Browse Articles

Atlas of proteomic signatures of brain structure and its links to brain disorders

Abstract Individual variation in brain structure influences deterioration due to disease and comprehensive profiling of the associated proteomic signature advances mechanistic understanding. Here, using data from 4997 UK Biobank participants, we analyzed the associations between 2920 plasma proteins and 272 neuroimaging-derived brain structure measures. We identified 5358 associations between 1143 proteins and 256 brain structure measures, with NCAN and LEP proteins showing the most associations. Functional enrichment implicated these proteins in neurogenesis, immune/apoptotic processes and neurons. Furthermore, bidirectional Mendelian randomization revealed 33 associations between 32 proteins and 23 brain structure measures, and 21 associations between nine brain structure associated proteins and ten brain disorders. Moreover, the significant associations between the identified proteins and mental health were mediated by brain volume and surface area. In summary, this study generates a comprehensive atlas mapping the patterns of association between proteome and brain structure, highlighting their potential value for studying brain disorders.

Correlation analysis of mitochondrial DNA maintenance-related genes with HCC prognosis, tumor mutation burden and tumor microenvironment features

Background Mitochondrial DNA (mtDNA) is an important genetic material in eukaryotic cells. Mitochondrial DNA maintenance-related gene (mtDNA MRG) variants contribute to mitochondrial dysfunction in cancer progression and are associated with cancer prognosis. However, the mechanism of mtDNA MRGs in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) remains unclear. Methods Data for a total of 487 HCC samples were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The mitochondrial regulatory pathway gene set was downloaded, and 22 mtDNA MRGs were identified by screening. Based on these 22 genes, the HCC samples were grouped by unsupervised clustering based on a machine learning model. Principal component analysis (PCA) was used to construct the mtDNA score model, and the relationships between the mtDNA score and clinicopathological features, tumor mutation burden (TMB), TME cell infiltration and biological processes were analyzed. Results The expression of 22 mtDNA MRGs significantly different in HCC samples vs. normal controls. In this study, HCC samples were divided into three molecular subtypes based on the expression of mtDNA MRGs. The three subtypes exhibit different clinical characteristics and immune infiltration profiles, and the cell infiltration profiles corresponded to the immune rejection, immune inflammation, and immune-desert phenotypes, respectively. A total of 740 core genes were obtained from different molecular subtypes, and these genes were divided into three gene subtypes. The mtDNA score model, which can be used to assess tumor immune cell invasion, clinicopathological features, genetic variation, and prognosis, was subsequently constructed. A high mtDNA score was associated with a high mutation burden, high clinical stage and poor prognosis. Conclusions mtDNA MRGs play important roles in HCC TMB, prognosis, clinicopathological features and the immune microenvironment. The mtDNA score can be used to evaluate HCC prognosis, TMB and the immune microenvironment, thereby providing guidance for treatment decision making and prognosis prediction in HCC patients.

An unbiased tissue transcriptome analysis identifies potential markers for skin phenotypes and therapeutic responses in atopic dermatitis

Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting

LSTM (Long Short-Term Memory Network) is currently extensively utilized for forecasting financial time series, primarily due to its distinct advantages in separating the long-term from the short-term memory information within a sequence. However, the experimental results presented in this paper indicate that LSTM may struggle to clearly differentiate between these two types of information. To overcome this limitation, we propose the ARMA-RNN-LSTM Hybrid Model, aimed at enhancing the separation between the long-term and short-term memory information on top of LSTM framework. The experiment in this paper is inspired by an observation: when LSTMs and RNNs are respectively used to forecast the same time series that contains only short-term memory information, LSTMs exhibit significantly lower forecasting accuracy than RNNs, and we attributed this to LSTMs potentially misclassifying some short-term memory information as long-term during forecasting process. Further, we speculate that this confusion might also arise when LSTMs are used to forecast the time series containing both the long-term and short-term memory information. To verify the aforementioned hypothesis and improve the forecasting accuracy for financial time series, this paper combines RNNs with LSTMs, proposing a method of ARMA-RNN-LSTM Hybrid Modelling, and conducts an experiment with stock index prices. Eventually, the experiment results show that the ARMA-RNN-LSTM Hybrid Model outperforms standalone RNNs and LSTMs in forecasting stock index series containing both long-term and short-term memory information, confirming that the ARMA-RNN-LSTM Hybrid Model has effectively enhanced the separation between the long-term and short-term memory information within sequence. This hybrid modelling approach has innovatively addressed the issue of the confusion between the long-term and the short-term memory information in a sequence during LSTM’s forecasting process, improving the accuracy of forecasting financial time series, and demonstrates that neural network’s forecasting errors is a area worth to explore in the future.

