Miracle-dz/UniFAR-FTUs huggingface dataset:
Prefrontal Regulation of Safety Learning during Ethologically Relevant Thermal Threat

Learning and adaptation during sources of threat and safety are critical mechanisms for survival. The prelimbic (PL) and infralimbic (IL) subregions of the medial prefrontal cortex (mPFC) have been broadly implicated in the processing of threat and safety. However, how these regions regulate threat and safety during naturalistic conditions involving thermal challenge still remains elusive. To examine this issue, we developed a novel paradigm in which adult mice learned that a particular zone that was identified with visuospatial cues was associated with either a noxious cold temperature (“threat zone”) or a pleasant warm temperature (“safety zone”). This led to the rapid development of avoidance behavior when the zone was paired with cold threat or approach behavior when the zone was paired with warm safety. During a long-term test without further thermal reinforcement, mice continued to exhibit robust avoidance or approach to the zone of interest, indicating that enduring spatial-based memories were formed to represent the thermal threat and thermal safety zones. Optogenetic experiments revealed that neural activity in PL and IL was not essential for establishing the memory for the threat zone. However, PL and IL activity bidirectionally regulated memory formation for the safety zone. While IL activity promoted safety memory during normal conditions, PL activity suppressed safety memory especially after a stress pretreatment. Therefore, a working model is proposed in which balanced activity between PL and IL is favorable for safety memory formation, whereas unbalanced activity between these brain regions is detrimental for safety memory after stress.
RUNOFF ESTIMATION FROM TWO MID-SIZE WATERSHEDS USING SWAT MODEL

SWAT model was applied to estimate daily stream flow for Yongdam and Bocheong watersheds in Korea. The model was calibrated and validated for the two watersheds and a new routine was added to analyze runoff process in paddy fields. The model efficiencies for two watersheds were 0.77 and 0.65 for the calibration period, and 0.76 and 0.50 for the validation period, respectively. It showed that water balance method simulated the runoff from paddy fields more precisely than CN method in SWAT. As results, the SWAT model is applicable to Korean watersheds, and more accurate estimation is possible using daily water balance method in paddy fields.
Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.
iVirP: An integrative, efficient, and user-friendly pipeline to annotate viral contigs from raw reads of metagenome or VLP sequencing

Metagenome sequencing and virus-like particles sequencing make it possible to explore the virome in the humans and other organisms. One way to analyze the sequencing data is to assemble reads into contigs according to the overlapping regions, and then the predicted viral contigs are screened out to conduct deeper dives. iVirP (integrative virome pipeline) is a user-friendly pipeline that includes the whole process of viral contigs discovery from the quality control of raw data to the filter of high quality viral contigs. This pipeline also features a branching function that can estimate the abundance of known eukaryotic viruses in a short period, while reducing host contamination. It is suitable for the rapid diagnosis of pathogens. Throughout iVirP, many details that might affect the experience of users are optimized carefully to reduce the time spent on dealing with usage and errors. iVirP was tested on a published, high-quality VLP sequencing dataset and was able to well reproduce the conclusions of the corresponding research. The benchmark indicates that iVirP could accurately assemble viral contigs from real sequencing data. iVirP is easy to install and currently available at https://github.com/li-bw18/iVirP.
Turnover rates of human muscle proteins in vivo reported in fractional, mole and absolute units

Protein fractional turnover rates (FTR) represent measurements of flux through a protein pool, i.e. net abundance (ABD) of the protein. If protein abundance is not measured or is different between experimental conditions the interpretation of FTR data may be confounded. This project investigates the consequences of reporting turnover rates of human muscle proteins in vivo in mole and absolute units (that incorporate protein abundance data) compared to fractional (%/d) data that ignore protein abundance. Three physically active males (21 ± 1 years) were recruited and underwent a 12-d protocol of daily deuterium oxide (D2O) consumption and biopsies of vastus lateralis on days 8 and 12. Protein abundances were normalised to yeast alcohol dehydrogenase, added during sample preparation, and FTR was calculated from time-dependent changes in peptide mass isotopomer profiles. FTR and abundance data (fmol/ μg protein) were combined to calculate mole turnover rates (MTR; fmol/ μg protein/ d) and absolute turnover rates (ATR; ng/ μg protein/ d). Abundance data were collected for 1,772 proteins and FTR data were calculated from 3,944 peptides representing 935 proteins (average 3 peptides per protein). The median (M), lower- (Q1) and upper-quartile (Q3) values for protein FTR (%/d) were M = 4.3, Q1 = 2.52, Q3 = 7.84. Our analyses suggest MTR data is preferred over FTR, particularly for studies on multiprotein complexes, wherein MTR takes account of potential differences amongst the molecular weight of the component subunits. ATR data may be preferred over MTR and FTR, particularly when comparing samples with different abundance profiles.
Functionalising the electrical properties of Kombucha zoogleal mats for biosensing applications

