Functional magnetic resonance imaging studies have demonstrated that back pain, particularly chronic back pain, is associated with altered functional brain connectivity, especially in regions involved in the modulation of pain and emotion regulation. Our study investigated dysconnectivity patterns in subacute back pain patients to distinguish those who develop chronic back pain from those who recover. This work utilized a publicly available longitudinal resting-state functional magnetic resonance imaging dataset from the OpenPain database, including clinical assessments collected across multiple sessions, and recorded pain scores. The dataset consists of 46 subacute back pain patients and 27 healthy controls. Based on longitudinal pain scores, patients were classified into recovery and chronic back pain groups. In contrast to prior analyses of the OpenPain dataset, which primarily relied on specific brain region's connectivity or predictive modeling, this study applies a voxel-wise dysconnectivity count framework combined with data-driven clustering to identify spatially precise, whole-brain signatures of abnormal functional connectivity. This approach enables detection of anatomically specific dysconnectivity patterns at a resolution not achieved in previous work. Our findings therefore extend the OpenPain literature by providing a fine-grained, network-wide characterization of early connectivity disturbances that distinguish recovery from back pain chronification. The results showed that the chronic back pain group exhibited greater dysconnectivity in regions linked to emotion regulation, pain processing, and attentional control. In contrast, the recovery group displayed connectivity deviations in sensorimotor, visual, and cognitive control regions. PERSPECTIVE: Baseline whole-brain dysconnectivity patterns differ between patients who recover and those who develop chronic pain. These findings highlight distributed network alterations associated with pain trajectories and provide insight into neural mechanisms underlying pain chronification.

Ising machines constitute a framework to solve combinatorial optimization problems efficiently utilizing dynamical interacting systems. Typical approaches simulate the dynamics on digital hardware, which is strongly dependent on matrix-vector operations, and were shown to experience acceleration of several orders of magnitude by parallel processing on FPGAs. However, optimization on single FPGAs has remained limited to small-scale problems due to memory limitations, and multi-FPGA networks are challenging to operate. In this work, we propose a high-accuracy and FPGA-based Ising machine design that achieves state-of-the-art solution speeds on integrated hardware and is scalable to problems of up to 20,000 variables on a single FPGA unit. Concretely, we introduce a sparse data format, called tiled coordinate list, for more efficient memory use and fast matrix-vector operations. Further, we propose a quantization mechanism to 8-bit integer precision that allows problem sizes to be scaled without reducing the accuracy of the solution. Co-optimization of the data flow and hardware design strongly accelerates the Ising machine while reducing computational costs. This enables 10- to 68-fold speedups on Max-Cut problems from the Gset graph collection compared to previous state-of-the-art FPGA-based Ising machines.

Fermented plant-based foods have obtained growing interests for their improved nutrition profile, enhanced flavor and taste, as well as their health-promoting properties. Fermentation using lactic acid bacteria (LAB) and yeasts can eliminate antinutritional components and off-flavor compounds present in plant matrices while also generating beneficial metabolites. The interaction between fermentative microbes and plant substrates is dependent on plant matrices, microbial strains, and processing conditions. Accumulating evidence indicates that fermentation modifies the generation, degradation, and bioavailability of food bioactive compounds such as bioactive peptides, vitamins, volatiles, phenolics, phytic acid and phytates, saponins, and raffinose-family oligosaccharides. This chapter reviews and critically examines research data on microbial transformations of bioactive compounds in fermented plant matrices and pinpoints key factors contributing to inconsistent findings. It also identifies key research directions for understanding and applying fermentation-driven changes to improve the nutritional and functional quality of plant-based fermented foods.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Roller compaction dry granulation is one of the pivotal dry granulation technologies in solid dosage form manufacturing, offering irreplaceable advantages in processing heat-sensitive or moisture-sensitive materials. With the advancement of large-scale production and intelligent manufacturing in the pharmaceutical industry, the scale-up challenge of roller compaction dry granulation from laboratory-scale to industrial-scale production has gradually emerged as a critical bottleneck restricting its widespread application. During the scale-up process, the complex coupling effects among parameters such as compaction force, roller gap, roller speed, and material properties can induce significant alterations in particle quality attributes (e.g., particle size distribution, solid fraction), thereby compromising product consistency and production efficiency. In recent years, emerging research efforts-including model-based scale-up approaches, the Johanson model, numerical simulation, and data-driven optimization strategies-have been continuously advancing. These endeavors have played a proactive role in unraveling the mechanistic correlations during scale-up and establishing predictable scale-up models. This review systematically expounds on mechanism-driven scale-up methods (e.g., those based on the Johanson model and numerical simulation), integrates the latest research progress in the application of simulation technologies, and clarifies the core principles and implementation strategies for process scale-up. It aims to provide theoretical guidance and practical references for the robust scale-up and industrial production of the roller compaction dry granulation process.

We used natural language processing (NLP) to improve the utility of clinical decision support (CDS) β-lactam allergy alerts and promote informed allergy evaluation. NLP was performed on a corpus of clinical notes from hospital-based encounters to identify previous tolerance of β-lactam products using a rule-based approach. Historical tolerance of β-lactams was then combined with structured electronic health records data to produce improved CDS alerts. A survey was used to evaluate the utility of the improved alerts compared to standard allergy alerts. The rule-based pipeline identified previous β-lactam tolerance in between 3% and 28.4% of clinical notes and performed with high positive predictive value (83.6-97.6%) and recall (71.2-79.4%). The surveyed clinicians (N = 9) reported increased confidence in using β-lactam products despite the presence of a documented β-lactam allergy when using the information presented by the NLP-enriched CDS alerts, and all surveyed clinicians indicated the alerts would improve the care of their patients. NLP of clinical notes shows potential to improve the utility of CDS allergy alerts. Clinicians were receptive to allergy alerts containing NLP-derived information. Allergy-related CDS alerts should be improved to provide additional information such as historical tolerance of relevant products to empower providers to make informed decisions regarding patient allergies.

