Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic subtype classification using a simplified algorithm and mutational characteristics of diffuse large B‐cell lymphoma in a Japanese cohort

Recent large‐scale genetic studies have proposed a new genetic classification of diffuse large B‐cell lymphoma (DLBCL), which is clinically and biologically heterogeneous. However, the classification methods were complicated to be introduced into clinical practice. Here we retrospectively evaluated the mutational status and copy number changes of 144 genes in 177 Japanese patients with DLBCL, using targeted DNA sequencing. We developed a simplified algorithm for classifying four genetic subtypes—MYD88, NOTCH2, BCL2, and SGK1—by assessing alterations in 18 representative genes and BCL2 and BCL6 rearrangement status, integrating the significant genes from previous studies. In our cohort and another validation cohort from published data, the classification results in our algorithm showed close agreement with the other established algorithm. A differential prognosis among the four groups was observed. The NOTCH2 group showed a particularly poorer outcome than similar groups in previous reports. Furthermore, our study revealed unreported genetic features in the DLBCL subtypes that are mainly reported in Japanese patients, such as CD5‐positive DLBCL and methotrexate‐associated lymphoproliferative disorders. These results indicate the utility of our simplified method for DLBCL genetic subtype classification, which can facilitate the optimisation of treatment strategies. In addition, our study highlights the genetic features of Japanese patients with DLBCL.
Impact of incomplete surgery and adjuvant chemotherapy for the intraoperative rupture of capsulated stage I epithelial ovarian cancer: a multi-institutional study with an in-depth subgroup analysis

Objective The aim of the present study was to examine the effects of incomplete surgery and adjuvant chemotherapy on the prognosis of patients with intraoperative rupture of capsulated stage I epithelial ovarian cancer (OvCa). Methods A regional retrospective study was conducted between 1986 and 2019. Among 4,730 patients with malignant ovarian tumors, 534 women with International Federation of Gynecology and Obstetrics stage IA and IC1 epithelial OvCa were eligible. Differences in survival outcomes were examined between patients with stage IA and IC1 tumors and the effects of uterine preservation, complete-staging lymphadenectomy, and adjuvant chemotherapy were investigated by an in-depth subgroup analysis. To analyze therapeutic effects, baseline imbalances were adjusted using propensity score (PS). Results The prognosis of patients with stage IC1 tumors was worse than those with stage IA. Surgical spill did not affect the site of recurrence. In the PS-adjusted subgroup analysis, uterine preservation (hazard ratio [HR]=1.669; 95% confidence interval [CI]=1.052–2.744), incomplete-staging lymphadenectomy (HR=1.689; 95% CI=1.211–2.355), and the omission of adjuvant chemotherapy (HR=3.729; 95% CI=2.090–6.653) significantly increased the HR of recurrence for patients with stage IC1 tumors compared to those with stage IA tumors. Adjuvant chemotherapy decreased the impact of rupture with uterine preservation (HR=0.159; 95% CI=0.230–1.168) or incomplete-staging lymphadenectomy (HR=0.987; 95% CI=0.638–1.527). Conclusion The present results suggest intraoperative rupture of capsulated stage I epithelial OvCa is associated with a poor prognosis. When chemotherapy is given for patients receiving incomplete surgery, there is no longer an increased risk of recurrence observed with the rupture.
Using Machine Learning to Predict Remission in Patients With Major Depressive Disorder Treated With Desvenlafaxine

