondrial function and morphology. On the other hand, all four ORFs altered mitochondrial dynamics and function, but only ORF3a and ORF9c induced a marked alteration in mitochondrial cristae structure. Genome-Scale Metabolic Models identified both metabolic flux reprogramming features both shared across all accessory proteins and specific for each accessory protein. Notably, a downregulated amino acid metabolism was observed in ORF9b, ORF9c and ORF10, while an upregulated lipid metabolism was distinctly induced by ORF3a. These findings reveal metabolic dependencies and vulnerabilities prompted by SARS-CoV-2 accessory proteins that may be exploited to identify new targets for intervention.

A dataset on eye movement tracking during the resolution of neuropsychological tests on a screen.

The dataset provides data obtained with eye-tracking while 55 volunteers solved 3 distinct neuropsychological tests on a screen inside a closed room. Among the 55 volunteers, 22 were women and 33 were men, all with ages ranging between 9 and 50, and 5 of whom were diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) [1]. The eye-tracker used for the collection of the data was an EyeTribe, which has a sampling rate of 60 Hz and an average visual angle between 0.5 and 1, which correspond to an on-screen error between 0.5 and 1cm (0.1969 to 0.393 inches aprox) respectively, when the distance to the user is around 60cm (23.62 in) [2], which was the case during the collection of these data. The neuropsychological tests were implemented in a software named NEURO-INNOVA KIDS® [3], which are the following: a domino test adapted from the D-48 intelligence test [4], an adaptation of the MASMI test consisting of unfolded cubes [5], the figures series completion test adapted from [6], and the Poppelreuter figures test [7]. Before each of the tests, a calibration process was performed, ensuring that the visual angle error was less than or equal to 0.5 cm (0.1969 in), which is considered an acceptable calibration. The collective mean duration of the four administered tests amounted to 20 minutes. This dataset exhibits significant promise for potential utilization due to the extensive prevalence of these neuropsychological assessments among healthcare practitioners for evaluating diverse cognitive faculties in individuals. Moreover, it has been empirically established that poor performance on these tests is associated with attention deficits [8].

Non-Invasive Prediction of Choledocholithiasis Using 1D Convolutional Neural Networks and Clinical Data.

This paper introduces a novel one-dimensional convolutional neural network that utilizes clinical data to accurately detect choledocholithiasis, where gallstones obstruct the common bile duct. Swift and precise detection of this condition is critical to preventing severe complications, such as biliary colic, jaundice, and pancreatitis. This cutting-edge model was rigorously compared with other machine learning methods commonly used in similar problems, such as logistic regression, linear discriminant analysis, and a state-of-the-art random forest, using a dataset derived from endoscopic retrograde cholangiopancreatography scans performed at Olive View-University of California, Los Angeles Medical Center. The one-dimensional convolutional neural network model demonstrated exceptional performance, achieving 90.77% accuracy and 92.86% specificity, with an area under the curve of 0.9270. While the paper acknowledges potential areas for improvement, it emphasizes the effectiveness of the one-dimensional convolutional neural network architecture. The results suggest that this one-dimensional convolutional neural network approach could serve as a plausible alternative to endoscopic retrograde cholangiopancreatography, considering its disadvantages, such as the need for specialized equipment and skilled personnel and the risk of postoperative complications. The potential of the one-dimensional convolutional neural network model to significantly advance the clinical diagnosis of this gallstone-related condition is notable, offering a less invasive, potentially safer, and more accessible alternative.

A Rapid, Equipment-Free DNA Isolation Method for DNA Barcoding.

This rapid, equipment-free DNA isolation procedure using chromatography paper is a simple method that can be performed in less than 30 min and requires no wet lab experience. With minimal expense, it offers an affordable alternative for anyone wanting to explore biodiversity. It also provides an excellent option for use in classrooms or other activities that are time limited. The method works best for plants or lichens, producing stable DNA on Whatman® chromatography paper at room temperature, which can be eluted as needed.

Investigation of Using Hyperspectral Vegetation Indices to Assess Brassica Downy Mildew.

