Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods for the analysis of multiple endpoints in small populations: A review

ABSTRACT While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. This paper reviews approaches based on a combination of test statistics or measurements across endpoints as well as multiple testing procedures that allow for confirmatory conclusions on individual endpoints. We especially focus on feasibility in trials with small sample sizes and do not solely rely on asymptotic considerations. A systematic literature search in the Scopus database, supplemented by a manual search, was performed to identify research papers on analysis methods for multiple endpoints with relevance to small populations. The identified methods were grouped into approaches that combine endpoints into a single measure to increase the power of statistical tests and methods to investigate differential treatment effects in several individual endpoints by multiple testing.
Eicosapentaenoic Acid and Prevalence of Cardiovascular Disease in Hemodialysis Patients

Eicosapentaenoic acid (EPA)/arachidonic acid (AA) ratio showed inverse associations with cardiovascular disease (CVD) in general population. However, this has not been examined enough in dialysis patients. We cross‐sectionally investigated the relationship between EPA/AA ratio and prevalence of CVD in 321 chronic hemodialysis patients (64 ± 11 years old; 110 women; dialysis vintage 10 ± 8 years) in an urban area of Tokyo. CVD was defined as a composite of ischemic heart disease, ischemic stroke and hemorrhagic stroke. The frequency of dietary fish intake was also examined. Logistic regression was used to quantify the association of EPA/AA ratio with CVD. EPA/AA ratio was 0.31 ± 0.19 and 154 patients (48%) consumed fish once or less weekly. One hundred and thirty patients (41%) had CVD, including 65 with ischemic heart disease, 70 with ischemic stroke, and 20 with hemorrhagic stroke. Age (odds ratio [OR], 1.04; P = 0.01), hypertension (OR, 2.25; P = 0.002), and dialysis vintage (OR, 1.04; P = 0.02) were associated with CVD; however, EPA/AA was not after adjustment for other risk factors. A similar relationship was observed between fish intake and CVD prevalence. We did not find any significant association between EPA/AA ratio and prevalence of CVD, although traditional risk factors such as age, hypertension and dialysis vintage were associated with CVD. These results might have been influenced by the fact that only a small proportion of our patients showed a high EPA/AA ratio.
Reasoning about Consensus when Opinions Diffuse through Majority Dynamics

Opinion diffusion is studied on social graphs where agents hold binary opinions and where social pressure leads them to conform to the opinion manifested by their neighbors. Within this setting, questions related to whether a minority/majority can spread the opinion it supports to all the other agents are considered.It is shown that, no matter of the graph given at hand, there always exists a group formed by a half of the agents that can annihilate the opposite opinion. Instead, the influence power of minorities depends on certain features of the underlying graphs, which are NP-hard to be identified. Deciding whether the two opinions can coexist in some stable configuration is NP-hard, too.
Compact-MDD: Efficiently Filtering (s)MDD Constraints with Reversible Sparse Bit-sets

Multi-Valued Decision Diagrams (MDDs) are instrumental in modeling combinatorial problems with Constraint Programming.In this paper, we propose a related data structure called sMDD (semi-MDD) where the central layer of the diagrams is non-deterministic.We show that it is easy and efficient to transform any table (set of tuples) into an sMDD.We also introduce a new filtering algorithm, called Compact-MDD, which is based on bitwise operations, and can be applied to both MDDs and sMDDs.Our experimental results show the practical interest of our approach, both in terms of compression and filtering speed.
Reconceptualization innovative moments as a predictor of symptomatology improvement in treatment for depression.

OBJECTIVES In previous studies, reconceptualization innovative moments were associated with successful psychotherapy. Reconceptualization has two components-(a) a positive temporal contrast between the past self and the present self (contrasting self [CS]) and (b) a description of how and/or why this change has occurred (change process [CP])-from the perspective of the client. The aim of this study is to analyse if CS and CP have the same association with outcomes as reconceptualization. METHOD Sixteen cases of clients with major depression (305 sessions) were analysed. Longitudinal regression models were used to explore if proportions of CS, CP, and reconceptualization predicted outcome measures and if outcome measures predicted CS, CP, and reconceptualization. RESULTS Reconceptualization is less frequent than CS and CP taken separately, but reconceptualization was a better predictor of treatment outcomes than were its separate components. Moreover, symptom improvement did not predict reconceptualization. CONCLUSION The construction of new meanings is important in improving depressive symptomatology. Psychotherapists can elicit these new meanings in their regular practice by posing questions that may help clients to conceptualize what is changing in themselves (CS) and questions of how this change is occurring (CP). The construction of an integrative account of these new meanings is associated with psychotherapeutic gains, and thus, reconceptualizing change could improve symptoms of depression.
A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization

In this paper, we propose a generic approach for accelerating the convergence of existing algorithms to solve the problem of stochastic zeroth-order convex optimization (SZCO). Standard techniques for accelerating the convergence of stochastic zeroth-order algorithms are by exploring multiple functional evaluations (e.g., two-point evaluations), or by exploiting global conditions of the problem (e.g., smoothness and strong convexity). Nevertheless, these classic acceleration techniques are necessarily restricting the applicability of newly developed algorithms. The key of our proposed generic approach is to explore a local growth condition (or called local error bound condition) of the objective function in SZCO. The benefits of the proposed acceleration technique are: (i) it is applicable to both settings with one-point evaluation and two-point evaluations; (ii) it does not necessarily require strong convexity or smoothness condition of the objective function; (iii) it yields an improvement on convergence for a broad family of problems. Empirical studies in various settings demonstrate the effectiveness of the proposed acceleration approach.
Recent Advances in Querying Probabilistic Knowledge Bases

We give a survey on recent advances at the forefront of research on probabilistic knowledge bases for representing and querying large-scale automatically extracted data. We concentrate especially on increasing the semantic expressivity of formalisms for representing and querying probabilistic knowledge (i) by giving up the closed-world assumption, (ii) by allowing for commonsense knowledge (and in parallel giving up the tuple-independence assumption), and (iii) by giving up the closed-domain assumption, while preserving some computational properties of query answering in such formalisms.
Hierarchical Graph Structure Learning for Multi-View 3D Model Retrieval

3D model retrieval has been widely utilized in numerous domains, such as computer-aided design, digital entertainment and virtual reality. Recently, many graph-based methods have been proposed to address this task by using multiple views of 3D models. However, these methods are always constrained by the many-to-many graph matching for similarity measure between pair-wise models. In this paper, we propose an hierarchical graph structure learning method (HGS) for 3D model retrieval. The proposed method can decompose the complicated multi-view graph-based similarity measure into multiple single-view graph-based similarity measures. In the bottom hierarchy, we present the method for single-view graph generation and further propose the novel method for similarity measure in single-view graph by leveraging both node-wise context and model-wise context. In the top hierarchy, we fuse the similarities in single-view graphs with respect to different viewpoints to get the multi-view similarity between pair-wise models. In this way, the proposed method can avoid the difficulty in definition and computation in the traditional high-order graph. Moreover, this method is unsupervised and is independent of large-scale 3D dataset for model learning. We conduct extensive evaluation on three popular and challenging datasets. The comparison demonstrates the superiority and effectiveness of the proposed method comparing with the state of the arts. Especially, this unsupervised method can achieve competing performance against the most recent supervised & deep learning method.
The Cognitive Impact of Chronic Diseases on Functional Capacity in Community-Dwelling Adults

