Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian target optimisation for high-precision holographic optogenetics

Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision.
Aging drives cerebrovascular network remodeling and functional changes in the mouse brain

Aging is the largest risk factor for neurodegenerative disorders, and commonly associated with compromised cerebrovasculature and pericytes. However, we do not know how normal aging differentially impacts the vascular structure and function in different brain areas. Here we utilize mesoscale microscopy methods (serial two-photon tomography and light sheet microscopy) and in vivo imaging (wide-field optical spectroscopy and two-photon imaging) to determine detailed changes in aged cerebrovascular networks. Whole-brain vascular tracing showed an overall ∼10% decrease in vascular length and branching density with ∼7% increase in vascular radii in aged brains. Light sheet imaging with 3D immunolabeling revealed increased arteriole tortuosity of aged brains. Notably, vasculature and pericyte densities showed selective and significant reductions in the deep cortical layers, hippocampal network, and basal forebrain areas. We also found a significant increase in blood extravasation, implying compromised blood-brain barrier function in aged brains. Moreover, in vivo imaging in awake mice identified a reduction of baseline and on-demand blood oxygenation despite relatively intact neurovascular coupling. Collectively, we uncover regional vulnerabilities of cerebrovascular network and physiological changes that can mediate cognitive decline in normal aging. Highlight - Brain-wide mapping of vasculature and pericyte changes with normal aging - Simplified vascular network with tortuous vessels in aged brains - Vascular rarefication in the deep cortical layers, hippocampus, and the basal forebrain - Intact neurovascular coupling but lower blood oxygenation in aged animals
Hijacking of internal calcium dynamics by intracellularly residing rhodopsins

Rhodopsins are ubiquitous light-driven membrane proteins with diverse functions, including ion transport. Widely distributed, they are also coded in the genomes of giant viruses infecting phytoplankton where their function is not settled. We examined the properties of three type 1 viral channelrhodopsins (VCR1s), and, unexpectedly, found that VCR1s accumulate exclusively intracellularly, and, upon illumination, induced calcium release from intracellular IP3-dependent stores. In vivo, this light-induced calcium release was sufficient to remote control muscle contraction and behavior in VCR1-expressing tadpoles. VCR1s are the first rhodopsins shown to natively confer light-induced Ca2+ release, suggesting an original mechanism for reshaping the response to light of virus-infected algae. The ability of VCR1s to photorelease calcium without altering plasma membrane electrical properties marks them as precursors for novel optogenetics tools, with potential applications in basic research and medicine.
Neurobiology of Aging

A missense variant in the tetratricopeptide repeat domain 3 ( TTC3 ) gene (rs377155188, p.S1038C, NM_003316.4:c 0.3113C > G) was found to segregate with disease in a multigenerational family with late-onset Alzheimer’s disease. This variant was introduced into induced pluripotent stem cells (iPSCs) derived from a cognitively intact individual using CRISPR genome editing, and the resulting isogenic pair of iPSC lines was differentiated into cortical neurons. Transcriptome analysis showed an enrichment for genes involved in axon guidance, regulation of actin cytoskeleton, and GABAergic synapse. Functional analysis showed that the TTC3 p.S1038C iPSC-derived neuronal progenitor cells had altered 3-dimensional morphology and increased migration, while the corresponding neurons had longer neurites, increased branch points, and altered expression levels of synaptic proteins. Pharmacological treatment with small molecules that target the actin cytoskeleton could revert many of these cellular phenotypes, suggesting a central role for actin in mediating the cellular phenotypes associated with the TTC3 p.S1038C variant. © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
RNA Targets and Function of Topoisomerase 3β in Reducing Stress in 1 Neuronal Physiology 2

13 Topoisomerase 3β (Top3β) relaxes DNA and RNA and its dysregulation is implicated in various 14 neurodevelopmental and cognitive disorders. We developed a novel protocol to isolate RNA 15 targets of the topoisomerase without artificial cross-linking and demonstrated that 16 topoisomerase activity toward long RNAs is useful in cells where mRNAs travel over long 17 distances and require strict translational control. We show that such mRNAs depend on the 18 RNA topoisomerase activity for their proper expression and localization. Lack of the enzymatic 19 activity of Top3β stresses neurons, affecting the morphology of the neuromuscular junction 20 (NMJ) in larvae and adult flies, aging, and the coordinated movement of older flies. The 21 interaction network of Top3β links it to all levels of posttranscriptional RNA expression, 22 splicing, localization, translation, and RNA stability. This study provides insights into the 23 mechanisms underlying neurological diseases, including schizophrenia, autism spectrum 24 disorders, and Fragile X syndrome, diseases to which Top3β and many of its interactors, 25 particularly Tdrd3 and FMR1, have been linked.
ZIPPI: proteome-scale sequence-based evaluation of protein-protein interaction models