Kombucha is a type of tea that is fermented using yeast and bacteria. During this process, a film made of cellulose is produced. This film has unique properties such as biodegradability, flexibility, shape conformability, and ability to self-grow, as well as be produced across customised scales. In our previous studies, we demonstrated that Kombucha mats exhibit electrical activity represented by spikes of electrical potential. We propose using microbial fermentation as a method for in situ functionalisation to modulate the electroactive nature of Kombucha cellulose mats, where graphene and zeolite were used for the functionalisation. We subjected the pure and functionalised Kombucha mats to mechanical stimulation by applying different weights and geometries. Our experiments demonstrated that Kombucha mats functionalised with graphene and zeolite exhibit memfractive properties and respond to load by producing distinctive spiking patterns. Our findings present incredible opportunities for the in situ development of functionalised hybrid materials with sensing, computing, and memory capabilities. These materials can self-assemble and self-grow after fusing their living and synthetic components. This study contributes to an emergent area of research on bioelectronic sensing and hybrid living materials, opening up exciting opportunities for use in smart wearables, diagnostics, health monitoring and energy harvesting applications.
Satb2 regulates pyramidal cell differentiation and the development of feedforward inhibitory circuits in CA1 hippocampus

In CA1 hippocampus, pyramidal cells (PCs) can be separated into two classes, deep or superficial, based on their radial position. In mice, superficial PCs receive fewer inhibitory synapses from parvalbumin (PV)-expressing interneurons than deep PCs, resulting in weaker feedforward inhibition of input from CA3 Schaffer collaterals. The molecular mechanisms that control PC differentiation and integration into hippocampal circuits remain unknown. Using mice, we found that the transcriptional regulator SATB2 is expressed in superficial PCs but absent from deep PCs at birth. Next, we conditionally knocked out (cKO) Satb2 from PCs during early development and then investigated inhibitory circuits using whole-cell recordings in acute slices from juveniles. In Satb2 cKO mice, feedforward inhibition of superficial PCs increased to match that observed in deep PCs. In paired recordings between PCs and PV+ interneurons, we found increased strength of inhibitory synapses selectively to superficial PCs. This was due to increased density of perisomatic PV+ synaptic puncta. Thus, Satb2 expression in superficial PCs suppresses PV+ interneuron synapse formation to establish differential feedforward inhibition in CA1 hippocampus.
Molecular mechanism of β-arrestin-2 interaction with phosphatidylinositol 4,5-bisphosphate

Phosphorylated residues of G protein-coupled receptors bind to the N-domain of arrestin, resulting in βXX release. This induces further allosteric conformational changes, such as polar core disruption, alteration of interdomain loops, and domain rotation, which transform arrestins into the active state. It is widely accepted that arrestin activation occurs by conformational changes propagated from the N-to the C-domain. However, recent studies have revealed that binding of phosphatidylinositol 4,5-bisphosphate (PIP2) to the C-domain transforms arrestins into an active state. In this study, we aimed to elucidate the mechanisms underlying PIP2-induced arrestin activation. We compared the conformational changes of β-arrestin-2 upon binding of PIP2 or phosphorylated C-tail peptide of vasopressin receptor type 2 using hydrogen/deuterium exchange mass spectrometry (HDX-MS). Introducing point mutations on the potential routes of the allosteric conformational changes and analyzing these mutant constructs with HDX-MS revealed that PIP2-binding at the C-domain affects the back loop, which destabilizes the gate loop and βXX to transform β-arrestin-2 into the pre-active state.
β-Cells as a Cell Factory for On-Demand Recombinant Protein Dosing: Harnessing the Neuroendocrine Cell Secretory Pathway for Controlled Release

