Analysis Of Datasets Research Articles

Investigating PPT2’s role in ovarian cancer prognosis and immunotherapy outcomes

Ovarian cancer (OC) remains the primary cause of mortality among gynecological malignancies, and the identification of reliable molecular biomarkers to prognosticate OC outcomes is yet to be achieved. The gene palmitoyl protein thioesterase 2 (PPT2), which has been sparsely studied in OC, was closely associated with metabolism. This study aimed to determine the association between PPT2 expression, prognosis, immune infiltration, and potential molecular mechanisms in OC. We obtained the RNA-seq and clinical data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) databases, then Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, nomogram, and calibration were conducted to assess and verify the role of PPT2. Gene set enrichment analysis (GSEA) was used to figure out the closely correlated pathways with PPT2. Overexpression experiment was performed to explore the function of PPT2. Our findings showed that PPT2 mRNA expression was apparent down-regulation in OC tissue compared to normal ovarian tissues in TCGA, GTEx datasets, and GEO datasets. This differential expression was also confirmed in our in-house datasets at both the mRNA and protein levels. Decreased PPT2 expression correlated with lower survival rates in TCGA, several GEO datasets, and our in-house datasets. Multivariate analysis revealed that PPT2 was an independent factor in predicting better outcomes for OC patients in TCGA and GEO. A negative correlation was revealed between immune infiltration and PPT2 expression through Single-sample GSEA (ssGSEA). Additionally, PPT2 was negatively correlated with an up-regulated immune score, stromal score, and estimate score, suggesting that patients with low PPT2 expression might benefit more from immunotherapy. Numerous chemical agents showed lower IC50 in patients with high PPT2 expression. In single-cell RNA sequencing (scRNA-seq) analysis of several OC datasets, we found PPT2 was mainly expressed in endothelial cells. Furthermore, we found that PPT2 inhibited OC cell proliferation in vitro. Our results demonstrated that PPT2 was considered a favorable prognostic biomarker for OC and may be vital in predicting response to immunotherapy and chemotherapy. Further research was needed to fully understand the relationship between PPT2 and immunotherapy efficacy in OC patients.

A large reverse-genetic screen identifies numerous regulators of testis nascent myotube collective cell migration and collective organ sculpting.

Collective cell migration is critical for morphogenesis, homeostasis, and wound healing. During development migrating mesenchymal cells form tissues that shape some of the body's organs. We have developed a powerful model for examining this, exploring how Drosophila testis nascent myotubes migrate onto the testis during pupal development, forming the muscles that ensheath it and also creating its characteristic spiral shape. To define genes that regulate this process, we have carried out RNAseq to define the genes expressed in myotubes during migration. Using this dataset, we curated a list of 131 ligands, receptors and cytoskeletal regulators, including all Rho-family GTPase GAPs and GEFs, as candidates. We then used the GAL4/UAS system to express 279 shRNAs targeting these genes, using the muscle specific driver dMef2>GAL4, and examined the adult testis. We identified 29 genes with diverse roles in testis morphogenesis. Some have phenotypes consistent with defects in collective cell migration, while others alter testis shape in different ways, revealing some of the underlying logic of testis morphogenesis. We followed up one of these genes in more detail-that encoding the Rho-family GEF dPix. dPix knockdown leads to a drastic reduction in migration and a substantial loss of muscle coverage. Our data suggest different isoforms of dPix play distinct roles in this process, reveal a role for its protein partner Git. We also explore whether cdc42 activity regulation or cell adhesion are among the dPix mechanisms of action. Together, our RNAseq dataset and genetic analysis will provide an important resource for the community to explore cell migration and organ morphogenesis.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis.

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren's disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease-associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Vitamin D levels and risk of ocular disorders: insights from bidirectional and multivariable Mendelian randomization analysis.