Predicting protein-protein interactions (PPI) is a challenging problem of central importance in fundamental biology. With the increasing number of available PPI prediction methods and databases, an effective evaluation model would be extremely valuable. Here we introduce ZIPPI (Z-score for Information about Protein-Protein Interfaces), which evaluates structural models of a complex based on sequence co-evolution and conservation involving residues that are in contact in the interface. The interface Z-score (ZIPPI score) is calculated by comparing metrics for interface contacts to metrics obtained from randomly chosen surface residues. Since contacting residues are defined by the structural model, this obviates the need of accounting for indirect interactions with methods such as Direct Coupling Analysis. Although ZIPPI relies on species-paired multiple sequence alignments, its focus on contacting interfacial residues and the avoidance of direct coupling methods makes it computationally efficient. The performance of ZIPPI is evaluated through applications to experimentally determined complexes from the Protein Data Bank (PDB) and to decoys from the Critical Assessment of PRedicted Interactions (CAPRI) experiment. We demonstrate how ZIPPI can be implemented on a genome-wide scale by calculating scores for millions of structural models of protein-protein interactions in the E. coli interactome as predicted by PrePPI. Many PrePPI predictions filtered by ZIPPI score are novel. In all, this proteome-scale method shows promising feasibility for applications to the full human protein interactome, which is not yet accessible to deep learning methods.
Genomic language model predicts protein co-regulation and function

Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we trained a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM’s unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.
Mapping the Computational Similarity of Individual Neurons within Large-scale Ensemble Recordings using the SIMNETS analysis framework

The expansion of large-scale neural recording capabilities has provided new opportunities to examine multi-scale cortical network activity at single neuron resolution. At the same time, the growing scale and complexity of these datasets introduce new conceptual and technical challenges beyond what can be addressed using traditional analysis techniques. Here, we present SIMNETS, a mathematically rigorous and efficient unsupervised relational analysis framework designed to generate intuitive, low-dimensional neuron maps that support a multi-scale view of the computational similarity (CS) relations among individual neurons. The critical innovation is the use of a novel measure of computational similarity that is based on comparing the intrinsic structure of latent spaces representing the spiking output of individual neurons. We use three publicly available neural population test datasets from the visual, motor, and hippocampal CA1 brain regions to validate the SIMNETS framework and demonstrate how it can be used to identify putative subnetworks (i.e., clusters of neurons with similar computational properties). Our analysis pipeline includes a novel statistical test designed to evaluate the likelihood of detecting spurious neuron clusters to validate network structure results. The SIMNETS framework can facilitate linking computational geometry representations across scales, from single neurons to subnetworks, within large-scale neural recording data.
Model Checking: Theories,Techniques and Applications

As computer hardware and software systems become more and more complex,how to assure the correctness and reliability of such system sbecomes an urgent problem. Among theories proposed as solutions to this problem, model checking has become a very attractive and appealing approach, because of its simplicity and high level of automation. Research on model checking covers the following subjects: modal/temporal logics,model checking algorithms,efficiency of model checking with respect to time and space(especially space complexity) ,and development of model checking tools.These aspects are closely related. Complexities of model checking algorithms vary very much for different modal/temporal logics, and optimizations are often targeted at certain types of logic formulas. Some new achievements and research directions are also discussed.
Production of bioethanol from sugarcane bagasse

The ever increasing dependence on fossil fuels, its environmental impacts and ecological degradations have led to relentless, nonstop, and intense search for renewable alternative energy sources, primarily aimed at controlling and eliminating the adverse environmental costs of energy generation from conventional fuels. Fossil fuels according to well sourced and collated statistical reports, contribute the largest quota to global energy generation due to abundant reserves across the globe. When energy is generated from fossil fuels, certain environmental pollutants chiefly gaseous are released into the environment. These gaseous substances constitute an effect known as greenhouse effects and thus are termed Green House Gases (GHG). A major breakthrough in the search for renewable alternative energy sources is the development of biofuels; which are simply conversion of liquid fuels directly from plant and animal materials. As an addition to the already existing body of knowledge, the scope of this study covers producing bioethanol from sugarcane bagasse, variation of certain process conditions and analysis of the potency of selected operational methods. The sugarcane bagasse which is a fibrous biomass residue of the sugarcane used for the analysis was preferred to other biomasses because of its non-competitiveness with food supply and / or availability. The bagasse was physically pretreated by drying and milling, hydrolyzed by acid and microbial methods (Aspergillus Niger). Fermentation was carried out at temperatures of 4 °C, 28 °C, and 37 °C for five days. The results of the
Social media marketing practices and consumer reactions: a content analysis on Facebook brand pages