This study explores the potential of utilizing β-cells, exemplified with R7T1 β-cell pseudoislets, as a transplantable cell factory for on-demand recombinant protein therapeutic delivery. While mammalian cell lines are widely used for in vitro protein production, the commonly utilized constitutive secretion pathway poses challenges to in vivo cell therapy, especially for delivering proteins requiring precise exposure kinetics. The proposed approach capitalizes on unique aspects of β-cells, including substantial vesicular protein storage capacity and electrochemically-regulated protein release, to facilitate timely and titratable in vivo therapeutic delivery. Examining a variety of strategies to acheive β-cell glucagon or glucagon-like peptide 1 (GLP-1) storage and secretion, we devised a flexible β-cell-based expression platform for efficient cellular peptide production and on-demand release. This platform utilizes the preproinsulin coding sequence as a template, wherein therapeutic peptides of interest (glucagon or GLP-1) are substituted for C-peptide while the A- and B-peptide insulin chains are mutated to prevent bio-active insulin production. This approach overcomes the challenge of efficient bio-active peptide expression by leveraging the endogenous β-cell peptide expression, translation, processing, storage and secretion machinery. Furthermore, β-cells provide a mechanism for scalable electyrochemnically-triggered peptide delivery. This transformative strategy, which may be extended to other proteins and peptide expression cassettes, holds significant promise for targeted and temporally controlled in vivo production and release of recombinant protein therapeutics. The study suggests potential applications in addressing challenges in metabolic disorders, blood disorders, and oncology. Future refinements may focus on optimizing vector design, peptide production, and in vivo adaptation.
REPrise: de novo interspersed repeat detection using inexact seeding

Motivation Interspersed repeats occupy a large part of many eukaryotic genomes, and thus their accurate annotation is essential for various genome analyses. Database-free de novo repeat detection approaches are powerful for annotating genomes that lack well-curated repeat databases. However, existing tools do not yet have sufficient repeat detection performance. Results In this study, we developed REPrise, a de novo interspersed repeat detection software program based on a seed-and-extension method. Although the algorithm of REPrise is similar to that of RepeatScout, which is currently the de facto standard tool, we incorporated three unique techniques into REPrise: inexact seeding, affine gap scoring and loose masking. Analyses of rice and simulation genome datasets showed that REPrise outperformed RepeatScout in terms of sensitivity, especially when the repeat sequences contained many mutations. Furthermore, when applied to the complete human genome dataset T2T-CHM13, REPrise demonstrated the potential to detect novel repeat sequence families. Availability The source code of REPrise is freely available at https://github.com/hmdlab/REPrise. Repeat annotations predicted for the T2T genome using REPrise are also available at https://waseda.box. com/v/REPrise-data. Contact fukunaga@aoni.waseda.jp and mhamada@waseda.jp
Activity modulation in anaerobic ribonucleotide reductases: nucleotide binding to the ATP-cone mediates long-range order-disorder transitions in the active site

A small, nucleotide-binding domain, the ATP-cone, is found at the N-terminus of most ribonucleotide reductase (RNR) catalytic subunits. By binding ATP or dATP it regulates the enzyme activity of all classes of RNR. Functional and structural work on aerobic RNRs has revealed a plethora of ways in which dATP inhibits activity by inducing oligomerization and preventing a productive radical transfer from one subunit to the active site in the other. Anaerobic RNRs, on the other hand, store a stable glycyl radical next to the active site and the basis for their dATP-dependent inhibition is completely unknown. We present biochemical, biophysical and structural information on the effects of ATP and dATP binding to the anaerobic RNR from Prevotella copri. The enzyme exists in a dimer-tetramer equilibrium biased towards dimers when two ATP molecules are bound and tetramers when two dATP molecules are bound. In the presence of ATP, P. copri NrdD is active and has a fully ordered glycyl radical domain (GRD) in one monomer of the dimer. Binding of dATP to the ATP-cone results in loss of activity and disordering of the GRD. The glycyl radical is formed even in the dATP-bound form, but the substrate does not bind, suggesting that dATP inhibition in anaerobic RNRs acts by disordering of the GRD more than 30 Å away from the dATP molecule, thereby preventing both substrate binding and radical mobilisation. The structures implicate a complex network of activity regulation involving the GRD, the allosteric substrate specificity site and a conserved but previously unseen flap over the active site.
Implementation of Data Science Methods in Armed Forces Budgeting: Challenges and Opportunities