This study aimed to assess the causal relationships between vitamin D levels and ocular disorders. Independent genetic variables were obtained from genome-wide association studies (GWAS) and publicly available databases. The summary statistics for 25-hydroxyvitamin D (25(OH)D) were obtained from two large-scale GWAS studies, with sample sizes of 324,105 and 417,580 European individuals. The genetic variants of myopia, primary open angle glaucoma (POAG), anterior iridocyclitis, senile cataract, diabetic retinopathy (DR), retinal vein occlusion (RVO), wet age-related macular degeneration (WAMD) and optic neuritis were extracted from the latest release of FinnGen consortium, which contains genome data from Finnish participants. Subsequently, Mendelian randomization (MR) analyses were conducted to obtain effect estimates. Additionally, we performed multivariable MR analysis and mediation analysis to validate the results. In the discovery dataset, genetically predicted vitamin D concentration was found to be causally associated with an increased risk of WAMD, (odd ratio (OR) = 1.35, 95% confidence interval (CI) = 1.09-1.67, P IVW = 0.005). However, no causal effects of genetically predisposed vitamin D levels on the risk of most types of ocular disorders were observed. Reverse MR revealed no causal relationships between the ocular diseases and vitamin D concentrations. The MR analyses of the validation dataset yielded consistent results. Additionally, the causal effect of vitamin D levels on the risk of WAMD remained significant after adjusting for potential confounders in the multivariable MR analysis (OR = 1.86, 95% CI = 1.26-2.73, P IVW = 0.002). Our MR analysis results provide robust evidence of a causal relationship between genetically predicted 25(OH)D levels and an increased risk of WAMD in European population. These findings offer important insights into the management and control of ocular disorders.

Quantifying Creep on the Laohushan Fault Using Dense Continuous GNSS

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan Fault Zone at the northeastern margin of the Tibetan Plateau, as revealed by InSAR observations, lacks confirmation from other observational methods, particularly high-precision GNSS studies. In this study, we utilized nearly seven years of observation data from a dense GNSS continuous monitoring profile (with a minimum station spacing of 2 km) that crosses the eastern segment of the Laohushan Fault. This dataset was integrated with GNSS data from regional continuous stations, such as those from the Crustal Movement Observation Network of China, and multiple campaign measurements to calculate GNSS baseline change time series across the Laohushan Fault and to obtain a high spatial resolution horizontal crustal velocity field for the region. A comprehensive analysis of this primary dataset indicates that the Laohushan Fault is currently experiencing left-lateral creep, characterized by a partially locked shallow segment and a deeper locked segment. The fault creep is predominantly concentrated in the shallow crustal region, within a depth range of 0–5.7 ± 3.4 km, exhibiting a creep rate of 1.5 ± 0.7 mm/yr. Conversely, at depths of 5.7 ± 3.4 km to 16.8 ± 4.2 km, the fault remains locked, with a loading rate of 3.9 ± 1.1 mm/yr. The shallow creep is primarily confined within 3 km on either side of the fault. Over the nearly seven-year observation period, the creep movement within approximately 5 km of the fault’s near field has shown no significant time-dependent variation, instead demonstrating a steady-state behavior. This steady-state creep appears unaffected by postseismic effects from historical large earthquakes in the adjacent region, although the deeper (far-field) tectonic deformation of the Laohushan Fault may have been influenced by the postseismic effects of the 1920 Haiyuan M8.5 earthquake.

AI in Scientific Research: Empowering Researchers with Intelligent Tools

This article explores the transformative impact of artificial intelligence (AI) on scientific research across various disciplines. It examines how AI-driven tools are revolutionizing data analysis, simulation, and hypothesis generation, particularly in fields such as genomics, climate science, and materials science. The article discusses the acceleration of discovery processes through AI, highlighting its role in enabling sophisticated analysis of complex datasets, developing predictive models, and facilitating automated experimentation. Ethical considerations, including the need for transparency and reproducibility in AI-assisted research, are addressed. The synergy between human creativity and AI capabilities is explored, emphasizing how AI augments human ingenuity and fosters interdisciplinary collaboration. Case studies illustrate successful implementations of AI in scientific inquiry, demonstrating its potential to enhance research methodologies and outcomes. The article also looks ahead to the prospects of AI in scientific research, considering emerging technologies and the evolving role of AI in the scientific process. By providing a comprehensive overview of AI's current applications and future potential in scientific research, this article underscores the pivotal role of AI in advancing scientific knowledge and addressing complex global challenges.

