Miracle-dz/UniFAR-FTUs huggingface dataset:
PhysMAP - interpretable in vivo neuronal cell type identification using multi-modal analysis of electrophysiological data

Cells of different types perform diverse computations and coordinate their activity during sensation, perception, and action. While electrophysiological approaches can measure the activity of many neurons simultaneously, assigning cell type labels to these neurons is an open problem. Here, we develop PhysMAP, a framework that weighs multiple electrophysiological modalities simultaneously in an unsupervised manner and obtain an interpretable representation that separates neurons by cell type. PhysMAP is superior to any single electrophysiological modality in identifying neuronal cell types such as excitatory pyramidal, PV+ interneurons, and SOM+ interneurons with high confidence in both juxtacellular and extracellular recordings and from multiple areas of the mouse brain. PhysMAP built on ground truth data can be used for classifying cell types in new and existing electrophysiological datasets, and thus facilitate simultaneous assessment of the coordinated dynamics of multiple neuronal cell types during behavior.
Proportion and distribution of neurotransmitter-defined cell types in the ventral tegmental area and substantia nigra pars compacta

Most studies on the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) have focused on dopamine neurons and their role in processes such as motivation, learning, movement, and associated disorders. However there has been increasing attention on other VTA and SNc cell types that release GABA, glutamate, or a combination of these neurotransmitters. Yet the relative distributions and proportions of neurotransmitter-defined cell types across VTA and SNc has remained unclear. Here, we used fluorescent in situ hybridization in male and female mice to label VTA and SNc neurons that expressed mRNA encoding the canonical vesicular transporters for dopamine, GABA, or glutamate: vesicular monoamine transporter VMAT2, vesicular GABA transporter (VGAT), and vesicular glutamate transporter (VGLUT2). Within VTA, we found that no one type was particularly more abundant, instead we observed similar numbers of VMAT2+ (44%), VGAT+ (37%) and VGLUT2+ (41%) neurons. In SNc we found that a slight majority of neurons expressed VMAT2 (54%), fewer were VGAT+ (42%), and VGLUT2+ neurons were least abundant (16%). Moreover, 20% of VTA neurons and 10% of SNc neurons expressed more than one vesicular transporter, including 45% of VGLUT2 neurons. We also assessed within VTA and SNc subregions and found remarkable heterogeneity in cell-type composition. And by quantifying density across both anterior-posterior and medial-lateral axes we generated heatmaps to visualize the distribution of each cell type. Our data complement recent single-cell RNAseq studies and support a more diverse landscape of neurotransmitter-defined cell types in VTA and SNc than is typically appreciated.
Evaluating generalizability of artificial intelligence models for molecular datasets

Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. We introduce SPECTRA, a spectral framework for comprehensive model evaluation. For a given model and input data, SPECTRA plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. We apply SPECTRA to 18 sequencing datasets with associated phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. With SPECTRA, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can generalize to previously unseen sequences on specific tasks. SPECTRA paves the way toward a better understanding of how foundation models generalize in biology.
A quantitative model for in vivo stem cell decisions in planarians

Stem cells contribute to organismal homeostasis by balancing division, self-renewal and differentiation. Elucidating the strategies by which stem cells achieve this balance is critical for understanding homeostasis, and for addressing pathogenesis associated with the disruption of this balance (e.g., cancer). Mathematical models have been developed to distill the principles underlying adult stem cell dynamics in vertebrates. Yet, valuable insights can be derived from modeling invertebrate stem cell systems that rely on a fundamentally different strategy. Here, integrating experimental, computational, and analytical approaches, we develop a quantitative model that reveals basic principles of clonal growth of individual neoblasts – pluripotent planarian stem cells. Deriving key physiological parameter values from experimental data, we show that neoblast colony growth can be well described as a straightforward stochastic decision process, without assuming memory or communication among cells. Crucially, by experimentally suppressing differentiation to major lineages, we reveal the interplay between colony growth and lineage decisions. Our findings suggest that neoblasts pre-select their progenitor lineage based on an underlying cell fate distribution, and that arresting differentiation into specific lineages disrupts neoblasts’ proliferative capacity – without inducing compensatory expression of other lineages. Our findings uncover essential aspects of stem cell regulation in planarians, demonstrating how principles distinct from those of vertebrate models can lead to robust homeostatic mechanisms.
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models

Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 10 compared to naive physics-based molecular docking.
Using a Förster-resonance energy transfer (FRET)-based detection system (FedEcs) to monitor nanoparticle cargo delivery to the brain

Nanotechnology holds great promise to improve delivery of therapeutics to the brain. Current experimental approaches are, however, hampered by the lack of tools to dynamically monitor cargo delivery in vivo. We developed highly fluorescent lipid nanodroplets (LNDs) that carry a Förster-resonance energy transfer (FRET)-based drug delivery detection system able to monitor cargo release (FedEcs) in vivo. We investigated the distribution, stability, and cargo release of FedEcs-LNDs in the healthy and ischemic mouse brain by intravital multiphoton microscopy. We dynamically observed that LNDs accumulated within cerebral microclots after ischemia, caused by magnetic nanoparticles (Nano-stroke), and released their cargo. Furthermore, the blood-brain barrier (BBB) became permeable at sites of microclots thereby allowing FedEcs-LNDs to cross the BBB and to deliver their cargo to the brain parenchyma. Consequently, FedEcs represents a novel tool to quantitatively investigate the nanocarriers biodistribution and cargo release using intravital microscopy and may thus tremendously ease their translational validation.
Synchronized photoactivation of T4K rhodopsin causes a chromophore-dependent retinal degeneration that is moderated by interaction with phototransduction cascade components

Multiple mutations in the Rhodopsin gene cause sector retinitis pigmentosa in humans and a corresponding light-exacerbated retinal degeneration (RD) in animal models. Previously we have shown that the rhodopsin mutation T4K requires photoactivation to exert its toxic effect. Here we further investigated the mechanisms involved in rod cell death caused by T4K rhodopsin in Xenopus laevis. In this model, RD was prevented by rearing animals in constant darkness but surprisingly also in constant light. RD was maximized by light cycles containing at least one hour of darkness and 20 minutes of light exposure, light of intensity 750 lux or greater, and by sudden light onset. Under conditions of frequent light cycling, RD occured rapidly and synchronously, with massive shedding of ROS fragments into the RPE initiated within hours, and subsequent death and phagocytosis of rod cell bodies. RD was minimized by reduced light levels, pre-treatment with constant light, and gradual light onset. RD was prevented by genetic ablation of the retinal isomerohydrolase RPE65, and exacerbated by ablation of phototransduction components GNAT1, SAG, and GRK1. Our results indicate that photoactivated T4K rhodopsin is toxic, that cell death requires synchronized photoactivation of T4K rhodopsin, and that toxicity is mitigated by interaction with other rod outer segment proteins regardless of whether they participate in activation or shutoff of phototransduction. In contrast, RD caused by P23H rhodopsin does not require photoactivation of the mutant protein, as it was exacerbated by RPE65 ablation, suggesting that these phenotypically similar disorders may benefit from different treatment strategies. Significance A large number of rhodopsin mutations are linked to the inherited degenerative disease retinitis pigmentosa. Although the end result in each case is the loss of photoreceptor cells and blindness, not all of these mutations cause cell death via the same mechanism. In order to design and test treatment therapies that target the disease at points as upstream as possible in the process, we require detailed understanding of the range and nature of these disease mechanisms. This study using a transgenic Xenopus laevis model has extended our understanding of how T4K rhodopsin and related mutations cause rod cell photoreceptor death via a phototoxic product, and how this mechanism differs from the more extensively researched protein misfolding mechanism underlying cell death caused by P23H rhodopsin.
Status, Quality, and Attention: What's in a (Missing) Name?

