Miracle-dz/UniFAR-FTUs huggingface dataset:
Piperazine Derivative Stabilizes Actin Filaments in Primary Fibroblasts and Binds G-Actin In Silico

Alzheimer’s disease (AD) is characterized by synaptic dysfunction, which is expressed through the loss of dendritic spines and changes in their morphology. Pharmacological compounds that are able to protect spines in the AD brain are suggested to be novel drugs that would be able to slow down the disease progression. We have recently shown that a positive modulator of transient receptor potential cation channel subfamily C member 6 (TRPC6), the compound N-(2-chlorophenyl)-2-(4-phenylpiperazine-1-yl) acetamide (51164), causes the upregulation of postsynaptic neuronal store-operated calcium entry, maintains mushroom spine percentage, and recovers synaptic plasticity in amyloidogenic mouse models of Alzheimer’s disease. Here, using confocal microscopy and calcium imaging methods, we present the experimental data indicating that 51164 possesses an alternative mechanism of action. We demonstrated that 51164 can increase the mushroom spine percentage in neurons with the downregulated activity of TRPC6-dependent neuronal store-operated calcium entry. Moreover, we report the binding of 51164 to G-actin in silico. We observed that 51164 interacts with Lys 336, Asp157, and Ser14 of G-actin, amino acids involved in the stabilization/polymerization of the G-actin structure. We showed that interactions of 51164 with G-actin are much stronger in comparison to the well-characterized F-actin stabilizing and polymerizing drug, jasplakinolide. The obtained results suggest an alternative protective mechanism of 51164 that is related to the preservation of actin filaments in vitro.
Oral Health Knowledge, Attitudes and Practices of People Living with Diabetes in South Asia: A Scoping Review

Diabetes increases the risk of oral health complications. This review aimed to synthesise the current evidence on the oral health knowledge, attitudes and practices of people living with diabetes in South Asian countries and provide recommendations on possible strategies for addressing the gaps in oral health care in this population, including the role of non-dental professionals. Using a scoping review framework, six electronic databases (Ovid Medline, CINAHL, ProQuest Central, Scopus, Web of Science and Embase) were searched to identify the relevant literature published between January 2000 and December 2021. The data were extracted into three main categories based on the review’s aims, and further refined into sub-categories. A total of 23 studies were included. The review identified that while people with diabetes living in South Asian countries had some level of awareness about oral health and limited care practices to maintain good oral health, there were gaps in knowledge, and there were areas where their oral health practices and attitudes could be improved. The findings suggest a need for developing targeted oral health policies as well as implementing integrated oral health care interventions involving non-dental professionals to improve the oral health outcomes of people with diabetes.
The deformed M2-brane

The superembedding formalism is used to study correction terms to the dynamics of the M2 brane in a flat background. This is done by deforming the standard embedding constraint. It is shown rigorously that the first such correction occurs at dimension four. Cohomological techniques are used to determine this correction explicitly. The action is derived to quadratic order in fermions, and the modified κ-symmetry transformations are given.
Bayesian modeling of risks of various origin

Background. Financial as well as many other types of risks are inherent to all types of human activities. The problem isto construct adequate mathematical description for the formal representation of risks selected and to use it for possibleloss estimation and forecasting. The loss estimation can be based upon processing available data and relevant expertestimates characterizing history and current state of the processes considered. An appropriate instrumentation for mod-elling and estimating risks of possible losses provides probabilistic approach including Bayesian techniques known todayas Bayesian programming methodology.Objective. The purpose of the paper is to perform overview of some Bayesian data processing methods providing apossibility for constructing models of financial risks selected. To use statistical data to develop a new model of Bayesiantype so that to describe formally operational risk that can occur in the information processing procedures.Methods. The methods used for data processing and model constructing refer to Bayesian programming methodology.Also Bayes theorem was directly applied to operational risk assessment in its formulation for discrete events and discreteparameters.Results. The proposed approach to modelling was applied to building a model of operational risk associated with in-correct information processing. To construct and apply the model to risk estimation the risk problem was analysed,appropriate variables were selected, and prior conditional probabilities were estimated. Functioning of the models con-structed was demonstrated with illustrative examples.Conclusions. Modelling and estimating financial and other type of risks is important practical problem that can besolved using the methodology of Bayesian programming providing the possibility for identification and taking intoconsideration uncertainties of data and expert estimates. The risk model constructed with the methodology proposedillustrates the possibilities of applying the Bayesian methods to solving the risk estimation problems
Status of soil available nickel (Ni) in different districts of eastern Uttar Pradesh, India