Selective targeting of genome amplifications and repeat elements by CRISPR-Cas9 nickases to promote cancer cell death

Abstract Focal gene amplification serves as an oncogenic driver during tumorigenesis and is a hallmark of many forms of cancer. Oncogene amplifications promote genomic instability, which is integral to cancer cell survival and evolution. However, focal gene amplification potentially affords an opportunity for therapeutic exploitation. As a proof-of-concept, we leverage CRISPR-Cas9 nickase to selectively promote cancer cell death in MYCN-amplified neuroblastoma in a gene amplification-dependent manner. Our analysis demonstrates that CRISPR-Cas9 nickase can generate a lethal number of highly toxic, replication-dependent double-strand breaks in cells harboring amplified loci. Furthermore, we demonstrate that Cas9 nickase—mediated toxicity can be modulated in combination with small molecule inhibitors targeting key regulators of the DNA-damage response or cell death pathways. Importantly, our findings in MYCN-amplified neuroblastoma translate to other cancer types with distinct oncogene amplifications.

Initial development of the Stress Monitoring and Response Tool (SMART): A holistic measure of stress following trauma

In the immediate aftermath of trauma exposure, individuals may experience an acute stress reaction (ASR). ASRs may be transient but for individuals operating in high-stakes occupations, these reactions can potentially endanger themselves and those around them. Thus, a better understanding of ASRs could facilitate development of early interventions that help prevent longer-term sequelae. Although existing measures (e.g., PCL-5, CAPS-5) target symptoms that occur in the weeks following trauma, they do not encompass the range of ASR symptoms identified in emerging research. Using data from a large-scale study conducted across emergency departments in the United States, we employed confirmatory factor analysis to identify survey items that sensitively assess ASR symptoms during the peri- and post-trauma phases. These analyses identified 23 core items that are appropriate for administration both immediately following trauma exposure and at later timepoints, as well as 11 supplementary items that can be added to the core items for assessment at later timepoints. Collectively, these items constitute the Stress Monitoring and Response Tool (SMART). Both the SMART Core Scale and the combined SMART Core Scale with Supplemental Items demonstrate good convergent and concurrent validity with several other measures of mental health, physical health, somatic symptoms, pain, and functioning. In addition, the SMART scale remains moderately-to-strongly correlated with multiple measures of symptoms and functional impairment at three months post-trauma. Consequently, the SMART can be used to assess individuals in clinical settings, predict trajectories of recovery, and inform tailoring of interventions across time. Future studies should be conducted to assess the potential utility of the SMART as a decision aid in high-intensity occupational contexts.

