Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-world Data: MCL2 Protocol Demonstrates Excellent Treatment Results Among Patients With Mantle Cell Lymphoma Not Fulfilling the Original Trial Inclusion Criteria

M antle cell lymphoma (MCL) is a rare and heterogeneous subgroup of B-cell lymphomas. The clinical course of MCL varies from an indolent form to an extremely aggressive phenotype. MCL is still an incurable disease. However, intensified chemoimmunotherapy regimens and targeted therapies have increased the overall survival (OS) of patients <65 years old from 5.6 years to 8.9 years. 1 Today, the standard treatment for fit patients <65–70 years is high-dose cytarabine-containing immunochemotherapy regimens followed by autologous stem cell transplantation (ASCT). 2 The MCL2 protocol developed by the Nordic Lymphoma Group is one of these approaches. 3,4 The MCL2 protocol has shown a median OS of 12.7 years at the 15-year follow-up and a median progression-free survival (PFS) of 8.5 years. 4 Similar results have been obtained in other high-dose regimen trials. 5–7 This study aimed to evaluate the efficacy of the MCL2 protocol in a population-based retrospective analysis and to compare treatment results in patients fulfilling the trial eligibility criteria with those who would have been trial ineligible. We collected data on patients diagnosed with MCL from 7 hospitals in Finland and 1 hospital
The variation characteristics of fecal microbiota in remission UC patients with anxiety and depression

Background Ulcerative colitis (UC) patients with relapsed disease are most likely to suffer from anxiety and depression. Increasing data indicates that psychological issues can change the composition of intestinal flora. Thus, we aim to seek the variation of intestinal microbiota composition in remission UC patients with anxiety and depression in Northwest China. Results In this study, 45 UC patients in remission were enrolled. The incidence of anxiety was 33.3%, and the prevalence of depression was 22.2%. There was no statistical difference in the alpha diversity of fecal microbiota, while beta diversity had a significant difference between the anxiety group and the non-anxiety group and the depression group and the non-depression group. Species composition analysis results showed that the ratio of Bifidobacterium and Lactobacilales significantly decreased. At the same time, the proportion of Escherichia-Shigella and Proteus_mirabilis increased in the anxiety group, and the ratio of Faecalibacterium and Bifidobacterium significantly decreased. In contrast, Escherichia-Shigella increased in the depression group at the gene levels. Conclusion Anxiety and depression still exist in UC patients even in the remission period. We first identify that the proportion of probiotics decreases while the proportion of pathogens increases in UC patients with anxiety and depression. These findings may provide a new pathophysiological mechanism for the recurrence of disease caused by impaired psychological function and a new method for the treatment strategy of UC patients with psychological issues.
Audio-Visual Contrastive and Consistency Learning for Semi-Supervised Action Recognition

Semi-supervised video learning is an increasingly popular approach for improving video understanding tasks by utilizing large-scale unlabeled videos along with a few labels. Recent studies have shown that multimodal contrastive learning and consistency regularization are effective techniques for generating high-quality pseudo-labels for semi-supervised action recognition. However, existing pseudo-labeling approaches are solely based on the model's class predictions and can suffer from confirmation biases due to the accumulation of false predictions. To address this issue, we propose exploiting audio-visual feature correlations to achieve high-quality pseudo-labels instead of relying on model confidence. To achieve this goal, we introduce Audio-visual Contrastive and Consistency Learning (AvCLR) for semi-supervised action recognition. AvCLR generates reliable pseudo-labels from audio-visual feature correlations using deep embedded clustering to mitigate confirmation biases. Additionally, AvCLR introduces two contrastive modules: intra-modal contrastive learning (ImCL) and cross-modal contrastive learning (XmCL) to discover complementary information from audio-visual alignments. The ImCL module learns informative representations within audio and video independently, while the XmCL module aims to leverage global high-level features of audio-visual information. Furthermore, the XmCL is constrained by introducing intra-instance negatives from one modality to the other. We jointly optimize the model with ImCL, XmCL, and consistency regularization in an end-to-end semi-supervised manner. Experimental results have demonstrated that the proposed AvCLR framework is effective in reducing confirmation biases and outperforms existing confidence-based semi-supervised action recognition methods.
The ARNO model.