Limited information is available regarding status of available Ni in Indian soil and almost no literatures available on Ni deficient soils. Hence, the present investigation was undertaken to generate database on Ni content in soil and identifying the potential Ni deficit areas. Plant available Ni in different districts soils was assessed by analyzing 526 surface soil samples from Varanasi, 684 from Mirzapur, 523 from Sant Ravidas Nagar and 558 from Chandauli district. The soil samples were analysed for soil reaction (pH), EC, organic carbon and DTPA extractable Ni. All the blocks of Varanasi were high in Ni and its content ranged from 0.03–20.88 mg kg-1. In Mirzapur district, overall 7.16% soil samples were Ni deficit, however at block level, highest deficiency was observed in Sikhar (31.67%) followed by Kon (27.78%) and Majhawa (24.32%). All the blocks of Sant Ravidas Nagar were high in nutrient index rating with variation in Ni content from 0.01–3.31 mg kg-1. Among the four districts, lowest number of Ni deficient soils (1.79 %) were found in Chandauli district with high nutrient index rating. While Mirzapur district had highest Ni deficit soil (7.16%) followed by Sant Ravidas Nagar (4.02%), however, potential threat of Ni deficiency was the highest in Sant Ravidas Nagar (22%) followed by Mirzapur (19%).
Effectiveness of Feedback Based on Log File Analysis in Introductory Programming Courses

Especially in large courses, feedback is often given only on the final results; less attention is paid to the programming process. Today, however, some programming environments, e.g., Thonny, log activities during programming and have the functionality of replaying the programming process. This information can be used to provide feedback, and this knowledge can be integrated into practical sessions in the classroom. This study aimed to analyse how feedback based on logs affects exam results, task completion time, the number of runs, error messages, and pastes (of the whole group and beginners and non-beginners separately). An experiment was conducted in 2020 and 2021 in the course “Introduction to Programming”. Some groups received additional feedback on homework throughout the course based on log information; the remaining groups worked as usual. Based on the information received from the logs, general recommendations were also offered in the practical sessions. Our study showed that feedback based on logs improved mainly exam test results and programming task solving time among beginners. Therefore, it would be a good method to use, especially in beginner groups.
Effects of Pollen Sources on Fruit Set and Fruit Characteristics of ‘Fengtangli’ Plum (Prunus salicina Lindl.) Based on Microscopic and Transcriptomic Analysis

Adequate yield and fruit quality are required in commercial plum production. The pollen source has been shown to influence fruit set and fruit characteristics. In this study, ‘Siyueli’, ‘Fenghuangli’ and ‘Yinhongli’ were used as pollinizers of ‘Fengtangli’ plum. Additionally, self-pollination, mixed pollination, and open pollination were performed. We characterized the differences in pollen tube growth, fruit set and fruit quality among pollination combinations. ‘Fengtangli’ flowers pollinated by ‘Fenghuangli’ had more pistils with pollen tubes penetrating the ovary and the highest fruit set rate, while the lowest fruit set rate was obtained from self-pollination. In self-pollinated flowers, 33% of pistils had at least one pollen tube reaching the ovary, implying that ‘Fengtangli’ is partially self-compatible. Pollen sources affected ‘Fengtangli’ fruit size, weight, pulp thickness, soluble solids, and sugar content. Transcriptome analysis of ‘Siyueli’-pollinated and ‘Yinhongli’-pollinated fruits revealed 2762 and 1018 differentially expressed genes (DEGs) involved in the response to different pollen sources. DEGs were enriched in plant hormone signal transduction, starch and sucrose metabolism, and MAPK signaling pathways. Our findings provide a reference for the selection of suitable pollinizers for ‘Fengtangli’ plum and promote future research on the metaxenia effect at the molecular level.
Dissecting the Many Faces of Frontotemporal Dementia: An Imaging Perspective

Frontotemporal dementia (FTD) is a heterogeneous clinical and neuropathological disorder characterized by behavioral abnormalities, executive dysfunctions and language deficits. FTD encompasses a wide range of different pathological entities, associated with the accumulation of proteins, such as tau and TPD-43. A family history of dementia is found in one third of cases, and several genes causing autosomal dominant inherited disease have been identified. The clinical symptoms are preceded by a prodromal phase, which has been mainly studied in cases carrying pathogenetic mutations. New experimental strategies are emerging, in both prodromal and clinical settings, and outcome markers are needed to test their efficacy. In this complex context, in the last few years, advanced neuroimaging techniques have allowed a better characterization of FTD, supporting clinical diagnosis, improving the comprehension of genetic heterogeneity and the earliest stages of the disease, contributing to a more detailed classification of underlying proteinopathies, and developing new outcome markers on clinical grounds. In this review, we briefly discuss the contribution of brain imaging and the most recent techniques in deciphering the different aspects of FTD.
Non-Commutative Field Theory on S4