This paper explores the challenges and opportunities of incorporating data science methods into military budgeting, with a focus on practical advice in the area of forecasting defense budgets and expenditures. The study uses aggregated monthly expenditure data from military bases and applies various forecasting models, including smoothing, linear regression, prophet and XGBoost. The three best performing models are selected to build a meta-model that improves the accuracy of the forecasts. The results suggest that incorporating data science methods into military budgeting can lead to better decision making and resource optimisation.
Using Parallelized Neural Networks to Detect Falsified Audio Information in Socially Oriented Systems

The problem of information counterfeiting with technology development is becoming more acute and can provoke social unrest. Modern algorithms can automatically create videos, audio, and text data, and more people are not able to reveal the fact of deception in them. This is especially true of audio information in socially oriented systems. Different mathematical models and concepts can be used to counteract this problem. The most effective of these are derivatives of convolutional and recurrent neural networks. However, the complexity of the data and its volume determine the problem of slowness of the classification. One of the approaches to solving this issue is the use of parallelism. With the framework of the current study, given the simplicity of monitoring, it was decided to focus on MapReduce technology. In the course of this investigation, the features were characteristic of audio information both in its text form and in the form of a signal. The possibilities for the rapid process of data and their augmentation using vector autoregression algorithms are determined. Based on the analysis of international experience, it was decided to use hybrid neural networks based on convolutional and recurrent networks. Several experiments were conducted to determine the effectiveness of the proposed approaches and the expediency of using MapReduce. The results obtained suggest that the most effective algorithm is the combination of two selected architectures and that it is advisable to use the specified parallelization technology to accelerate the selected models. This fact shows the possibility of practical application of the proposed approach to detecting falsified audio.
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation

Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It is particularly appealing in medical image segmentation, where the annotation process is laborious and expertise-demanding. However, the direct application of SAM often yields inferior results compared to conventional fully supervised segmentation networks. An alternative approach is to use SAM as the initial stage to generate pseudo labels for further network training. However, the performance is limited by the quality of pseudo labels. In this paper, we propose a novel label correction framework to push the boundary of SAM-based segmentation. Our model utilizes a label quality evaluation module to distinguish between noisy labels and clean labels. This enables the correction of the noisy labels using an uncertainty-based self-correction module, thereby enriching the clean training set. Finally, we retrain the segmentation network with updated labels to optimize its weights for future predictions. One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance. We demonstrate the effectiveness of our proposed model on three public datasets, indicating its ability to improve segmentation accuracy and outperform baseline methods in label correction.
Camera-based Motion Correction for PET/MR Brain Imaging

In this work, we evaluated the performance of a camera-based rigid body motion correction solution in PET studies. We compared the image quality obtained by reconstructing a static phantom scan to those obtained by reconstructing a moving phantom scan with and without applying motion correction. Our suggested solution uses a camera to track a 3D marker attached to the object to be imaged (in this work, a resolution phantom). This data was incorporated in the multiple acquisition frames (MAF) image reconstruction algorithm to compensate for motion. While we got multiple spatially shifted replicas of the resolution phantom in the motion study's reconstructed image when motion correction was not applied, the motion-corrected image was comparable to the static image, with some degradation in spatial resolution (3.2-4.2 vs. 2.8-3.2 mm) and lower peak to valley ratio (indicating lower contrast to noise ratio). The context of the results presented is in PET/MR imaging, but in principle, this motion compensation method could be used in any type of medical imaging modality.
Charge Transport and Signal Modeling for Sub-Cathode Positioning in a CZT PET System

This paper investigates the possibility of improved spatial resolution of a cadmium zinc telluride positron emission tomography system through sub-electrode positioning of the 511 keV annihilation photons. For this aim, research was conducted to improve our understanding on the physics of charge transport in the semiconductor system. Simulation studies were conducted on a 2D model which included five cathode strips and one anode strip using the MATLAB Finite Element Analysis Tool, which provided accurate data on the electric potential, electric field, and weighting potential present in the CZT crystal. Through implementing MATLAB post-processing tools and consulting the Shockley-Ramo theorem, analysis was performed on the moving charge in the detector to understand its effect on the induced charge on the cathodes. We have shown that, for a charge with an initial position directly above a specific cathode, the relative intensity of the induced charge is between 0.7 and 0.9 on that electrode, between 0.1 and 0.3 on its two adjacent cathodes, and negligible on the remaining electrodes. We predict that performing an analysis on the relative induced charges on the adjacent cathodes will allow us to successfully achieve sub-strip positioning of each coincident event.