Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib’s mechanism of Mpro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1–M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.
Single-Exponential FPT Algorithms for Enumerating Secluded F-Free Subgraphs and Deleting to Scattered Graph Classes

The celebrated notion of important separators bounds the number of small $(S,T)$-separators in a graph which are 'farthest from $S$' in a technical sense. In this paper, we introduce a generalization of this powerful algorithmic primitive that is phrased in terms of $k$-secluded vertex sets: sets with an open neighborhood of size at most $k$. In this terminology, the bound on important separators says that there are at most $4^k$ maximal $k$-secluded connected vertex sets $C$ containing $S$ but disjoint from $T$. We generalize this statement significantly: even when we demand that $G[C]$ avoids a finite set $\mathcal{F}$ of forbidden induced subgraphs, the number of such maximal subgraphs is $2^{O(k)}$ and they can be enumerated efficiently. This allows us to make significant improvements for two problems from the literature. Our first application concerns the 'Connected $k$-Secluded $\mathcal{F}$-free subgraph' problem, where $\mathcal{F}$ is a finite set of forbidden induced subgraphs. Given a graph in which each vertex has a positive integer weight, the problem asks to find a maximum-weight connected $k$-secluded vertex set $C \subseteq V(G)$ such that $G[C]$ does not contain an induced subgraph isomorphic to any $F \in \mathcal{F}$. The parameterization by $k$ is known to be solvable in triple-exponential time via the technique of recursive understanding, which we improve to single-exponential. Our second application concerns the deletion problem to scattered graph classes. Here, the task is to find a vertex set of size at most $k$ whose removal yields a graph whose each connected component belongs to one of the prescribed graph classes $\Pi_1, \ldots, \Pi_d$. We obtain a single-exponential algorithm whenever each class $\Pi_i$ is characterized by a finite number of forbidden induced subgraphs. This generalizes and improves upon earlier results in the literature.
Long-Form End-to-End Speech Translation via Latent Alignment Segmentation

Current simultaneous speech translation models can process audio only up to a few seconds long. Contemporary datasets provide an oracle segmentation into sentences based on human-annotated transcripts and translations. However, the segmentation into sentences is not available in the real world. Current speech segmentation approaches either offer poor segmentation quality or have to trade latency for quality. In this paper, we propose a novel segmentation approach for a low-latency end-to-end speech translation. We leverage the existing speech translation encoder-decoder architecture with ST CTC and show that it can perform the segmentation task without supervision or additional parameters. To the best of our knowledge, our method is the first that allows an actual end-to-end simultaneous speech translation, as the same model is used for translation and segmentation at the same time. On a diverse set of language pairs and in- and out-of-domain data, we show that the proposed approach achieves state-of-the-art quality at no additional computational cost.
Design of Fast Model Predictive Controller for Permanent Magnet Synchronous Motor Drive System

In this article, a new predictive controller based on fast model predictive control has been proposed for permanent magnet synchronous motor(PMSM) drive system to solve the quadratic programming problem online in a very short period. The mathematical model of PMSM has been described in terms of state space and discretized by Euler method firstly. Then, the discrete model is linearized at the steady state point. Based on this, the optimization objective function which is used to measure the performance of the PMSM is designed properly. The approximate primal barrier interior point method is employed to speed up the optimization process. Finally, the simulation results declare that the proposed algorithm is useful for PMSM drive system. In addition, the proposed approach enables the model parameters, the horizon size as well as the weighting factors updated in real time so that it has potential to solve problems such as model mismatch in the future.
Studying Lobby Influence in the European Parliament

We present a method based on natural language processing (NLP), for studying the influence of interest groups (lobbies) in the law-making process in the European Parliament (EP). We collect and analyze novel datasets of lobbies' position papers and speeches made by members of the EP (MEPs). By comparing these texts on the basis of semantic similarity and entailment, we are able to discover interpretable links between MEPs and lobbies. In the absence of a ground-truth dataset of such links, we perform an indirect validation by comparing the discovered links with a dataset, which we curate, of retweet links between MEPs and lobbies, and with the publicly disclosed meetings of MEPs. Our best method achieves an AUC score of 0.77 and performs significantly better than several baselines. Moreover, an aggregate analysis of the discovered links, between groups of related lobbies and political groups of MEPs, correspond to the expectations from the ideology of the groups (e.g., center-left groups are associated with social causes). We believe that this work, which encompasses the methodology, datasets, and results, is a step towards enhancing the transparency of the intricate decision-making processes within democratic institutions.
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement

In high-stakes settings, Machine Learning models that can provide predictions that are interpretable for humans are crucial. This is even more true with the advent of complex deep learning based models with a huge number of tunable parameters. Recently, prototype-based methods have emerged as a promising approach to make deep learning interpretable. We particularly focus on the analysis of deepfake videos in a forensics context. Although prototype-based methods have been introduced for the detection of deepfake videos, their use in real-world scenarios still presents major challenges, in that prototypes tend to be overly similar and interpretability varies between prototypes. This paper proposes a Visual Analytics process model for prototype learning, and, based on this, presents ProtoExplorer, a Visual Analytics system for the exploration and refinement of prototype-based deepfake detection models. ProtoExplorer offers tools for visualizing and temporally filtering prototype-based predictions when working with video data. It disentangles the complexity of working with spatio-temporal prototypes, facilitating their visualization. It further enables the refinement of models by interactively deleting and replacing prototypes with the aim to achieve more interpretable and less biased predictions while preserving detection accuracy. The system was designed with forensic experts and evaluated in a number of rounds based on both open-ended think aloud evaluation and interviews. These sessions have confirmed the strength of our prototype based exploration of deepfake videos while they provided the feedback needed to continuously improve the system.
Pharmacodynamics of ATI-2307 in a rabbit model of cryptococcal meningoencephalitis

ABSTRACT Cryptococcal meningoencephalitis (CM) is a devastating fungal disease with high morbidity and mortality. The current regimen that is standard-of-care involves a combination of three different drugs administered for up to one year. There is a critical need for new therapies due to both toxicity and inadequate fungicidal activity of the currently available antifungal drugs. ATI-2307 is a novel aryl amidine that disrupts the mitochondrial membrane potential and inhibits the respiratory chain complexes of fungi—it thus represents a new mechanism for direct antifungal action. Furthermore, ATI-2307 selectively targets fungal mitochondria via a fungal-specific transporter that is not present in mammalian cells. It has very potent in vitro anticryptococcal activity. In this study, the efficacy of ATI-2307 was tested in a rabbit model of CM. ATI-2307 demonstrated significant fungicidal activity at dosages between 1 and 2 mg/kg/d, and these results were superior to fluconazole and similar to amphotericin B treatment. When ATI-2307 was combined with fluconazole, the antifungal effect was greater than either therapy alone. While ATI-2307 has potent anticryptococcal activity in the subarachnoid space, its ability to reduce yeasts in the brain parenchyma was relatively less over the same study period. This new drug, with its unique mechanism of fungicidal action and ability to positively interact with an azole, has demonstrated sufficient anticryptococcal potential in this experimental setting to be further evaluated in clinical studies.
On the convergence conditions of Laplace importance sampling with randomized quasi-Monte Carlo

The study further explores randomized QMC (RQMC), which maintains the QMC convergence rate and facilitates computational efficiency analysis. Emphasis is laid on integrating randomly shifted lattice rules, a distinct RQMC quadrature, with IS,a classic variance reduction technique. The study underscores the intricacies of establishing a theoretical convergence rate for IS in QMC compared to MC, given the influence of problem dimensions and smoothness on QMC. The research also touches on the significance of IS density selection and its potential implications. The study culminates in examining the error bound of IS with a randomly shifted lattice rule, drawing inspiration from the reproducing kernel Hilbert space (RKHS). In the realm of finance and statistics, many problems boil down to computing expectations, predominantly integrals concerning a Gaussian measure. This study considers optimal drift importance sampling (ODIS) and Laplace importance sampling (LapIS) as common importance densities. Conclusively, the paper establishes that under certain conditions, the IS-randomly shifted lattice rule can achieve a near $O(N^{-1})$ error bound.
Understanding the Impact of Service Convenience on Customer Satisfaction in Home Delivery: Evidence from Karachi

The rise of e-commerce caused by the growing popularity of the world wide web, electronic gadgets, and social media, has led to a constant upward trend in online shopping in Pakistan. Home delivery is crucial to online shoppers because if the product does not reach their desired destination, the purchase made will be conceived as inconvenient by the buyer. This study aims to identify how five kinds of conveniences, i.e., decision convenience, access convenience, transaction convenience, benefit convenience, and post benefit convenience, impact how satisfied customers are by the services provided by online retailers during home delivery. 110 individuals who availed a home delivery service over the past year took part in the study, and customer experience was observed as they were offered different conveniences at the various stages of home delivery. After performing a regression analysis, results show that four independent variables have a positive and significant impact on the dependent variable, while the impact of benefit convenience, the fifth variable, is insignificant. These results show us that the timely and correct delivery of products does not impact satisfaction as long as the provider offers a convenient post purchase return policy. They also show that decision convenience, post benefit, and transaction convenience have the greatest impacts on customer satisfaction. Access convenience and benefit convenience are also shown to have a high impact on the dependent variable. Based on these findings, we recommend that online retailers pay more attention to offering convenience services to their customers to improve satisfaction.
PLVS: A SLAM System with Points, Lines, Volumetric Mapping, and 3D Incremental Segmentation