Background Major depressive disorder (MDD) is a common and burdensome condition that has low rates of treatment success for each individual treatment. This means that many patients require several medication switches to achieve remission; selecting an effective antidepressant is typically a sequential trial-and-error process. Machine learning techniques may be able to learn models that can predict whether a specific patient will respond to a given treatment, before it is administered. This study uses baseline clinical data to create a machine-learned model that accurately predicts remission status for a patient after desvenlafaxine (DVS) treatment. Methods We applied machine learning algorithms to data from 3,399 MDD patients (90% of the 3,776 subjects in 11 phase-III/IV clinical trials, each described using 92 features), to produce a model that uses 26 of these features to predict symptom remission, defined as an 8-week Hamilton Depression Rating Scale score of 7 or below. We evaluated that learned model on the remaining held-out 10% of the data (n = 377). Results Our resulting classifier, a trained linear support vector machine, had a holdout set accuracy of 69.0%, significantly greater than the probability of classifying a patient correctly by chance. We demonstrate that this learning process is stable by repeatedly sampling part of the training dataset and running the learner on this sample, then evaluating the learned model on the held-out instances of the training set; these runs had an average accuracy of 67.0% ± 1.8%. Conclusions Our model, based on 26 clinical features, proved sufficient to predict DVS remission significantly better than chance. This may allow more accurate use of DVS without waiting 8 weeks to determine treatment outcome, and may serve as a first step toward changing psychiatric care by incorporating clinical assistive technologies using machine-learned models.
Motivation‐based segmentation of game meat consumers: A look at the beliefs of food consumers during the COVID‐19 crisis in China

Abstract Objectives This research aims to explore the factors motivate consumers to eat game meat during a multi‐state disease outbreak. Methods It proposes a segmentation of consumers based on their attitudes toward and reveals the consumers' food beliefs that motivate their actions. Three segments of game meat consumers were identified: identity seekers, health seekers, and taste seekers. Results A survey of the potential impact that the COVID‐19 crisis has on these three clusters' future food choices showed that the identity and health seekers are more open to a change in food choices. However, the taste seekers are less likely to be influenced by external factors. Conclusions This research indicates that for the policymakers, the key is to take game meat consumers as an effective intervention entry point. It is crucial to facilitate healthy food choices and to promote socially‐ and culturally‐appropriate food beliefs by improving public awareness of the risks of game meat, and invest in organic food. Research Implications This research provides new insights into the food beliefs of game meat consumers via motivation‐based segmentation.
Triazole‐Linked Iminosugars and Aromatic Systems as Simplified UDP‐Galf Mimics: Synthesis and Preliminary Evaluation as Galf‐Transferase Inhibitors

The convenient preparation of 1,4-dideoxy-1,4-imino-L-arabinitol derivatives carrying a propargyl group by way of the addition of TMS-propargyl Grignard reagent to a N-sulfinylglycosylamine is reported. This compound was coupled via a triazole-tether to various aromatic and heteroaromatic groups, thus providing structures that can be considered as simplified mimics of UDP-Galf, the substrate of GlfT2, a galactofuranosyltransferase present in mycobacteria. These new compounds were evaluated as inhibitors of this enzyme and found to have significant activity (IC50 values in the high M to low mM range) in spite of the absence of the diphospho linkage present in the parent sugar nucleotide.
Remodeling of Tumor Microenvironment by Tumor-Targeting Nanozymes Enhances Immune Activation of CAR T Cells for Combination Therapy.

Targeting B7-H3 chimeric antigen receptor (CAR) T cells has antitumor potential for therapy of non-small cell lung cancer (NSCLC) in preclinical studies. However, CAR T cell therapy remains a formidable challenge for the treatment of solid tumors due to the heterogeneous and immunosuppressive tumor microenvironment (TME). Nanozymes exhibit merits modulating the immunosuppression of the tumor milieu. Here, a synergetic strategy by combination of nanozymes and CAR T cells in solid tumors is described. This nanozyme with dual photothermal-nanocatalytic properties is endowed to remodel TME by destroying its compact structure. It is found that the B7-H3 CAR T cells infused in mice engrafted with the NSCLC cells have superior antitumor activity after nanozyme ablation of the tumor. Importantly, it is found that the changes altered immune-hostile cancer environment, resulting in enhanced activation and infiltration of B7-H3 CAR T cells. The first evidence that the process of combination nanozyme therapy effectively improves the therapeutic index of CAR T cells is presented. Thus, this study clearly supports that the TME-immunomodulated nanozyme is a promising tool to improve the therapeutic obstacles of CAR T cells against solid tumors.
Modeling Misretrieval and Feature Substitution in Agreement Attraction: A Computational Evaluation

