Miracle-dz/UniFAR-FTUs huggingface dataset:
Screening and introduction of key cell cycle microRNAs deregulated in colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men worldwide. Impaired cell cycle regulation leads to many cancers and is also approved in CRC. Therefore, cell cycle regulation is a critical therapeutic target for CRC. Furthermore, miRNAs have been discovered as regulators in a variety of cancer‐related pathways. This study is designed to investigate how miRNAs and mRNAs interact to regulate the cell cycle in CRC patients. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Expression Omnibus (GEO), and Therapeutic Target Database (TTD), cell cycle‐associated genes were identified and evaluated. Seven of the 22 differentially expressed genes (DEGs) implicated in the cell cycle in three GSEs (GSE24514, GSE10950, and GSE74604) were identified as potential therapeutic targets. Then, using PyRx software, we performed docking proteins with selected drugs. The results demonstrated that these drugs are appropriate molecules for targeting cell cycle DEGs. Tarbase, miRTarbase, miRDIP, and miRCancer databases were used to find miRNAs that target the indicated genes. The ability of these six miRNAs to impact the cell cycle in colorectal cancer may be concluded. These miRNAs were found to be downregulated in SW480 cells when compared to the normal tissue. Our data imply that a precise selection of bioinformatics tools can facilitate the identification of miRNAs that impact mRNA translation at different stages of the cell cycle. The candidates can be investigated more as targets for cell cycle arrest in cancers.
Microvascular and neural alterations in carotid cavernous fistulas: An optical coherence tomography angiography study

Current modalities for diagnosing carotid cavernous fistula (CCF) are inaccurate in analysing retinal microcirculations and nerve fibre changes. Retinal microvascular and neural alterations occur in CCF patients and can be quantitatively measured using optical coherence tomography angiography (OCTA). We measured the neurovascular changes in the eyes of CCF patients and used OCTA as a supplementary method.
A Simple Constraint-solving Decision Procedure for Protocols with Exclusive or

We present a procedure for deciding security of protocols employing the Exclusive or operator. This procedure relies on a direct combination of a constraint solver for security protocol with a unification algorithm for the exclusive-or theory. Hence compared to the previous ones it is much simpler and easily amenable to automation. The principle of the approach can be applied to other theories too.
Complex plumages spur rapid color diversification in kingfishers (Aves: Alcedinidae)

Colorful signals in nature provide some of the most stunning examples of rapid phenotypic evolution. Yet, studying color pattern evolution has been historically difficult owing to differences in perceptual ability of humans and analytical challenges with studying how complex color patterns evolve. Island systems provide a natural laboratory for testing hypotheses about the direction and magnitude of phenotypic change. A recent study found that plumage colors of island species are darker and less complex than continental species. Whether such shifts in plumage complexity are associated with increased rates of color evolution remains unknown. Here, we use geometric morphometric techniques to test the hypothesis that plumage complexity and insularity interact to influence color diversity in a species-rich clade of colorful birds—kingfishers (Aves: Alcedinidae). In particular, we test three predictions: (1) plumage complexity enhances interspecific rates of color evolution, (2) plumage complexity is lower on islands, and (3) rates of plumage color evolution are higher on islands. Our results show that more complex plumages result in more diverse colors among species and that island species have higher rates of color evolution. Importantly, we found that island species did not have more complex plumages than their continental relatives. Thus, complexity may be a key innovation that facilitates evolutionary response of individual color patches to distinct selection pressures on islands, rather than being a direct target of selection itself. This study demonstrates how a truly multivariate treatment of color data can reveal evolutionary patterns that might otherwise go unnoticed.
European Committee on Antimicrobial Susceptibility Testing-Recommended Rapid Antimicrobial Susceptibility Testing of Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus From Positive Blood Culture Bottles

