Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Vitamin D Supplementation in Pregnant Women with Overweight and Obesity: A Randomised Controlled Trial

The impact of vitamin D supplementation on 25-hydroxyvitamin D (25OHD) levels, metabolic status, and pregnancy outcomes in pregnant women with overweight and obesity (OW/OB) is uncertain. This study aimed to examine whether administrating 800 IU of vitamin D3 orally would improve maternal serum 25OHD levels, lipid profile, and pregnancy outcomes compared to 400 IU. This was a two-arm, parallel, non-blinded randomised controlled trial involving 274 pregnant women recruited from KK Women’s and Children’s Hospital, with a body mass index of ≥25 kg/m2 within 16 weeks gestation. The participants were randomly assigned to receive 800 IU/day (intervention group) or 400 IU/day (control group) of oral vitamin D3 supplements. The primary outcomes were maternal serum 25OHD and lipid levels at 24–28 weeks gestation. The secondary outcomes included maternal and birth outcomes. Compared with controls (n = 119), the intervention group (n = 112) exhibited higher 25OHD levels at 24–28 weeks gestation (adjusted mean difference 6.52 nmol/L; 95% confidence interval 2.74, 10.31). More women in the intervention group achieved sufficient 25OHD levels (77.7% vs. 55.5%; p < 0.001). No differences were observed in lipid profiles or maternal or birth outcomes between the groups. An additional 400 IU of oral vitamin D3 supplementation increased serum 25OHD levels but did not impact lipid profiles or pregnancy outcomes.
Natalizumab for the Treatment of Relapsing-Remitting Multiple Sclerosis

Introduction: Worldwide, more than 2.5 million people suffer from Multiple Sclerosis (MS), which is a progressive degenerating disease of the central nervous system (CNS). At present, there is no definitive cure. Thus, available pharmacological therapies aim to reduce the disease activity by either suppressing or modulating the immune system. The humanised monoclonal antibody natalizumab (Tysabri™) was approved for the treatment of relapsing-remitting MS by the FDA and the EMA in 2006. Although, natalizumab is deemed to be highly effective, the treatment is associated with an increased risk of developing progressive multifocal encephalopathy (PML). Methods: The aim of this systematic review was to investigate whether natalizumab is more effective and safer than alternative pharmacological therapies or placebo over a minimum follow-up period of 36 months with respect to annualised relapse rate, disability progression, quality of life (QoL) and number of serious adverse events (SAEs). A systematic literature search was conducted in four databases. The risk of bias (RoB) was assessed using the Cochrane RoB tool for randomised controlled studies and the ROBINS-I tool for non-randomised controlled trials. The quality of evidence was assessed using the GRADE-method (Grading of Recommendations, Assessment, Development and Evaluation). Results: For the assessment of clinical effectiveness, three studies met the inclusion criteria. No significant differences regarding the annualised relapse rate and disability progression were found, when natalizumab was compared to fingolimod. A single trial investigated the patient-reported outcome quality of life. Yet, no significant difference was observed between the intervention group and a placebo control. For the assessment of safety, seven studies met the inclusion criteria. The proportion of patients suffering from SAEs ranged from 2.4% to 16.0%. Among them, infections and infestations (up to 4.0%), neoplasms (up to 2.0%) and hypersensitivity reactions (0.5% to 2.0%) were reported most frequently. In total, 35 cases of PML occurred. Conclusion: Concerning clinical effectiveness and safety, the quality of evidence was low to very low. Thus, future research should provide more head-to-head RCTs comparing natalizumab with other disease modulating drugs along with a comprehensive documentation of adverse events.
Utilidad de diferentes técnicas serológicas y moleculares en el diagnóstico y seguimiento de la enfermedad de Chagas y en la caracterización de Trypanosoma cruzi

Los movimientos migratorios han permitido la expansion de la enfermedad de Chagas a zonas no endemicas. Se dispone de una gran cantidad de pruebas para el diagnostico de la enfermedad, la mayoria de ellas serologicas, pero su utilidad en diferentes grupos de poblacion, fases de la infeccion y seguimiento de la enfermedad todavia no es bien conocida. Recientemente se han desarrollado tecnicas de nueva generacion que no han sido tenidas en cuenta en los protocolos existentes, los que implican el analisis de un gran numero de muestras por varias pruebas serologicas, con costes elevados en el presupuesto de salud. Por otro lado, el diagnostico serologico tiene el inconveniente de la elevada persistencia de los anticuerpos tipo inmunoglobulina G (IgG) en pacientes cronicos y la deteccion de IgG pasivas de origen materno en recien nacidos. La caracterizacion molecular de Trypanosoma cruzi en muestras de los pacientes busca identificar asociaciones entre las Unidades Discretas de Tipificacion (UDTs) del parasito y el desarrollo clinico de la enfermedad. Existen diferentes marcadores moleculares, pero no hay consenso sobre un protocolo. El objetivo principal del presente trabajo fue evaluar la utilidad de varias tecnicas serologicas para el diagnostico y seguimiento de la infeccion cronica y congenita por T. cruzi y para caracterizar los genotipos de T. cruzi en una poblacion latinoamericana atendida en hospitales de Barcelona (Espana). Los resultados mostraron que el Inmuno-ensayo de Microparticulas Quimioluminiscentes (CMIA), con una sensibilidad del 100% en pacientes cronicos, permitio el diagnostico correcto de la mayoria de las muestras cuando se aplicaba como unica tecnica. Solo las muestras en zona gris y positivas con un resultado de ≤ 6 unidades relativas de luz/punto de corte (S/CO) necesitarian ser confirmadas. En los recien nacidos no infectados, se observo una tendencia decreciente continua de anticuerpos IgG pasivos pero ninguno de los tests serologicos utilizados serorevertio en todos los casos antes de los 12 meses. A partir de ese momento, todas las pruebas serologicas obtuvieron resultados negativos en todas las muestras analizadas excepto CMIA. Los protocolos propuestos permitirian importantes ahorros en el diagnostico de la enfermedad de Chagas y en el manejo y el control de la enfermedad. Los diagramas de identificacion basados en la Reaccion en Cadena de la Polimerasa (PCR) se mostraron muy utiles para caracterizar las UDTs de T. cruzi durante la infeccion aguda pero no lo suficientemente sensibles para el analisis de pacientes con cargas parasitarias bajas. En cambio, la secuenciacion del ADN satelite (ADNsat) no permitio discriminar las poblaciones a nivel de una unica UDT, pero si aumentar el numero de casos caracterizados en pacientes cronicos. La UDT mas identificada fue TcV, comun en Bolivia y predominante en sangre periferica.
How to play Notakto: Can reinforcement learning achieve optimal play on combinatorial games?

