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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.

Hybrid Gaussian process regression with temporal feature extraction for partially interpretable remaining useful life interval prediction in Aeroengine prognostics

Belief in belief: Even atheists in secular countries show intuitive preferences favoring religious belief

We find evidence of belief in belief —intuitive preferences for religious belief over atheism, even among atheist participants—across eight comparatively secular countries. Religion is a cross-cultural human universal, yet explicit markers of religiosity have rapidly waned in large parts of the world in recent decades. We explored whether intuitive religious influence lingers, even among nonbelievers in largely secular societies. We adapted a classic experimental philosophy task to test for this intuitive belief in belief among people in eight comparatively nonreligious countries: Canada, China, Czechia, Japan, the Netherlands, Sweden, the United Kingdom, and Vietnam (total N = 3,804). Our analyses revealed strong evidence that 1) people intuitively favor religious belief over atheism and that 2) this pattern was not moderated by participants’ own self-reported atheism. Indeed, 3) even atheists in relatively secular societies intuitively prefer belief to atheism. These inferences were robust across different analytic strategies and across other measures of individual differences in religiosity and religious instruction. Although explicit religious belief has rapidly declined in these countries, it is possible that belief in belief may still persist. These results speak to the complex psychological and cultural dynamics of secularization.

Injectable ECM-mimetic dynamic hydrogels abolish ferroptosis-induced post-discectomy herniation through delivering nucleus pulposus progenitor cell-derived exosomes

Maximum likelihood estimators are ineffective for acoustic detection of rare bat species

Acoustic monitoring is an important tool for determining presence or probable absence of threatened and endangered bats in the United States (US). Federal guidance requires the use of automated identification programs that classify audio files and calculate a Maximum Likelihood Estimator (MLE) for each bat species during each night of a survey. Acoustic presence or absence of species is based on a significant or non-significant MLE, which can have profound regulatory effects, positive or negative. Despite relying on this metric to determine presence of rare species for the past ten years, little is known about the number of files required by available programs to trigger significant MLE or the effect of species ratio on this calculation. We used 1,120 audio files containing echolocation calls from nine northeastern US bat species to simulate survey nights containing variable absolute counts and ratios of species’ audio files. We developed models to estimate the number of audio files that Kaleidoscope Pro (KPro) and SonoBat programs required to establish acoustic presence for each species, and we then applied our best model to a long-term acoustic dataset collected at the Fort Drum Military Installation in New York. Each program required a similar number of files to detect presence for some species, such as Myotis septentrionalis and M. sodalis (8 to 10 files), but differed in file requirements for other species, such as Lasiurus cinereus (KPro =  4; SonoBat =  7) and Perimyotis subflavus (KPro =  10; SonoBat =  6). Both programs performed poorly with determining presence for any species at low species ratio (<25%). Applying our model to the Fort Drum dataset revealed that the total number of audio files recorded within a night had a great effect on whether a rare species was correctly determined to be present. We conclude that MLE should be used with caution during surveys of rare species and could produce misleading results in certain conditions.

Prognostic value of sarcopenia in patients with unresectable colorectal liver metastases after drug-eluting beads transcatheter arterial chemoembolization: a single center retrospective study

Explaining human motor coordination via the synergy expansion hypothesis

The search for an answer to Bernstein’s degrees of freedom problem has propelled a large portion of research studies in human motor control over the past six decades. Different theories have been developed to explain how humans might use their incredibly complex neuro-musculo-skeletal system with astonishing ease. Among these theories, motor synergies appeared as one possible explanation. In this work, the authors investigate the nature and role of synergies and propose a theoretical framework, namely the “expansion hypothesis,” to answer Bernstein’s problem. The expansion hypothesis is articulated in three propositions: mechanical, developmental, and behavioral. Each proposition addresses a different question on the nature of synergies: i) How many synergies can humans have? ii) How do we learn and develop synergies? iii) How do we use synergies? An example numerical simulation is presented and analyzed to clarify the hypothesis propositions. The expansion hypothesis is contextualized with respect to the existing literature on motor synergies both in healthy and impaired individuals, as well as other prominent theories in human motor control and development. The expansion hypothesis provides a framework to better comprehend and explain the nature, use, and evolution of human motor skills.

Author Correction: On-surface synthesis of triangulene trimers via dehydration reaction

Correction: Reconstructing prehistoric lifeways using multi-Isotope analyses of human enamel, dentine, and bone from Legaire Sur, Spain

Cross-sectional analysis of eating disorder risk and risk correlates in candidates for bariatric surgery from the BariPredict cohort

Abstract Bariatric surgery is an effective intervention for managing obesity. Persons with obesity are a high-risk population for eating disorders (ED), and these can negatively impact perioperative and long-term outcomes of surgery. We aim to understand prevalence and correlates of ED in preintervention patients, identifying those needing psychological support. Baseline cross-sectional analysis of 275 patients of the BariPredict cohort (NCT06480058), a study to assess predictors of long-term surgery outcomes. Psychological assessments were conducted using SCOFF, KUAS, and BDI tools. Data were analyzed for prevalence of high ED risk and for associations of clinical, biological and demographic factors. Mean age was 38.5 years, mean BMI was 42.3 kg/m², with 62.5% being female. 65.8% of patients had a SCOFF score ≥ 2 indicating high ED risk. Class II obesity (p < 0.05), younger age (p < 0.01), and higher depression (p < 0.01) were associated with ED risk in a logistic regression adjusted for age, obesity class, diabetes, HbA1c, depression and anxiety scores. We report high preintervention prevalence of ED, with a risk profile corresponding to BMI of 35-39.9 Kg/m2 in younger adults with concurrent depression. This patient profile should be prioritized for psychological assessment and support to potentially improve outcomes of bariatric surgery.

The emergence of eukaryotes as an evolutionary algorithmic phase transition

The origin of eukaryotes represents one of the most significant events in evolution since it allowed the posterior emergence of multicellular organisms. Yet, it remains unclear how existing regulatory mechanisms of gene activity were transformed to allow this increase in complexity. Here, we address this question by analyzing the length distribution of proteins and their corresponding genes for 6,519 species across the tree of life. We find a scale-invariant relationship between gene mean length and variance maintained across the entire evolutionary history. Using a simple model, we show that this scale-invariant relationship naturally originates through a simple multiplicative process of gene growth. During the first phase of this process, corresponding to prokaryotes, protein length follows gene growth. At the onset of the eukaryotic cell, however, mean protein length stabilizes around 500 amino acids. While genes continued growing at the same rate as before, this growth primarily involved noncoding sequences that complemented proteins in regulating gene activity. Our analysis indicates that this shift at the origin of the eukaryotic cell was due to an algorithmic phase transition equivalent to that of certain search algorithms triggered by the constraints in finding increasingly larger proteins.

Global data-driven prediction of fire activity

Abstract Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting.