Miracle-dz/UniFAR-FTUs huggingface dataset:
MYC Expression and Metabolic Redox Changes in Cancer Cells: A Synergy Able to Induce Chemoresistance

Chemoresistance is due to multiple factors including the induction of a metabolic adaptation of tumor cells. In fact, in these cells, stress conditions induced by therapies stimulate a metabolic reprogramming which involves the strengthening of various pathways such as glycolysis, glutaminolysis and the pentose phosphate pathway. This metabolic reprogramming is the result of a complex network of mechanisms that, through the activation of oncogenes (i.e., MYC, HIF1, and PI3K) or the downregulation of tumor suppressors (i.e., TP53), induces an increased expression of glucose and/or glutamine transporters and of glycolytic enzymes. Therefore, in order to overcome chemoresistance, it is necessary to develop combined therapies which are able to selectively and simultaneously act on the multiple molecular targets responsible for this adaptation. This review is focused on highlighting the role of MYC in modulating the epigenetic redox changes which are crucial in the acquisition of therapy resistance.
Same-Day HIV Pre-Exposure Prophylaxis (PrEP) Initiation During Drop-in Sexually Transmitted Diseases Clinic Appointments Is a Highly Acceptable, Feasible, and Safe Model that Engages Individuals at Risk for HIV into PrEP Care

Abstract Objective Strategies to increase pre-exposure prophylaxis (PrEP) uptake are needed. We hypothesized that same-day PrEP initiation in a sexually transmitted diseases (STD) clinic would be acceptable, feasible, and safe, and that individuals would engage in ongoing PrEP care. Method Individuals aged ≥ 18 years were evaluated for PrEP. Exclusion criteria were HIV, history of renal dysfunction or chronic hepatitis B infection, pregnancy, indications for HIV post-exposure prophylaxis, or positive screen for acute HIV symptoms. One hundred individuals received a free 30-day PrEP starter pack and met with a patient navigator to establish ongoing care. Bivariate analysis and multivariable logistic regression were used to compare individuals who did and did not attend at least 1 PrEP follow-up appointment within 180 days of enrollment. Client satisfaction surveys were given 3 months after enrollment. Results The majority (78%) of participants completed at least 1 PrEP follow-up appointment, and 57% attended at least 2 follow-up appointments. After adjusting for race and ethnicity, age, health insurance status, and annual income, only income was associated with follow-up appointment attendance. Each additional $10,000 increase in income was associated with a 1.7-fold increase in the odds of attending a PrEP follow-up appointment (95% confidence interval, 1.07–2.66, P = .02). The majority (54%) of individuals completed the satisfaction survey and all respondents liked the option of same-day PrEP initiation. Conclusions Our study suggests STD clinic-based, same-day PrEP initiation is acceptable, feasible, safe, and links a high proportion of individuals into ongoing PrEP care. Additional resources may be needed to support low-income individuals’ retention in care.
Evolutionary dynamics and molecular epidemiology of West Nile virus in New York State: 1999–2015

Abstract Following its introduction into New York State (NYS) in 1999, West Nile virus (WNV; Flavivirus, Flaviviridae) underwent a rapid expansion throughout the USA and into Canada and Latin America. WNV has been characterized as being evolutionarily stable, with weak geographic structure, a dominance of purifying selection and limited adaptive change. We analyzed all available full-genome WNV sequences, focusing on the 543 available sequences from NYS, which included 495 newly sequenced 2000–15 isolates. In addition, we analyzed deep-sequencing data from 317 of these isolates. While our data are generally in agreement with the limited pace of evolutionary change and broad geographic and temporal mixing identified in other studies, we have identified some important exceptions. Most notably, there are 14 codons which demonstrated evidence of positive selection as determined by multiple models, including some positions with evidence of selection in NYS exclusively. Coincident with increased WNV activity, genotypes possessing one or more of these mutations, designated NY01, NY07, and NY10, have increased in prevalence in recent years and displaced historic strains. In addition, we have found a geographical bias with many of these mutations, which suggests selective pressures and adaptations could be regional. Lastly, our deep-sequencing data suggest both increased overall diversity in avian tissue isolates relative to mosquito isolates and multiple non-synonymous minority variants that are both host-specific and retained over time and space. Together, these data provide novel insight into the evolutionary pressures on WNV and the need for continued genetic surveillance and characterization of emergent strains.
Taxonomy, systematics and geographic distribution of ground frogs (Alsodidae, Eupsophus): a comprehensive synthesis of the last six decades of research