Microbial potential to mitigate neurotoxic methylmercury accumulation in farmlands and rice

An enhanced adaptive dynamic metaheuristic optimization algorithm for rainfall prediction depends on long short-term memory

Sorting and analyzing different types of rainfall according to their intensity, duration, distribution, and associated meteorological circumstances is the process of rainfall prediction. Understanding rainfall patterns and predictions is crucial for various applications, such as climate studies, weather forecasting, agriculture, and water resource management. Making educated decisions about things like agricultural planning, effective use of water resources, precise weather forecasting, and a greater comprehension of climate-related phenomena is made more accessible when many components of rainfall are analyzed. The capacity to confront and overcome this obstacle is where machine learning and metaheuristic algorithms shine. This study introduces the Adaptive Dynamic Particle Swarm Optimization enhanced with the Guided Whale Optimization Algorithm (AD-PSO-Guided WOA) for rainfall prediction. The AD-PSO-Guided WOA overcomes limitations of conventional optimization algorithms, such as premature convergence by balancing global search (exploration) and local refinement (exploitation). This effectively balances exploration and exploitation, and addresses the early convergence problem of the original algorithms. To choose the most crucial characteristics of the dataset, the feature selection method employs the binary format of AD-PSO-Guided WOA. Next, the desired features are trained on five different models: Decision Trees (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and K-Nearest Neighbor (KNN). Out of all the models, LSTM produced the best results. The AD-PSO-Guided WOA algorithm was used to adjust the hyperparameters for the LSTM model. With coefficient of determination (R2) of 0.9636, the results demonstrate the superior efficacy and performance of the suggested methodology (AD-PSO-Guided WOA-LSTM) compared to other alternative optimization techniques.

Your time is valuable. Don’t give it away just for ‘exposure’

ELMO2 is an essential regulator of carotid artery development

Abstract Engulfment and cell motility 2 (ELMO2) is a cytoskeletal adaptor protein necessary for cell migration and apoptotic cell removal. Loss-of-function mutations in ELMO2 cause intraosseous vascular malformation (VMOS), a human disease involving progressive expansion of craniofacial bones in combination with anomalies in blood vessels that emerge from the external carotid artery, as well as aneurysms in the internal carotid artery. Here we show that global inactivation of Elmo2 in mice leads to midgestation embryonic lethality due to dilation of the 3 rd pharyngeal arch arteries and aneurysm formation in the common carotids. These vascular malformations are associated to defects in vascular smooth muscle cell organization and are phenocopied upon neural crest-specific deletion. In vitro experiments further confirm that ELMO2 regulates vascular smooth muscle cell adhesion, spreading and contractility through Rac1 activation and modulation of actin dynamics. Our findings provide new insights into the biological function of ELMO2 with relevant implications for understanding VMOS pathogenesis and raise the possibility of vessel-targeted diagnostic and treatment strategies.

Decline of salt marsh-nesting birds within the lower Chesapeake Bay (1992–2021)

Bird species that depend on tidal marshes throughout the world are threatened by ongoing sea-level rise. How species that differ in their level of marsh-dependency may respond to change over time remains unclear. I surveyed a network of patches (N = 186) within tidal salt marshes located in the lower Chesapeake Bay (1992, 2021) for 12 species of breeding birds to evaluate changes in abundance. Marsh-nesting bird abundance declined by 65.7% during the course of the survey interval. Significant declines in abundance were discovered for eight of ten species evaluated with declines in abundance ranging from 34 to 100%. Four species were extirpated or nearly extirpated within focal marshes during the study period. The magnitude of decline was highest for facultative nesting species (84.2%) followed by marsh obligates (81.6%) and salt marsh obligates (44.2%) respectively. Salt marsh obligates have become an increasingly dominant portion of the species assemblage over time reaching 83% of all detections by 2021. This pattern supports the prediction that specialists may persist longer than generalists as habitats are subjected to change. Despite their relative stability, salt marsh obligates are of high conservation concern over the longer term due to their specialization on a habitat that is currently experiencing rapid disruption. Even though this study did not evaluate the causes of population decline, results are aligned with other recent work within other regions that have implicated ongoing sea-level rise and nest predation.

