Transient Receptor Potential (TRP) channels: Quantitative biophysical correlates of cognitive function across brain regions.

Visual processing and sensorimotor integration in flute music reading: Eye movements, anticipation, and breathing patterns during practice.

Ventral temporal-frontal pathways in the human brain based on diffusion MRI tractography.

Continuous-time causal distribution learning with identifiability for brain dynamic effective connectivity inference.

Decoding the neural mechanisms of salty peptide perception via electroencephalography and machine learning.

Biotechnology-enabled plastic valorization across enzymatic, microbial, and hybrid conversion systems: A review.

Traditional SONAR systems are widely used for underwater object detection and navigation; however, they suffer from high energy consumption, noise interference, and signal degradation in varying aquatic conditions. Inspired by biological echolocation, we propose a novel machine learning-enhanced sonar model to improve accuracy, robustness, and energy efficiency in underwater sensing applications. The proposed model employs bio-inspired echolocation principles, where transmitted acoustic pulses dynamically adjust in response to environmental conditions. Deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are employed to classify sonar echoes and enhance object detection. Additionally, digital signal processing (DSP) techniques, including Butterworth filtering, wavelet decomposition, and adaptive thresholding, are integrated to mitigate noise and improve signal clarity. Experimental evaluations demonstrate that the proposed sonar model, SonarNet, achieves 92.7% classification accuracy, outperforming conventional SONAR-based methods, which achieved 85.4% accuracy. The integration of adaptive signal processing leads to a 20.8% reduction in energy consumption compared to standard SONAR models, improves the Signal-to-Noise Ratio (SNR) by 15.3dB, and reduces the false positive rate by 18.6%. Additionally, the sound velocity profile (SVP) correction mechanism improves depth estimation accuracy by 12.4%. All performance results reported in this work were obtained using a simulation environment parameterized by real-world oceanographic datasets.

ABSTRACT Platforms are key intermediaries of public and political communication, both heralded as facilitators of bottom-up democracy and condemned as democracy’s greatest danger. From misinformation to hate speech to election integrity concerns, the perils of platforms have become obvious in recent years, and platforms have increasingly developed rules and processes to handle problematic content. Elections are a catalyst for this development as both moments of peak attention on platforms’ roles in democracy and of vulnerability to political interference. In this paper, we identify and compare election-related policies on key social media platforms (YouTube, Facebook, Instagram, Twitter/X, and TikTok), and examine their changes over time. We identify an early phase addressing threats to concrete election-related processes and persons (2018–2020), a much more political phase taking their roles as hosts of public conversation seriously and affirming responsibility for the integrity of civic processes (2020–2022), and a more recent phase of profound backlash (since 2022). Contextualizing these phases, we assume that general dynamics in discourses about the responsibility of platforms, as well as specific election-related developments, most notably US elections 2016–2024, explain these changes.

PurposeInformed consent is a cornerstone of ethical practice. Eliciting patient questions during informed consent increases patient engagement and understanding, thus enhancing integrity of the consent process. However, a patient in visible pain can add an additional challenge to the informed consent process. The purpose of this research was to examine if and how anesthesiology residents ask for questions from a patient displaying severe pain during an informed consent simulation.MethodsAnesthesiology residents recruited from three anesthesia residency programs were video recorded performing an informed consent with a 52-year-old White male simulated patient awaiting urgent repair of a perforated gastric ulcer who was displaying verbal and nonverbal signs of pain. Two independent coders evaluated whether, when and how residents elicited patient questions during the informed consent process.ResultsAmong a sample of 65 first- and third-year anesthesiology residents, more than 20% of residents did not elicit questions during the informed consent encounter. Those who elicited questions typically did so late in the process, with approximately 10% inviting questions only after the consent form had been signed. Most questions were closed-ended (yes/no questions). Although residents did not typically incorporate the patient’s name when eliciting questions, most residents demonstrated eye contact. There were no differences in the number or characteristics of questions elicited based on resident gender or level of training.ConclusionOur findings suggest there is a need for increased education regarding the purpose and approach to eliciting patient questions during the informed consent process, especially for patients who are experiencing pain.

Division of labor in workers of the leafcutter ant Atta sexdens (Hymenoptera: Formicidae) is correlated with brain structure.

