Browse Articles

Personalized models of disorders of consciousness reveal complementary roles of connectivity and local parameters in diagnosis and prognosis

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients’ fMRI data into a low-dimension latent-space . We define the latent space’s dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patient’s diagnosis, and, (2) the local node parameters correlate to the patient’s etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

Using large language models to categorize strategic situations and decipher motivations behind human behaviors

By varying prompts to a large language model, we can elicit the full range of human behaviors in a variety of different scenarios in classic economic games. By analyzing which prompts elicit which behaviors, we can categorize and compare different strategic situations, which can also help provide insight into what different economic scenarios might induce people to think about. We discuss how this provides a step toward a nonstandard method of inferring (deciphering) the motivations behind the human behaviors. We also show how this deciphering process can be used to categorize differences in the behavioral tendencies of different populations.

Deep computer vision with artificial intelligence based sign language recognition to assist hearing and speech-impaired individuals

Sonar image denoising based on clustering and Bayesian sparse coding

Side-scan sonar image (SSI) are often affected by a combination of multiplicative speckle noise and additive noise, which degrades image quality and hinders target recognition and scene interpretation. To address this problem, this paper proposes a denoising algorithm that integrates non-local similar block clustering with Bayesian sparse coding. The proposed method leverages cross-scale structural features and noise statistical properties of image patches, and employs a similarity metric based on the Equivalent Number of Looks (ENL) along with an improved K-means clustering algorithm to achieve accurate classification and enhance intra-class noise consistency. Subsequently, a joint training strategy is used to construct dictionaries for each cluster, and Bayesian Orthogonal Matching Pursuit (BOMP) is applied for sparse representation. This enables effective modeling and suppression of mixed noise while preserving structural details. Experimental results demonstrate that the proposed method outperforms several classical approaches in both objective metrics such as PSNR and SSIM, and in visual quality, particularly in preserving target edges and textures under severe noise conditions.

Abiotic synthesis during the interaction of ferrous chloride–rich silicic fluids with marble under high-grade metamorphic conditions

Ferrous chloride–rich silicic fluid and melt infiltration led to the decarbonation of dolomitic marble in the Chinese Sulu ultrahigh-pressure metamorphic terrain under temperatures ranging from 670 to 800 °C, pressures from the aragonite + albite to calcite stability fields and the oxygen fugacities between the hematite–magnetite and pyrite–pyrrhotite–magnetite buffers, resulting in the formation of olivine marble and diopsidite. The inclusions in zircons trapped during the decarbonation process suggest that the H 2 -producing reaction 3FeCl 2 (aq) + 3CaCO 3 + H 2 O = Fe 3 O 4 (magnetite) + 3CaCl 2 (aq) + 3CO 2 + H 2 occurred and that it induced magnetite-catalyzed Fischer–Tropsch-type synthesis, as indicated by the presence of whewellite, disordered carbonaceous material, CH 4 , and CO in the inclusions. The results of this study highlight the role of aqueous Fe in generating H 2 and magnetite and have far-reaching implications for carbon speciation and solubility in deep fluids and for endogenic abiotic synthesis, which may be pivotal in the prebiotic occurrence of organic compounds on Earth.

Optimal estimation of power Chris-Jerry distribution parameters using ranked set sampling design with application

Kinematical error analysis and autonomous calibration of a 5PUS-RPUR parallel robot

Kinematic calibration is essential for improving the absolute accuracy of parallel robots, but conventional identification methods often struggle with the complex, non-linear coupling of their numerous geometric error parameters. This can lead to convergence to local rather than global optima, limiting the effectiveness of the calibration. To address this challenge, this paper proposes a novel self-calibration methodology based on a global optimization strategy. Taking the 5PUS-RPUR parallel robot as an example, its inverse kinematics is established based on screw theory. A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. An objective function for the GA is constructed by integrating the actuator displacement errors from each kinematic chain with the overall pose error of the moving platform. Non-linear constraints are handled using a penalty function approach. Based on measurement data from an onboard IMU and joint encoders, the identification results are obtained. The experimental results demonstrate that the proposed method significantly improves the robot’s positional accuracy across its entire workspace. The superiority and efficacy of this approach are further corroborated by a benchmark comparison with three recent, state-of-the-art calibration methodologies.

Profile of Johannes Lehmann

Soil scientist Johannes Lehmann has spent his career examining how to improve soil quality to secure agricultural production, regulate climate, and keep water clean. His work with dark earths in the Brazilian Amazon led to the discovery of the importance of biochar in soil fertility and nutrient recycling from excreta. For more than 24 years, Lehmann has held a faculty position at Cornell University, where his research initially focused on carbon and nutrient cycles and went on to challenge the existence of soil humus and delve into the role of functional complexity in soil management.