We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better accounted for by an encoding-based model of agreement attraction, compared to a retrieval-based model. A novel methodological contribution of our study is the use of comprehension questions with open-ended responses, so that both misinterpretation of the number feature of the subject phrase and misassignment of the thematic subject role of the verb can be investigated at the same time. We find evidence for both types of misinterpretation in our study, sometimes in the same trial. However, the specific error patterns in our data are not fully consistent with any previously proposed model.
Progress toward quantitative in vivo chemical exchange saturation transfer (CEST) MRI

Chemical exchange saturation transfer (CEST) MRI provides a sensitive detection mechanism for imaging dilute labile protons, complementing the routine radiological exams. Enormous progress has been achieved in CEST MRI and image analysis, from the mathematical modeling, CEST agent design, and most importantly, increasing adoption of CEST imaging in the clinical setting. Therefore, CEST imaging represents an emerging field that involves multiple disciplines and together made a remarkable transition from the simplistic CEST-weighted MRI to quantitative CEST (qCEST) analysis. This review focuses on the recent advancements in CEST quantification techniques and findings of in vivo CEST imaging in representative disorders of ischemia, tumor, and epilepsy. In addition, limitations of current CEST methodologies are examined that should help guide future development of more sensitive and quantitative CEST imaging techniques and ultimately, facilitate their adoption in the clinical setting.
Dynamic data evaluation for solid-liquid equilibria

The accuracy and reliability of the measured data sets to be used in regression of model parameters is an important issue related to modeling of phase equilibria. It is clear that good parameters for any model cannot be obtained from low quality data. A thermodynamic consistency test for solid-liquid systems using a relation between the solid and liquid activity coefficients for systems containing metals [1], where the data from the two phases are given were proposed. However, as consistency tests based on the Gibbs–Duhem equation are not feasible, new consistency tests have been developed [2]. Some of the developed tests were based in the quality tests proposed for VLE data by Kang et al. [3] and a methodology that combines solute activity coefficients in the liquid phase at infinite dilution and a theoretically based term to account for the non-ideality in dilute solutions are discussed. In this work, case studies considering the methodology proposed for SLE thermodynamic consistency tests and data from open literature and databases such as NIST-TDE®, DIPPR® and DECHEMA® are presented. The SLE consistency test and data evaluation is performed in a software containing option for data analysis, model analysis and parameter regression. The paper will highlight the data collection, the data analysis for SLE data and the thermodynamic
A Low Phase Noise 2 GHz VCO using 0 . 13 μ m CMOS process

A 2 GHz LC VCO with a large improvement in phase noise is designed and implemented in 0.13 μm CMOS process. It has phase noise of -100.7 dBc/Hz, -130.6 dBc/Hz, and -140.8 dBc/Hz at 100 kHz, 1 MHz, and 3 MHz offset frequencies from the carrier, respectively. The phase noise reduction of about 10 dB is observed for all controllable voltage range, as compared with a comparable conventional VCO. This VCO consumes 3.29 mA from a 1.8 V supply with the silicon area of 500μm x 850 μm.
Engineering sperm-binding IgG antibodies for the development of an effective nonhormonal female contraception