Background: Tinnitus is an auditory phantom perception in the absence of any corresponding acoustic stimulus whose pathophysiology remains poorly understood. This study aimed to investigate alterations in the functional organization of the brain in individuals with tinnitus using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory analysis.Methods: We conducted a study including 44 individuals with tinnitus and 32 healthy controls. Using rs-fMRI and graph theory measures, we characterized whole-brain topological properties, including network segregation, integration, small-worldness, and global efficiency. In addition, regional segregation and integration were assessed using clustering coefficient and participation coefficient analyses to identify alterations in brain hub regions.Results: Our findings revealed altered topological properties in the tinnitus brain, particularly in the balance between cerebral segregation and integration, leading to deviations from optimal small-world architecture. We also observed alterations in the topology of specific auditory and nonauditory brain regions associated with phantom sound perception. Notably, patients with tinnitus exhibited a decreased nodal participation coefficient in the thalamus, suggesting reduced connectivity between this region and different functional modules as well as long-range connections.Conclusions: These results suggest that tinnitus is associated with alterations in the functional organization of the brain, leading to disrupted information processing and sensory integration.

Generative artificial intelligence in nursing care research: Applications across the research process.

This study aims to demonstrate that speech sound disorders associated with central auditory processing disorder stem from a processing disorder in the central auditory pathways during listening, rather than from an articulation-based disorder. Included 25 children with articulation problems (WAP; 7 females, 18 males) and 25 typically developing children without articulation problems (WoAP; 10 females, 15 males), aged 5-8years. Assessments comprised the Hacettepe Articulation Test, Scan-C subtests (Filtered Words, Auditory Figure-Ground, Competing Words, Competing Sentences), and the Denver II Developmental Screening Test. The Children in the WAP group scored low on the left ear only in the dichotic words and dichotic sentences tests. Left ear performance was significantly lower in the dichotic words test (p = 0.026, Cohen's d = 0.609) and the dichotic sentences (p = 0.001, r = 0.427). These results suggest that articulation disorders may indicate a central auditory processing deficit, particularly a delay in right-hemisphere auditory maturation. It has been observed that in children with central auditory processing disorder, whatever speech sounds they process defectively during listening, they also produce those same sounds defectively. In this context, this study offers a new perspective on the relationship between articulation disorders and central auditory processing. Therefore, it is recommended that central auditory processing screening be conducted in children with articulation disorders to distinguish the underlying disorder.

The Li-rich Mn-based oxides have attracted extensive attention due to the specific anion redox reaction to provide high capacity. However, the poor reversibility of anion redox leads to serious lattice oxygen loss and surface structure evolution. Here, we report an approach by integrating chemical disorder-based crystallographic texture into the cathode surface to solve these questions, which involves the spatial rearrangement of lattice oxygen by supersaturated occupation of cations in the lattice. This makes the oxygen electronic structure delocalized and diversified, strengthens the metal-oxygen orbital hybridization, and effectively improves the reversibility and kinetics of anion redox reactions. Meanwhile, the robust surface architecture effectively inhibits superficial detrimental phase evolution and electrode/electrolyte interface side reactions, maintaining the structural integrity of the electrochemical process. Accordingly, the as-designed modified cathode delivered a promising capacity (291.8 mAh g-1), excellent long cycling stability, and voltage retention (90.5% capacity retention and 0.68mV/cycle voltage fade with 300 cycles). This work highlights the role of surface chemical disorder and the strongly correlated chemical environment of transition metals with oxygen, which is expected to provide a new paradigm for the structural design of cathode materials.

Despite over a century of research into the neural code, the fundamental principles by which the brain encodes sensory information remain debated. In this study we provide converging evidence for the presence of a dynamic, fast-switching integration of rate and temporal coding in the thalamus, primary visual cortex, and higher-order visual cortical areas of mice viewing an array of visual stimuli. This scheme is primarily characterized by the presence of distinct, temporally coordinated "bracket"s that tile the duration of each trial, are rate-coded within, and are separated by boundaries that are precisely-timed and synchronized across the population. Using large-scale Neuropixels recordings from the Allen Institute Visual Coding dataset, we provide evidence for the robust- ness and generality of bracket coding across several visual tasks and brain regions, as well as its optimality for information decoding, functional relevance for information representation, pronounced hierarchical organization, long-range bottom-up synchrony across visual regions, and coherence with low-frequency local field oscillations. These findings were all subsequently validated in a second, independent dataset provided by the International Brain Laboratory consortium. Finally, we provide a computational model that can serve as a potential mechanism for the generation of bracket-coded population spiking activity. Together, our results demonstrate the presence of a novel form of sensory information encoding in the brain, with broad implications for neuroscience and neuroengineering.

