Browse Articles
Discover research articles across all indexed journals
Explainable classification of goat vocalizations using convolutional neural networks
Efficient precision livestock farming relies on having timely access to data and information that accurately describes both the animals and their surrounding environment. This paper advances classification of goat vocalizations leveraging a publicly available dataset recorded at diverse farms breeding different species. We developed a Convolutional Neural Network (CNN) architecture tailored for classifying goat vocalizations, yielding an average classification rate of 95.8% in discriminating various goat emotional states. To this end, we suitably augmented the existing dataset using pitch shifting and time stretching techniques boosting the robustness of the trained model. After thoroughly demonstrating the superiority of the designed architecture over the contrasting approaches, we provide insights into the underlying mechanisms governing the proposed CNN by carrying out an extensive interpretation study. More specifically, we conducted an explainability analysis to identify the time-frequency content within goat vocalisations that significantly impacts the classification process. Such an XAI-driven validation not only provides transparency in the decision-making process of the CNN model but also sheds light on the acoustic features crucial for distinguishing the considered classes. Last but not least, the proposed solution encompasses an interactive scheme able to provide valuable information to animal scientists regarding the analysis performed by the model highlighting the distinctive components of the considered goat vocalizations. Our findings underline the effectiveness of data augmentation techniques in bolstering classification accuracy and highlight the significance of leveraging XAI methodologies for validating and interpreting complex machine learning models applied to animal vocalizations.
Source-specific fine particulates emission linked to prevalence of ophthalmic cases in India
Second coordination sphere regulates nanozyme inhibition to assist early drug discovery
Clinical breast cancer screening uptake and associated factors among reproductive age women in Kenya: further analysis of Kenyan Demographic and Health Survey 2022
Introduction Breast cancer is a global public health problem among reproductive-age women, with an estimated 670,000 deaths in 2022. It’s also a pressing health challenge in Sub-Saharan African countries, driven by late-stage diagnoses and limited healthcare access, underscoring the urgent need for early screening and treatment initiatives to combat this growing epidemic. In Kenya, the burden was also significant, and multilevel factors such as individual, household, and community level factors that influence screening uptake are undermined. Furthermore, there were no nationally representative studies. Therefore, this study aimed to assess clinical breast cancer screening uptake (CBCSU) and associated factors among reproductive-age women: further analysis of Kenyan demographic and health survey (KDHS) 2022. Methods This study used a weighted nationally representative sample of 16,649 women from the 2022 KDHS. A Multilevel mixed effects binary logistic regression analysis was performed and in the multivariable analysis, variables with a p-value less than 0.05 were considered statistically significant. The strength of the association was evaluated using Adjusted Odds Ratios (AOR) along with their corresponding 95% confidence intervals (CI). STATA version 17 software was to for data management and statistical analysis. Results The weighted prevalence of CBCSU in Kenya was 13.91% (95% CI: 13.33, 14.44). Besides, women aged 25 to 34 years (AOR = 1.86, 95% CI: 1.57, 2.21), and 35 to 49 years (AOR = 2.87, 95% CI: 2.40, 3.42) had higher odds of CBCSU. Women with primary education (AOR = 2.14, 95% CI: 1.56, 2.94) and those with secondary or higher education (AOR = 3.09, 95% CI: 2.23, 4.28) had higher odds of CBCSU. In addition, the odds of CBCSU were higher among women with a middle (AOR = 1.41, 95% CI: 1.19, 1.67) and a rich wealth index (AOR = 2.19, 95% CI: 1.84, 2.61). On the other hand, CBCSU was lower among non-contraceptive user women (AOR 0.78, 95% CI: 0.69, 0.87). Furthermore, women in communities with a high proportion of media exposure had higher CBCSU (AOR = 1.17, 95% CI: 1.04, 1.33). Conclusion In this study, the prevalence of CBCSU among reproductive-age women in Kenya was found to be low. Besides, factors such as age, educational status, wealth index, family planning utilization, and community media exposure were identified as significant contributors to screening uptake. Therefore, policymakers and stakeholders should design interventions that address factors contributing to low breast cancer screening uptake, particularly targeting women in areas with limited media exposure, to increase the uptake of clinical breast cancer screening.