ICS-LTU2022: A dataset for ICS vulnerabilities

Industrial control systems (ICS) are a collection of control systems and associated instrumentation for controlling and monitoring industrial processes. Critical infrastructure relies on supervisory control and data acquisition (SCADA), a subset of ICS specifically designed for monitoring and controlling industrial processes over large geographic areas. Cyberattacks like the Colonial Pipeline ransomware case have demonstrated how an adversary may compromise critical infrastructure. The Colonial Pipeline ransomware attack led to a week’s pipeline shutdown, causing a gas shortage in the United States. As existing vulnerability assessment tools cannot be used in the context of ICS systems, vulnerability datasets specified for ICSs are needed to evaluate the security weaknesses. Our secondary metadata, ICS-LTU2022, consists of multiple features that can be used for vulnerability assessment and risk evaluation in industrial control systems. A description of the dataset, its characteristics, and data analysis are also presented in this paper. Vulnerability analysis was conducted based on the top 10 vulnerabilities in terms of severity, frequency by year, impact, components of the ICS, and common weaknesses. The ICS-LTU2022 vulnerabilities dataset is updated biannually. Our proposed dataset provides security researchers with the most recent ICS critical vulnerabilities.

Two new species of Pholiota (Agaricales, Strophariaceae) from the southwest of China.

Two new mushroom species from Southwest China, Pholiotacylindrospora and P.subterrestris, are described in this study. Pholiotacylindrospora is characterized by its dry pileus with slightly recurved and triangular scales, cylindrical basidiospores, and two types of pleurocystidia-leptocystidia and chrysocystidia-as well as its growth on soil. Pholiotasubterrestris is identified by a brownish-orange pileus with numerous brown fibrillose scales; pale brown lamellae with even edges; a stipe covered with recurved fibrillose scales; elliptical spores with a distinct but small germ pore; and pleurocystidia containing typical amorphous refractive inclusions of chrysocystidia. Both species are described and illustrated, and a phylogenetic analysis of a multigene dataset (ITS + 28S) is presented. Morphological and phylogenetic analyses confirm that P.cylindrospora and P.subterrestris are distinct from the other Pholiota species, and both belong to the subgenus Pholiota. A key to the species of subgenus Pholiota from China is provided.

Jun modulates endoplasmic reticulum stress-associated ferroptosis in dorsal root ganglia neurons during neuropathic pain by regulating Timp1

Neuropathic pain (NP) is a complex disorder caused by lesions or diseases affecting the somatosensory nervous system, severely impacting patients' quality of life. Recent studies suggest ferroptosis may be involved in NP induction, but its precise mechanisms remain unclear. We used GO and KEGG pathway enrichment analyses to functionally annotate ferroptosis-related differentially expressed genes (FRDs). Through STRING and the maximum cluster centrality (MCC) algorithm, we identified five hub FRDs (Jun, Timp1, Egfr, Cdkn1a, Cdkn2a). Single-cell analysis revealed significant expression of Jun and Timp1 in neurons. Our study confirmed the association between ferroptosis and endoplasmic reticulum stress (ERS) in NP and validated changes in hub FRD expression across various NP animal models. In vitro experiments demonstrated that Jun regulates neuronal ferroptosis and ERS, particularly by modulating Timp1 expression. Transcription factor prediction and JASPAR binding site analysis elucidated the regulatory network involving Jun. ROC curve analysis of external datasets highlighted the diagnostic potential of hub FRDs and ERS-related differentially expressed genes (ERSRDs) in NP. Using the Comparative Toxicogenomics Database (CTD), we identified estradiol (E2) as a potential therapeutic drug targeting hub FRDs and ERSRDs. Molecular docking predicted its binding sites with Jun and Timp1, and in vivo experiments confirmed that E2 alleviated NP and reversed the expression of Jun and Timp1. This study underscores the crucial role of Jun and Timp1 in the interplay between ferroptosis and ERS, offering new insights and promising avenues for NP treatment.

The syntaxin-binding protein STXBP5 regulates progerin expression.