Abstract The genus Eupsophus (ground frogs) inhabits exclusively the temperate forests of southern South America (Chile and Argentina). The current delimitation of the genus was reached in the late 1970s, when only two species were recognized, but since then the number of described species steadily increased, reaching a maximum of 11 by 2012. Subsequent studies that applied explicit species delimitation approaches decreased the number of species to six in 2017 and raised it again to 11 the following year, including an undescribed putative species. Despite these taxonomic changes, the two species groups traditionally recognized, roseus and vertebralis, have been maintained. Another recent contribution to the taxonomy of the genus was the explicit recognition of the extremely high level of external phenotypic variation exhibited by species of the roseus group, which undermines the utility of some diagnostic characters. Here we provide a critical review of the extensive taxonomic and systematic literature on the genus over the last six decades, to examine the evidence behind the recurrent taxonomic changes and advances in its systematics. We also update and complete a 2017 review of geographic information, provide additional qualitative observations of external characters commonly used in the diagnoses of species of the roseus group, and reassess the phylogenetic position of a putative new species from Tolhuaca (Chile), which was not included in the last species delimitation study. The present review shows that: 1) there is no congruence between the patterns of phenotypic and genetic/phylogenetic differentiation among species of both groups; 2) in the roseus group, the intraspecific variation in some external characters is as high as the differences described among species; 3) there is little morphological and bioacoustic differentiation within species groups, and inconsistencies in the chromosomal evidence at the genus level; 4) under the latest taxonomic proposal (2018), species of the roseus group still lack consistent and reliable diagnoses and their distribution limits are poorly defined; and 5) the population from Tolhuaca represents an additional undescribed species under the most recent taxonomic framework. Finally, we discuss the implications of these findings for the taxonomy and biogeography of the genus, pointing out some areas that require further research to understand their patterns and processes of diversification.
New Insights into the Process of Placentation and the Role of Oxidative Uterine Microenvironment

For a successful pregnancy to occur, a predecidualized receptive endometrium must be invaded by placental differentiated cells (extravillous trophoblast cells (EVTs)) and, at the same time, continue decidualization. EVT invasion is aimed at anchoring the placenta to the maternal uterus and ensuring local blood supply increase necessary to provide normal placental and foetal development. The first is achieved by migrating through the maternal endometrium and deeper into the myometrium, while the second by transforming uterine spiral arteries into large vessels. This process is a tightly regulated battle comprising interests of both the mother and the foetus. Invading EVTs are required to perform a scope of functions: move, adhere, proliferate, differentiate, interact, and digest the extracellular matrix (ECM); tolerate hypoxia; transform the maternal spiral arteries; and die by apoptosis. All these functions are modulated by their surrounding microenvironment: oxygen, soluble factors (e.g., cytokines, growth factors, and hormones), ECM proteins, and reactive oxygen species. A deeper comprehension of oxidative uterine microenvironment contribution to trophoblast function will be addressed in this review.
Switching Tenofovir/Emtricitabine/Efavirenz (TDF/FTC/EFV) to TDF/FTC/Rilpivirine vs Continuing TDF/FTC/EFV in HIV-Infected Patients With Virological Suppression: A Randomized Controlled Trial

Abstract A randomized controlled noninferiority trial was conducted in HIV-infected patients receiving tenofovir/emtricitabine/efavirenz (TDF/FTC/EFV) with virological suppression in a resource-limited setting. Switching to TDF/FTC/rilpivirine was noninferior to continuing TDF/FTC/EFV in terms of maintaining compete viral suppression at 24 weeks and provided better lipid profiles and fewer central nervous system adverse effects.
Proppy: A System to Unmask Propaganda in Online News

We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the news into events, and organizes the articles about an event on the basis of the likelihood that they contain propagandistic content. The system is trained on known propaganda sources using a variety of stylistic features. The evaluation results on a standard dataset show stateof-the-art results for propaganda detection.
New Directions in Sparse Models for Image Analysis and Restoration

Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery. By considering graph relations between data samples while learning sparse models, graph-embedded codes can be obtained for use in unsupervised, supervised and semi-supervised problems. Using experiments on standard datasets, it is demonstrated that the codes obtained from the proposed methods outperform several baseline algorithms. In order to facilitate sparse learning with large scale data, the paradigm of ensemble sparse coding is proposed, and different strategies for constructing weak base models are developed. Experiments with image recovery and clustering demonstrate that these ensemble models perform better when compared to conventional sparse coding frameworks. When examples from the data manifold are available, manifold constraints can be incorporated with sparse models and two approaches are proposed to combine sparse coding with manifold projection. The improved performance of the proposed techniques in comparison to sparse coding approaches is demonstrated using several image recovery experiments. In addition to these approaches, it might be required in some applications to combine multiple sparse models with different regularizations. In particular, combining an unconstrained sparse model with non-negative sparse coding is important in image analysis, and it poses several algorithmic and theoretical challenges. A convex and an efficient greedy algorithm for recovering combined representations are proposed. Theoretical guarantees on sparsity thresholds for exact recovery using these algorithms are derived and recovery performance is also demonstrated using simulations on synthetic data. Finally, the problem of non-linear compressive sensing, where the measurement process is carried out in feature space obtained using non-linear transformations, is considered. An optimized non-linear measurement system is proposed, and improvements in recovery performance are demonstrated in comparison to using random measurements as well as optimized linear measurements.
Automating Analysis and Feedback to Improve Mathematics Teachers' Classroom Discourse

Our work builds on advances in deep learning for natural language processing to automatically analyze transcribed classroom discourse and reliably generate information about teachers’ uses of specific discursive strategies called ”talk moves. ” Talk moves can be used by both teachers and learners to construct conversations in which students share their thinking, actively consider the ideas of others, and engage in sustained reasoning. Currently, providing teachers with detailed feedback about the talk moves in their lessons requires highly trained observers to hand code transcripts of classroom recordings and analyze talk moves and/or one-on-one expert coaching, a time-consuming and expensive process that is unlikely to scale. We created a bidirectional long short-term memory (bi-LSTM) network that can automate the annotation process. We have demonstrated the feasibility of this deep learning approach to reliably identify a set of teacher talk moves at the sentence level with an F1 measure of 65%.
The Rensselaer Mandarin Project - A Cognitive and Immersive Language Learning Environment

The Rensselaer Mandarin Project enables a group of foreign language students to improve functional understanding, pronunciation and vocabulary in Mandarin Chinese through authentic speaking situations in a virtual visit to China. Students use speech, gestures, and combinations thereof to navigate an immersive, mixed reality, stylized realism game experience through interaction with AI agents, immersive technologies, and game mechanics. The environment was developed in a black box theater equipped with a human-scale 360◦ panoramic screen (140h, 200r), arrays of markerless motion tracking sensors, and speakers for spatial audio.
Envisioning AI for K-12: What Should Every Child Know about AI?

The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA's national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
Integration and Selection of Linear SVM Classifiers in Geometric Space

: Integration or fusion of the base classiﬁers is the ﬁnal stage of creating multiple classiﬁers system. Known methods in this step use base classiﬁer outputs, which are class labels or values of the conﬁdence (predicted probabilities) for each class label. In this paper we propose an integration process which takes place in the geometric space. It means that the fusion of base classiﬁers is done using their decision boundaries. In order to obtain one decision boundary from boundaries deﬁned by base classiﬁers the median or weighted average method will be used. In addition, the proposed algorithm uses the division of the entire feature space into disjoint regions of competence as well as the process of selection of base classiﬁers is carried out. The aim of the experiments was to compare the proposed algorithms with the majority voting method and assessment which of the analyzed approaches to integration of the base classiﬁers creates a more eﬀective ensemble.
Estimation of sample size in systematic review and Meta-analysis: trial sequential analysis

The estimation of sample size is required by the repeatability principle of clinical trials aiming at the study power in statistics.Similarly,in systematic review and Meta-analysis,when the number of included trials or the total sample size was too small,the curative effect will be exaggerated due to random errors.The paper briefly introduces a method for estimating sample size,which can adjust random errors and calculate the sample size needed by systematic review and Meta-analysis through applying trial sequential analysis(TSA).
Highs and Lows in Motor Control Development

Abstract Motor control is classically described as relying on two components: anticipatory control (feedforward processing) and online control (feedback processing). Here we aimed to unveil the developmental steps of both feedback and feedforward control in 5–10 years old children, using a simple and ecological task. We manipulated object’s weight in a reach-to-displace paradigm. When the weight was known before lifting it, anticipatory processes were quantifiable during the reaching phase. Conversely, an unknown weight triggered online corrections during the displacing phase. Movement kinematics revealed that children anticipate this objet property as young as 5 y-o. This anticipation becomes adequate around 7 y-o and is paralleled by poor online corrections. This simple yet relevant paradigm should allow quantifying deviations from neurotypical patterns in disorders of motor control.
A simulator study of driving behavior and mental workload in mixed-use arterial road environments