Lipoic acid-plumbagin conjugate protects pancreatic beta cells against high glucose-induced toxicity
Structural basis for allosteric modulation of M. tuberculosis proteasome core particle
Abstract The Mycobacterium tuberculosis (Mtb) proteasome system selectively degrades damaged or misfolded proteins and is crucial for the pathogen’s survival within the host. Targeting the 20S core particle (CP) offers a viable strategy for developing tuberculosis treatments. The activity of Mtb 20S CP, like that of its eukaryotic counterpart, is allosterically regulated, yet the specific conformations involved have not been captured in high-resolution structures to date. Here, we use single-particle electron cryomicroscopy and H/D exchange mass spectrometry to determine the Mtb 20S CP structure in an auto-inhibited state that is distinguished from the canonical resting state by the conformation of switch helices at the α/β interface. The rearrangement of these helices collapses the S1 pocket, effectively inhibiting substrate binding. Biochemical experiments show that the Mtb 20S CP activity can be altered through allosteric sites far from the active site. Our findings underscore the potential of targeting allostery to develop antituberculosis therapeutics.
Retraction: Specific inhibition of tumor cells by oncogenic EGFR specific silencing by RNA interference
A population based study to analyse amyotrophic lateral sclerosis as a multi-step process
Dopaminergic neurons in the paraventricular hypothalamus extend the food consumption phase
Feeding behavior is controlled by various neural networks in the brain that are involved in different feeding phases: Food procurement, consumption, and termination. However, the specific neural circuits controlling the food consumption phase remain poorly understood. Here, we investigated the roles of dopaminergic neurons in the paraventricular nucleus of the hypothalamus (PVH) in the feeding behavior in mice. Our results indicated that the PVH dopaminergic neurons were critical for extending the food consumption phase and involved in the development of obesity through epigenetic mechanisms. These neurons synchronized with proopiomelanocortin neurons during consumption, were stimulated by proopiomelanocortin activation, and projected to the lateral habenula (LHb), where dopamine receptor D2 was involved in the increase in food consumption. In addition, upregulated tyrosine hydroxylase (TH) expression in PVH was associated with obesity and indispensable for obesity induction in mice lacking Dnmt3a . Taken together, our results highlight the roles of PVH dopaminergic neurons in promoting food consumption and obesity induction.
Catechol-based chemistry for hypoglycemia-responsive delivery of zinc-glucagon via hydrogel-based microneedle patch technology
Colonization with extended-spectrum β-lactamase and carbapenemase-producing Enterobacterales in Ethiopia: A systematic review and meta-analysis
Background The human intestinal tract contains many commensals. However, during an imbalance of the normal microbiota following exposure to antibiotics, extended-spectrum β-lactamase- and carbapenemase-producing Enterobacterales emerge. Individuals colonized with these bacteria may develop subsequent infections themselves. Therefore, this review aimed to estimate the colonization rate of extended-spectrum β-lactamase- and carbapenemase-producing Enterobacterales in Ethiopia. Methods The protocol was registered (PROSPERO ID: CRD42024550137). A systematic literature search was conducted in electronic databases, including PubMed, Google Scholar, and Hinari, to retrieve potential studies. The quality of the included studies was assessed using the Joanna Briggs Institute critical appraisal tool. The data were extracted from the eligible studies using Microsoft Excel 2019 and analyzed using STATA version 11. Heterogeneity between studies was checked using I2 test statistics. Publication bias was assessed using funnel plots and Egger’s test. A random-effects model of DerSimonian-Laird method was employed to estimate the outcomes. Results A total of 15 studies with 4713 participants were included in the meta-analysis. The overall pooled colonization rates of extended-spectrum β-lactamase-producing and carbapenemase-producing Enterobacterales in Ethiopia were 28.5% (95% CI: 16.4-40.5%, I2 = 95.9%, p < 0.001) and 4.4% (95% CI: 0.9–7.9%, I2 = 0.0%, p = 0.64), respectively. The majority of the extended-spectrum β-lactamase producers were E. coli (20.6%, 95% CI: 9.3–31.9%, I2 = 94.4%, p < 0.001), followed by Klebsiella spp. (11.1%, 95% CI: 7.7–14.6%, I2 = 20.2%, p = 0.245). Similarly, the predominant carbapenemase producers were E. coli (2.7%, 95% CI: -1.3–6.7, I2 = 0.0%, p = 0.941) and Klebsiella spp. (2.1%, 95% CI: -1.7–5.9%, I2 = 0.0%, p = 0.999). Furthermore, the pooled estimate of multidrug resistance among extended-spectrum β-lactamase producers was 71.7% (95% CI: 55.25–88.05%, I2 = 92.9%, p < 0.001). Conclusion and recommendations Approximately one-quarter of Ethiopians are colonized with ESBL-PE, while about one in 25 is colonized with CPE. These findings were obtained from studies with a moderate-to-low risk of bias. However, the results for ESBL-PE showed significant variability, indicating high heterogeneity among the studies. This colonization may lead to subsequent extraintestinal infections. Therefore, proactive action from all stakeholders is required to combat the unrecognized spread of extended-spectrum β-lactamase- and carbapenemase-producing Enterobacterales in humans.