Single cell RNA seq reveals the pro-regenerative phenotype of thrombospondin-2 deficient dermal fibroblasts

The MCTOT app: A publicly available tool for statistical cycle-to-threshold analysis and inference of informative but uncertainly determined qPCR data

As a common experimental technique, qPCR (Quantitative Real-time Polymerase Chain Reaction) is widely used to measure levels of nucleic acids, e.g., microRNAs and messenger RNA. While providing accurate and complete data, researchers have inevitably encountered uncertainly determined qPCR data because of intrinsically low amounts of biological material. The presence of incomplete or uncertain qPCR data challenges interpretation accuracy. This study presents a web application that integrates two sophisticated statistical methods – a flexible regression approach and a two-group hypothesis testing technique – to enhance the accuracy and robustness of qPCR data analysis with informative but uncertainly determined observations. To demonstrate the versatility and efficacy of our MCTOT (Multi-Functional Cycle-To-Threshold Statistical Analysis Tool) application, this study presents two distinct examples employing two-group hypothesis testing. The first example delves into an analysis of pathogens in wastewater, an area gaining increasing relevance for public health surveillance. The second example illustrates an application in the realm of liquid biopsy, a rapidly evolving field in disease diagnostics, monitoring, and early treatment. Moreover, the application’s process is further exhibited through another liquid biopsy example, wherein the flexible regression method is employed to detect the hemolysis effect on a molecular target. These examples demonstrate the tool’s capacity to not only identify significant differences between groups but also to quantify the effect size, a crucial aspect in biomedical research. The MCTOT web application stands as a pioneering step toward empowering researchers to harness the full potential of qPCR data, especially when dealing with informative but uncertainly determined observations. It also paves the way for further development of web-based tools that adhere to the refined CTOT (Cycle-To-Threshold) methodology, opening new avenues in qPCR data analysis and interpretation. The developed application can be accessed online through Shinyapps.io at https://ctot.shinyapps.io/bioinformatics/ for open access.

Evolutionarily conserved grammar rules viral factories of amoeba-infecting members of the hyperdiverse <i>Nucleocytoviricota</i> phylum

Despite sharing fewer than 10 core genes, the hyperdiverse Nucleocytoviricota phylum (ranging from poxviruses to giant viruses) universally assembles viral factories (VFs) resembling biomolecular condensates. Regardless, it is unclear how these viruses achieve such a level of functional conservation without clear conserved genetic information. We demonstrate that the VFs produced by amoeba-infecting viruses have liquid-like properties and identify a conserved molecular grammar governing viral factory scaffold protein: charge-patterned intrinsically disordered regions that drive phase separation independently of sequence homology. This grammar predicts functional scaffold proteins across the 15 viral families, revealing evolutionary constraints invisible to sequence or structural analysis. Strikingly, VFs exhibit subcompartmentalization analogous to nuclei, segregating transcription and mRNA processing (inner condensates) from replication (interphase zones) and translation (host cytoplasm). Our work establishes phase separation as a fundamental organizational principle bridging extreme genomic diversity, explaining how biological complexity emerges without gene conservation. This grammar is likely also conserved in non-amoeba-infecting members of the phylum and thus may represent a primordial solution for organelle-like organization, with broad implications for antiviral targeting.

Serum bone turnover biomarkers in early postoperative period related to the spontaneous resolution of disc herniation

Correction: Effectiveness and satisfaction of mindfulness-based cognitive therapy for children on anxiety, depression, and internet addiction in adolescents: Study protocol for a randomized control trial

The US–Israel Blavatnik Scientific Forum on alleviating global water scarcity by desalination and water reuse

Dual-actuator-type active noise control in vibro-acoustic systems with openings

Abstract Openings in plate structures are essential in various engineering applications, particularly in vibro-acoustic systems where airflow is required. This paper investigates noise control in vibro-acoustic systems with noise barriers incorporating structural openings, focusing on active noise control and Active Structural Acoustic Control (ASAC). It also introduces a novel approach, Dual-Actuator-Type Active Noise Control (DATANC), which combines loudspeakers and inertial actuators into the same barrier to address the challenges of noise reduction. A sound power estimation method is proposed to account for sound transmission through the opening and is integrated into an analytical model for optimizing actuator placement; predictions show strong agreement with observed behavior. Among the ASAC configurations, experimental analysis shows that actuators placed near the edge of the opening achieve the greatest noise reduction in the 100–200 Hz range, where acoustic leakage is dominant. DATANC consistently outperformed all single-actuator configurations, delivering superior attenuation of dominant vibro-acoustic resonances while maintaining reasonable computational complexity. The analysis is extended to a plate with a transparent lid over the opening to evaluate the contribution of acoustic leakage to the system performance. The findings of this study demonstrate that optimized actuator placement, combined with DATANC, provides a practical solution for noise control in systems where structural openings are required.

