Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-needle Aspiration Cytology to Identify a Rare Mimicker of Breast Cancer: Plasma Cell Mastitis

Abstract There are rare benign diseases that can mimic malignant breast neoplasms in the clinical exam and in mammography. We evaluated the contribution of an accessible procedure to most clinicians, the fine-needle aspiration cytology, to identify a rare mimicker of malignant breast neoplasms. A type 2 diabetic 85-year-old female presented with a 6-month history of a left breast lump. The physical exam and mammography were compatible with breast cancer. Nevertheless, after fine-needle aspiration cytology, the diagnosis was plasma cell mastitis. Once this rare diagnosis was established, the tumor was extirpated, and the final histologic diagnosis corroborated chronic plasma cell mastitis. The patient's postoperative evolution was uneventful, and no other treatment was needed. Fine-needle aspiration cytology could be a valuable tool to identify rare mimickers of malignant breast neoplasms.
Nucleoporin 54 contributes to homologous recombination repair and post-replicative DNA integrity

Abstract The nuclear pore complex (NPC) machinery is emerging as an important determinant in the maintenance of genome integrity and sensitivity to DNA double-strand break (DSB)-inducing agents, such as ionising radiation (IR). In this study, using a high-throughput siRNA screen, we identified the central channel NPC protein Nup54, and concomitantly its molecular partners Nup62 and Nup58, as novel factors implicated in radiosensitivity. Nup54 depletion caused an increase in cell death by mitotic catastrophe after IR, and specifically enhanced both the duration of the G2 arrest and the radiosensitivity of cells that contained replicated DNA at the time of IR exposure. Nup54-depleted cells also exhibited increased formation of chromosome aberrations arisen from replicated DNA. Interestingly, we found that Nup54 is epistatic with the homologous recombination (HR) factor Rad51. Moreover, using specific DNA damage repair reporters, we observed a decreased HR repair activity upon Nup54 knockdown. In agreement with a role in HR repair, we also demonstrated a decreased formation of HR-linked DNA synthesis foci and sister chromatid exchanges after IR in cells depleted of Nup54. Our study reveals a novel role for Nup54 in the response to IR and the maintenance of HR-mediated genome integrity.
Senescence Induces Dysfunctions in Endothelial Progenitor Cells and Osteoblasts by Interfering Translational Machinery and Bioenergetic Homeostasis

Age-related bone diseases are partly caused by impaired bone integrity, which are closely related to osteoblasts’ activity and angiogenesis. Endothelial progenitor cells (EPCs) are the initiators of angiogenesis and found to have senescent-induced dysfunctions. The aim of this study is to investigate the effects of senescence in EPCs on osteogenesis and angiogenesis. Human primary EPCs and a murine osteoblast cell line (MC3T3-E1) are utilized in this study. The senescence of EPCs are induced by serial passages. When co-cultured with senescent EPCs, the osteoblasts demonstrate weakened alkaline phosphatase (ALP) activity and mineral deposition. On the other hand, osteoblast-induced migration decreases in senescent EPCs. As for the intracellular alterations of senescent EPCs, the activation of Akt/mTOR/p70S6K pathway, MnSOD and catalase are diminished. In contrast, the level of reactive oxygen species are significantly higher in senescent EPCs. Furthermore, senescent EPCs has decreased level intracellular ATP level and coupling efficiency for oxidative phosphorylation while the non-mitochondrial respiration and glycolysis are elevated. The senescence of EPCs impairs the functions of both osteoblasts and EPCs, suggesting EPCs’ role in the pathophysiology of age-related bone diseases. Targeting the alterations found in this study could be potential treatments.
Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking

Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.
Analyse d'opinions multi-aspects pour la recommandation fine de restaurants(Aspect-based opinion analysis for restaurant discovery)

In this paper, we present the task of multi-aspect opinion mining and its application for recommendation in an industrial context. The texts used are comments in French about restaurants, posted on the Internet. These texts have been manually labeled according to a domain ontology, so as to extract the various aspects of the user experience and attach a polarity to them. Several Machine Learning experiments have been conducted to reproduce this annotation on new texts. The best results have been obtained by using two consecutive CRFs.
Androgens downregulate anti-Müllerian hormone promoter activity in the Sertoli cell through the androgen receptor and intact steroidogenic factor 1 sites†