How much are we influenced by an author's identity when evaluating his or her work? This paper exploits a natural experiment to measure the impact of status signals in the context of open standards development. For a period of time, e-mails announcing new submissions to the Internet Engineering Task Force would replace individual author names with “et al.” if submission volumes were unusually high. We measure the impact of status signals by comparing the effect of obscuring high-versus low-status author names. Our results show that name-based signals can explain up to three-quarters of the difference in publication rates between high-and low-status authors. The signaling effect disappears for a set of prescreened proposals that receive more scrutiny than a typical submission, suggesting that status signals are more important when attention is scarce (or search costs high). We also show that submissions from high-status authors receive more attention on electronic discussion boards, which may help high-status authors to develop their ideas and bring them forward to publication. This paper was accepted by Jesper Sorensen, organizations.
Feature selection for classification of BGP anomalies using Bayesian models

Traffic anomalies in communication networks greatly degrade network performance. Early detection of such anomalies alleviates their effect on network performance. A number of approaches that involve traffic modeling, signal processing, and machine learning techniques have been employed to detect network traffic anomalies. In this paper, we develop various Naive Bayes (NB) classifiers for detecting the Internet anomalies using the Routing Information Base (RIB) of the Border Gateway Protocol (BGP). The classifiers are trained on the feature sets selected by various feature selection algorithms. We compare the Fisher, minimum redundancy maximum relevance (mRMR), extended/weighted/multi-class odds ratio (EORIWORIMOR), and class discriminating measure (CDM) feature selection algorithms. The odds ratio algorithms are extended to include continuous features. The classifiers that are trained based on the features selected by the WOR algorithm achieve the highest F-score.
Future trends in SoC interconnect

Self-timed packet-switched networks are poised to take a major role in addressing the complex system design and timing closure problems of future complex systems-on-chip. The robust, correct-by-construction characteristics of self-timed communications enables each IP block on the SoC to operate in its own isolated timing domain, greatly simplifying the problems of timing verification. Design automation software can remove the need for expertise in self-timed design, enabling the on-chip interconnect to be treated as an additional IP block within a conventional (synchronous) design flow. The paradigm shift from viewing the SoC design problem as a matter of organizing complex hierarchies of buses with multiple coupled timing domains, where every interface between timing domains must be verified carefully, to viewing the SoC as a problem in network design where those timing issues are automatically isolated, promises significant improvements in designer productivity, component reuse and SoC functionality.
O Uso de Simuladores no Ensino da Medicina

Este trabalho descreve o desenvolvimento de simulacao de casos clinicos em um ambiente computadorizado na web. O simulador foi inserido no Moodle e 18 alunos do 7o semestre de medicina realizaram a tarefa e responderam a um questionario validado para a avaliacao de softwares educativos com perguntas sobre: o aprendizado, a interatividade, motivacao, concentracao, atratividade, operacionalidade e autonomia. A experiencia foi considerada extremamente valida. O desafio e enorme no sentido de buscar alternativas de interatividade que estimulem e que simultaneamente abriguem conteudo, com um aprofundamento adequado ao objetivo do curso. As criticas na avaliacao servirao de base para ajustes nos proximos casos.
e-Government Systems Success and User Acceptance in Developing Countries: The Role of Perceived Support Quality

The concept of e-Government was first developed and implemented in industrialised countries. Consequently, it should not be assumed that this concept is automatically appropriate for developing countries. This study aims to examine the roles of perceived support quality and support satisfaction in the context of government-to-citizen (G2C) transactions towards behavioural intention and usage in developing countries. To test the proposed model, the official website of Kuwait Government Online Services (KGOS) was selected. A survey with a total of 628 responses was analysed using Structural Equation Modelling (SEM) t o investigate the goodness of fit of the model and the underlying indicators. The findings confirm the significance of support quality perceived by citizens and provide insights to explain citizens’ intention to use e-Government systems.
A New Indoor Positioning System Using Artificial Encoded Magnetic Fields