In a previous paper (hep-th/0402010) we proposed a matrix configuration for a non-commutative S 4 (NC4S) and constructed a non-commutative (star) product for field theories on NC4S. In the present paper we will show that any matrix can be expanded in terms of the matrix configuration representing NC4S just like any matrix can be expanded into symmetrized products of the matrix configuration for non-commutative S 2 . Then a scalar field theory on NC4S is constructed. Our matrix configuration describes two S 4 joined at the circle and the Matrix theory action contains a projection matrix inside the trace to restrict the space of matrices to that for one S 4 .
The role of lake basin history on palynological records from the Upper Kolyma region (northeastern Siberia)

Abstract Paleoecological and modern studies at Priyatnoye Lake, which is located within an intermontane depression in the interior of northeastern Siberia, indicate a similar paleovegetation record as has been documented for nearby mountain valleys, but a history of basin stability and instability that is uncharacteristic of the valley lakes. Analyses of a 385-cm-long core from the western basin of Priyatnoye Lake shows that sediment accumulation began in late Marine Oxygen Isotope Stage 3 (MIS 3), followed by a hiatus during MIS 2, and then continuous accumulation over the past ca. 14,000 cal yr BP. The eastern basin of the lake has a sediment thickness of ~35 cm, suggesting that it intermittently contained water and/or is younger than the western basin. A drop in lake levels between AD 2005 and AD 2009 resulted in the formation of two distinct lakes. This change was caused by the melting of underlying ice wedges and the formation of sinkholes through which the lake water drained. Although the northern coastal lowlands have been the geographic focus of permafrost global warming research, the Priyatnoye study draws attention to the intermontane depressions in northeastern Siberia. While less extensive, these depressions contain organic-rich deposits, are underlain by permafrost, and have the potential to affect future carbon budgets as global temperatures rise and permafrost melts.
Exact renormalization group flow equations for nonrelativistic fermions: Scaling toward the Fermi surface

We construct exact functional renormalization group (RG) flow equations for nonrelativistic fermions in arbitrary dimensions, taking into account not only mode elimination, but also the rescaling of the momenta, frequencies, and fermionic fields. The complete RG flow of all relevant, marginal, and irrelevant couplings can be described by a system of coupled flow equations for the irreducible n-point vertices. Introducing suitable dimensionless variables, we obtain flow equations for generalized scaling functions which are continuous functions of the flow parameter, even if we consider quantities which are dominated by momenta close to the Fermi surface, such as the density-density correlation function at long wavelengths. We also show how the problem of constructing the renormalized Fermi surface can be reduced to the problem of finding the RG fixed point of the irreducible two-point vertex at vanishing momentum and frequency. We argue that only if the degrees of freedom are properly rescaled is it possible to reach scale-invariant non-Fermi-liquid fixed points within a truncation of the exact RG flow equations.
AKRUAL: Jurnal Akuntansi

The auditor change issue is getting attention from scholarly. Nonetheless, the studies that analyzed the grade (size) change of accounting firms are still rare. This study analyzes the nonfinancial and financial factors that affect accounting firms switching, namely upgrade, samegrade, or downgrade. Non-financial factors are proxied by managerial ownership and management turnover, while financial factors are proxied by profitability and financial distress. The population was manufacturing companies on the Indonesia Stock Exchange in 2017-2019. The research sample was selected using the purposive sampling method, and the total sample was 93 company units. The analytical tool is multinomial logistic regression analysis. The results show that managerial ownership does not affect the three types of turnover. Meanwhile, the management turnover variable positively affects the upgrade, samegrade, and downgrade. In terms of financial factors, profitability does not affect upgrade and downgrade accounting firms of switching. Nevertheless, profitability has a positive effect on the samegrade type of switching. Hypotheses testing of financial distress revealed that this variable could not influence the three types of accounting firm switching. These results indicate that managerial ownership and financial distress variables do not affect any auditor turnover at all. However, the management turnover and profitability variables have a different effects for each type of turnover.
Unimodal Likelihood Models for Ordinal Data

Ordinal regression (OR) is the classiﬁcation of ordinal data, in which the underlying target variable is categorical and considered to have a natural ordinal relation for the explanatory variables. In this study, we suppose the unimodality of the conditional probability distribution of the target variable given a value of the explanatory variables as a natural ordinal relation of the ordinal data. Under this supposition, unimodal likelihood models are considered to be promising for achieving good generalization performance in OR tasks. Demonstrating that previous unimodal likelihood models have a weak representation ability, we thus develop more representable unimodal likelihood models, including the most repre-sentable one. OR experiments in this study showed that the developed more representable unimodal likelihood models could yield better generalization performance for real-world ordinal data compared with previous unimodal likelihood models and popular statistical OR models having no unimodality guarantee.
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts

Training machine learning models robust to distribution shifts is critical for real-world applications. Some robust training algorithms (e.g., Group DRO) specialize to group shifts and require group information on all training points. Other methods (e.g., CVaR DRO) that do not need group annotations can be overly conservative, since they naively upweight high loss points which may form a contrived set that does not correspond to any meaningful group in the real world (e.g., when the high loss points are randomly mislabeled training points). In this work, we address limitations in prior approaches by assuming a more nuanced form of group shift: conditioned on the label, we assume that the true group function (indicator over group) is simple. For example, we may expect that group shifts occur along low bitrate features (e.g., image background, lighting). Thus, we aim to learn a model that maintains high accuracy on simple group functions realized by these low bitrate features, that need not spend valuable model capacity achieving high accuracy on contrived groups of examples. Based on this, we consider the two-player game formulation of DRO where the adversary's capacity is bitrate-constrained. Our resulting practical algorithm, Bitrate-Constrained DRO (BR-DRO), does not require group information on training samples yet matches the performance of Group DRO on datasets that have training group annotations and that of CVaR DRO on long-tailed distributions. Our theoretical analysis reveals that in some settings BR-DRO objective can provably yield statistically efficient and less conservative solutions than unconstrained CVaR DRO.
SG-Drop: Faster Skip-Gram by Dropping Context Words 1

Many natural language processing (NLP) models utilize pre-trained word embeddings to leverage latent information. One of the most successful word embedding model is the Skip-gram (SG). In this paper, we propose a Skip-gram drop (SG-Drop) model, which is a variation of the SG model. The SG-Drop model is designed to reduce training time efficiently. Furthermore, the SG-Drop allows controlling training time with its hyperparameter. It could train word embedding faster than reducing training epochs while better preserving the quality.
Gowdy S1(X)S2 and S3 inhomogeneous cosmological models

The ADM-type Hamiltonian formulation of Gowdy (1971, 1974, 1975) S1(X)S2 and S3 vacuum inhomogeneous cosmological models is given. The canonical and constraint equations are explicitly solved and the asymptotic behaviour of the most general solution in the neighbourhood of the initial and final inhomogeneous scalar singularities is shown to be of the generalised Kasner type.
ER-01-2019-0041_proof 686..701

Purpose – The purpose of this paper is to advance a conceptualization of sustainable HRM that builds on scholarship focusing on the pluralistic nature of human resource management. The paper seeks to advance the promise of sustainable HRM as an alternative to HRM scholarship that adopts a unitarist frame of reference. Design/methodology/approach – The paper draws on a variety of HRM-related literatures to offer new insights about what a pluralist perspective on sustainable HRM from the perspective of employees would look like and what it would accomplish, and in so doing allow sustainable HRM to become socially sustainable. Findings – Taking a pluralistic perspective is essential for making the concept of sustainable HRM more distinct and robust. Sustainable HRM can offer a challenge to the dominant unitarist perspective on the employment relationship, focusing the attention of researchers on the extent to which employment practices benefit both employers and employees while contributing to social sustainability outside of the employment context. Originality/value – This paper adds analyses of pluralism and unitarism to the current literature on sustainable HRM while also focusing attention on how sustainable HRM might be more robustly conceptualized and also more normative in its orientation.
Efficient method for calculating Raman spectra of solids with impurities and alloys and its application to two-dimensional transition metal dichalcogenides

Raman spectroscopy is a widely used, powerful, and nondestructive tool for studying the vibrational properties of bulk and low-dimensional materials. Raman spectra can be simulated using first-principles methods, but due to the high computational cost calculations are usually limited only to fairly small unit cells, which makes it difficult to carry out simulations for alloys and defects. Here, we develop an efficient method for simulating Raman spectra of alloys, benchmark it against full density-functional theory calculations, and apply it to several alloys of two-dimensional transition metal dichalcogenides. In this method, the Raman tensor for the supercell mode is constructed by summing up the Raman tensors of the pristine system weighted by the projections of the supercell vibrational modes to those of the pristine system. This approach is not limited to 2D materials and should be applicable to any crystalline solids with defects and impurities. To efficiently evaluate vibrational modes of very large supercells, we adopt mass approximation, although it is limited to chemically and structurally similar atomic substitutions. To benchmark our method, we first apply it to Mo$_x$W$_{(1-x)}$S$_2$ monolayer in the H-phase, where several experimental reports are available for comparison. Second, we consider Mo$_x$W$_{(1-x)}$Te$_2$ in the T'-phase, which has been proposed to be 2D topological insulator, but where experimental results for the monolayer alloy are still missing. We show that the projection scheme also provides a powerful tool for analyzing the origin of the alloy Raman-active modes in terms of the parent system eigenmodes. Finally, we examine the trends in characteristic Raman signatures for dilute concentrations of impurities in MoS$_2$.