The paper describes the ARNO model, a continuous time semi-distributed rainfall-runoff model, widely used in the last two decades as the basic tool for several applications such as water master planning, analysis of extreme floods, realisation of real-time flood forecasting systems and last but not least to represent the soil component in General Circulation Models (GCM). The ARNO model is based upon a parametric description of the main hydrological processes taking place at the catchment scale, such as infiltration, evapotranspiration, drainage, percolation, routing, etc., and can represent a large catchment as a tree shaped cascade of subcatchments. The paper will also briefly discuss the parameter calibration and present the application of the ARNO model to the Tiber river in Italy.
Efficacy of Metallic Nanoparticles and Nanocarriers as an Advanced Tool for Imaging and Diagnosis: Insight into Theranostic Applications

The development of nanotechnology has brought diagnosis and therapy closer together, making theranostic compounds a promising area of study. Theranostic substances can both diagnose and treat a patient's condition. For their potential use in treating a wide range of ailments, including cancer, TB, diabetes, etc., metallic nanoparticles are currently the subject of intensive research. Modern nanotechnology allows for the precise assembly of medicinal chemicals onto metallic nanoparticles, which can then be used for targeted drug delivery, illness detection, and even imaging. Problems that must be solved before metallic theranostic agents can be successfully implemented in clinical settings include their complicated design, instability, toxicity, drug release regulation, mass production, and the potential for behavioral, psychological, and metabolic side effects. This article reviews the recent progress made in using metallic nanoparticles like gold, carbon, and iron oxide nanoparticles as theranostic agents. We also provide a brief summary of the various imaging properties of metallic nanoparticles. Finally, this in-depth analysis discusses new patents associated with metallic nanoparticles as a theranostic agent.
The Open Bioinformatics

: Purpose: Diabetes is a condition where the body cannot utilize insulin properly. Maintenance of the levels of insulin in the body is mandatory, otherwise it will lead to several disorders of kidney failure, heart attack, nervous weakness, blindness, etc . Among the 10 majority diseases, diabetes is occupying the second role by covering 34.2 million individuals as for the National Diabetes Statistics report. According to the World Health Organization, diabetes is playing the 7th role in cause of death. Thus early identification of diabetes can overcome these severe damages. Methods: Accurate predictions require a lot of data, which is introducing the curse of dimensionality. In the present research, PIMA Indians diabetes data set is considered and different classification models viz . , K-means clustering with logistic regression, SVM (Support Vector Machine), Random Forest, etc . are implemented in predicting the accuracy of diabetes.
Willingness to Pay for Conservation of the Vietnamese Rhino

This study is part of a bigger research on Local Willingness to Pay (WTP) for the Conservation of Endangered Species in Southeast Asia. The research measures WTP using contingent valuation method for a conservation program for the Vietnamese Rhinoceros and a regional conservation program for sea turtles, which are now critically endangered. Five bid levels were used based on the results of a pretest of 120 questionnaires in Ho Chi Minh City. A drop-off survey of 800 households was done in two cities: Ho Chi Minh City and Hanoi; 690 households responded. The mean WTP was estimated at USD 2.50/household.
Light-Sensitive Open Channel Block of Ionotropic Glutamate Receptors by Quaternary Ammonium Azobenzene Derivatives