The demand for accurate indoor positioning continues to grow but the predominant positioning technologies such as Global Navigation Satellite Systems (GNSS) are not suitable for indoor environments due to multipath effects and Non-Line-Of-Sight (NLOS) conditions. This paper presents a new indoor positioning system using artificial encoded magnetic fields, which has good properties for NLOS conditions and fewer multipath effects. The encoded magnetic fields are generated by multiple beacons; each beacon periodically generates unique magnetic field sequences, which consist of a gold code sequence and a beacon location sequence. The position of an object can be determined with measurements from a tri-axial magnetometer using a three-step method: performing time synchronisation between sensor and beacons, identifying the beacon field and the beacon location, and estimating the position of the object. The results of the simulation and experiment show that the proposed system is capable of achieving Two-Dimensional (2D) and Three-Dimensional (3D) accuracy at sub-decimetre and decimetre levels, respectively.
Is biodiversity aging? Heuristic questions on the taxonomic diversity in the Phanerozoic

Received 02.05. 2018; accepted 12.06. 2018; printed 30.06. 2018; published online 05.07. 2018 The trend of numerosity component of diversity was analysed from palaeontological data, according to various time intervals and taxonomical ranks. Taxa numerosity of lower (with respect to family), with a nearly exponential increase, vs. higher ranks (from order to macrotaxon), with mainly a logarithmic trend, confirms to follow quite different patterns over time. This dataset seems to fit with a more assortative hypothesis, for higher taxa and a more divisive one for lower taxa. Then, a model was built to quantify the relative weight, over time, of the above hypothetical evolutionary components of various taxa ranks; such ranks were identified, in a palaeontological approach, by the maximum number of recorded taxa or, in a phyletic approach, by the time duration based on genetic data. The trends obtained by this model agree with observed records and the hypothesis, to be verified, of a quite different evolutionary origin of macro(phylum, class, order ranks) and micro-taxa (genus and species), during the transition between two main time phases: the first (evidently more assortative), mainly linked to the lateral sharing of characters, the second (evidently more divisive), mainly influenced by the ever growing morpho-physio-genetic isolation even for the protection of complex adaptations, as in the present, “modern species”.
FeatFSDA: Towards Few-shot Domain Adaptation for Video-based Activity Recognition

Domain adaptation is essential for activity recognition, as common spatiotemporal architectures risk overfitting due to increased parameters arising from the temporal dimension. Unsupervised domain adaptation methods have been extensively studied, yet, they require large-scale unlabeled data from the target domain. In this work, we address few-shot domain adaptation for video-based activity recognition (FSDA-AR) , which leverages a very small amount of labeled target videos to achieve effective adaptation. This setting is attractive and promising for applications, as it requires recording and labeling only a few, or even a single example per class in the target domain, which often includes activities that are rare yet crucial to recognize. We construct FSDA-AR benchmarks us-ing five established datasets: UCF101, HMDB51, EPIC-KITCHEN, Sims4Action, and Toyota Smart Home. Our results demonstrate that FSDA-AR performs comparably to unsupervised domain adaptation with significantly fewer (yet labeled) target examples. We further pro-pose a novel approach, FeatFSDA, to better leverage the few labeled target domain samples as knowledge guidance. FeatFSDA incorporates a latent space semantic adjacency loss, a domain prototypical similarity loss, and a graph-attentive-network-based edge dropout technique. Our approach achieves state-of-the-art performance on all datasets within our FSDA-AR benchmark. To encourage future research of few-shot domain adaptation for video-based activity recognition, we will release our benchmarks and code at FeatFSDA.
Madridge Journal of Bioinformatics and Systems Biology

By the start of cloud computing as well as its benefits the data owners submits their files to cloud service providers which allow the data users to download the searched files. For maintaining the privacy of the files andto have secure search over encrypted files. We have developed the privacy preserving keyword search for multiple data user’s incloud. There are many existing papers which supports the same privacy as well as the secure search by using the single owner model. But, utmost cloud servers in real time do not just support one data owner, as a substitute, the cloud server will maintain multiple data owners to enjoy the advantages provided by the cloud. In this research paper, we propose a protocol which will support the short and secure trapdoor to have the secure search over the cloud. By using our protocol even the cloud service providers will not able to find the corresponding search request as well as the data user trapdoor. We have developed the secure trapdoor phase to have the secure search over the cloud. We have developed the cloud matching phase to have the ranked results. We also have developed the decryption phase to decrypt the encrypted files. The performance evaluation and the security evaluation are presented to prove that our protocol is better in terms of computation complexity as well as the communication complexity.