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.

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.

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.

Mechanisms of Abrupt Loss of Virus Control in a Cohort of Previous HIV Controllers.

Elite and viremic HIV controllers are able to control their HIV infection and maintain undetectable or low-level viremia in the absence of antiretroviral treatment. Despite extensive studies, the immune factors responsible for such exclusive control remain poorly defined. We identified a cohort of 14 HIV controllers that suffered an abrupt loss of HIV control (LoC) to investigate possible mechanisms and virological and immunological events related to the sudden loss of control. The in-depth analysis of these subjects involved the study of cell tropism of circulating virus, evidence for HIV superinfection, cellular immune responses to HIV, as well as an examination of viral adaptation to host immunity by Gag sequencing. Our data demonstrate that a poor capacity of T cells to mediate in vitro viral suppression, even in the context of protective HLA alleles, predicts a loss of viral control. In addition, the data suggest that inefficient viral control may be explained by an increase of CD8 T-cell activation and exhaustion before LoC. Furthermore, we detected a switch from C5- to X4-tropic viruses in 4 individuals after loss of control, suggesting that tropism shift might also contribute to disease progression in HIV controllers. The significantly reduced inhibition of in vitro viral replication and increased expression of activation and exhaustion markers preceding the abrupt loss of viral control may help identify untreated HIV controllers that are at risk of losing control and may offer a useful tool for monitoring individuals during treatment interruption phases in therapeutic vaccine trials.IMPORTANCE A few individuals can control HIV infection without the need for antiretroviral treatment and are referred to as HIV controllers. We have studied HIV controllers who suddenly lose this ability and present with high in vivo viral replication and decays in their CD4+ T-cell counts to identify potential immune and virological factors that were responsible for initial virus control. We identify in vitro-determined reductions in the ability of CD8 T cells to suppress viral control and the presence of PD-1-expressing CD8+ T cells with a naive immune phenotype as potential predictors of in vivo loss of virus control. The findings could be important for the clinical management of HIV controller individuals, and it may offer an important tool to anticipate viral rebound in individuals in clinical studies that include combination antiretroviral therapy (cART) treatment interruptions and which, if not treated quickly, could pose a significant risk to the trial participants.

Multi-level immune response network in mild-moderate Chronic Obstructive Pulmonary Disease (COPD).

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is associated with an abnormal pulmonary and systemic immune response to tobacco smoking. Yet, how do immune cells relate within and between these two biological compartments, how the pulmonary infiltrate influences the lung transcriptome, and what is the role of active smoking vs. presence of disease is unclear. METHODS: To investigate these questions, we simultaneously collected lung tissue and blood from 65 individuals stratified by smoking habit and presence of the disease. The immune cell composition of both tissues was assessed by flow cytometry, whole lung transcriptome was determined with Affymetrix arrays, and we used Weighted Gene Co-expression Network Analysis (WGCNA) to integrate results. RESULTS: Main results showed that: (1) current smoking and the presence of COPD were both independently associated with a reduction in the proportion of lung T cells and an increase of macrophages, specifically those expressing CD80 + CD163+; (2) changes in the proportion of infiltrating macrophages, smoking status or the level of airflow limitation were associated to different WGCNA modules, which were enriched in iron ion transport, extracellular matrix and cilium organization gene ontologies; and, (3) circulating white blood cells counts were correlated with lung macrophages and T cells. CONCLUSIONS: Mild-moderated COPD lung immune infiltrate is associated with the active smoking status and presence of disease; is associated with changes in whole lung tissue transcriptome and marginally reflected in blood.

Telomere attrition in heart failure: a flow-FISH longitudinal analysis of circulating monocytes.

BACKGROUND: Cross-sectional investigations report shorter telomeres in patients with heart failure (HF); however, no studies describe telomere length (TL) trajectory and its relationship with HF progression. Here we aimed to investigate telomere shortening over time and its relationship to outcomes. METHODS: Our study cohort included 101 ambulatory patients with HF. Blood samples were collected at baseline (n = 101) and at the 1-year follow-up (n = 54). Using flow-FISH analysis of circulating monocytes, we simultaneously measured three monocyte subsets-classical (CD14++CD16-), intermediate (CD14++CD16+), and nonclassical (CD14+CD16++)-and their respective TLs based on FITC-labeled PNA probe hybridization. The primary endpoints were all-cause death and the composite of all-cause death or HF-related hospitalization, assessed at 2.3 ± 0.6 years. All statistical analyses were executed by using the SPSS 15.0 software, and included Student's t test and ANOVA with post hoc Scheffe analysis, Pearson or Spearman rho correlation and univariate Cox regression when applicable. RESULTS: We found high correlations between TL values of different monocyte subsets: CD14++CD16+ vs. CD14++CD16-, R = 0.95, p < 0.001; CD14++CD16+ vs. CD14+CD16++, R = 0.90, p < 0.001; and CD14++CD16- vs. CD14+CD16++, R = 0.89, p < 0.001. Mean monocyte TL exhibited significant attrition from baseline to the 1-year follow-up (11.1 ± 3.3 vs. 8.3 ± 2.1, p < 0.001). TL did not significantly differ between monocyte subsets at either sampling time-point (all p values > 0.1). Cox regression analyses did not indicate that TL or ΔTL was associated with all-cause death or the composite endpoint. CONCLUSIONS: Overall, this longitudinal study demonstrated a ~ 22% reduction of TL in monocytes from ambulatory patients with HF within 1 year. TL and ΔTL were not related to outcomes over long-term follow-up.