This document presents PLVS: a real-time system that leverages sparse SLAM, volumetric mapping, and 3D unsupervised incremental segmentation. PLVS stands for Points, Lines, Volumetric mapping, and Segmentation. It supports RGB-D and Stereo cameras, which may be optionally equipped with IMUs. The SLAM module is keyframe-based, and extracts and tracks sparse points and line segments as features. Volumetric mapping runs in parallel with respect to the SLAM front-end and generates a 3D reconstruction of the explored environment by fusing point clouds backprojected from keyframes. Different volumetric mapping methods are supported and integrated in PLVS. We use a novel reprojection error to bundle-adjust line segments. This error exploits available depth information to stabilize the position estimates of line segment endpoints. An incremental and geometric-based segmentation method is implemented and integrated for RGB-D cameras in the PLVS framework. We present qualitative and quantitative evaluations of the PLVS framework on some publicly available datasets. The appendix details the adopted stereo line triangulation method and provides a derivation of the Jacobians we used for line error terms. The software is available as open-source.
Degradation of p0071 and p120-catenin during adherens junction disassembly by Leptospira interrogans

Leptospira interrogans disseminates hematogenously to reach the target organs by disrupting epithelial adherens junctions (AJs), thus causing leptospirosis, which is a globally neglected zoonotic disease. L. interrogans induces E-cadherin (E-cad) endocytosis and cytoskeletal rearrangement during AJ disassembly, but the detailed mechanism remains unknown. Elucidation of AJ disassembly mechanisms will guide new approaches to developing vaccines and diagnostic methods. In this study, we combine proteomic and imaging analysis with chemical inhibition studies to demonstrate that disrupting the AJs of renal proximal tubule epithelial cells involves the degradation of two armadillo repeat-containing proteins, p0071 and p120-catenin, that stabilize E-cad at the plasma membrane. Combining proteasomal and lysosomal inhibitors substantially prevented p120-catenin degradation, and monolayer integrity destruction without preventing p0071 proteolysis. In contrast, the pan-caspase inhibitor Z-VAD-FMK inhibited p0071 proteolysis and displacement of both armadillo repeat-containing proteins from the cell-cell junctions. Our results show that L. interrogans induces p120-catenin and p0071 degradation, which mutually regulates E-cad stability by co-opting multiple cellular degradation pathways. This strategy may allow L. interrogans to disassemble AJs and disseminate through the body efficiently.
Expression of the Embryonic Cancer Stem Cells’ Biomarkers SOX2 and OCT3/4 in Oral Leukoplakias and Squamous Cell Carcinomas: A Preliminary Study

Introduction: Cancer stem cells (CSCs) are incriminated for initiating the process of carcinogenesis either de novo or through the transformation of oral potentially malignant disorders (OPMDs) to oral squamous cell carcinoma (OSCC). The aim of this study was to detect the expression of embryonic-type CSC markers OCT3/4 and SOX2 in OSCCs and oral leukoplakias (OLs), the most common of OPMDs. Materials and methods: The study type is experimental, and the study design is characterized as semiquantitative research, which belongs to the branch of experimental research. The experiment was conducted in the Department of Oral Medicine/Pathology, School of Dentistry, Aristotle University of Thessaloniki, Greece. This study focuses on the semiquantitative immunohistochemical (IHC) pattern of expression of CSCs protein-biomarkers SOX2 and OCT3/4, in paraffin embedded samples of 21 OSCCs of different grades of differentiation and 30 cases of OLs with different grades of dysplasia, compared to five cases of normal oral mucosa in both terms of cells’ stain positivity and intensity. Statistical analysis was performed through SPSS 2017 Pearson Chi-square and the significance level was set at 0.05 (p=0.05). The expression of the respective genes of SOX2 and OCT3/4 was studied through quantitative polymerase chain reaction (qPCR), in paraffin-embedded samples of 12 cases of OLs with mild/non dysplasia and 19 cases moderately/poorly differentiated OSCCs(n=19) and five normal mucosa using the Independent Paired T-test. Results: The genes SOX2 and Oct3/4 were expressed in all examined cases although no statistically significant correlations among normal, OL and OSCC, were established. A nuclear/membrane staining of OCT3/4 was noticed only in three out of 21 OSCCs but in none of OLs or normal cases (without statistical significance). A characteristic nuclear staining of SOX2 was noticed in the majority of the samples, mostly in the basal and parabasal layers of the epithelium. SOX2 was significantly detected in the OSCCs group (strong positivity in 17/21) than in the OL group (30 cases, mostly mildly stained) (p-value=0.007), and the normal oral epithelium (mild stained, p=0.065). Furthermore, SOX2 was overexpressed in well differentiated OSCCs group (5/OSCCs, strongly stained) rather than in mildly dysplastic and non-dysplastic OLs samples (14/OLs, mildly stained) (p-value =0.035). Conclusion:The characteristic expression of SOX2 but not of OCT3/4 in OLs' and OSCCs’ lesions suggests the presence of neoplastic cells with certain CSC characteristics whose implication in the early stages of oral tumorigenesis could be further evaluated. The clinical use of SOX2, as prognostic factor, requires further experimental evaluation in larger number of samples.
Analysis on the Characteristics of High-Temperature Heat Wave Weather in Jilin Province from 1961 to 2018