Glutamate ionotropic receptors mediate fast excitation processes in the central nervous system of vertebrates and play an important role in synaptic plasticity, learning, and memory. Here, we describe the action of two azobenene-containing compounds, AAQ (acrylamide–azobenzene–quaternary ammonium) and QAQ (quaternary ammonium–azobenzene–quaternary ammonium), which produced rapid and fully reversible light-dependent inhibition of glutamate ionotropic receptors. The compounds demonstrated voltage-dependent inhibition with only minor voltage-independent allosteric action. Calcium-impermeable AMPA receptors had weaker sensitivity compared to NMDA and calcium-permeable AMPA receptors. We further revealed that the compounds bound to NMDA and calcium-permeable AMPA receptors in different modes. They were able to enter the wide selectivity filter of AMPA receptors, and strong negative voltages caused permeation into the cytoplasm. The narrow selectivity filter of the NMDA receptors did not allow the molecules to bypass them; therefore, QAQ and AAQ bound to the shallow channel site and prevented channel closure by a foot-in-the-door mechanism. Computer simulations employing available AMPA and NMDA receptor structures readily reproduced the experimental findings, allowing for the structure-based design of more potent and selective drugs in the future. Thus, our work creates a framework for the development of light-sensitive blockers of calcium-permeable AMPA receptors, which are desirable tools for neuroscience.
Enhanced Charge Transfer from Coinage Metal Doped InP Quantum Dots

This paper describes coinage-metal-doped InP quantum dots (QDs) as a platform for enhanced electron transfer to molecular acceptors relative to undoped QDs. A synthetic strategy is developed to prepare doped InP/ZnSe QDs. First-principles DFT calculations show that Ag+ and Cu+ dopants localize photoexcited holes while leaving electrons delocalized. This charge carrier wave function modulation is leveraged to enhance electron transfer to molecular acceptors by up to an order of magnitude. Examination of photoluminescence quenching data suggests that larger electron acceptors, such as anthraquinone and methyl viologen, bind to the QD surface in two ways: by direct adsorption to the surface and by adsorption following displacement of a weakly bound surface cation-ligand complex. Reactions with larger acceptors show the greatest increases in electron transfer between doped and undoped quantum dots, while smaller acceptors show smaller enhancements. Specifically, benzoquinone shows the smallest, followed by naphthoquinone and then methyl viologen and anthraquinone. These results demonstrate the benefits of dopant-induced excited-state carrier localization on photoinduced charge transfer and highlight design principles for improved implementation of quantum dots in photoredox catalysis.
during the

Background Corona Virus Disease 2019 (COVID-19) patients display risk factors for intensive care unit acquired weakness (ICUAW). The pandemic increased existing barriers to mobilisation. This study aimed to compare mobilisation practices in COVID-19 and non-COVID-19 patients. Methods This retrospective cohort study was conducted at Charité-Universitätsmedizin Berlin, Germany, including adult patients admitted to one of 16 ICUs between March 2018, and November 2021. The effect of COVID-19 on mobilisation level and frequency, early mobilisation (EM) and time to active sitting position (ASP) was analysed. Subgroup analysis on COVID-19 patients and the ICU type influencing mobilisation practices was performed. Mobilisation entries were converted into
Analysis of the population genetics of the Han and Hui of Liaoning province, Peoples Republic of China

Throughout recorded Chinese history, regions of the country populated by persons of non-Han ancestry often fluctuated significantly in population numbers :t. '1d in their political and commercial influence. However, at all times they were /consid"ered as important contributors to the nation. Many of these peoples had moved from their homelands, settled in China and had intermarried with Han · Chinese. Over the generations they became accepted as fully-fledged Chinese citizens although, in many instances, they retained their traditional customs and religious practices, and frequently their own language. The Hui Muslims are a good example of this process of integration, and today they comprise some 8.6 million individuals thus forming approximately half of the total Muslim population ofPR China. The purpose oft his study is to investigate the genetic structure of two populations, the Han and Hui of Liaoning, Northeast PR China. The study seeks to provide a better understanding of the effect of population subdivision on the genetic diversity of human populations, by comparing genome-based investigations using single tandem repeat markers with historical and anthropological information. As the Hui of Liaoning are endogamous, and they are known to contract consanguineous marriages, the study also attempts to assess the effect of consanguinity on overall genetic diversity in the Hui. Genetic analysis of the Han and Hui was undertaken by surveying the allele distribution patterns at ten autosomal and seven y "chromosome microsatellite loci in both study populations. Various population genetic
Dichlorido[1-(1,10-phenanthrolin-2-yl)-2-pyridone]copper(II)