Abstract Testicular anti-Müllerian hormone (AMH) production is inhibited by androgens around pubertal onset, as observed under normal physiological conditions and in patients with precocious puberty. In agreement, AMH downregulation is absent in patients with androgen insensitivity. The molecular mechanisms underlying the negative regulation of AMH by androgens remain unknown. Our aim was to elucidate the mechanisms through which androgens downregulate AMH expression in the testis. A direct negative effect of androgens on the transcriptional activity of the AMH promoter was found using luciferase reporter assays in the mouse prepubertal Sertoli cell line SMAT1. A strong inhibition of AMH promoter activity was seen in the presence of both testosterone and DHT and of the androgen receptor. By site-directed mutagenesis and chromatin immunoprecipitation assays, we showed that androgen-mediated inhibition involved the binding sites for steroidogenic factor 1 (SF1) present in the proximal promoter of the AMH gene. In this study, we describe for the first time the mechanism behind AMH inhibition by androgens, as seen in physiological and pathological conditions in males. Inhibition of AMH promoter activity by androgens could be due to protein–protein interactions between the ligand-bound androgen receptor and SF1 or by blockage of SF1 binding to its sites on the AMH promoter. Summary Sentence Androgens act directly on prepubertal Sertoli cells to inhibit AMH promoter activity in the presence of the androgen receptor and intact SF1 sites.
Centrosome Remodelling in Evolution

The centrosome is the major microtubule organizing centre (MTOC) in animal cells. The canonical centrosome is composed of two centrioles surrounded by a pericentriolar matrix (PCM). In contrast, yeasts and amoebozoa have lost centrioles and possess acentriolar centrosomes—called the spindle pole body (SPB) and the nucleus-associated body (NAB), respectively. Despite the difference in their structures, centriolar centrosomes and SPBs not only share components but also common biogenesis regulators. In this review, we focus on the SPB and speculate how its structures evolved from the ancestral centrosome. Phylogenetic distribution of molecular components suggests that yeasts gained specific SPB components upon loss of centrioles but maintained PCM components associated with the structure. It is possible that the PCM structure remained even after centrosome remodelling due to its indispensable function to nucleate microtubules. We propose that the yeast SPB has been formed by a step-wise process; (1) an SPB-like precursor structure appeared on the ancestral centriolar centrosome; (2) it interacted with the PCM and the nuclear envelope; and (3) it replaced the roles of centrioles. Acentriolar centrosomes should continue to be a great model to understand how centrosomes evolved and how centrosome biogenesis is regulated.
Three-Level Longitudinal Mediation with Nested Units: How does an Upper-Level Predictor Influence a Lower-Level Outcome via an Upper-Level Mediator Over Time?

ABSTRACT In the study, extending from the cross-lagged panel model (CLPM) and the 2-2-1 cross-sectional multilevel mediation model, we proposed a three-level longitudinal mediation model to evaluate the causal process among variables at different levels over time. Given the complexity of the proposed model, Bayesian estimation was used. A simulation study was conducted to examine the estimation accuracy of Bayesian estimation for the proposed model. Factors considered in the simulation study included average sample sizes of the lower-level units within each upper-level unit (or cluster size; ), numbers of upper-level units (or clusters; J), numbers of time points (T), fixed direct and indirect effect sizes, and variances and covariances of upper-level random effects. Results indicated that the fixed effect estimates for the indirect effect components (a and b), fixed direct effect (c), and the average mediation effect estimate were reasonably accurate when , J > 20, and T ⩾ 4. Comparing the proposed model against the CLPMs coupled with aggregation or disaggregation techniques, we found that the CLPMs yielded misleading fixed-effects estimates and low coverage rates when variation existed between upper-level units. The proposed model was illustrated using a study on early childhood education. We compared estimates and inferences from the proposed three-level longitudinal mediation model, the CLPMs with aggregation and disaggregation, and the traditional 2-2-1 mediation model. Limitations and extensions of the current study were discussed.
Modeling and Dynamic Control of a Class of Semibiomimetic Robotic Fish

This paper proposes a new robotic fish which avoids the complex mechanical structure and reduces the model complexity comparing to the existing bioinspired robotic fish, giving rise to a semibiomimetic robotic fish. The generalized Lagrange equation is adopted to establish the dynamic model of the robotic fish. The controllability of the system is analyzed, upon which a trajectory tracking control algorithm is designed by using the feedback linearization technique. The simulation results show that the dynamic model adopted in this paper can achieve better control performance.
Genomic Investigation of a Mycobacterium tuberculosis Outbreak Involving Prison and Community Cases in Florida, United States