Downy mildew caused by Hyaloperonospora brassicae is a severe disease in Brassica oleracea that significantly reduces crop yield and marketability. This study aims to evaluate different vegetation indices to assess different downy mildew infection levels in the Brassica variety Mildis using hyperspectral data. Artificial inoculation using H. brassicae sporangia suspension was conducted to induce different levels of downy mildew disease. Spectral measurements, spanning 350 nm to 1050 nm, were conducted on the leaves using an environmentally controlled setup, and the reflectance data were acquired and processed. The Successive Projections Algorithm (SPA) and signal sensitivity calculation were used to extract the most informative wavelengths that could be used to develop downy mildew indices (DMI). A total of 37 existing vegetation indices and three proposed DMIs were evaluated to indicate downy mildew (DM) infection levels. The results showed that the classification using a support vector machine achieved accuracies of 71.3%, 80.7%, and 85.3% for distinguishing healthy leaves from DM1 (early infection), DM2 (progressed infection), and DM3 (severe infection) leaves using the proposed downy mildew index. The proposed new downy mildew index potentially enables the development of an automated DM monitoring system and resistance profiling in Brassica breeding lines.

Modeling of Particulate Pollutants Using a Memory-Based Recurrent Neural Network Implemented on an FPGA.

The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). Understanding the behavior of pollutants and assessing air quality in specific geographical regions is crucial. Overexposure to these pollutants can cause harm to both natural ecosystems and living organisms, including humans. Therefore, it is essential to develop a solution that can accurately evaluate pollution levels. One potential approach is to implement a modified LSTM neural network on an FPGA board. This implementation obtained an 11% improvement compared to the original LSTM network, demonstrating that the proposed architecture is able to maintain its functionality despite reducing the number of neurons in its initial layers. It shows the feasibility of integrating a prediction network into a limited system such as an FPGA board, but easily coupled to a different system. Importantly, this implementation does not compromise the prediction accuracy for both 24 h and 72 h time frames, highlighting an opportunity for further enhancement and refinement.

Fast-screening flow cytometry method for detecting nanoplastics in human peripheral blood.

Plastic pollution is a global problem. Animals and humans can ingest and inhale plastic particles, with uncertain health consequences. Nanoplastics (NPs) are particles ranging from 1 nm to 1000 nm that result from the erosion or breakage of larger plastic debris, and can be highly polydisperse in physical properties and heterogeneous in composition. Potential effects of NPs exposure may be associated with alterations in the xenobiotic metabolism, nutrients absorption, energy metabolism, cytotoxicity, and behavior. In humans, no data on NPs absorptions has been reported previously. Given that their detection relies significantly on environmental exposure, we have prospectively studied the presence of NPs in human peripheral blood (PB). Specifically, we have used fluorescence techniques and nanocytometry, together with the staining of the lipophilic dye Nile Red (NR), to demonstrate that NPs can be accurately detected using flow cytometry.•Potential effects of nanoplastics exposure.•Fluorescence techniques and nanocytometry.•Accurate detection using flow cytometry.

Deep genomic analysis of malignant peripheral nerve sheath tumor cell lines challenges current malignant peripheral nerve sheath tumor diagnosis.

Malignant peripheral nerve sheath tumors (MPNSTs) are soft-tissue sarcomas of the peripheral nervous system that develop either sporadically or in the context of neurofibromatosis type 1 (NF1). MPNST diagnosis can be challenging and treatment outcomes are poor. We present here a resource consisting of the genomic characterization of 9 widely used human MPNST cell lines for their use in translational research. NF1-related cell lines recapitulated primary MPNST copy number profiles, exhibited NF1, CDKN2A, and SUZ12/EED tumor suppressor gene (TSG) inactivation, and presented no gain-of-function mutations. In contrast, sporadic cell lines collectively displayed different TSG inactivation patterns and presented kinase-activating mutations, fusion genes, altered mutational frequencies and COSMIC signatures, and different methylome-based classifications. Cell lines re-classified as melanomas and other sarcomas exhibited a different drug-treatment response. Deep genomic analysis, methylome-based classification, and cell-identity marker expression, challenged the identity of common MPNST cell lines, opening an opportunity to revise MPNST differential diagnosis.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs.

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

Study of CD27, CD38, HLA-DR and Ki-67 immune profiles for the characterization of active tuberculosis, latent infection and end of treatment.