Background: People with chronic diseases may experience poor cognitive functioning associated with advanced age, progression of disease, or other comorbid chronic conditions. Empirical evidence of this phenomenon is limited despite the clinical relevance of cognitive decline and associated adverse outcomes such as poor physical functioning. Purpose: The purpose of this study was to examine cognitive functioning in the domains of memory, attention, and executive function and its association with functional capacity in a sample of community-dwelling adults with a spectrum of chronic diseases. Methods: An exploratory cross-sectional study was conducted in a sample of community-dwelling adults with chronic diseases, including hypertension (58.9%), diabetes mellitus (DM; 20.0%), and dyslipidemia (14.4%). Participants’ mean age was 64.1 ± 11.2 years, and 48.9% were male. Ninety persons completed the face-to-face interviews, which evaluated cognitive functioning in the domains of memory, attention, and executive function using neuropsychological tests and the physical well-being test, which measured functional capacity using the Duke Activity Status Index. Results: Compared with those with other chronic diseases, our sample with hypertension and DM had significantly more memory loss and poorer executive function. These significant differences were nullified when adjusting for age, gender, and education. Approximately one third had functional limitations (n = 29, 32.2%), using a cutoff point of 35 or less (Duke Activity Status Index). Memory loss (delayed recall, b = 1.5, p = .016) and poor executive function (Trail Making Test Part A, b = −0.2, p < .001) were predicting factors of functional decline, independent of age, gender, education, and comorbidity. Conclusions/Implications for Practice: Cognitive function, particularly memory and executive function, was poorer among chronically ill Korean adults in the community with hypertension or DM than their counterparts. Functional decline was worse in the presence of memory loss and poor executive function. Studies examining the mechanism by which overall functioning is impacted by cognitive decline and its relevance to functional declines in a larger representative sample are warranted.
Oncolytic Viruses as Therapeutic Tools for Pediatric Brain Tumors

In recent years, we have seen an important progress in our comprehension of the molecular basis of pediatric brain tumors (PBTs). However, they still represent the main cause of death by disease in children. Due to the poor prognosis of some types of PBTs and the long-term adverse effects associated with the traditional treatments, oncolytic viruses (OVs) have emerged as an interesting therapeutic option since they displayed safety and high tolerability in pre-clinical and clinical levels. In this review, we summarize the OVs evaluated in different types of PBTs, mostly in pre-clinical studies, and we discuss the possible future direction of research in this field. In this sense, one important aspect of OVs antitumoral effect is the stimulation of an immune response against the tumor which is necessary for a complete response in preclinical immunocompetent models and in the clinic. The role of the immune system in the response of OVs needs to be evaluated in PBTs and represents an experimental challenge due to the limited immunocompetent models of these diseases available for pre-clinical research.
Inferring Temporal Knowledge for Near-Periodic Recurrent Events

We define the novel problem of extracting and predicting occurrence dates for a class of recurrent events -- events that are held periodically as per a near-regular schedule (e.g., conferences, film festivals, sport championships). Knowledge-bases such as Freebase contain a large number of such recurring events, but they also miss substantial information regarding specific event instances and their occurrence dates. We develop a temporal extraction and inference engine to fill in the missing dates as well as to predict their future occurrences. Our engine performs joint inference over several knowledge sources -- (1) information about an event instance and its date extracted from text by our temporal extractor, (2) information about the typical schedule (e.g., ``every second week of June") for a recurrent event extracted by our schedule extractor, and (3) known dates for other instances of the same event. The output of our system is a representation for the event schedule and an occurrence date for each event instance. We find that our system beats humans in predicting future occurrences of recurrent events by significant margins. We release our code and system output for further research.
Superinfection and cure of infected cells as mechanisms for hepatitis C virus adaptation and persistence