In the title mononuclear complex, [CuCl2(C17H11N3O)], the CuII ion is in a distorted square-pyramidal coordination environment. The crystal structure is stabilized by various π–π stacking interactions in which the benzene ring, a pyridine ring and the five-membered CuN2C2 ring are involved. The centroid–centroid distances range from 3.5631 (15) to 3.5666 (16) Å.
Large scale optimization : state of the art

Preface. Restarting Strategies for the DQA Algorithm A.J. Berger, J.M. Mulvey, A. Ruszczynski. Mathematical Equivalence of the Auction Algorithm for Assignment and the epsilon-Relaxation (Preflow-Push) Method for Min Cost Flow D.P. Bertsekas. Preliminary Computational Experience with Modified Log-Barrier Functions for Large-Scale Nonlinear Programming M.G. Breitfeld, D.F. Shanno. A New Stochastic/Perturbation Method for Large-Scale Global Optimization and its Application to Water Cluster Problems R.H. Byrd, T. Derby, E. Eskow, K.P.B. Oldenkamp, R.B. Schnabel. Improving the Decomposition of Partially Separable Functions in the Context of Large-Scale Optimization: a First Approach A.R. Conn, N. Gould, P.L. Toint. Gradient-Related Constrained Minimization Algorithms in Function Spaces: Convergence Properties and Computational Implications J.C. Dunn. Some Reformulations and Applications of the Alternating Direction Method of Multipliers J. Eckstein, M. Fukushima. Experience with a Primal Presolve Algorithm R. Fourer, D.M. Gay. A Trust Region Method for Constrained Nonsmooth Equations S.A. Gabriel, Jong-Shi Pang. On the Complexity of a Column Generation Algorithm for Convex or Quasiconvex Feasibility Problems J.-L. Goffin, Zhi-Quan Luo, Yinyu Ye. Identificiation of the Support of Nonsmoothness C.T. Kelley. On Very Large Scale Assignment Problems Y. Lee, J.B. Orlin. Numerical Solution of Parabolic State Constrained Control Problems Using SQP and Interior-Point-Methods F. Leibfritz, E.W. Sachs. A Global Optimization Method for Weber's Problem with Attraction and Repulsion C.D. Maranas, C.A. Floudas. Large-Scale Diversity Minimization via Parallel Genetic Algorithms R.R. Meyer, J. Yackel. A Numerical Comparison of Barrier andModified Barrier Methods for Large-Scale Bound-Constrained Optimization S.G. Nash, R. Polyak, A. Sofer. A Numerical Study of Some Data Association Problems Arising in Multitarget Tracking A.B. Poore, N. Rijavec. Identifying the Optimal Face of a Network Linear Program with a Globally Convergent Interior Point Method M.G. C. Resende, T. Tsuchiya, G. Veiga. Solution of Large Scale Stochastic Programs with Stochastic Decomposition Algorithms S. Sen, J.Mai, J.L. Higle. A Simple, Quadratically Convergent Interior Point Algorithm for Linear Programming and Convex Quadratic Programming A.L. Tits, J.L. Zhou. On Two Algorithms for Nonconvex Nonsmooth Optimization Problems in Structural Mechanics M.Ap. Tzaferopoulos, E.S. Mistakidis, C.D. Bisbos, P.D. Panagiotopoulos.
METAVerse: Meta-Learning Traversability Cost Map for Off-Road Navigation

Autonomous navigation in off-road conditions requires an accurate estimation of terrain traversability. However, traversability estimation in unstructured environments is subject to high uncertainty due to the variability of numerous factors that influence vehicle-terrain interaction. Consequently, it is challenging to obtain a generalizable model that can accurately predict traversability in a variety of environments. This paper presents METAVerse, a meta-learning framework for learning a global model that accurately and reliably predicts terrain traversability across diverse environments. We train the traversability prediction network to generate a dense and continuous-valued cost map from a sparse LiDAR point cloud, leveraging vehicle-terrain interaction feedback in a self-supervised manner. Meta-learning is utilized to train a global model with driving data collected from multiple environments, effectively minimizing estimation uncertainty. During deployment, online adaptation is performed to rapidly adapt the network to the local environment by exploiting recent interaction experiences. To conduct a comprehensive evaluation, we collect driving data from various terrains and demonstrate that our method can obtain a global model that minimizes uncertainty. Moreover, by integrating our model with a model predictive controller, we demonstrate that the reduced uncertainty results in safe and stable navigation in unstructured and unknown terrains.
Interactions of Cells and Biomaterials for Nerve Tissue Engineering: Polymers and Fabrication