Background Early diagnosis and treatment are important for a good prognosis of bloodstream infections. The European Committee on Antimicrobial Susceptibility Testing (EUCAST) recommends rapid antimicrobial susceptibility testing (RAST) based on the disk diffusion methodology for 4, 6, and 8 hours of incubation. We evaluated EUCAST-RAST of Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus from positive blood culture bottles. Methods Twenty strains of E. coli, K. pneumoniae, and S. aureus were tested using EUCAST-RAST. Ten antimicrobial agents against E. coli and K. pneumoniae and four agents against S. aureus were tested. The diameter of the inhibition zone (mm) was compared with the minimal inhibitory concentration (μg/mL) obtained using the Sensititre AST system (TREK Diagnostic Systems, East Grinstead, UK). Results For E. coli, the percentage of total categorical agreement (CA) was 69.5% at 4 hours, and 87% at 8 hours. For K. pneumoniae, the total CA was 89% at 4 hours, and 95.5% at 6 hours. For S. aureus, the total CA was 100% after 4 hours. Discrepancies were observed mainly for E. coli with β-lactam antimicrobial agents, and the numbers of errors decreased over time. Conclusions EUCAST-RAST for K. pneumoniae and S. aureus met the United States Food and Drug Administration criteria at 6 and 4 hours, respectively, whereas that for E. coli did not meet the criteria for up to 8 hours. RAST can shorten the turn-around testing time by more than one day; therefore, if applied accurately according to laboratory conditions, antimicrobial agent results can be reported faster.
A TLR4 agonist liposome formulation effectively stimulates innate immunity and enhances protection from bacterial infection

Stimulation of innate immunity can protect against infectious insult and could be used in combination with other therapies. Since antibiotic resistance is an increasing concern, strategies to reduce the dose or eliminate the need for these drugs are warranted. Lipo-CRX is a formulation in which the TLR4 agonist CRX-527 is incorporated into lipid membranes in liposomes. Lipo-CRX is less inflammatory than either CRX-527 or LPS, but retains unique capacity to enhance host defense responses. We compared lipo-CRX to other agonists in vitro using mammalian cells and in vivo in mice, and assessed indicators of innate immune responses and protection from bacterial infection. Lipo-CRX is similar to E. coli LPS in its capacity to activate bovine γδ T cells and to recruit neutrophils into mouse lungs, but with less reactivity in the LAL assay. However, lipo-CRX uniquely induced the production of systemic innate immune cytokines. In the mouse model of brucellosis, delivery of lipo-CRX to the lungs reduced the dissemination of B. abortus. While lipo-CRX or the antibiotic ampicillin alone did not alter B. abortus burdens in the lung, the combination had a synergistic beneficial effect. Our data suggest that stimulating the innate immune system with lipo-CRX, either alone or when combined with antibiotics, can enhance bacterial clearance in the mouse model of brucellosis.
A Finite-Element Model of the Atmospheric Boundary Layer Suitable for Use with Numerical Weather Prediction Models

Abstract We give a detailed description of an atmospheric boundary layer model capable of simulating the diurnal cycles of wind, temperature and humidity. The model includes a formulation of various physical processes (radiative effects, variation of soil surface temperature and humidity, etc.) and uses a first-order closure for turbulent fluxes that relies upon a time-dependent equation for turbulent kinetic energy and on a mixing length governed by a relaxation process. The exchange processes taking place in the surface layer are dealt with in a separate micrometeorological module. The one-dimensional model uses a Galerkin technique based on linear finite elements, variable resolution in the vertical, and a time discretization of the Crank-Nicholson type. A simulation test based on day 33 of the Wangara Australian experiment indicates that the model, despite its relative simplicity, gives realistic results that compare favorably with those from higher order models while taking much less space and time o.
Wavelet-Based Density Estimation in Presence of Additive Noise under Various Dependence Structures

We study the following model: . The aim is to estimate the distribution of X when only are observed. In the classical model, the distribution of is assumed to be known, and this is often considered as an important drawback of this simple model. Indeed, in most practical applications, the distribution of the errors cannot be perfectly known. In this paper, the author will construct wavelet estimators and analyze their asymptotic mean integrated squared error for additive noise models under certain dependent conditions, the strong mixing case, the β-mixing case and the ρ-mixing case. Under mild conditions on the family of wavelets, the estimator is shown to be -consistent and fast rates of convergence have been established.
EVOLVING CASCADE NEURAL NETWORK BASED ON MULTIDIMESNIONAL EPANECHNIKOV'S KERNELS AND ITS LEARNING ALGORITHM

