Browse Articles

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.

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.

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.

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.

Inkjet-printed transparent electrodes: Design, characterization, and initial in vivo evaluation for brain stimulation

Electrical stimulation is a powerful tool for investigating and modulating brain activity, as well as for treating neurological disorders. However, understanding the precise effects of electrical stimulation on neural activity has been hindered by limitations in recording neuronal responses near the stimulating electrode, such as stimulation artifacts in electrophysiology or obstruction of the field of view in imaging. In this study, we introduce a novel stimulation device fabricated from conductive polymers that is transparent and therefore compatible with optical imaging techniques. The device is manufactured using a combination of microfabrication and inkjet printing techniques and is flexible, allowing better adherence to the brain’s natural curvature. We characterized the electrical and optical properties of the electrodes, focusing on the trade-off between the maximum current that can be delivered and optical transmittance. We found that a 1 mm diameter, 350 nm thick PEDOT:PSS electrode could be used to apply a maximum current of 130 μA while maintaining 84% transmittance (approximately 50% under 2-photon imaging conditions). We then evaluated the electrode performance in the brain of an anesthetized mouse by measuring the electric field with a nearby recording electrode and found values up to 30 V/m. Finally, we combined experimental data with a finite-element model of the in vivo experimental setup to estimate the distribution of the electric field underneath the electrode in the mouse brain. Our findings indicate that the device can generate an electric field as high as 300 V/m directly beneath the electrode, demonstrating its potential for studying and manipulating neural activity using a range of electrical stimulation techniques relevant to human applications. Overall, this work presents a promising approach for developing versatile new tools to apply and study electrical brain stimulation.

Author Correction: Dual-branch image projection network for geographic atrophy segmentation in retinal OCT images

Manipulation of interoceptive signaling biases decision making in rhesus macaques

Several influential theories have proposed that interoceptive signals, sent from the body to the brain, contribute to neural processes that coordinate complex behaviors. We altered the physiological state of the body using compounds that have minimal effect on the brain and evaluated their effect on decision making in rhesus monkeys. We used glycopyrrolate, a nonspecific muscarinic (parasympathetic) antagonist, and isoproterenol, a beta-1/2 (sympathetic) agonist, to create a sympathetic-dominated state in the periphery, that was indexed by increased heart rate. Rhesus monkeys were trained on two variants of an approach-avoidance conflict task. The tasks offered a choice between enduring mildly aversive stimuli in exchange for a steady flow of rewards, or canceling the aversive stimuli, forgoing the rewards. The latency to interrupt the aversive stimuli was used as a measure of monkeys’ tolerance for contact with a hot but not painful stimulus or airflow directed at their muzzle. Both drugs reduced tolerance for the aversive stimuli. To determine whether the drug-induced autonomic state reduced the subjective value of the reward, we tested the effects of glycopyrrolate on a food preference task. Food preference was unaltered, suggesting that the sympathetic dominated state in the periphery selectively reduces tolerance for aversive stimuli without altering reward-seeking behaviors. As the drugs used are expected to have little or no direct effect on the brain, the observed biases in decision making are likely induced by interoceptive afferents that signal to the brain the physiological state of the body.

Association of overweight/obesity and digestive system cancers: A meta-analysis and trial sequential analysis of prospective cohort studies

Background Previous researches have reported correlations between overweight/obesity and common digestive system cancers (DSCs), including gastric, liver, esophageal, colorectal, and pancreatic cancers. However, the inconsistency in defining overweight/obesity and the risk of recall bias from case-control and retrospective cohort studies may influence existing results. Therefore, we aimed to validate the relationship between overweight/obesity and common DSCs by combining prospective cohort studies based on the World Health Organization (WHO) criteria for defining overweight/obesity. Methods A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases, covering all publications up to February 7, 2024. The inclusion criteria focused on prospective cohort studies that examined the link between overweight/obesity and risks of DSCs. R software 4.1.3 and STATA 12 were utilised to calculate the relative risk (RR), with 95% confidence interval (CI) and prediction interval (PI). TSA v0.9.5.10 Beta software was used for trial sequential analysis (TSA). Results The meta-analysis encompassed 39 articles. The overall analysis showed that compared with normal weight, overweight/obesity increased the risks of liver cancer (overweight: RR [95% CI] =  1.237 [1.112-1.377]; 95% PI: 0.888-1.725; obesity: RR [95% CI] =  1.642 [1.466-1.839]; 95% PI: 1.143-2.358) and colorectal cancer (overweight: RR [95% CI] =  1.124 [1.056-1.197]; 95% PI: 0.931-1.357; obesity: RR [95% CI] =  1.366 [1.242-1.503]; 95% PI: 0.959-1.945) in the total population. Subgroup analysis revealed that overweight (RR [95% CI] =  1.237 [1.165-1.314]; 95% PI: 1.154-1.327) and obesity (RR [95% CI] =  1.306 [1.152-1.480]; 95% PI: 1.108-1.539) were associated with an increased risk of pancreatic cancer only in women, and overweight also increased the gastric cancer risk of women (RR [95% CI] =  1.041 [1.013-1.070], 95% PI: 0.806-1.230). No significant association of overweight/obesity and esophageal cancer was observed in both male and female. Conclusion Our study suggested that overweight/obesity elevated the risks of liver and colorectal cancer in both men and women. No significant association was found between overweight/obesity and the risk of developing esophageal cancer. Clinicians are advised to consider weight control as an effective measure for preventing pancreatic, liver, and colorectal cancers.