Background: Current blood-based diagnostic tools for TB are insufficient to properly characterize the distinct stages of TB, from the latent infection (LTBI) to its active form (aTB); nor can they assess treatment efficacy. Several immune cell biomarkers have been proposed as potential candidates for the development of improved diagnostic tools. Objective: To compare the capacity of CD27, HLA-DR, CD38 and Ki-67 markers to characterize LTBI, active TB and patients who ended treatment and resolved TB. Methods: Blood was collected from 45 patients defined according to clinical and microbiological criteria as: LTBI, aTB with less than 1 month of treatment and aTB after completing treatment. Peripheral blood mononuclear cells were stimulated with ESAT-6/CFP-10 or PPD antigens and acquired for flow cytometry after labelling with conjugated antibodies against CD3, CD4, CD8, CD27, IFN-γ, TNF-α, CD38, HLA-DR, and Ki-67. Conventional and multiparametric analyses were done with FlowJo and OMIQ, respectively. Results: The expression of CD27, CD38, HLA-DR and Ki-67 markers was analyzed in CD4+ T-cells producing IFN-γ and/or TNF-α cytokines after ESAT-6/CFP-10 or PPD stimulation. Within antigen-responsive CD4+ T-cells, CD27- and CD38+ (ESAT-6/CFP-10-specific), and HLA-DR+ and Ki-67+ (PPD- and ESAT-6/CFP-10-specific) populations were significantly increased in aTB compared to LTBI. Ki-67 demonstrated the best discriminative performance as evaluated by ROC analyses (AUC > 0.9 after PPD stimulation). Data also points to a significant change in the expression of CD38 (ESAT-6/CFP-10-specific) and Ki-67 (PPD- and ESAT-6/CFP-10-specific) after ending the anti-TB treatment regimen. Furthermore, ratio based on the CD27 median fluorescence intensity in CD4+ T-cells over Mtb-specific CD4+ T-cells showed a positive association with aTB over LTBI (ESAT-6/CFP-10-specific). Additionally, multiparametric FlowSOM analyses revealed an increase in CD27 cell clusters and a decrease in HLA-DR cell clusters within Mtb-specific populations after the end of treatment. Conclusion: Our study independently confirms that CD27-, CD38+, HLA-DR+ and Ki-67+ populations on Mtb-specific CD4+ T-cells are increased during active TB disease. Multiparametric analyses unbiasedly identify clusters based on CD27 or HLA-DR whose abundance can be related to treatment efficacy. Further studies are necessary to pinpoint the convergence between conventional and multiparametric approaches.

Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter.

Atmospheric pollution refers to the presence of substances in the air such as particulate matter (PM) which has a negative impact in population ́s health exposed to it. This makes it a topic of current interest. Since the Metropolitan Zone of the Valley of Mexico's geographic characteristics do not allow proper ventilation and due to its population's density a significant quantity of poor air quality events are registered. This paper proposes a methodology to improve the forecasting of PM10 and PM2.5, in largely populated areas, using a recurrent long-term/short-term memory (LSTM) network optimized by the Ant Colony Optimization (ACO) algorithm. The experimental results show an improved performance in reducing the error by around 13.00% in RMSE and 14.82% in MAE using as reference the averaged results obtained by the LSTM deep neural network. Overall, the current study proposes a methodology to be studied in the future to improve different forecasting techniques in real-life applications where there is no need to respond in real time.Implications: This contribution presents a methodology to deal with the highly non-linear modeling of airborne particulate matter (both PM10 and PM2.5). Most linear approaches to this modeling problem are often not accurate enough when dealing with this type of data. In addition, most machine learning methods require extensive training or have problems when dealing with noise embedded in the time-series data. The proposed methodology deals with this data in three stages: preprocessing, modeling, and optimization. In the preprocessing stage, data is acquired and imputed any missing data. This ensures that the modeling process is robust even when there are errors in the acquired data and is invalid, or the data is missing. In the modeling stage, a recurrent deep neural network called LSTM (Long-Short Term Memory) is used, which shows that regardless of the monitoring station and the geographical characteristics of the site, the resulting model shows accurate and robust results. Furthermore, the optimization stage deals with enhancing the capability of the data modeling by using swarm intelligence algorithms (Ant Colony Optimization, in this case). The results presented in this study were compared with other works that presented traditional algorithms, such as multi-layer perceptron, traditional deep neural networks, and common spatiotemporal models, which show the feasibility of the methodology presented in this contribution. Lastly, the advantages of using this methodology are highlighted.

Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA.

Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm.

High-Dimensional Immunophenotyping with 37-Color Panel Using Full-Spectrum Cytometry.

A comprehensive study of the cellular components of the immune system demands both deep and broad immunophenotyping of numerous cell subsets in an effective and practical way. Novel full-spectrum technology reveals the complete emission spectrum of each dye maximizing the amount of information that can be obtained on a single sample regarding conventional flow cytometry and provide an expanded knowledge of biological processes. In this chapter, we describe a 37-color protocol that allows to identify more than 45 different cell populations on whole blood samples of SARS-CoV-2-infected patients.

Identifying Changes in Peripheral Lymphocyte Subpopulations in Adult Onset Type 1 Diabetes.

T- and B-lymphocytes play an important role in the pathogenesis of type 1 diabetes (T1D), a chronic disease caused by the autoimmune destruction of the insulin-producing cells in the pancreatic islets. Flow cytometry allows their characterization in peripheral blood, letting to investigate changes in cellular subpopulations that can provide insights in T1D pathophysiology. With this purpose, CD4+ and CD8+ T cells (including naïve, central memory, effector memory and terminally differentiated effector (TEMRA), Th17 and Tregs) and B cells subsets (naïve, unswitched memory, switched memory and transitional B cells) were analysed in peripheral blood of adult T1D patients at disease onset and after ≥2 years using multiparametric flow cytometry. Here we report changes in the percentage of early and late effector memory CD4+ and CD8+ T cells as well as of naïve subsets, regulatory T cells and transitional B cells in peripheral blood of adult patients at onset of T1D when compared with HD. After 2 years follow-up these changes were maintained. Also, we found a decrease in percentage of Th17 and numbers of T cells with baseline. In order to identify potential biomarkers of disease, ROC curves were performed being late EM CD4 T cell subset the most promising candidate. In conclusion, the observed changes in the percentage and/or absolute number of lymphocyte subpopulations of adult T1D patients support the hypothesis that effector cells migrate to the pancreas and this autoimmune process perseveres along the disease. Moreover, multiparametric flow allows to identify those subsets with potential to be considered biomarkers of disease.

Immune Profiles Identification by Vaccinomics After MVA Immunization in Randomized Clinical Study.

Background: Our previous work has demonstrated the benefits of transcutaneous immunization in targeting Langerhans cells and preferentially inducing CD8 T-cell responses. Methods: In this randomized phase Ib clinical trial including 20 HIV uninfected volunteers, we compared the safety and immunogenicity of the MVA recombinant vaccine expressing HIV-B antigen (MVA-B) by transcutaneous and intramuscular routes. We hypothesized that the quality of innate and adaptive immunity differs according to the route of immunization and explored the quality of the vector vaccine-induced immune responses. We also investigated the early blood transcriptome and serum cytokine levels to identify innate events correlated with the strength and quality of adaptive immunity. Results: We demonstrate that MVA-B vaccine is safe by both routes, but that the quality and intensity of both innate and adaptive immunity differ significantly. Transcutaneous vaccination promoted CD8 responses in the absence of antibodies and slightly affected gene expression, involving mainly genes associated with metabolic pathways. Intramuscular vaccination, on the other hand, drove robust changes in the expression of genes involved in IL-6 and interferon signalling pathways, mainly those associated with humoral responses, and also some levels of CD8 response. Conclusion: Thus, vaccine delivery route perturbs early innate responses that shape the quality of adaptive immunity. Clinical Trial Registration: http://ClinicalTrials.gov, identifier PER-073-13.

KIF11 and KIF15 mitotic kinesins are potential therapeutic vulnerabilities for malignant peripheral nerve sheath tumors.