Significance Viral populations exhibit an extraordinary ability to survive abrupt changes in host environment by rapidly generating adaptive mutations. However, our understanding of how viral populations respond to selection pressure and the underlying molecular mechanisms supporting viral adaptation in vivo is limited. Here, we report a set of clinical data sampled from subjects chronically infected by hepatitis C virus (HCV). The data show rapid expansion and turnover of drug-resistant viruses following treatment with an HCV protease inhibitor. By fitting mathematical models to the data, we propose that superinfection and cure of infected cells play critical roles in facilitating the rapid expansion and turnover of viral populations. Our results highlight the importance of considering intracellular viral competition in understanding rapid viral adaptation. RNA viruses exist as a genetically diverse quasispecies with extraordinary ability to adapt to abrupt changes in the host environment. However, the molecular mechanisms that contribute to their rapid adaptation and persistence in vivo are not well studied. Here, we probe hepatitis C virus (HCV) persistence by analyzing clinical samples taken from subjects who were treated with a second-generation HCV protease inhibitor. Frequent longitudinal viral load determinations and large-scale single-genome sequence analyses revealed rapid antiviral resistance development, and surprisingly, dynamic turnover of dominant drug-resistant mutant populations long after treatment cessation. We fitted mathematical models to both the viral load and the viral sequencing data, and the results provided strong support for the critical roles that superinfection and cure of infected cells play in facilitating the rapid turnover and persistence of viral populations. More broadly, our results highlight the importance of considering viral dynamics and competition at the intracellular level in understanding rapid viral adaptation. Thus, we propose a theoretical framework integrating viral and molecular mechanisms to explain rapid viral evolution, resistance, and persistence despite antiviral treatment and host immune responses.
Prognostic factors among critically ill patients with community-acquired acute bacterial meningitis and acute kidney injury

Objective To investigate prognostic factors among critically ill patients with community-acquired bacterial meningitis and acute kidney injury. Methods A retrospective study including patients admitted to a tertiary infectious disease hospital in Fortaleza, Brazil diagnosed with community-acquired bacterial meningitis complicated with acute kidney injury. Factors associated with death, mechanical ventilation and use of vasopressors were investigated. Results Forty-one patients were included, with a mean age of 41.6 ± 15.5 years; 56% were males. Mean time between intensive care unit admission and acute kidney injury diagnosis was 5.8 ± 10.6 days. Overall mortality was 53.7%. According to KDIGO criteria, 10 patients were classified as stage 1 (24.4%), 18 as stage 2 (43.9%) and 13 as stage 3 (31.7%). KDIGO 3 significantly increased mortality (OR = 6.67; 95%CI = 1.23 - 36.23; p = 0.028). Thrombocytopenia was not associated with higher mortality, but it was a risk factor for KDIGO 3 (OR = 5.67; 95%CI = 1.25 - 25.61; p = 0.024) and for mechanical ventilation (OR = 6.25; 95%CI = 1.33 - 29.37; p = 0.02). Patients who needed mechanical ventilation by 48 hours from acute kidney injury diagnosis had higher urea (44.6 versus 74mg/dL, p = 0.039) and sodium (138.6 versus 144.1mEq/L; p = 0.036). Conclusion Mortality among critically ill patients with community-acquired bacterial meningitis and acute kidney injury is high. Acute kidney injury severity was associated with even higher mortality. Thrombocytopenia was associated with severer acute kidney injury. Higher urea was an earlier predictor of severer acute kidney injury than was creatinine.
Highly Ductile and Ultra-Thick P-Doped FeSiB Amorphous Alloys with Excellent Soft Magnetic Properties

Herein, we demonstrate the successful synthesis of novel Fe80Si9B(11−x)Px (x = 0, 1, 3, 5, 7) ultra-thick amorphous ribbons by planar flow casting. The influence of P alloying on glass forming ability (GFA), microstructure, thermal stability, soft magnetic properties, and ductility has been systematically investigated. The results reveal that introduction of P into Fe80Si9B11 alloy can remarkably enhance the GFA and increase critical thickness (tc) of the alloy from 45 to 89 um. Furthermore, the annealed FeSiBP amorphous alloys exhibited excellent soft magnetic properties, including high saturation magnetic flux density of 1.54 T, the low coercivity of 1.5 A/m, and low core losses of 0.15 W/kg. In addition, the representative Fe80Si9B8P3 ultra-thick amorphous alloy demonstrate excellent ductility even after annealing at 400 °C for 10 min, which indicates the superior performance of P-doped FeSiB alloys as compared to the commercial Fe78Si9B13 (Metglas 2605 S2) alloy. The combination of high GFA, excellent ductility, and low core losses of newly developed FeSiBP amorphous soft magnetic alloys makes them attractive candidates for magnetic applications in the high-frequency and high-speed electric devices.
Multi-scale and Discriminative Part Detectors Based Features for Multi-label Image Classification