: In this study, meteorological data from 47 stations in Jilin Province during June to August from 1961 to 2018 are examined. The data includes daily maximum temperature, wind speed, and relative humidity. The analysis focuses on the change patterns of daily maximum temperature (Tmax), perceived temperature (ATmax), and high temperature heat waves (3d ≥ 35°C). The impact of meteorological factors on daily maximum perceived temperature is assessed using gray correlation degree and partial correlation coefficient. The researchresultsshow that from 1961 to 2018, the average values of Tmax from June to August of each meteorological station in Jilin Province were 25.6℃, 27.5℃ and 26.6℃ respectively, and the average climatic inclination rates were 0.192, 0.134 and 0.153℃ (10a) -1 respectively; the average values of ATmax were 26.6℃, 30.2℃ and 29.3℃ respectively, and the average climatic inclination rates were 0.339, 0.238 and 0.234℃(10a) -1 . From June to August, the average numbers of days with the maximum air temperature ≥35℃ of each station were 0.29, 0.26 and 0.10 dof -1 , and the overall average is 0.64 dd -1 , with the maximum in June, the secondary in July and the minimum in August. The increase rate of the number of days with the maximum air temperature ≥35℃ from June to August was 0.132d second -1 . The maximum number of days with high temperature occurred in the western part of Jilin Province, and the minimum occurred in the central and southern part. The average number of times of high-temperature heat waves at each station was 0.041 per year, and the increase rate of heat waves was 0.012 times(10a) -1 . The high-value areas were mainly distributed in the west of Jilin Province, and basically no high-temperature heat waves occurred in the central and southern regions. Analysis of the grey correlation coefficient shows that the maximum somatosensory temperature is most closely related to the maximum air temperature, followed by relative humidity, and wind speed is the least relewant.
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models

We investigate the approximation efficiency of score functions by deep neural networks in diffusion-based generative modeling. While existing approximation theories utilize the smoothness of score functions, they suffer from the curse of dimensionality for intrinsically high-dimensional data. This limitation is pronounced in graphical models such as Markov random fields, common for image distributions, where the approximation efficiency of score functions remains unestablished. To address this, we observe score functions can often be well-approximated in graphical models through variational inference denoising algorithms. Furthermore, these algorithms are amenable to efficient neural network representation. We demonstrate this in examples of graphical models, including Ising models, conditional Ising models, restricted Boltzmann machines, and sparse encoding models. Combined with off-the-shelf discretization error bounds for diffusion-based sampling, we provide an efficient sample complexity bound for diffusion-based generative modeling when the score function is learned by deep neural networks.
EquiPrEP: An implementation science protocol for promoting equitable access and uptake of long-acting injectable HIV pre-exposure prophylaxis (LAI-PrEP)

Background Long-acting injectable HIV pre-exposure prophylaxis (LAI-PrEP) was approved by the U.S. Food and Drug Administration in December 2021. This initial phase of implementation represents a prime opportunity to ensure equitable LAI-PrEP provision to communities often underrepresented in PrEP care before disparities in access and uptake emerge. Herein, we describe the EquiPrEP Project which utilizes an equity-oriented implementation science framework to optimize LAI-PrEP rollout in an urban safety-net clinic in New York City. Methods The primary objectives of this project are to: (1) increase LAI-PrEP initiation overall; (2) increase uptake among groups disproportionately impacted by the HIV epidemic; (3) preserve high PrEP retention while expanding use; and (4) identify barriers and facilitators to LAI-PrEP use. EquiPrEP will enroll 210 PrEP-eligible participants into LAI-PrEP care with planned follow-up for one year. We will recruit from the following priority populations: Black and/or Latine men who have sex with men, Black and/or Latine cisgender women, and transgender women and nonbinary individuals. To evaluate implementation of LAI-PrEP, we will utilize equity-focused iterations of the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework and the Consolidated Framework for Implementation Research (CFIR), in addition to longitudinal surveys and qualitative interviews. Discussion Novel LAI-PrEP formulations carry tremendous potential to revolutionize the field of HIV prevention. Implementation strategies rooted in equity are needed to ensure that marginalized populations have access to LAI-PrEP and to address the structural factors that hinder initiation and retention in care.