Semi-transparent and stable In2S3/CdTe heterojunction photoanodes for unbiased photoelectrochemical water splitting

An attribute-enhanced relationship-aware neighborhood matching model with dual attention

The entity alignment task aims to match semantically corresponding entities in different knowledge graphs, which is important for knowledge fusion. Traditional graph-based methods often lose information due to insufficient use of attributes and imperfect relationship modeling, which makes it difficult to capture the deep semantic relationship between entities fully. To improve the effect of entity alignment, we propose a new model named ARNM-DAE2A, which strengthens the information aggregation capability of GCN by introducing a dual-attention mechanism to ensure a more balanced and comprehensive structural representation. The model contains the entity structure embedding module, the attribute structure embedding module, the joint alignment module and the relationship-aware neighborhood matching module. The entity structure embedding module optimizes the structure learning capability of GCN by introducing the pairwise attention mechanism. The attribute structural embedding module utilizes GCN to acquire entity attribute information. The joint alignment module weights and fuses the relationship structure information and attribute information as a comprehensive representation of entities. The relationship-aware neighborhood matching module then corrects the noise in the GCN aggregated information by comparing the neighborhood relationships of entity pairs. Experiments conducted on DBP15K and SRPRS datasets illustrate that the proposed ARNM-DAE2A outperforms baselines.

Laser activation of single group-IV colour centres in diamond

Abstract Spin-photon interfaces based on group-IV colour centres in diamond offer a promising platform for quantum networks. A key challenge in the field is realising precise single-defect positioning and activation, which is crucial for scalable device fabrication. Here we address this problem by demonstrating a two-step fabrication method for tin vacancy (SnV−) centres that uses site-controlled ion implantation followed by local femtosecond laser annealing with in-situ spectral monitoring. The ion implantation is performed with sub-50 nm resolution and a dosage that is controlled from hundreds of ions down to single ions per site, limited by Poissonian statistics. Using this approach, we successfully demonstrate site-selective creation and modification of single SnV− centres. Our in-situ spectral monitoring opens a window onto materials tuning at the single defect level, and provides new insight into defect structures and dynamics during the annealing process. While demonstrated for SnV− centres, this versatile approach can be readily generalised to other implanted colour centres in diamond and wide-bandgap materials.

Patient satisfaction in regional referral hospitals of Bhutan: Insights from a cross-sectional study

Background Patient satisfaction is crucial for evaluating healthcare quality and guiding continuous quality improvement. Globally, patient satisfaction has been extensively studied; however, there is limited research on this topic in Bhutan, where the healthcare system is in the early stages of developing a quality-oriented culture. To address this gap, we aimed to evaluate patient satisfaction levels among different socio-demographic and clinical groups and identify the predictors of patient satisfaction in Bhutan. Methods We conducted a retrospective analysis of patient satisfaction survey responses archived in the quality assurance unit of two tertiary healthcare centres in Bhutan: Mongar Eastern Regional Referral Hospital and Gelephu Central Regional Referral Hospital. The routine surveys, administered throughout April 2024, utilised an adapted version of the Patient Satisfaction Questionnaire-18. The data were analysed using descriptive and inferential statistics. Results Our study revealed significant variations in patient satisfaction across socio-demographic and clinical groups. Ethnicity (P-value = 0.017), occupation (P-value = 0.014), and education level (P-value = 0.021) emerged as significant predictors of satisfaction. Sharchop and other ethnic groups (P-value= < 0.001); farmers, religious personnel, and other occupational groups (P-value= < 0.001); and illiterate (P-value= < 0.001) individuals exhibited significantly higher satisfaction levels. While patient type (P-value = 0.472), age (P-value = 0.553), and marital status (P-value = 0.448) influenced satisfaction levels, they did not emerge as significant predictors when considering other variables. Overall, patient satisfaction in Bhutan is 4.06 on a 5-point Likert scale. Satisfaction is highest in the financial domain, while accessibility and convenience received the lowest scores. Conclusions Overall, with a score of 4.06 on a 5-point Likert scale, patient satisfaction in Bhutan is high. However, our findings highlight the need to address socio-demographic disparities in patient satisfaction. As the Bhutanese socio-demographic landscape evolves, satisfaction levels may decline. To enhance overall satisfaction, healthcare policymakers should focus on improving accessibility and convenience. Strategies such as establishing dynamic limits on free services, exploring private sector engagement in advanced healthcare service, and strengthening the healthcare workforce are essential for sustainable and quality healthcare service delivery.