Convolutional neural networks (CNNs) have shown their promise for image classification task. However, global CNN features still lack geometric invariance for addressing the problem of intra-class variations and so are not optimal for multi-label image classification. This paper proposes a new and effective framework built upon CNNs to learn Multi-scale and Discriminative Part Detectors (MsDPD)-based feature representations for multi-label image classification. Specifically, at each scale level, we (i) first present an entropy-rank based scheme to generate and select a set of discriminative part detectors (DPD), and then (ii) obtain a number of DPD-based convolutional feature maps with each feature map representing the occurrence probability of a particular part detector and learn DPD-based features by using a task-driven pooling scheme. The two steps are formulated into a unified framework by developing a new objective function, which jointly trains part detectors incrementally and integrates the learning of feature representations into the classification task. Finally, the multi-scale features are fused to produce the predictions. Experimental results on PASCAL VOC 2007 and VOC 2012 datasets demonstrate that the proposed method achieves better accuracy when compared with the existing state-of-the-art multi-label classification methods.
Estrogen-regulated feedback loop limits the efficacy of estrogen receptor–targeted breast cancer therapy

Significance Estrogen receptor-positive (ER+) breast cancer is treated with endocrine therapies, although therapeutic resistance almost invariably develops in advanced disease. Using genome-wide CRISPR screens, we identified genes whose loss confers endocrine resistance, as well as synthetic lethal vulnerabilities to overcome such resistance. These findings reveal an estrogen-induced negative feedback loop that constrains the growth of ER+ tumors, thereby limiting the efficacy of therapies that inhibit ER, and suggest a previously unappreciated therapeutic route to overcoming endocrine resistance. Endocrine therapy resistance invariably develops in advanced estrogen receptor-positive (ER+) breast cancer, but the underlying mechanisms are largely unknown. We have identified C-terminal SRC kinase (CSK) as a critical node in a previously unappreciated negative feedback loop that limits the efficacy of current ER-targeted therapies. Estrogen directly drives CSK expression in ER+ breast cancer. At low CSK levels, as is the case in patients with ER+ breast cancer resistant to endocrine therapy and with the poorest outcomes, the p21 protein-activated kinase 2 (PAK2) becomes activated and drives estrogen-independent growth. PAK2 overexpression is also associated with endocrine therapy resistance and worse clinical outcome, and the combination of a PAK2 inhibitor with an ER antagonist synergistically suppressed breast tumor growth. Clinical approaches to endocrine therapy-resistant breast cancer must overcome the loss of this estrogen-induced negative feedback loop that normally constrains the growth of ER+ tumors.
Stakeholder Perspectives on Using and Developing the MYPLAN Suicide Prevention Mobile Phone Application: A Focus Group Study

The objective of this study was to explore different stakeholder perspectives on the MYPLAN app for suicide prevention safety planning. The study was a comparative analysis of 4 focus groups with Danish MYPLAN stakeholders, young users, adult users, relatives, and clinicians. The focus groups were audio recorded, transcribed, and subjected to a thematic analysis. The analysis contextualized the participants’ experiences of the benefits and limitations of MYPLAN. While participants believed that MYPLAN could potentially interrupt early stages of a suicidal process, clinicians’ involvement in safety planning was considered important. MYPLAN could potentially give users a sense of increased personal control but learning how to effectively safety plan was not perceived to be simple and additional support should be considered for MYPLAN users.