Red blood cell (RBC) deformability is critical for in vivo tissue oxygenation and has been studied in ideal, canonical flows. In vitro frameworks are needed to study RBC deformation in more complex flows than those studied previously, as complex hemodynamics are common in blood-wetted medical devices and valvular pathologies. In this study, pseudo-3-dimensional RBC deformation and orientation angles are investigated in vitro in a microfluidic sudden expansion under two viscosity conditions (2.05, 4.17 cP) and three flow rates (100, 200, and 400 μL/min). The velocity gradient in the sudden expansion is supraphysiological (3,000 - 200,000 s[-1]) with coexisting shear, rotational, and extensional components to produce complex flows. An image post-processing workflow is developed to generate two-dimensional contour maps of RBC deformation and orientation throughout the sudden expansion. RBCs remain ellipsoidal throughout the channel, except when transitioning between extended and compressed, where they exhibit a teardrop shape. Peak deformation index (DI) magnitudes in the complex flow regions reach 0.53 and 0.33 for 4.17 and 2.05 cP, respectively. DI magnitudes are 20 - 27% higher in extension-dominant compared to compressive-dominant flows for 4.17 cP, while they are 11 - 14% lower in extension-dominant compared to compressive-dominant flows for 2.05 cP. The pseudo-3-dimensional DI behavior between viscosity conditions also differ, with DI differences observed between 4.17 and 2.05 cP in the membrane cross section orthogonal to the principal straining direction. Two-dimensional contour maps of RBC deformation and orientation show high spatial variability, a result of the velocity gradient tensor's heterogeneity throughout the device. Contour map data also reveal higher RBC behavior variability in compressive and mixed flows compared to homogeneous extension. The study's novelty is capturing RBC deformation in off-centered regions of a sudden expansion across a suite of conditions, offering new insights to RBC behavior in complex flows.

Biofilms are a major cause of delayed wound healing, yet current biofilm identification methods are limited by invasiveness, processing times, or specificity. This study investigates the potential of metal-oxide electronic noses for identifying bacterial cultures of Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus faecium, and Staphylococcus epidermidis grown in blood-based growth medium. We conducted in-vitro experiments to capture volatilome signatures from cultures grown under biofilm-promoting conditions and analyzed data using an interpretable machine learning workflow to disentangle algorithmic limitations from biological variability. This workflow incorporated feature extraction and selection, correlation-based clustering, and Shapley analysis. Six classification models were evaluated using cross-validation. Considering all five classes, classification accuracy reached at most 55.6%, which Shapley-based interpretation attributed mainly to biological factors: E. faecium and S. aureus exhibited high signal similarity to control samples and strong inter-day variability. Accuracy increased to 100.0% for species with distinct volatile signatures, and dimensionality reduction resulted in a model using two constructed features. These findings demonstrate that classification performance in biological sensing cannot be explained solely by algorithmic factors. Interpretable machine learning workflows help for distinguishing biological sources of complexity from algorithmic ones. We provide a proof-of-principle for electronic nose-based identification of bacterial growth under biofilm-promoting conditions.

Talkative Power Conversion (TPC) is an instance of simultaneous information and power transfer, in which data modulation is integrated in the power conversion process. The TPC framework is generalized in this work to incorporate multiple-input multiple-output (MIMO) processing. In this innovative concept, called MIMO-TPC, power electronic converters with multiple switching units are applied that use coordinated or uncoordinated, data-dependent switching patterns in order to increase the degrees of freedom for data modulation. A selection of power converter topologies is investigated that fit within the framework of MIMO-TPC, including H-bridge DC/DC converters, multi-phase DC/DC converters, multi-phase DC/AC inverters, multi-input converters, multi-modular converters, multi-level converters, and multi-port converters. These switched-mode converter classes are characterized by an elementary building block and elementary combinations. Encouraging advantages of MIMO-TPC are elaborated, potential applications of MIMO-TPC are discussed, and open challenges as well as future research directions are suggested.

Early-life tobacco exposure is a significant risk factor for depression and anxiety. Understanding its immune and neural mechanisms-and how risks differ by age of smoking initiation-is essential for designing effective prevention and intervention strategies. Using data from 375,903 participants in the UK Biobank, we found that in utero tobacco exposure was associated with increased risk of depression and anxiety, elevated inflammatory markers, and reduced structure-function (SF) coupling in the thalamus and putamen. Childhood smoking initiation was associated with a significantly higher risk of depression, anxiety, and systemic inflammation compared to adulthood initiation, whereas adolescent initiation did not differ significantly from adulthood initiation. Both childhood and adolescent initiation were associated with reduced SF coupling in brain regions involved in reward processing and cognitive control, relative to never-smokers. Mediation analyses revealed that systemic inflammation and SF coupling jointly mediated the association between early-life tobacco exposure and psychiatric risk. CRP emerged as a key mediator linking early smoking initiation to SF coupling alterations. Moreover, interaction models revealed that in utero tobacco exposure exacerbated the pro-inflammatory effects of childhood smoking initiation. Together, these findings underscore that early-life tobacco exposure contributes to long-term mental health vulnerability via neuroimmune pathways, with childhood representing a particularly sensitive period-underscoring the need for developmental-stage prevention and mechanism-based interventions.