A panel of anti-sperm IgGs that effectively agglutinate human sperm in vitro and in sheep has been developed for nonhormonal contraception. Stopping sperm in its tracks Many women have contraindications to or are dissatisfied with hormone-based birth control. Here, Shrestha and colleagues developed a panel of sperm-binding antibodies based on an immunoglobulin against human sperm isolated from an infertile woman. By engineering the antibodies to contain additional antigen-binding fragments while maintaining the portion of the IgG that mediates trapping of sperm, the authors generated a panel of highly multivalent IgGs (HM-IgGs) that were substantially more potent and faster at agglutinating sperm than the original IgG in vitro and effectively reduced progressively motile sperm in the sheep vagina. Although tests in humans are needed, these HM-IgGs may offer women another alternative to hormonal contraception. Many women risk unintended pregnancy because of medical contraindications or dissatisfaction with contraceptive methods, including real and perceived side effects associated with the use of exogenous hormones. We pursued direct vaginal delivery of sperm-binding monoclonal antibodies (mAbs) that can limit progressive sperm motility in the female reproductive tract as a strategy for effective nonhormonal contraception. Here, motivated by the greater agglutination potencies of polyvalent immunoglobulins but the bioprocessing ease and stability of immunoglobulin G (IgG), we engineered a panel of sperm-binding IgGs with 6 to 10 antigen-binding fragments (Fabs), isolated from a healthy immune-infertile woman against a unique surface antigen universally present on human sperm. These highly multivalent IgGs (HM-IgGs) were at least 10- to 16-fold more potent and faster at agglutinating sperm than the parent IgG while preserving the crystallizable fragment (Fc) of IgG that mediates trapping of individual spermatozoa in mucus. The increased potencies translated into effective (>99.9%) reduction of progressively motile sperm in the sheep vagina using as little as 33 μg of the 10-Fab HM-IgG. HM-IgGs were produced at comparable yields and had identical thermal stability to the parent IgG, with greater homogeneity. HM-IgGs represent not only promising biologics for nonhormonal contraception but also a promising platform for engineering potent multivalent mAbs for other biomedical applications.
Adversarial Robustness in Deep Learning: From Practices to Theories

Deep neural networks (DNNs) have achieved unprecedented accomplishments in various machine learning tasks. However, recent studies demonstrate that DNNs are extremely vulnerable to adversarial examples. They are manually synthesized input samples which look benign but can severely fool the prediction of DNN models. For machine learning practitioners who are applying DNNs, understanding the behavior of adversarial examples will not only help them improve the safety of their models, but also can help them have deeper insights into the working mechanism of the DNNs. In this tutorial, we provide a comprehensive overview on the recent advances of adversarial examples and their countermeasures, from both practical and theoretical perspectives. From the practical aspect, we give a detailed introduction of the popular algorithms to generate adversarial examples under different adversary's goals. We also discuss how the defending strategies are developed to resist these attacks, and how new attacks come out to break these defenses. From the theoretical aspect, we discuss a series of intrinsic behaviors of robust DNNs which are different from traditional DNNs, especially about their optimization and generalization properties. Finally, we introduce DeepRobust, a Pytorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. Via our tutorial, the audience can grip the main ideas of adversarial attacks and defenses and gain a deep insight of DNN's robustness. The tutorial official website is at https://sites.google. com/view/kdd21-tutorial-adv-robust.
Scalable Query Processing and Query Engines over Cloud Databases: Models, Paradigms, Techniques, Future Challenges

Scalable query processing and scalable query engines over Cloud databases is a vibrant area of research, which has recently emerged within both the academic and industrial research community. This area has been further stirred-up by the current explosion of big data management and analytics models and techniques that, usually executed within the internal layer of public as well as private Clouds, pose severe (and new! ) challenges to the annoying distributed query processing optimization problem in (distributed) database systems. Among other, taming the complexity of query execution plays a leading role, especially considering the typical Cloud environment that includes tens and tens of different-in-granularity data processing tasks (also at a different scale) over large-scale clusters. Inspired by these considerations, this paper focuses on models, paradigms, techniques and future challenges of scalable query processing and query engines over Cloud databases, by reporting on state-of-the-art results as well as emerging trends, with also criticisms on future work that we should expect from the community.
Creating Recommender Systems Datasets in Scientific Fields