The regulatory effect of CoL10A1 to the intracranial vascular invasion and cell proliferation in breast cancer via EMT pathway
Linear Recursive Feature Machines provably recover low-rank matrices
A fundamental problem in machine learning is to understand how neural networks make accurate predictions, while seemingly bypassing the curse of dimensionality. A possible explanation is that common training algorithms for neural networks implicitly perform dimensionality reduction—a process called feature learning. Recent work [A. Radhakrishnan, D. Beaglehole, P. Pandit, M. Belkin, Science 383 , 1461–1467 (2024).] posited that the effects of feature learning can be elicited from a classical statistical estimator called the average gradient outer product (AGOP). The authors proposed Recursive Feature Machines (RFMs) as an algorithm that explicitly performs feature learning by alternating between 1) reweighting the feature vectors by the AGOP and 2) learning the prediction function in the transformed space. In this work, we develop theoretical guarantees for how RFM performs dimensionality reduction by focusing on the class of overparameterized problems arising in sparse linear regression and low-rank matrix recovery. Specifically, we show that RFM restricted to linear models (lin-RFM) reduces to a variant of the well-studied Iteratively Reweighted Least Squares (IRLS) algorithm. Furthermore, our results connect feature learning in neural networks and classical sparse recovery algorithms and shed light on how neural networks recover low rank structure from data. In addition, we provide an implementation of lin-RFM that scales to matrices with millions of missing entries. Our implementation is faster than the standard IRLS algorithms since it avoids forming singular value decompositions. It also outperforms deep linear networks for sparse linear regression and low-rank matrix completion.
Somatic NAP1L1 p.D349E promotes cardiac hypertrophy through cGAS-STING-IFN signaling
Abstract Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease, often caused by sarcomere gene mutations, though many sporadic cases remain genetically unexplained. Here we show that the somatic variant NAP1L1 p.D349E was involved in cardiac hypertrophy in sporadic HCM patients. Through next generation sequencing, we found that somatic variant NAP1L1 p.D349E was recurrent in the cardiomyocytes of gene-elusive sporadic HCM patients. Subsequent in vivo and in vitro functional analysis confirmed that NAP1L1 p.D349E contributes to HCM by triggering an innate immunity response. This mutation destabilizes nucleosome formation, causing DNA to leak into the cytoplasm. This leakage activates a key immune pathway, cGAS-STING, which leads to the release of inflammatory molecules and promotes heart muscle thickening. Our findings reveal a new mechanism driving HCM and suggest that somatic variants could be important in understanding and management of HCM.
Viral and host factors associated with SARS-CoV-2 disease severity in Georgia, USA
While SARS-CoV-2 vaccines have shown strong efficacy, the continued emergence of new viral variants raises concerns about the ongoing and future public health impact of COVID-19, especially in locations with suboptimal vaccination uptake. We investigated viral and host factors, including vaccination status, that were associated with SARS-CoV-2 disease severity in a setting with low vaccination rates. We analyzed clinical and demographic data from 1,957 individuals in the state of Georgia, USA, coupled with viral genome sequencing from 1,185 samples. We found no specific mutations associated with disease severity. Compared to those who were unvaccinated, vaccinated individuals experienced less severe SARS-CoV-2 disease, and the effect was similar for both variants. Vaccination within the prior 3-9 months was associated with decreased odds of moderate disease, severe disease, and death. Older age and underlying health conditions, especially immunosuppression and renal disease, were associated with increased disease severity. Overall, this study provides insights into the impact of vaccination status, variants/mutations, and clinical factors on disease severity in SARS-CoV-2 infection when vaccination rates are low. Understanding these associations will help refine and reinforce messaging around the crucial importance of vaccination in mitigating the severity of SARS-CoV-2 disease.