Impact of genotypes, environmental stresses, and genotype by environment interactions on growth and yield of quinoa at flowering stage

Flowering is a critical growth stage of quinoa (Chenopodium quinoa Willd.), with a strong influence on growth and grain yield. To understand factors affecting such flowering stage effects, we measure the differential effects of genotype (G), environmental stress (E), and genotype by environment interaction (G × E) on quinoa growth and yield-related traits during the flowering stage. A semi-controlled pot experiment was conducted in a greenhouse using a Randomized Complete Block Design (RCBD) with five replications. Five quinoa genotypes (Q1, Cahuil, G18, Isluga, and Q3) were evaluated under four climate-related stress vs non-stress treatment conditions: control (E1), waterlogging (E2), salinity (E3), and drought (E4). Morphological and yield traits, including plant height, number of tillers and leaves, leaf area, soil plant analysis development (SPAD) values, fresh and dry biomass, panicle length, 1000-grain weight, and individual grain yield were measured. There were significant effects of G, E, and G × E interaction on all measured traits, indicating considerable variation in genotype adaptability to abiotic stresses. The order of stress severity was E2 &gt; E4 &gt; E3 &gt; E1, with waterlogging causing the most substantial reductions across growth and yield traits. The AMMI analysis highlighted strong genotype-specific responses across environments. Our findings provide insights into how quinoa responds to environmental stresses, supporting the development of research strategies and and irrigation management for quinoa under climate change related stresses.

Communication increases cooperation among students in a coordination game

Cooperation often requires individuals to balance personal risk with mutual gain. The Stag Hunt game provides a well-established paradigm for studying such decision-making. Prior research suggested that verbal communication about the game correlated with participants finding the optimal coordinated solution, but these studies either did not manipulate communication directly or informed participants of the payoff structure in advance. This study examined whether communication improves cooperative decision-making among college students under conditions in which the payoff structure had to be inferred through repeated play. A total of 127 same-sex dyads ( M age = 22.8 y, 51.2% female) played 40 rounds of an online Stag Hunt game, with dyads randomly assigned to either a no communication or communication condition. Participants were not informed about the game’s payoff structure in advance and had to infer it during play. Results showed that coordination on the payoff-dominant outcome (Stag–Stag) increased across trials, but only when communication was possible. No significant sex differences were observed. These findings highlight the central role of communication in fostering cooperation, particularly in environments in which information must be jointly discovered. This is an important consideration for developing and interpreting future research. Moreover, subsequent research should explore how the content, timing, and relevance of communication shape cooperative outcomes over time.

Development and validation of a risk prediction model for pulmonary tuberculosis in presumptive tuberculosis patients in Tigray, northern Ethiopia

Analysis and intelligent prediction of domino effect accidents in chemical storage tanks with a focus on accident chain length

The compact arrangement of chemical storage tanks significantly increases the occurrence probability of domino effect accidents. The accident chain length, a critical parameter for assessing accident severity, enables rapid comprehension of potential accident impacts and serves as a foundation for constructing accident scenarios in domino effect risk assessment. This study centers on domino effect accidents within chemical storage tanks and conducting a detailed analysis of factors influencing the accident chain length. Given the limitations in historical statistical data and quantitative risk evaluations, an intelligent prediction method is developed to forecast the accident chain length. A fully connected feedforward neural network (FC-FNN) is utilized to analyze 255 pertinent accident cases spanning from 1970 to 2024, with key features such as the type of substances implicated and the operating conditions during accidents being judiciously screened. To compensate for the insufficiency of data regarding the volume of storage tanks, a small-scale augmentation is implemented within the tolerable error range. Additionally, Shapley Additive Explanations (SHAP) is applied to optimize the feature set, reducing the number of features from 15 to 10 based on their contribution to the model’s predictions. The results show that the combined application of feature selection, data augmentation, and SHAP-based optimization significantly improves the model’s prediction performance. The test set prediction accuracy exceeds 0.978, demonstrating the effectiveness of the proposed approach.

Precise antibody delivery to the brain via nanobubble-actuated focused ultrasound alleviates depression

Precise, noninvasive drug delivery to small but important brain regions is challenging and highly desired given the brain’s inherent complexity and heterogeneous nature. Here, we report an approach utilizing focused ultrasound (FUS) combined with nanobubbles to successfully navigate this challenge. Compared to traditional microbubbles, nanobubbles exhibit superior acoustic properties. The nanobubbles, when exposed to FUS, induce a highly localized and reversible opening of the blood–brain barrier (BBB) with significantly enhanced precision (up to fourfolds compared to microbubbles, as measured by the precision loss metric). Repeated multitarget FUS-NB precisely delivers macromolecular human-derived anti-N-methyl-D-aspartate receptors monoclonal antibodies (HuMAbs) into the small brain region within a 2-h half-life window per opening. Fluorescence images confirm HuMAb retention in the brain parenchyma for at least 10 d postadministration. With this approach, we targeted the lateral habenula, a small but effective brain target for antidepressant treatments, and significantly alleviated depression-like symptoms at least 2 wk in a mouse model (tail suspension test/forced swim test: P &lt; 0.01/0.05). Moreover, minimal red blood cell extravasation (0.9‱ affected area) was observed in the treated region after multiple FUS treatments, indicating the safety and tolerability of FUS-nanobubble-mediated BBB opening. The enhanced delivery precision, coupled with a favorable safety profile, positions our approach as a promising strategy for antibody therapy with significant clinical translation potential.