Performance of Metal and Polymeric O-Ring Seals during Beyond-Design-Basis Thermal Conditions.

This paper summarizes the small scale thermal exposure test results of the performance of metallic and polymeric O-ring seals typically used in radioactive material transportation packages. Five different O-ring materials were evaluated: Inconel/silver, ethylene-propylene diene monomer (EPDM), polytetrafluoroethylene (PTFE), silicone, butyl, and Viton. The overall objective of this study is to provide test data and insights to the performance of these Oring seals when exposed to beyond-design-basis temperature conditions due to a severe fire. Tests were conducted using a small-scale stainless steel pressure vessel pressurized with helium to 2 bar or 5 bar at room temperature. The vessel was then heated in an electric furnace to temperatures up to 900 °C for a pre-determined period (typically 8 h to 9 h). The pressure drop technique was used to determine if leakage occurred during thermal exposure. Out of a total of 46 tests performed, leakage (loss of vessel pressure) was detected in 13 tests.

Brilliant violet fluorochromes in simultaneous multicolor flow cytometry-fluorescence in situ hybridization measurement of monocyte subsets and telomere length in heart failure.

Conventional analytical methods to determine telomere length (TL) have been replaced by more precise and reproducible procedures, such as fluorescence in situ hybridization coupled with flow cytometry (flow-FISH). However, simultaneous measurement of TL and cell phenotype remains difficult. Relatively expensive and time-consuming cell-sorting purification is needed to counteract the loss, due to stringent FISH conditions, of prehybridization fluorescence by the organic fluorochromes conventionally used in the phenotyping step. Here, we sought to assess whether the newly developed Brilliant Violet (BV) dyes are valuable to specifically and simultaneously assess the distribution and telomere attrition of monocyte subsets circulating in the blood of a cohort of patients with heart failure. We performed flow-FISH on blood samples from 28 patients with heart failure. To differentiate among monocyte subsets, we used BV and conventional fluorochromes conjugated to antibodies against CD86, CD14, CD16, and CD15. We simultaneously assessed the TLs of the monocyte subsets with a telomere-specific peptide nucleic acid probe labeled with fluorescein isothiocyanate. The BV dyes completely tolerated the harsh conditions required for adequate DNA denaturation and simultaneously provided accurate identification of monocyte subpopulations and respective TLs. The presented protocol may be faster and less expensive than those used currently for purposes such as establishing associations among patient categories, disease progression, monocyte heterogeneity, and aging in the context of heart failure.

Alternative effector-function profiling identifies broad HIV-specific T-cell responses in highly HIV-exposed individuals who remain uninfected.

The characterization of host immune responses to human immunodeficiency virus (HIV) in HIV controllers and individuals with high exposure but seronegativity to HIV (HESN) is needed to guide the development of effective preventive and therapeutic vaccine candidates. However, several technical hurdles severely limit the definition of an effective virus-specific T-cell response. By using a toggle-peptide approach, which takes HIV sequence diversity into account, and a novel, boosted cytokine staining/flow cytometry strategy, we here describe new patterns of T-cell responses to HIV that would be missed by standard assays. Importantly, this approach also allows detection of broad and strong virus-specific T-cell responses in HESN individuals that are characterized by a T-helper type 1 cytokine-like effector profile and produce cytokines that have been associated with potential control of HIV infection, including interleukin 10, interleukin 13, and interleukin 22. These results establish a novel approach to improve the current understanding of HIV-specific T-cell immunity and identify cellular immune responses and individual cytokines as potential markers of relative HIV resistance. As such, the findings also help develop similar strategies for more-comprehensive assessments of host immune responses to other human infections and immune-mediated disorders.

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.

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.

Alignment of the measurement scale mark during immersion hydrometer calibration using an image processing system.

The present work presents an improved method to align the measurement scale mark in an immersion hydrometer calibration system of CENAM, the National Metrology Institute (NMI) of Mexico, The proposed method uses a vision system to align the scale mark of the hydrometer to the surface of the liquid where it is immersed by implementing image processing algorithms. This approach reduces the variability in the apparent mass determination during the hydrostatic weighing in the calibration process, therefore decreasing the relative uncertainty of calibration.

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.

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.

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.