Social media marketing (SMM) is becoming relevant in marketing communication strategies for successful firms in recent years. Even with the COVID-19 pandemic, social media allowed the companies to interact with the consumers and all people to stay in contact, even if they were far away. These digital tools are important for the consumer and firms due to the connection that can be created for sustaining competitive advantage from a marketing perspective. Companies invest in developing innovative communication solutions that create a relationship with consumers to understand their opinions and needs. The purpose of the paper is to analyze the role of SMM strategies to support communication processes that generate higher levels of engagement in order to stimulate greater consumer responses. The work uses a content analysis on a sample of four Facebook brand pages operating in the automotive industry (BMW, Honda, Mercedes-Benz, and Toyota). Regression analysis is used to test the proposed model.
Plant Diversity in Alpine Area of Kalatop-Khajjiar Wild life Sanctuary of District Chamba, Himachal Pradesh

A study was conducted to understand the plant diversity along an altitudinal gradient with elevations varying from 2400 – 2625 m above msl in alpine area of Dainkund in Kalatop-Khajjiar wild life sanctuary in district Chamba, Himachal Pradesh during 2010. Total number of plant species was 61 belonging to 34 families and 60 genera. The dominant families were Asteraceae, Rosaceae and Ranunculaceae. The number of shrub species at 2400 – 2625 m elevation was 10 with dominance of Vibernum erubescens . The number of herb species was 53 with the dominance of Erigeron multiradiatus . The distribution pattern of most of the plant species was contiguous in this region. Index of diversity for shrub and herb species was 1.719 and 3.200 whereas dominance index was 0.184 and 0.051 for 2400 – 2625 m elevation range respectively. Richness Index for shrub and herb species was 1.463 and 7.148 whereas evenness index was 0.746 and 0.805 for 2400 – 2625 m elevation range respectively. Out of 36 medicinal plant species recorded from the area, 3 species i.e. Aconitum heterophyllum, Podophyllum hexandrun and Taxus wallichiana fall in the category of threatened plants.
Statistical design, structural analysis, and in vitro susceptibility assay of antimicrobial peptoids to combat bacterial infections

Antimicrobial peptoids (ampetoids) have been established as a new and attractive class of antibiotic agent to combat bacterial infections with high bioavailability, strong metabolic stability, and low in vivo toxicity. Here, we reported successful design of short ampetoids by integrating chemometrics/cheminformatics techniques and in vitro susceptibility test. In the procedure, a novel panel of natural and N‐substituted amino acid descriptors was derived by principal component analysis of hundreds of constitutional, topological, geometrical, and physicochemical properties, which was then used to characterize and model the statistical regression relationship between the structural feature and antibacterial activity of known antimicrobial peptides. The resulting best quantitative structure‐activity relationship predictor confirmed by Monte Carlo cross validation was employed to perform high‐throughput virtual screening against a combinatorial ampeptod library, from which five ampeptod candidates were identified and their antibacterial potencies against two clinical bacterial strains, that is, Staphylococcus aureus ATCC33591 and Pseudomonas aeruginosa ATCC27853, were measured using a standard microtiter broth dilution method. Consequently, three ampeptoids (IK‐Nssb‐NLys‐VRK‐Nssb‐NH2, KYW‐NHis‐N‐Nspe‐RLR‐NH2, and NLys‐L‐NLys‐W‐Nsmb‐IKRW‐NH2) were found to have high antibacterial activity with minimum inhibitory concentration <30 µg/lm against the two strains. Molecular dynamics simulations revealed that two of the three potent ampeptoids can fold into a typical amphipathic helix that allows them to interact directly with and then destruct microbial membranes, while another is unstructured in water environment and possesses a hybrid sequence pattern mixing with hydrophobic and hydrophilic residues. Copyright © 2016 John Wiley & Sons, Ltd.
A Typical Slotted SIW Cavity-backed Antenna for Dual frequency operations in U-NII Bands