Recommender systems (RS) have been successfully explored in a vast number of domains, e.g. movies and tv shows, music, or e-commerce. In these domains we have a large number of datasets freely available for testing and evaluating new recommender algorithms. For example, Movielens and Netflix datasets for movies, Spotify for music, and Amazon for e-commerce, which translates into a large number of algorithms applied to these fields. In scientific fields, such as Health and Chemistry, standard and open access datasets with the information about the preferences of the users are scarce. First, it is important to understand the application domain, i.e. "what the recommended item is". Second, who are the end users: researchers, pharmacists, clinicians or policy makers. Third, the availability of data. Thus, if we wish to develop an algorithm for recommending scientific items, we do not have access to datasets with information about the past preferences of a group of users. Given this limitation, we developed a methodology, called LIBRETTI - LIterature Based RecommEndaTion of scienTific Items, whose goal is the creation of datasets, related with scientific fields. These datasets are created based on the major resource of knowledge that Science has: scientific literature. We consider the users as the authors of the publications, the items as the scientific entities (for example chemical compounds or diseases), and the ratings as the number of publications an author wrote about an entity. In this tutorial we will approach state-of-the-art recommender systems in scientific fields, explain what is Named Entity Recognition/Linking (NER/NEL) in research literature, and to demonstrate how to create a dataset for recommending drugs and diseases through research literature related to COVID-19. Our goal is to spread the use of LIBRETTI methodology in order to help in the development of recommender algorithms in scientific fields. These datasets are created based on the major resource of knowledge that Science has: scientific literature. We consider the users as the authors of the publications, the items as the scientific entities (for example chemical compounds or diseases), and the ratings as the number of publications an author wrote about an entity. In this tutorial we will approach state-of-the-art recommender systems in scientific fields, explain what is Named Entity Recognition/Linking (NER/NEL) in research literature, and to demonstrate how to create a dataset for recommending drugs and diseases through research literature related to COVID-19. Our goal is to spread the use of LIBRETTI methodology in order to help in the development of recommender algorithms in scientific fields. More info about the tutorial at https://lasigebiotm.github. io/RecSys.Scifi/.
Spectral Clustering of Attributed Multi-relational Graphs

Graph clustering aims at discovering a natural grouping of the nodes such that similar nodes are assigned to a common cluster. Many different algorithms have been proposed in the literature: for simple graphs, for graphs with attributes associated to nodes, and for graphs where edges represent different types of relations among nodes. However, complex data in many domains can be represented as both attributed and multi-relational networks. In this paper, we propose SpectralMix, a joint dimensionality reduction technique for multi-relational graphs with categorical node attributes. SpectralMix integrates all information available from the attributes, the different types of relations, and the graph structure to enable a sound interpretation of the clustering results. Moreover, it generalizes existing techniques: it reduces to spectral embedding and clustering when only applied to a single graph and to homogeneity analysis when applied to categorical data. Experiments conducted on several real-world datasets enable us to detect dependencies between graph structure and categorical attributes, moreover, they exhibit the superiority of SpectralMix over existing methods.
Exploring EFL Students' Writing Performance and Their Acceptance of AI-based Automated Writing Feedback

This study examined the EFL writing performance of using AI-based writing feedback called Grammarly and students’ acceptance of this new technology. Fifty-three EFL students from two convenience sample classes in China participated in this quasi-experimental design study. They were randomly assigned to the experimental and control groups. The students in the experimental group (EG) applied Grammarly to edit and revise their essays, while those in the control group (CG) received traditional instruction without Grammarly intervention. A total of five essays were implemented during the 16-week intervention period. EG students' perceptions of Grammarly via a survey were additionally collected by the researchers at the end of the intervention. Results from an independent t-test showed that students in the EG significantly outperformed those in the CG in respect of posttest writing performance. The effect size value of the Cohen's d = 0.603, indicating a medium effect. Results from a survey with open-ended questions showed that students appreciated the AI-based instant grammar correction given by Grammarly. The disadvantages of Grammarly and future teaching strategies in EFL writing classrooms are provided.