Abstract Objectives: Mixed-use urban environments, such as arterial roads with adjacent commercial land uses, represent crash locations with the highest risk. These locations are often characterized by high volumes of motor vehicle traffic, on-street parking, and interactions with multiple road user groups such as pedestrians, cyclists, and public transportation. The objective of this study was to investigate previously identified crash risk factors for mixed-use urban environments and assess how parking occupancy, center medians, and cyclist volume influence performance and workload in a driving simulator study. Methods: Thirty participants were recruited for the study. Participants completed 6 drives that presented different combinations of cyclist volume, median condition, and parking occupancy. Incorporated into the simulator drives was a secondary peripheral detection task (PDT) designed to measure mental workload. Participants provided subjective assessments of workload using the Rating Scale Mental Effort (RSME). Results: Mean lateral lane position was found to significantly vary across the 3 independent variables of parking occupancy, cyclist volume, and median conditions. No significant changes were identified for mean speed across the conditions. Subjective and objective measures of workload identified changes due to the presence of cyclists with slower reaction times for the PDT task when cyclists were present. Conclusion: The findings provide insight into the interaction of road design elements in mixed-use urban road environments and demonstrate that increasingly complex environments increase driver demand. This has important road design implications for mixed-use arterial roads, which are often characterized by complex interactions between multiple road user groups.
Sperm Proteome Analysis and Identification of Fertility-Associated Biomarkers in Unexplained Male Infertility

Up to 30% of men with normal semen parameters suffer from infertility and the reason for this is unknown. Altered expression of sperm proteins may be a major cause of infertility in these men. Proteomic profiling was performed on pooled semen samples from eight normozoospermic fertile men and nine normozoospermic infertile men using LC-MS/MS. Furthermore, key differentially expressed proteins (DEPs) related to the fertilization process were selected for validation using Western blotting. A total of 1139 and 1095 proteins were identified in normozoospermic fertile and infertile men, respectively. Of these, 162 proteins were identified as DEPs. The canonical pathway related to free radical scavenging was enriched with upregulated DEPs in normozoospermic infertile men. The proteins associated with reproductive system development and function, and the ubiquitination pathway were underexpressed in normozoospermic infertile men. Western blot analysis revealed the overexpression of annexin A2 (ANXA2) (2.03 fold change; P = 0.0243), and underexpression of sperm surface protein Sp17 (SPA17) (0.37 fold change; P = 0.0205) and serine protease inhibitor (SERPINA5) (0.32 fold change; P = 0.0073) in men with unexplained male infertility (UMI). The global proteomic profile of normozoospermic infertile men is different from that of normozoospermic fertile men. Our data suggests that SPA17, ANXA2, and SERPINA5 may potentially serve as non-invasive protein biomarkers associated with the fertilization process of the spermatozoa in UMI.
Using a two-sample Mendelian randomization design to investigate a possible causal effect of maternal lipid concentrations on offspring birth weight

Abstract Background The intrauterine environment is critical for fetal growth and development. However, observational associations between maternal gestational lipid concentrations and offspring birth weight (BW) have been inconsistent and ascertaining causality is challenging. Methods We used a novel two-sample Mendelian randomization (MR) approach to estimate the causal effect of maternal gestational high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride concentrations on offspring BW. Single nucleotide polymorphisms (SNPs) associated with serum HDL-C, LDL-C and triglyceride concentrations identified in the Global Lipids Genetics Consortium genome-wide association study meta-analysis (n = 188 577 European-ancestry individuals; sample 1) were selected as instrumental variables. The effects of these SNPs on offspring BW were estimated using a structural equation model in the UK Biobank and Early Growth Genetics consortium (n = 230 069 European-ancestry individuals; sample 2) that enabled partitioning of the genetic associations into maternal- (intrauterine) and fetal-specific effects. Results We found no evidence for a causal effect of maternal gestational HDL-C, LDL-C or triglyceride concentrations on offspring BW [standard deviation change in BW per standard deviation higher in HDL-C = −0.005 (95% confidence interval: −0.039, 0.029), LDL-C = 0.014 (−0.017, 0.045), and triglycerides = 0.014 (−0.025, 0.052)]. Conclusions Our findings suggest that maternal gestational HDL-C, LDL-C and triglyceride concentrations play a limited role in determining offspring BW. However, we cannot comment on the impact of these and other lipid fractions on fetal development more generally. Our study illustrates the power and flexibility of two-sample MR in assessing the causal effect of maternal environmental exposures on offspring outcomes.