A low profile circular shaped cavity-backed substrate integrated waveguide (SIW) antenna with two-typical intersecting rectangular slots on the ground plane is designed to operate in the dominant TM010 mode at a frequency of 5.19 GHz in U-NII-1 band for wireless applications. The initially designed antenna produces a gain of 4 dBi with a narrow impedance bandwidth extending from 5.17 – 5.22 GHz. The antenna design is further modified by insertion of another shifted two-typical intersecting rectangular slots to finally resemble that of Hash shape; resulting in dual band antenna operation at 4.9 and 5.93 GHz. The gains obtained are 3.7 dBi and 1.4 dBi for 4.9 and 5.93 GHz respectively with an impedance bandwidth covering 4.88 - 4.92 GHz and 5.92 – 5.94 GHz respectively. The antenna prototype is fabricated using Arlon AD270 substrate material. Parametric studies are performed in terms of return loss and gain of the antenna. All simulations are carried out using HFSS v19.0 and show similar behavior to their experimentally measured counterparts.
An Exploration to Heritage Tourism in Chester: The Curious ‗Deva‘

— This paper deals with the heritage tourism management in Chester, a historic city in North West England. In spite of the Roman heritage of the Chester, the contemporary shopping facilities, and other modern amenities are criticised to evanescent the historical importance of the location. This qualitative research study focused on the visitor management, preferences, and tourism marketing criteria in the Chester to understand the current scenario of tourist’s preference in the place. The results indicate that the glorious Roman history, as well as the variety of heritage attraction, are the leading pull factor for the rising demand for Chester. Many of the visitors selected Chester Cathedral, Shopping, Historic attraction, and Zoo as their preferred attractions. Moreover, both inbound and outbound tourists visit the location, in which specific heritage tourists are females than males with peculiar ardors in the history, high education, income in their middle ages .
Workplace Ostracism, Employee Identity and Initiative Behavior – A Mediation Mechanism

Workplace ostracism has been one of the major issues of current world workplaces. It has been found to influence employees (its victims) negatively. Considering the value of ostracism, the current study aims to find its relationship with employee initiative taking behavior. It also covers the mediating role of employee identity. The study aims at achieving two objectives, where the first objective is to explore the level of prevalence of ostracism at workplace. Here the sample of the study comprised employees from public and private sector organizations and all in all 1429 responses were received. The findings revealed that the ostracism is present at both the workplace and employees face it on regular basis. The second objective was achieved by collecting data from 163 service sector employees. The statistical analysis revealed that the ostracism reduces employee initiative taking behavior and identity mediates the said relationship. Implications are provided on the basis of study results
Deep learning correction for image reconstruction in electrical impedance tomography using UNet model

This article was inspired by a similar Deep DBar algorithm, where a modified UNet convolutional model was used to correct the output of the DBar algorithm using the UNet model. However, instead of the DBar algorithm, another deterministic electrical impedance tomography reconstruction algorithm was used in this solution. The modified UNet model was used to successfully correct the initial reconstructions, which were computed using Kotre regularities using pseudo-inversion of the sensitivity matrix. Streszczenie. Ten artyku ł zosta ł
Improvement of Facilitation and Management Skills by Whole Systems Approach

The whole systems approach (WSA) is a generic term for varieties of methods such as Open Space Technology, Appreciative Inquiry, World Café and so on, which are commonly based on dialogue and attach great importance to communication among all stakeholders. It is widely introduced in the business scene for the purpose of organization reform, making innovative team and so on. The WSA has been introduced to reconsider the role of teacher and to introduce the rational educational policy intended to the PBL classes in NIT, Tsuruoka college. For this purpose, we held a workshop for the teachers in charge of the PBL classes. Through the workshop, the teachers could learn the essence of the WSA, focusing on “Being” aspect of facilitators and teachers. In the next step, they try to introduce its know-how in the classes. This activity apparently improves the teacher’s approach to the classes and quality of communications among teachers and students.
Voice Recognition and Document Classification-Based Data Analysis for Voice Phishing Detection

Phishing crime has become a serious issueworldwide. Damagescaused by phishing have been increasing continuously ever since the first phishing attacks occurred. Voice phishing, in which criminals impersonate financial institutions over the telephonein order to damage consumers, account for the majority of such attacks.This study aimed to convert phishing sound source files to text files through voice recognition and to classify and evaluate whether such texts can be judged as voice phishing. From the proposed methodology, it was confirmed that the Doc2Vec embedding method and the similarity determination method performed better than the methods used in previous studies. Through this, the proposed methodology confirmed that voice phishing can be judged by document data that are textualized by voice recognition for voice phishing sound sources.