Loss of the chromatin remodeler WSTF (BAZ1B), a gene deleted in Williams syndrome, causes reproducible, genome-scale reprogramming of chromatin states and transcript processing that links altered chromatin composition to misprocessed transcripts and aberrant signaling. Using engineered HCT116-WSTFKO cells, we combine transcriptome profiling, microscopy, chromatin CUT&RUN, and histone post-translational modification (HPTM)-defined chromatin state modeling to show that WSTF localizes to promoters and gene bodies of actively transcribed loci together with ASH2L and CBP. Loss of WSTF causes depletion of ASH2L/CBP, selective loss of H3K4me2 and multiple acetylation marks, and gain of Polycomb components. This loss results in systematic conversion from a multi-mark active promoter/enhancer chromatin landscape to a hypoacetylated, H3K4me2-depleted, PRC-enriched landscape. These chromatin changes coincide with widespread isoform switching and splicing alterations in genes encoding chromatin regulators and signaling pathways. This WSTF deficiency produces Wnt/β-catenin hyperactivation. A locus-specific example at TCF7L2 demonstrates how gene body loss of active marks drives isoform switching that alters DNA binding domains and decouples stabilized nuclear β-catenin from canonical target engagement. Our results establish a mechanistic chain from WSTF-dependent chromatin architecture to co-transcriptional RNA processing integrity, signaling pathway fidelity, and developmental gene program regulation relevant to Williams syndrome.

Modern organizations face increasing operational complexity due to fragmented tooling, manual task allocation, and reactive workflow management. Traditional systems rely on rigid rule-based automation, lacking predictive foresight and adaptive decision-making capabilities. This paper presents an AIBased Workflow & Task Optimization System thatintegrates Machine Learning (ML), Natural Language Processing (NLP), and Predictive Process Monitoring (PPM) into a unified microservices architecture. The platform features an intelligent task assignment engine powered by XGBoost, a real-time Service Level Agreement (SLA) breach prediction module using gradient boosting classifiers, and a conversational NLP interface leveraging GPT-4. Developed with Django REST Framework, React.js, PostgreSQL, and Celery, the system supports horizontal scalability, eventdriven processing, and seamless third-party integration. Experimental evaluation demonstrates a weighted F1-score of 0.887 for task assignment, an AUC-ROC of 0.921 for SLA prediction, and a 52% reduction in workflow bottlenecks. User acceptance testing yielded an overall satisfaction score of 4.4/5. The system delivers enterprise-grade intelligent automation while maintaining costefficiency through open-source technologies.

The rapid growth of internet technology has significantly transformed the way businesses operate, leading to the widespread adoption of electronic commerce (e-commerce). This paper presents the design and development of a fully functional E-Commerce website that enables users to browse products, add items to a cart, and securely complete purchases online. The system is developed with the objective of providing a seamless, userfriendly, and efficient online shopping experience. The proposed platform includes user authentication with OTPbased email verification, product management, shopping cart functionality, order processing, and PayPal payment integration. The application is built using the MERN Stack — MongoDB, Express.js, React.js, and Node.js — ensuring scalability, security, and maintainability. All 18 test cases passed successfully and performance metrics confirmed API response times under 150ms with a Lighthouse score of 92/100.

Chemical engineers have played a vital role in the pharmaceutical industry for more than a century, bridging the gap between scientific discovery and large-scale drug manufacturing. This review examines the evolution in the role of chemical engineers and their impact on the development of small molecules, biologics, and emerging modalities such as oligonucleotides and gene therapies. We provide historical context, from early breakthroughs in insulin and penicillin production to the integration of continuous processing and advanced modeling. Key areas of focus include reaction engineering, separations, crystallization, fluid dynamics, process control, and continuous manufacturing. Looking ahead, chemical engineers will be central to addressing challenges in sustainability, advanced delivery systems, and the application of artificial intelligence and data-driven technologies. As therapeutic complexity grows, the application of engineering fundamentals, integrated with life and natural sciences, remains essential for ensuring safe, efficient, and scalable manufacturing of medicines that advance global health.

Physiological palpation serves as a primary clinical modality for identifying pathological changes in tissue compliance. However, its diagnostic precision is inherently limited by the subjective nature of human haptic perception and the lack of quantifiable mechanical metrics. This work describes a bio-inspired, portable tactile interface engineered for the non-invasive and real-time characterization of tissue stiffness. The system incorporates multimodal piezoresistive sensing elements that emulate the specific mechanotransduction functions of cutaneous receptors, namely Merkel disks and Ruffini endings. By integrating Hertzian contact mechanics to decouple pressure and strain signals, the platform analytically derives the effective Young's modulus of heterogeneous soft tissues. The developed sensor architecture exhibits a functional range of 0-600 kPa and a gauge factor of 10.8, facilitating high-fidelity detection of subcutaneous anomalies. Validation against various nodule geometries and depths demonstrates that the system achieves a diagnostic resolution surpassing conventional manual assessments. Furthermore, the integration of wireless data processing enables instantaneous, on-site mechanical profiling. This platform provides a scalable framework for objective diagnostics, robotic haptics, and continuous physiological monitoring, establishing a robust bridge between qualitative clinical observation and quantitative biomechanical analysis.