Large live biomass carbon losses from droughts in the northern temperate ecosystems during 2016-2022

Influence of shot peening on the microstructure and friction-wear performance of CF53 steel

In order to further enhance the wear resistance of the camshaft surface, this study conducted shot peening reinforcement on CF53 steel. The research involves the analysis of microstructure, microhardness, and residual stress evolution. Additionally, a pin-on-disk friction and wear test machine was used to investigate the influence of shot peening on the friction-wear characteristics of CF53 steel. The results show that a certain depth of plastic deformation layer is formed on the surface after shot peening, accompanied by an increase in surface roughness. With the increase of shot peening pressure, the surface roughness, microhardness, and residual stress values of the samples correspondingly increase. Shot peening treatment significantly improves the friction-wear performance of CF53 steel, with a reduction of 22.5% and 54.6% in the friction coefficient and wear rate for SP3 and SP4 groups, respectively, compared to untreated samples. The wear mechanism of untreated samples is characterized by severe fatigue wear with prominent features of plowing grooves and cracks. In contrast, the wear mechanism of peened samples shifts to fatigue wear dominated by delamination.

Cancer-fighting CAR T cells show promising results for hard-to-treat tumours

Performance of deep-learning-based approaches to improve polygenic scores

Abstract Polygenic scores, which estimate an individual’s genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonlinear phenomena, which may be adapted to exploit gene-gene and gene-environment interactions to potentially improve polygenic scores. We fit neural-network models to both simulated and 28 real traits in the UK Biobank. To infer the amount of nonlinearity present in a phenotype, we also present a framework using neural-networks, which controls for the potential confounding effect of linkage disequilibrium. Although we found evidence for small amounts of nonlinear effects, neural-network models were outperformed by linear regression models for both genetic-only and genetic+environmental input scenarios. In this work, we find that the usefulness of neural-networks for generating polygenic scores may currently be limited and confounded by joint tagging effects due to linkage disequilibrium.

Wavelet analysis text classification algorithm based on typical features of data samples

Currently, traditional text feature extraction methods fail to fully capture category-specific features when handling text data with existing category labels, thereby limiting classification performance. Meanwhile, text classification methods based on wavelet analysis have yet to achieve optimal performance due to the limitations of their feature extraction and analysis techniques. To address these issues, this paper proposes two novel algorithms: (1) Average Term Frequency-Document Frequency (ATF-DF), which adopts a forward-thinking approach to comprehensively extract category-specific features from labeled text samples, resulting in class feature vectors that effectively represent the text categories; (2) Average Term Frequency-Document Frequency-Wavelet Analysis (ATF-DF-WA), which transforms class feature vectors into waveforms and utilizes wavelet analysis to extract typical class feature layer waveforms and feature layer waveforms of the text to be classified. Text classification is then performed by calculating waveform similarity. Experimental results on the THUCHNews dataset demonstrate that compared to two baseline algorithms, ATF-DF improves Precision, Recall, and F1-score by 13.71%, 28.94%, and 20.74%, respectively. Furthermore, experimental results on the THUCHNews, Sogou, and CNTC datasets indicate that ATF-DF-WA outperforms four baseline algorithms, achieving an average Precision improvement of 2.80% to 80.36%, an average Recall improvement of 0.10% to 54.65%, and an average F1-score improvement of 2.62% to 60.82%. Additionally, experimental results on the THUCHNews dataset reveal that ATF-DF-WA demonstrates advantages in both classification performance and training speed compared to baseline algorithms based on pre-trained models, highlighting its promising potential for practical applications.