Polarisation reconfigurable anisotropic dielectric resonator antenna

Abstract A novel polarization reconfigurable anisotropic dielectric resonator antenna (ADRA) is presented utilizing a new modulation scheme to exploit the degree of freedom in the polarization domain. The ADRA comprises periodic assembly dielectric resonators with two different dielectrics constant, equal in size, a vertically positioned metal strip, one varactor diode, and six PIN diode switches. The modulation scheme utilizes the tilt angle and axial ratio (AR) of a wireless signal to convey additional information, enabling the realization of different working modes ranging from circular polarization (CP) to nearly linear polarization (LP). The proposed modulation scheme yields significantly better bit error rate (BER) performance and higher spectral efficiency in bits/s/Hz/antenna. Additionally, the paper presents an antenna design capable of generating an arbitrary polarization state, highlighting the system benefits of polarization modulation. Post-fabrication, the proposed approach is validated by comparing simulated and measured results. The proposed antenna provides a total efficiency higher than 93% in the desired frequency bands and consistent gain at approximately 7.64 dBi and 7.09 dBi at 3.8 GHz, with the impedance matching bandwidth ranging from 3.53 to 3.90 GHz and 3.56 to 3.91 GHz fully overlapping across all polarization states for the simulated and measured results, respectively. Experimental results affirm the robust performance of the proposed ADRA.

Modulation of host gene expression by the zinc finger antiviral protein

The zinc finger antiviral protein (ZAP) depletes nonself RNAs through recognition of their elevated CpG dinucleotide content. CpG dinucleotides are sparse in most endogenous mammalian mRNAs, but a subset might potentially be modulated by ZAP. While CpG frequency alone is insufficient to predict ZAP-regulation, we developed an algorithm using experimentally determined compositional features to predict which endogenous mRNAs may be ZAP-regulated. Using ZAP-knockout mice, we demonstrate that levels of many host mRNAs that are algorithmically predicted ZAP targets are indeed increased when ZAP is absent. ZAP is interferon-inducible, and we also identify genes that are downregulated by ZAP during an innate immune response. Many ZAP-regulated gene products are extracellular matrix or of nucleosome components, whose ZAP-mediated control is conserved in human cells. Overall, we provide a tool for the prediction of ZAP target genes and reveal host mRNAs that are ZAP-regulated.

Correction: Protective effects of Euphorbia heterophylla against testicular degeneration in streptozotocin-induced diabetic rats in relation to phytochemical profile

Cable vibration and girder displacement measurement via morphology recognition and feature matching methods in bridge structure

Bayesian inference by visuomotor neurons in the prefrontal cortex

Perceptual judgments of the environment emerge from the concerted activity of neural populations in decision-making areas downstream of the sensory cortex. When the sensory input is ambiguous, perceptual judgments can be biased by prior expectations shaped by environmental regularities. These effects are examples of Bayesian inference, a reasoning method in which prior knowledge is leveraged to optimize uncertain decisions. However, it is not known how decision-making circuits combine sensory signals and prior expectations to form a perceptual decision. Here, we study neural population activity in the prefrontal cortex of macaque monkeys trained to report perceptual judgments of ambiguous visual stimuli under two different stimulus distributions. We isolate the component of the neural population response that represents the formation of the perceptual decision (the decision variable, DV), and find that its dynamical evolution reflects the integration of sensory signals and prior expectations. Prior expectations impact the DV’s trajectory both before and during stimulus presentation such that DV trajectories with a smaller dynamic range result in more biased and less sensitive perceptual decisions. We show that these results resemble a specific variant of Bayesian inference known as approximate hierarchical inference. Our findings expand our understanding of the mechanisms by which prefrontal circuits can execute Bayesian inference.