Hutchinson-Gilfor progeria syndrome (HGPS) is caused by a mutation in Lamin A resulting in the production of a protein called progerin. The accumulation of progerin induces inflammation, cellular senescence and activation of the P53 pathway. In this study, through public dataset analysis, we identified Syntaxin Binding Protein 5 (STXBP5) as an influencing factor of progerin expression. STXBP5 overexpression accelerated the onset of senescence, while STXBP5 deletion suppressed progerin expression, delayed senility, and decreased the expression of senescence-related factors. STXBP5 and progerin have synergistic effects and a protein-protein interaction. Through bioinformatics analysis, we found that STXBP5 affects ageing-related signalling pathways such as the mitogen-activated protein kinase (MAPK) pathway, the hippo pathway and the interleukin 17 (IL17) signalling pathway in progerin-expressing cells. In addition, STXBP5 overexpression induced changes in transposable elements (TEs), such as the human endogenous retrovirus H internal coding sequence (HERVH-int) changes. Our protein coimmunoprecipitation (Co-IP) results indicated that STXBP5 bound directly to progerin. Therefore, decreasing STXBP5 expression is a potential new therapeutic strategy for treating ageing-related phenotypes in patients with HGPS.

Malware Detection using Deep Learning (DL)

The attack that occurred recently involved the utilization of malicious software, commonly referred to as malware, along with advanced techniques such as machine learning, specifically deep learning, code transformation, and polymorphism. This makes it harder for cyber experts to detect malware using traditional analysis methods. In view of the low accuracy and high false positive rate of traditional malware detection methods, this research proposes a fine-tuned deep learning model with a novel dataset that is compared with ANN, Support Vector Machine (SVM), random forest (RF), K-Nearest Neighbourhood (KNN) classifiers. Converting a malware code into an image could allow users to effectively identify the presence of malware, even if the original code is modified by the creator. This is due to the fact that the attributes of images remain unchanged, allowing for reliable identification. So, researchers used deep learning technology to detect malware, like detecting malaria from red blood cells. The deep learning model found that a more detailed analysis of malware data sets, focusing on RGB and greyscale images, is needed. These data sets currently rely on publicly available data, but the accuracy of the traditional model could be a lot higher. It also produces many false positive results. The main goal is to create a new data set and model using malware images to identify and categorize malware using deep learning without relying on existing image detection and transfer learning models. The researcher adjusted different hyper parameters, like the number of neurons, filters, stride, hidden units, layers, learning rate, batch size, activation function, optimizer, and epochs. Identifying and correcting issues in models, improving their clarity, stability, and fairness, as well as debugging and monitoring them, is a challenging task. To eliminate this obstacle, assess the model's behaviour and performance by employing various methods such as logging, profiling, testing, visualization, and model clarity. To overcome the challenges posed by the electricity shutdown, we utilized Google's cloud-based GPU and Python 3.7 language to conduct the experiment and train the model. Kali Linux is an operating system that can automatically encrypt file systems and has a lower chance of crashing the system due to malware in a virtual sandbox. The researcher used techniques like early stopping and cross-validation to prevent over fitting and assess generalization in addition to monitoring and evaluating the model's behaviour and performance. The researcher used different methods like L1 and L2 regularization, dropout, and batch normalization to improve the model's performance and avoid over fitting. The binary portable executable (PE) malware dataset is collected from Kaggle, Malimg, Virusshare, Malvis, MS Big2015, and VX-underground and finally converted to greyscale and RGB images to create a novel dataset to fill the lack of image dataset. The raw dataset was then rescaled to a 128x128 greyscale and RGB (red, green, blue) image and flattened to 1024-byte vector images input into convolution neural network (CNN) interpolation to extract features for malware detection. The newly acquired dataset is utilized as an input for the innovative DL algorithms in order to create a tailor-made model capable of accurately predicting malware. The findings in this customized CNN model by fine-tuning hyper-parameters with the three-channel RGB dataset outperformed a greyscale dataset with an accuracy of 98.7% and error rate of 1.3% in current malware compared with other artificial neural network (ANN), ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and also Random Forest (RF) models. The proposed approach is compatible with all operating systems (Windows, Linux, and Mac) and can identify different types of malwares, such as packed, polymorphic, obfuscated, metamorphic, or variations of a malware family. There are some limitations to the shortage of malware image datasets and computational costs for fine-tuning hyper parameters in the whole model. Furthermore, the proposed approach detects malware and groups it into families without using common techniques like disassembly, decompilation, de-obfuscation, or running malicious code in a virtual environment.

Identifying patient subgroups in MASLD and MASH-associated fibrosis: molecular profiles and implications for drug development

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

Habitat structure and an introduced predator limit the abundance of an endangered ground-nesting bird.

Understanding the factors that limit the abundance of threatened species is critical for the development of effective conservation strategies. However, gaining such knowledge from monitoring programs and using it to inform decision-making for rare species can be difficult due to methodological issues posed by the problems of distinguishing true absences from false absences and the analysis of datasets dominated by zero counts. The plains-wanderer (Pedionomus torquatus) is a critically endangered ground-nesting bird that occurs in grasslands of southeastern Australia. Decline of the plains-wanderer has been attributed to habitat modification but little emphasis has been placed on the role of introduced predators, such as the red fox (Vulpes vulpes), which have had a devastating effect on small ground-dwelling vertebrates in dryland regions of Australia. Here, we use a 9-year time series of spotlight counts to investigate the impact of vegetation structure and fox presence on plains-wanderer occupancy and abundance. We used distance sampling to determine the effective strip width for sighting plains-wanderers during spotlight surveys. We then used a hurdle model approach whereby binomial generalized additive models were fitted to presence/absence data within the effective strip-width across all sites and negative-binomial models were fitted to an index of abundance at sites where plains-wanderers were observed. Plains-wanderer occupancy and abundance fluctuated markedly through time. Where foxes were absent, occupancy (but not abundance) of plains-wanderers showed a humped relationship with grass height with an optimal height between 50 and 150 mm. Where foxes were present however, this relationship broke down and plains-wanderers were rarely recorded. Our results suggest that plains-wanderers should benefit from management strategies that maintain grass height at optimal levels and exclude foxes or effectively suppress their populations. A key message from this study is that if statistical analyses of data generated by monitoring programs for rare species are intended to inform management decisions by identifying relationships between threatened species and drivers of their abundance, there should be consideration of analytic approaches that account for true and false zeroes, high prevalence of zeroes, and the possibility of nonlinear responses.

Integrative study to determine the anti-tumor role and mechanism of Chouchunpi San in colorectal cancer

IntroductionThe herbal formula Chouchunpi San (CCPS) is a traditional formula that has been widely used and proven effective in various clinical cancer treatments. However, the mechanism of its anticancer effect remains unclear. This study aims to determine the anti-tumor effect of CCPS on colorectal cancer (CRC) in vivo and in vitro and to explore the underlying molecular mechanisms. MethodsCell counting kit-8 assays, colony-forming assays, and flow cytometry for cell cycle and apoptosis assays were employed to determine the anti-tumor roles of CCPS. Liquid chromatography-mass spectrometry (LC-MS), network pharmacology, molecular docking, analysis of real-world clinical datasets of CRC, and western blotting were conducted to explore the molecular mechanism of CCPS on CRC. CRC xenografted mouse model, western blotting, and public CRC data analysis were conducted to evaluate the anti-tumor efficacy and mechanisms of CCPS on CRC. ResultsCCPS suppresses the growth of CRC cells and increases the number of apoptotic cells dose-dependently. CCPS targets and significantly downregulates RPA1 and enhances the phosphorylation of its downstream effectors, ATR and CHK1, which are critical for CRC progression. Additionally, CCPS shows a comparable anti-tumor effect in CRC xenografted mouse models compared to Capecitabine, a chemotherapy drug commonly used in clinics. DiscussionOur findings demonstrate the potential use of CCPS in cancer treatment by suppressing cancer cell growth and modulating the RPA1/ATR/CHK1 signaling pathway in CRC. Further investigations on the application of CCPS in cancer therapies could be extended to evaluate the potential in vivo toxicity and adverse events, as well as the synergistic effect of CCPS in combination with other chemotherapeutic agents.