At present time neural networks based on Group Method of Data Handling (GMDH-NN), nodes of which are two-input N-Adalines, is well-known. Each of N-Adalines contains the set of adjustable synaptic weights that are estimated using standard least squares method and provides quadratic approximation of restoring nonlinear mapping. On the other hand, for needed approximation quality ensuring this NN can require considerable number of hidden layers. Approximating properties of GMDH-NN can be improved by uniting the approaches based on Group Method of Data Handling and Radial-Basis-Functions Networks that have only one hidden layer, formed by, so-called, R-neurons. Such networks learning reduces, as a rule, to the tuning of synaptic weights of output layer that are formed by adaptive linear associators. In contrast to neurons of multilayer structures with polynomial or sigmoidal activation functions R-neurons have bell-shaped activation functions. In this paper as activation functions multidimensional Epanechnikov's kernels are used. The advantage of activation function is that its derivatives are linear according all the parameters that allows to adjust sufficiently simply not only synaptic weights but also centers with receptive fields. Proposed network combines Group Method of Data Handling, Radial-Basis-Functions Networks and cascade networks and isn't inclined to the "curse of dimensionality", is able to real time mode information processing by adapting its parameters and structure to problem conditions. The multidimensional Epanechnikov's kernels were used as activation functions, that allowed to introduce numerically simple learning algorithms, which are characterized by high speed.
Foreign Direct Investment in Neoclassical Theory of International Trade: A Conceptual Weak Spot

Abstract This article analyses the role of foreign direct investment (FDI) in neoclassical theory of trade with a focus on the relocation of production into low wage countries. The conceptual analysis shows that FDI not only constitutes a weak spot in comparative advantage theory, but also that the latter is incompatible with efficiency-seeking FDI—with significant implications for policy. In Ricardian and Heckscher-Ohlin models of trade, FDI is ruled out via the assumption of factor immobility. In models in which factor immobility is relaxed, the closest FDI comes to is a generic reference to capital imports and exports, which affect factor endowments and, therefore, comparative advantage. Due to the assumption that input factors are determined by wage-rental ratios, this leads to the condition that firms operating in advanced economies must produce more labor-intensively, if investing in facilities in low-wage countries. Efficiency and market-seeking FDI, which combines productive, capital-intensive modes of production with cheap labor to exploit unit labor cost differentials, is a theoretical and mathematical impossibility in this framework. This conceptual weakness questions the validity of conventional approaches to development policy, which largely rely on market liberalization and free capital mobility.
Developmental Couple Therapy for Complex Trauma Survivors: A detailed case study of a manualized, time-limited, psychoanalytically informed treatment

ABSTRACT Many couples in distress encounter significant barriers to accessing psychoanalytic treatments, which leaves psychoanalysis far out of the reach of many of those who would benefit from its rich and deep clinical processes. This paper will briefly outline Developmental Couple Therapy for Complex Trauma (DCTCT). DCTCT is a manualized, evidence-based, time-limited therapy built upon the psychoanalytic foundations of attachment, emotion regulation, mentalizing, and repetition-enactment. This four-stage model integrates psychoeducation about psychoanalytic concepts such as attachment, emotion regulation, and mentalizing, to help couples learn about these developmental self-capacities prior to a second stage of explicit skills building, a third stage of deeper analytic working through of traumatic memories, challenges with sexuality, and developing attachment security prior to a fourth stage of consolidation. The case of Cheryl and Cat will illustrate the process of DCTCT and show some of the advantages specific to couple therapy.
Joint Latent Space and Label Inference Estimation with Adaptive Fused Data and Label Graphs

Recently, structured computing has become an interesting topic in the world of artificial intelligence, especially in the field of machine learning, as most researchers focus on the development of graph-based semi-supervised learning models. In this article, we present a new framework for graph-based semi-supervised learning. We present a powerful method for simultaneous label inference and linear transform estimation. The targeted linear transformation is used to obtain a discriminant subspace. To improve semi-supervised learning, our framework focuses on exploiting the data structure and soft labels of the available unlabeled samples. In the iterative optimization scheme used, the prior estimation of the label increases the supervision information indirectly through an introduced informative matrix called the label graph, thus avoiding the use of hard confidence-based decisions as used in self-supervised methods. In addition, the estimation of labels and projected data is made more robust by using smoothing concepts based on hybrid graphs. For each type of smoothing, the hybrid graph is an adaptive fusion of the two graphs encoding the similarity of the data and the similarity of the labels. The proposed method leads to an improved discriminative linear transformation. Several experimental results on real image datasets confirm the effectiveness of the proposed method. They also show superior performance compared to semi-supervised methods that use integration and label inference simultaneously.
Data Provenance in Security and Privacy

Provenance information corresponds to essential metadata that describes the entities, users, and processes involved in the history and evolution of a data object. The benefits of tracking provenance information have been widely understood in a variety of domains; however, only recently have provenance solutions gained interest in the security community. Indeed, on the one hand, provenance allows for a reliable historical analysis enabling security-related applications such as forensic analysis and attribution of malicious activity. On the other hand, the unprecedented changes in the threat landscape place demands for securing provenance information to facilitate its trustworthiness. With the recent growth of provenance studies in security, in this work we examine the role of data provenance in security and privacy. To set this work in context, we outline fundamental principles and models of data provenance and explore how the existing studies achieve security principles. We further review the existing schemes for securing data provenance collection and manipulation known as secure provenance and the role of data provenance for security and privacy, which we refer to as threat provenance.
OPTIMAL-EM: Optimised Population Sourcing for Web Accessibility Evaluation