An efficient graph attention framework enhances bladder cancer prediction
Abstract Bladder (BL) cancer is the 10th most common cancer worldwide, ranking 9th in males and 13th in females in the United States, respectively. BL cancer is a quick-growing tumor of all cancer forms. Given a malignant tumor’s high malignancy, rapid metastasis prediction and accurate treatment are critical. The most significant drivers of the intricate genesis of cancer are complex genetics, including deoxyribonucleic acid (DNA) insertions and deletions, abnormal structure, copy number variations (CNVs), and single nucleotide variations (SNVs). The proposed method enhances the identification of driver genes at the individual patient level by employing attention mechanisms to extract features of both coding and non-coding genes and predict BL cancer based on the personalized driver gene (PDG) detection. The embedded vectors are propagated through the three dense blocks for the binary classification of PDGs. The novel constructure of graph neural network (GNN) with attention mechanism, called Multi Stacked-Layered GAT (MSL-GAT) leverages graph attention mechanisms (GAT) to identify and predict critical driver genes associated with BL cancer progression. In order to pick out and extract essential features from both coding and non-coding genes, including long non-coding RNAs (lncRNAs), which are known to be crucial to the advancement of BL cancer. The approach analyzes key genetic changes (such as SNVs, CNVs, and structural abnormalities) that lead to tumorigenesis and metastasis by concentrating on personalized driver genes (PDGs). The discovery of genes crucial for the survival and proliferation of cancer cells is made possible by the model’s precise classification of PDGs. MSL-GAT draws attention to certain lncRNAs and other non-coding elements that control carcinogenic pathways by utilizing the attention mechanism. Tumor development, metastasis, and medication resistance are all facilitated by these lncRNAs, which are frequently overexpressed or dysregulated in BL cancer. In order to reduce the survival of cancer cells, the model’s predictions can direct specific treatment approaches, such as RNA interference (RNAi), to mute or suppress the expression of these important genes. MSL-GAT is followed by three dense blocks that spread the embedded vectors to categorize PDGs, making it possible to determine which genes are more likely to cause BL cancer in a certain patient. The model facilitates the identification of new treatment targets by offering a thorough understanding of the molecular landscape of BL cancer through the integration of multi-omics data, encompassing as genomic, transcriptomic, and epigenomic metadata. We compared the novel approach with classical machine learning methods and other deep learning-based methods on benchmark TCGA-BLCA, and the leave-one-out experimental results showed that MSL-GAT achieved better performance than competitive methods. This approach achieves accuracy with 97.72% and improves specificity and sensitivity. It can potentially aid physicians during early prediction of BL cancer.
Expectation-dependent stimulus selectivity in the ventral visual cortical pathway
The hierarchical view of the ventral object recognition pathway is primarily based on feedforward mechanisms, starting from a fixed basis set of object primitives and ending on a representation of whole objects in the inferotemporal cortex. Here, we provide a different view. Rather than being a fixed “labeled line” for a specific feature, neurons are continually changing their stimulus selectivities on a moment-to-moment basis, as dictated by top–down influences of object expectation and perceptual task. Here, we also derive the selectivity for stimulus features from an ethologically curated stimulus set, based on a delayed match-to-sample task, that finds components that are informative for object recognition in addition to full objects, though the top–down effects were seen for both informative and uninformative components. Cortical areas responding to these stimuli were identified with functional MRI in order to guide placement of chronically implanted electrode arrays.
Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors
Deubiquitination of epidermal growth factor receptor by ubiquitin-specific peptidase 54 enhances drug sensitivity to gefitinib in gefitinib-resistant non-small cell lung cancer cells
A precise balance between ubiquitination and deubiquitination is crucial for cellular regulation. Ubiquitin-specific peptidase 54 (USP54), an active deubiquitinase (DUB), modulates the ubiquitination of the epidermal growth factor receptor (EGFR). While the significance of USP54 in tumorigenesis is known, its specific function in cancer progression remains unclear. This study investigates the role of USP54 in gefitinib sensitivity in gefitinib-resistant non-small cell lung cancer (NSCLC) cells. Using western blotting and next-generation sequencing, we examined gene expression changes in ubiquitination pathways. USP54 deficiency and its impact on cell viability and gefitinib response were evaluated in 2D and 3D spheroid cancer models. Prolonged gefitinib exposure altered the expression of 20 deubiquitinase-regulating genes. Notably, ubiquitin C-terminal hydrolase L3, downregulated by gefitinib, was identified as a key regulator of EGFR ubiquitination in gefitinib-sensitive PC9 cells. Silencing USP54 in resistant NSCLC cells increased gefitinib-induced EGFR ubiquitination and G0/G1 cell cycle arrest, enhancing drug susceptibility in resistant spheroids. USP54 upregulation in gefitinib-treated cells was associated with reduced EGFR ubiquitination, stabilizing EGFR and promoting cell survival. These findings suggest USP54 as a critical modulator of EGFR stability and a potential therapeutic target to overcome gefitinib resistance in NSCLC.