Psychoacoustic metrics and affective responses in indoor soundscapes: Implications for restorative soundscapes

This study investigates the links between psychoacoustic metrics and affective responses in indoor soundscapes, guided by the ISO/TS 12913 standard. Through the analysis of an indoor soundscapes dataset, we examine how various psychoacoustic metrics, namely sharpness (S), fluctuation strength (FS), and roughness (R), influence affective responses such as pleasant and eventful. Our research reveals that eventful and annoying soundscapes show higher values of (S), (FS), and (R). Conversely, pleasant soundscapes tend to show lower values in these metrics. The findings suggest that while these metrics can be effective in designing eventful soundscapes, they may be less useful for creating pleasant acoustic environments. This study contributes to the understanding of the applicability of current psychoacoustic metrics for the type of soundscape intervention and calls for the development of new psychoacoustic metrics to design restorative soundscapes that enhance well-being.

Functional Optimization in Distinct Tissues and Conditions Constrains the Rate of Protein Evolution.

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues, specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species' levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

Protected areas and disparate data: understanding geospatial data synthesis in poaching mitigation

Wildlife poaching remains a global threat to endangered species and human health, yet knowledge gaps remain. As new geospatial technologies are developed, evaluated, and put into practice in protected areas we need to know how protected areas synthesize geospatial data derived from these technologies. In this article, we evaluate results from a survey of protected area personnel and from in-depth interviews with personnel from two protected areas. Our structured survey focused on identifying workflows, characterizing the use of geospatial technologies, understanding how geospatial information is synthesized, and learning about the geospatial tools used by practitioners to make sense of protected area data. In addition, we conducted semi-structured interviews with protected area practitioners to provide added context to complement online survey results. We investigate how geospatial data from protected areas is synthesized by practitioners through the collection, organization, analysis, and fusion of geospatial datasets to support the protection of endangered species from poaching activities. Our results show how protected areas currently utilize two key types of geospatial data: device-based and human-generated. We highlight five themes which emerge via analysis of survey, interview, and observational data. These themes include organizational bureaucracy, trust in data collection and sharing, technical limitations, workforce limitations, and financial stressors. We conclude by outlining a series of challenges and opportunities in geographic information science to guide further research to understand geospatial synthesis in the context of protected area management.

Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol.

Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently, Δ-machine learning has been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H3O+ to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here, we investigate the generality of this approach for the PBE, M06, M06-2X, and PBE0 + MBD functionals, using ethanol as the example molecule. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE analysis for training and test data sets, and then general fidelity tests such as energetics of stationary points, normal-mode frequencies, and torsional potentials are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradients were not used to correct the low-level PES. Finally, we present some results for correcting a recent molecular mechanics force field for ethanol and comment on the possible generality of this approach.

Immunotherapy may be more appropriate for ERBB2 low-expressing extramammary paget’s disease patients: a prognosis analysis and exploration of targeted therapy and immunotherapy of extramammary paget’s disease patients

Extramammary Paget’s disease (EMPD) is a rare cutaneous malignancy characterized by its uncertain etiology and metastatic potential. Surgery remains the first-line clinical treatment for EMPD, but the efficacy of radiotherapy and chemotherapy remains to be fully evaluated, and new therapies for EMPD are urgently needed. In this study, we initially screened 815 EMPD patients in the Surveillance, Epidemiology, and End Results (SEER) database and analyzed their clinical features and prognostic factors. Using the dataset from the Genome Sequence Archive (GSA) database, we subsequently conducted weighted gene coexpression network analysis (WGCNA), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analyses, grouping the samples based on EMPD disease status and the levels of ERBB2 expression. The prognostic analysis based on the SEER database identified increased age at diagnosis, distant metastasis, and receipt of radiotherapy as independent risk factors for EMPD. Moreover, our results indicated that patients who received chemotherapy had worse prognoses than those who did not, highlighting the urgent need for novel treatment approaches for EMPD. Functional analysis of the GSA-derived dataset revealed that EMPD tissues were significantly enriched in immune-related pathways compared with normal skin tissues. Compared with those with high ERBB2 expression, tissues with low ERBB2 expression displayed greater immunogenicity and enrichment of immune pathways, particularly those related to B cells. These findings suggest that patients with low ERBB2 expression are likely to benefit from immunotherapy, especially B-cell-related immunotherapy.

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.