Abstract. We used whole-genome sequencing to investigate a tuberculosis outbreak involving U.S.-born persons in the prison system and both U.S.- and foreign-born persons in the community in Florida over a 7-year period (2009–2015). Genotyping by spacer oligonucleotide typing and 24-locus mycobacterial interspersed repetitive unit-variable number tandem repeat suggested that the outbreak might be clonal in origin. However, contact tracing could not link the two populations. Through a multidisciplinary approach, we showed that the cluster involved distinct bacterial transmission networks segregated by country of birth. The source strain is of foreign origin and circulated in the local Florida community for more than 20 years before introduction into the prison system. We also identified novel transmission links involving foreign and U.S.-born cases not discovered during contact investigation. Our data highlight the potential for spread of strains originating from outside the United States into U.S. “high-risk” populations, such as prisoners, with subsequent movement back to the general community.
Building Ethics into Artificial Intelligence

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.
First-degree atrioventricular block on basal electrocardiogram predicts future arrhythmic events in patients with Brugada syndrome: a long-term follow-up study from the Veneto region of Northeastern Italy

Aims This study was designed to assess the prognostic value of clinical and electrocardiographic parameters in Brugada syndrome (BrS). Methods and results The study population included 272 consecutive patients (82% males; mean age 43 ± 12 years), with either a spontaneous (n = 137, 50%) or drug-induced (n = 135, 50%) Type 1 Brugada electrocardiogram (ECG) pattern. The study combined endpoint included sudden cardiac death (SCD), cardiac arrest, and appropriate intervention of implantable cardioverter-defibrillator (ICD). A first-degree atrioventricular (AV) block (PR = 219 ± 17 ms) was documented at basal ECG in 45 patients (16.5%); 27 of these underwent an electrophysiological study with recording in 21 (78%) of an HV interval ≥55 ms (mean 61 ± 3 ms). Patients with first-degree AV block had a wider QRS complex (median 110 ms vs. 95 ms; P = 0.04) and more often showed a left anterior hemiblock pattern (n = 13, 29% vs. n = 35, 16%; P = 0.056). During a mean follow-up of 85 ± 55 months, 17 patients (6.3%) experienced ≥1 major arrhythmic events (appropriate ICD intervention, n = 13 and SCD, n = 4). At univariate analysis, the occurrence of major arrhythmic events was significantly associated with a history of syncope or cardiac arrest (P < 0.001), Type 1 ECG pattern (P = 0.04), and first-degree AV block (P < 0.001). Univariate and multivariable predictors of events included a history of syncope or cardiac arrest [hazard ratio (HR) 5.8, 95% confidence interval (95% CI) 2.04-16.5; P < 0.001; and HR 6.68, 95% CI 2.34-19.1; P < 0.001; respectively], a spontaneous Type 1 ECG pattern (HR 1.56, 95% CI 1.03-4.24; P = 0.033; and HR 1.84, 95% CI 1.01-4.29; P = 0.044; respectively) and a first-degree AV block at baseline ECG (HR 3.84, 95% CI 1.47-9.99; P = 0.006; and HR 4.65, 95% CI 2.34-19.1; P = 0.002; respectively). Conclusion Besides a history of cardiac arrest or syncope, first-degree AV block on basal ECG is an independent predictor of malignant arrhythmic events and a stronger marker of arrhythmic risk than a spontaneous 'coved-type' ECG pattern in patients with BrS.
A Deep Modular RNN Approach for Ethos Mining

Automatically recognising and extracting the reasoning expressed in natural language text is extremely demanding and only very recently has there been significant headway. While such argument mining focuses on logos (the content of what is said) evidence has demonstrated that using ethos (the character of the speaker) can sometimes be an even more powerful tool of influence. We study the UK parliamentary debates which furnish a rich source of ethos with linguistic material signalling the ethotic relationships between politicians. We then develop a novel deep modular recurrent neural network, DMRNN, approach and employ proven methods from argument mining and sentiment analysis to create an ethos mining pipeline. Annotation of ethotic statements is reliable and its extraction is robust (macro-F1 = 0.83), while annotation of polarity is perfect and its extraction is solid (macro-F1 = 0.84). By exploring correspondences between ethos in political discourse and major events in the political landscape through ethos analytics, we uncover tantalising evidence that identifying expressions of positive and negative ethotic sentiment is a powerful instrument for understanding the dynamics of governments.
Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback

Matrix factorization has been widely adopted for recommendation by learning latent embeddings of users and items from observed user-item interaction data. However, previous methods usually assume the learned embeddings are static or homogeneously evolving with the same diffusion rate. This is not valid in most scenarios, where users’ preferences and item attributes heterogeneously drift over time. To remedy this issue, we have proposed a novel dynamic matrix factorization model, named Dynamic Bayesian Logistic Matrix Factorization (DBLMF), which aims to learn heterogeneous user and item embeddings that are drifting with inconsistent diffusion rates. More specifically, DBLMF extends logistic matrix factorization to model the probability a user would like to interact with an item at a given timestamp, and a diffusion process to connect latent embeddings over time. In addition, an efficient Bayesian inference algorithm has also been proposed to make DBLMF scalable on large datasets. The effectiveness of the proposed method has been demonstrated by extensive experiments on real datasets, compared with the state-of-the-art methods.
Teaching NeuroImages: Cerebral cortex swelling in Creutzfeldt-Jakob disease with V180I mutation

A 74-year-old woman developed amnesia that rapidly progressed over 6 months. She exhibited no apparent neurologic abnormalities, except for cognitive decline. Mini-Mental State Examination score was 20/30, mainly involving orientation and recent memory. EEG revealed no periodic synchronous discharge. CSF analysis was negative for 14-3-3 protein. Brain MRI revealed abnormal signals in the cerebral cortices (cortical ribboning) with swelling (figure). Analysis of the prion protein gene (PRNP) revealed V180I mutation. Premortem diagnosis of Creutzfeldt-Jakob disease with V180I mutation, especially in early stage, merely based on clinical features is difficult.1,2 MRI revealed cortical swelling that is associated with V180I mutation and prompts genetic testing.1,2
Personality-Aware Personalized Emotion Recognition from Physiological Signals

Emotion recognition methodologies from physiological signals are increasingly becoming personalized, due to the subjective responses of different subjects to physical stimuli. Existing works mainly focused on modelling the involved physiological corpus of each subject, without considering the psychological factors. The latent correlation among different subjects has also been rarely examined. We propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we assign each of them with weights. The emotion relevance learned on the vertex-weighted multi-modal multi-task hypergraphs is employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method.
Biharmonic Distance Related Centrality for Edges in Weighted Networks

The Kirchhoff index, defined as the sum of effective resistances over pairs all of nodes, is of primary significance in diverse contexts of complex networks. In this paper, we propose to use the rate at which the Kirchhoff index changes with respect to the change of resistance of an edge as a measure of importance for this edge in weighted networks. For an arbitrary edge, we explicitly determine the change of the Kirchhoff index and express it in terms of the biharmonic distance between its end nodes, and thus call this centrality as biharmonic distance related centrality (BDRC). We show that BDRC has a better discriminating power than those commonly used metrics, such as edge betweenness and spanning edge centrality. We give an efficient algorithm that provides an approximation of biharmonic distance for all edges in nearly linear time of the number of edges, with a high probability. Experiment results validate the efficiency and accuracy of the presented algorithm.
Answering Mixed Type Questions about Daily Living Episodes

We propose a physical-world question-answering (QA) method, where the system answers a text question about the physical world by searching a given sequence of sentences about daily-life episodes. To address various information needs in a physical world situation, the physical-world QA methods have to generate mixed-type responses (e.g. word sequence, word set, number, and time as well as a single word) according to the content of questions, after reading physical-world event stories. Most existing methods only provide words or choose answers from multiple candidates. In this paper, we use multiple decoders to generate a mixed-type answer encoding daily episodes with a memory architecture that can capture short- and long-term event dependencies. Results using house-activity stories show that the use of multiple decoders with memory components is effective for answering various physical-world QA questions.
Seeking Practical CDCL Insights from Theoretical SAT Benchmarks

Over the last decades Boolean satisfiability (SAT) solvers based on conflict-driven clause learning (CDCL) have developed to the point where they can handle formulas with millions of variables. Yet a deeper understanding of how these solvers can be so successful has remained elusive. In this work we shed light on CDCL performance by using theoretical benchmarks, which have the attractive features of being a) scalable, b) extremal with respect to different proof search parameters, and c) theoretically easy in the sense of having short proofs in the resolution proof system underlying CDCL. This allows for a systematic study of solver heuristics and how efficiently they search for proofs. We report results from extensive experiments on a wide range of benchmarks. Our findings include several examples where theory predicts and explains CDCL behaviour, but also raise a number of intriguing questions for further study.