BACKGROUND: Malignant peripheral nerve sheath tumor (MPNST) constitutes the leading cause of neurofibromatosis type 1-related mortality. MPNSTs contain highly rearranged hyperploid genomes and exhibit a high division rate and aggressiveness. We have studied in vitro whether the mitotic kinesins KIF11, KIF15, and KIF23 have a functional role in maintaining MPNST cell survival and can represent potential therapeutic vulnerabilities. METHODS: We studied the expression of kinesin mRNAs and proteins in tumors and cell lines and used several in vitro functional assays to analyze the impact of kinesin genetic suppression (KIF15, KIF23) and drug inhibition (KIF11) in MPNST cells. We also performed in vitro combined treatments targeting KIF11 together with other described MPNST targets. RESULTS: The studied kinesins were overexpressed in MPNST samples. KIF15 and KIF23 were required for the survival of MPNST cell lines, which were also more sensitive than benign control fibroblasts to the KIF11 inhibitors ispinesib and ARRY-520. Co-targeting KIF11 and BRD4 with ARRY-520 and JQ1 reduced MPNST cell viability, synergistically killing a much higher proportion of MPNST cells than control fibroblasts. In addition, genetic suppression of KIF15 conferred an increased sensitivity to KIF11 inhibitors alone or in combination with JQ1. CONCLUSIONS: The mitotic spindle kinesins KIF11 and KIF15 and the cytokinetic kinesin KIF23 play a clear role in maintaining MPNST cell survival and may represent potential therapeutic vulnerabilities. Although further in vivo evidences are still mandatory, we propose a simultaneous suppression of KIF11, KIF15, and BRD4 as a potential therapy for MPNSTs.

Evidence of human occupation in Mexico around the Last Glacial Maximum.

The initial colonization of the Americas remains a highly debated topic1, and the exact timing of the first arrivals is unknown. The earliest archaeological record of Mexico-which holds a key geographical position in the Americas-is poorly known and understudied. Historically, the region has remained on the periphery of research focused on the first American populations2. However, recent investigations provide reliable evidence of a human presence in the northwest region of Mexico3,4, the Chiapas Highlands5, Central Mexico6 and the Caribbean coast7-9 during the Late Pleistocene and Early Holocene epochs. Here we present results of recent excavations at Chiquihuite Cave-a high-altitude site in central-northern Mexico-that corroborate previous findings in the Americas10-17of cultural evidence that dates to the Last Glacial Maximum (26,500-19,000 years ago)18, and which push back dates for human dispersal to the region possibly as early as 33,000-31,000 years ago. The site yielded about 1,900 stone artefacts within a 3-m-deep stratified sequence, revealing a previously unknown lithic industry that underwent only minor changes over millennia. More than 50 radiocarbon and luminescence dates provide chronological control, and genetic, palaeoenvironmental and chemical data document the changing environments in which the occupants lived. Our results provide new evidence for the antiquity of humans in the Americas, illustrate the cultural diversity of the earliest dispersal groups (which predate those of the Clovis culture) and open new directions of research.

Repellency and mortality effects of sunscreens on the shrimp Palaemon varians: Toxicity dependent on exposure method.

Contamination by sunscreens has become a serious environmental problem due to the increasing use of these products in coastal regions. Their complex chemical composition supposes an input of different chemical compounds capable of producing toxic effects and repelling organisms. The aim of the current study was to experimentally check the repellency of three commercial sunscreens [A (lotion), B (gel) and C (milk spray)] by assessing the escape (displacement towards areas with lower sunscreen levels) of the estuarine shrimp Palaemon varians exposed (4 h) to a gradient (0-300 mg/L) of the sunscreens in a heterogeneous non-forced exposure scenario. Additionally, mortality and immobility (72 h) were checked in a traditional forced exposure scenario. Considering that the toxicity of sunscreens is a little controversial regarding their chemical availability in the medium, two different methods of sunscreen solubilisation were tested: complete homogenization and direct immersion. Very low mortality was observed in the highest concentration of sunscreens A and C applied by direct immersion; however, for sunscreen B, the main effect was the loss of motility when homogenization was applied. Repellency was evidenced for two sunscreens (A and B) applied by direct immersion. The homogenization in the medium seemed to lower the degree of repellency of the sunscreens, probably linked to the higher viscosity in the medium, preventing the motility of shrimps. By integrating both short-term responses (avoidance and mortality/immobility), the PID (population immediate decline) calculated showed that avoidance might be the main factor responsible for the reduction of the population at the local scale.