This paper presents a prototypical accessibility tool and associated framework. Our tool supports a framework of six metrics: coverage, representativeness, complexity, popularity, freshness and accessibility, designed to optimise the web accessibility evaluation process through representative pages. We divided our tool into three stages: an initial step focused on crawling and downloading pages to run preliminary clustering. The second step calculates the complexity of the population, and the third stage calculates the sample size and produces a final report. Our approach significantly reduces the number of pages an auditor must review, as they will inspect a page representative of the others in their cluster. Collectively, these clusters are statistically representative of the pages in the target population.
UnlockedMaps: A Web-Based Map for Visualizing the Real-Time Accessibility of Urban Rail Transit Stations

Limited visibility into real-time accessibility in current web-based maps disenfranchises urban rail commuters who rely on functioning station elevators (e.g., wheelchair users). We shed light on the experiences of such commuters through interviews with representatives from five stakeholder groups (people with mobility disabilities, pregnant people, cyclists/stroller users/heavy-equipment carriers, disability advocates, and civic hackers). Motivated by our findings, we present UnlockedMaps—an open-data map that visualizes the real-time accessibility status (accessible, experiencing at least one elevator outage, not-accessible) of urban rail stations in six North American cities and displays nearby accessible restaurants and restrooms, assisting users in making informed decisions regarding their commute. We assessed UnlockedMaps by conducting (1) an exploratory user study, (2) controlled and “in-the-wild” longitudinal studies, and (3) follow-up interviews with our stakeholder groups. Our findings show that UnlockedMaps is a “for-us-by-us” solution that benefits all stakeholder groups, including those who do not rely on functioning elevators.
Spatial phylogenetics of Fagales: Investigating the history of temperate forests

Aim Quantifying the phylogenetic diversity of temperate trees is essential for understanding what processes are implicated in shaping the modern distribution of temperate broadleaf forest and other major forest biomes. Here we focus on Fagales, an iconic member of forests worldwide, to uncover global diversity and endemism patterns and investigate potential drivers responsible for the spatial distribution of fagalean forest communities. Location Global. Taxon Fagales. Methods We combined phylogenetic data covering 60.2% of living species, fine-scale distribution models covering 90% of species, and nodulation data covering all species to investigate the distribution of species richness at fine spatial scales and compare this to relative phylogenetic diversity (RPD) and phylogenetic endemism. Further, we quantify phylogenetic betadiversity and bioregionalization of Fagales and determine hotspots of Fagales species engaging in root nodule symbiosis (RNS) with nitrogen-fixing actinomycetes. Results We find the highest richness in temperate east Asia, eastern North America, and equatorial montane regions of Asia and Central America. By contrast, RPD is highest at higher latitudes, where RNS also predominates. We found a strong spatial structuring of regionalizations of Fagales floras as defined by phylogeny and traits related to RNS, reflecting distinct Northern and Southern Hemisphere floras (with the exception of a unique Afro-Boreal region) and highly distinct tropical montane communities. Main conclusions Species richness and phylogenetic regionalization accord well with traditional biogeographic concepts for temperate forests, but RPD does not. This may reflect ecological filtering specific to Fagales, as RNS strategies are almost universal in the highest RPD regions. Our results highlight the importance of global-scale, clade-specific spatial phylogenetics and its utility for understanding the history behind temperate forest diversity.
A reproducible benchmark of resting-state fMRI denoising strategies using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a fully reproducible denoising benchmark featuring a range of denoising strategies and evaluation metrics, built primarily on the fMRIPrep and Nilearn software packages. We apply this reproducible benchmark to investigate the robustness of the conclusions across two different datasets and two versions of fMRIPrep. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks, especially ICA-AROMA. These results demonstrate that a reproducible denoising benchmark can effectively assess the robustness of conclusions across multiple datasets and software versions. Technologies such as BIDS-App, the Jupyter Book and Neurolibre provided the infrastructure to publish the metadata and report figures. Readers can reproduce the report figures beyond the ones reported in the published manuscript. With the denoising benchmark, we hope to provide useful guidelines for the community, and that our software infrastructure will facilitate continued development as the state-of-the-art advances.