Neural injuries affect millions globally, significantly impacting their quality of life. The inability of these injuries to heal, limited ability to regenerate, and the lack of available treatments make regenerative medicine and tissue engineering a promising field of research for developing methods for nerve repair. This review evaluates the use of natural and synthetic polymers, and the fabrication methods applied that influence a cell’s behavior. Methods include cross-linking hydrogels, incorporation of nanoparticles, and 3D printing with and without live cells. The endogenous cells within the injured area and any exogenous cells seeded on the polymer construct play a vital role in regulating healthy neural activity. This review evaluates the body’s local and systemic reactions to the implanted materials. Although numerous variables are involved, many of these materials and methods have exhibited the potential to provide a biomaterial environment that promotes biocompatibility and the regeneration of a physical and functional nerve. Future studies may evaluate advanced methods for modifying material properties and characterizing the tissue–biomaterial interface for clinical applications.
Generic Second Order Traffic Flow Modelling

This paper presents a generic second order family, which generalizes the Aw-Rascle-Zhang (ARZ) model. This family, called Extended Aw-Rascle-Zhang (EARZ) is characterized by a variable fundamental diagram, by the presence along car trajectories of an invariant related to the fundamental diagram, and by a wide scatter of traffic data in the congested domain. It is shown that the resolution methods developed for the ARZ model can be suitably extended to the EARZ family. A bi-phase traffic flow model introduced by Colombo is shown to be close to a model belonging to the EARZ family. The properties of this model are easily deduced from those of the EARZ models.
Transition Experiences of Mature Students at Ontario Colleges

Drawing on 24 semi-structured interviews, this small-scale qualitative study delves into mature students’ social, academic, and career preparation experiences pursuing college education. Using Schlossberg’s (1989) transition model, the findings reveal that mature students’ overall experience is influenced by their mature status, as they possess greater confidence derived from their previouswork and education experiences and the acquisition of new skills from their programs. However, some mature students perceive the career services offered by the college as being geared toward younger students, often disregarding their previous work experience. Consequently, they find these services less applicable to their needs. The transitioning-in stage for mature students is characterized by initial uncertainty that gradually transforms into a growing sense of confidence, fuelled by their experiences. These experiences motivate mature students to actively contribute to the college community by becoming mentors and assuming a supportive role for younger students during the transition-through stage. The transitioning-out for mature students involves evaluating career options and harbouring some skepticism. Nonetheless, the majority of participants expressed positive experiences and excitement about the new chapters in their lives. This study highlights the significance of tailored support and resources that acknowledge the specific needs and experiences of mature students throughout their college education.
Literacy at Work: Korean Adult Immigrants’ Lived Experiences

Despite the increasing emphasis on the language and literacy practices of immigrant communities, our understanding of Asian immigrants’ everyday experiences in their workplaces remains limited. This study examines the language and literacy practices of two Korean adult immigrants within their professional environment—a family-owned dry cleaning business. Drawing on the concept of Literacy as Social Practice, this study explores their lived experiences. Research was conducted using an ethnographic approach, incorporating participant observations, interviews, and field notes. The collected data was analyzed using an inductive method, facilitating a deeper understanding of the family's evolving social practices and the crucial role their workplace plays in the context of immigrant families. Findings suggest that the family refined their professional skills and essential English proficiency through self-initiated learning, utilizing available resources. The workplace emerged as a significant platform for the family's immigrant experience, markedly shaping their language and literacy practices. This research provides insight into the underrepresented population's literacy practices and spaces, contributing valuable understanding to the field of adult literacy.