Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650-900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.

Characterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Technological developments have enabled the collection of hundreds of neural recordings; this calls for new tools capable of performing feature extraction efficiently. To address the urgent need for a powerful and accessible tool, we developed ElecFeX, an open-source MATLAB-based toolbox that (1) has an intuitive graphical user interface, (2) provides customizable measurements for a wide range of electrophysiological features, (3) processes large-size datasets effortlessly via batch analysis, and (4) yields formatted output for further analysis. We implemented ElecFeX on a diverse set of neural recordings; demonstrated its functionality, versatility, and efficiency in capturing electrical features; and established its significance in distinguishing neuronal subgroups across brain regions and species. ElecFeX is thus presented as a user-friendly toolbox to benefit the neuroscience community by minimizing the time required for extracting features from their electrophysiological datasets.

UNASSIGNED: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.
UNASSIGNED: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.
UNASSIGNED: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.
UNASSIGNED: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Natural products, while valuable for drug discovery, encounter limitations like uncertainty in targets and toxicity. As an important active ingredient in traditional Chinese medicine, celastrol exhibits a wide range of biological activities, yet its mechanism remains unclear. In this study, they introduced an innovative \"Degradation-based protein profiling (DBPP)\" strategy, which combined PROteolysis TArgeting Chimeras (PROTAC) technology with quantitative proteomics and Immunoprecipitation-Mass Spectrometry (IP-MS) techniques, to identify multiple targets of natural products using a toolbox of degraders. Taking celastrol as an example, they successfully identified its known targets, including inhibitor of nuclear factor kappa B kinase subunit beta (IKKβ), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PI3Kα), and cellular inhibitor of PP2A (CIP2A), as well as potential new targets such as checkpoint kinase 1 (CHK1), O-GlcNAcase (OGA), and DNA excision repair protein ERCC-6-like (ERCC6L). Furthermore, the first glycosidase degrader is developed in this work. Finally, by employing a mixed PROTAC toolbox in quantitative proteomics, they also achieved multi-target identification of celastrol, significantly reducing costs while improving efficiency. Taken together, they believe that the DBPP strategy can complement existing target identification strategies, thereby facilitating the rapid advancement of the pharmaceutical field.

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the \'full picture\' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.

Reproducible functional assays to study in vitro neuronal networks represent an important cornerstone in the quest to develop physiologically relevant cellular models of human diseases. Here, we introduce DeePhys, a MATLAB-based analysis tool for data-driven functional phenotyping of in vitro neuronal cultures recorded by high-density microelectrode arrays. DeePhys is a modular workflow that offers a range of techniques to extract features from spike-sorted data, allowing for the examination of functional phenotypes both at the individual cell and network levels, as well as across development. In addition, DeePhys incorporates the capability to integrate novel features and to use machine-learning-assisted approaches, which facilitates a comprehensive evaluation of pharmacological interventions. To illustrate its practical application, we apply DeePhys to human induced pluripotent stem cell-derived dopaminergic neurons obtained from both patients and healthy individuals and showcase how DeePhys enables phenotypic screenings.

BACKGROUND: The digital transformation in medicine, particularly in technology-orientated areas such as rhythmology, is leading to a rapid change in diagnostic and therapeutic options. Digital skills are helpful and need to keep up with this pace of change.
OBJECTIVE: Which digital technologies and resources with rhythmological relevance play a role today and in the future?
METHODS: Review of the various digital technologies for rhythm detection and monitoring, as well as current digital resources for training and education.
RESULTS: Rhythm detection and monitoring can be optimized with smart devices and telemedicine, while digital platforms such as social media and virtual reality offer new perspectives in the training of rhythmology specialists.
CONCLUSIONS: Acquiring digital skills will be the basis for future work in rhythmology.
UNASSIGNED: HINTERGRUND: Die digitale Transformation in der Medizin führt, insbesondere in technikaffinen Bereichen wie der Rhythmologie, zu einem raschen Wandel an diagnostischen und therapeutischen Möglichkeiten. Um diesem Rhythmus an Veränderungen gerecht zu werden, sind digitale Kompetenzen hilfreich und notwendig.
UNASSIGNED: Welche digitalen Technologien und Ressourcen mit rhythmologischer Relevanz spielen heute und zukünftig eine Rolle?
METHODS: Überblick und Einordnung der verschiedenen digitalen Technologien zur Rhythmusdetektion und zum Rhythmusmonitoring sowie der aktuellen digitalen Ressourcen zum Training und zur Weiterbildung.
UNASSIGNED: Durch den Einsatz von Smart Devices sowie von Telemedizin können Rhythmusdetektion und -monitoring optimiert werden, während digitale Plattformen wie Social Media und Virtual Reality neue Perspektiven in der Weiterbildung von Rhythmologen bieten.
UNASSIGNED: Die Aneignung digitaler Kompetenzen wird in der Rhythmologie Grundlage für die zukünftige Tätigkeit sein.

Gut microbes are closely related with human health, but remain much to learn. Clostridium symbiosum is a conditionally pathogenic human gut bacterium and regarded as a potential biomarker for early diagnosis of intestinal tumors. However, the absence of an efficient toolbox that allows diverse genetic manipulations of this bacterium limits its in-depth studies. Here, we obtained the complete genome sequence of C. symbiosum ATCC 14940, a representative strain of C. symbiosum. On this basis, we further developed a series of genetic manipulation methods for this bacterium. Firstly, following the identification of a functional replicon pBP1 in C. symbiosum ATCC 14940, a highly efficient conjugative DNA transfer method was established, enabling the rapid introduction of exogenous plasmids into cells. Next, we constructed a dual-plasmid CRISPR/Cas12a system for genome editing in this bacterium, reaching over 60 % repression for most of the chosen genes as well as efficient deletion (>90 %) of three target genes. Finally, this toolbox was used for the identification of crucial functional genes, involving growth, synthesis of important metabolites, and virulence of C. symbiosum ATCC 14940. Our work has effectively established and optimized genome editing methods in intestinal C. symbiosum, thereby providing strong support for further basic and application research in this bacterium.

Respiratory sinus arrhythmia (RSA), the natural variation in heart rate synchronized with respiration, has been extensively studied in emotional and cognitive contexts. Various time or frequency-based methods using the cardiac signal have been proposed to analyze RSA. In this study, we present a novel approach that combines respiratory phase and heart rate to enable a more detailed analysis of RSA and its dynamics throughout the respiratory cycle. To facilitate the application of this method, we have implemented it in an open-source Python toolbox called physio This toolbox includes essential functionalities for processing electrocardiogram (ECG) and respiratory signals, while also introducing this new approach for RSA analysis. Inspired by previous research conducted by our group, this method enables a cycle-by-cycle analysis of RSA providing the possibility to correlate any respiratory feature to any RSA feature. By employing this approach, we aim to gain a more accurate understanding of the neural mechanisms associated with RSA.