Reinforcement learning has achieved great success in learning strategies for games with minimal supervision. Here we consider a speciﬁc simple combinatorial game called notakto, in which players cover squares on a tic-tac-toe board until one of them loses by ﬁnishing a whole row, column, or diagonal. We introduce optimal strategies for this game played on small boards, but larger boards have proven difﬁcult to analyze mathematically. We set out to use reinforcement learning to help give insight into optimal strategies for larger boards. Sur-prisingly, we found that, despite the simplicity of the game, AlphaGo Zero struggled to learn an optimal strategy, even though we know that they exist. We developed two ways to accelerate AlphaGo Zero’s learning on this problem: one is a targeted sampling strategy that biases towards states that are likely to appear in competent play, and the other is to set the threshold for updating a model based on statistics in the training data. Both techniques make AlphaGo Zero converge faster on notakto, but they are speciﬁc to this game and still do not seem to ﬁnd a near-optimal strategy. We conjecture that this difﬁculty may show some fundamental limitations of reinforcement learning on combinatorial games that need to be further investigated.
A moment estimator for the conditional extreme-value index

In extreme value theory, the so-called extreme-value index is a parameter that controls the behavior of a distribution function in its right tail. Knowing this parameter is thus essential to solve many problems re- lated to extreme events. In this paper, the estimation of the extreme-value index is considered in the presence of a random covariate, whether the conditional distribution of the variable of interest belongs to the Frechet, Weibull or Gumbel max-domain of attraction. The pointwise weak consis- tency and asymptotic normality of the proposed estimator are established. We examine the finite sample performance of our estimator in a simulation study and we illustrate its behavior on a real set of fire insurance data.
Provenance in Databases : Why , How , and Where James Cheney

Different notions of provenance for database queries have been proposed and studied in the past few years. In this article, we detail three main notions of database provenance, some of their applications, and compare and contrast amongst them. Specifically, we review why, how, and where provenance, describe the relationships among these notions of provenance, and describe some of their applications in confidence computation, view maintenance and update, debugging, and annotation propagation. Full text available at: http://dx.doi. org/10.1561/1900000006 Full text available at: http://dx.doi. org/10.1561/1900000006
On Precisely Detecting Censorship Circumvention in Real-World Networks

—The understanding of realistic censorship threats enables the development of more resilient censorship circumvention systems, which are vitally important for advancing human rights and fundamental freedoms. We argue that current state-of-the-art methods for detecting circumventing flows in Tor are unrealistic: they are overwhelmed with false positives ( > 94% ), even when considering conservatively high base rates ( 10 − 3 ). In this paper, we present a new methodology for detecting censorship circumvention in which a deep-learning flow-based classifier is combined with a host-based detection strategy that incorporates information from multiple flows over time. Using over 60,000,000 real-world network flows to over 600,000 destinations, we demonstrate how our detection methods become more precise as they temporally accumulate information, allowing us to detect circumvention servers with perfect recall and no false positives. Our evaluation considers a range of circumventing flow base rates spanning six orders of magnitude and real-world protocol distributions. Our findings suggest that future circumvention system designs need to more carefully consider host-based detection strategies, and we offer suggestions for designs that are more resistant to these attacks.
How Consumers Resolve Con ﬂ ict over Branded Products: Evidence from Mouse Cursor Trajectories

Consumers differ in the extent to which brands drive their choices. The current research investigates the psychology underlying such decisions by using a cursor-tracking paradigm that captures consumers ’ decision-making processes in real time. Results indicate that while consumers typically process brand attributes relatively later than product attributes, the timing of this processing varies across individuals and affects choice. Speci ﬁ cally, when consumers trade off brand and product desirability (i.e., when deciding between a more [less] preferred product from a less [more] preferred brand), the earlier that brand attributes are considered, the more likely consumers are to choose the option from the preferred brand. Increasing the prominence of brand (vs. product) attributes leads to earlier brand attribute processing and a higher likelihood of choosing the preferred brand. These ﬁ ndings hold across a limited number of choice trials and for decisions involving three attributes (brand, product, and price). This research highlights the applicability of cursor tracking in revealing the psychological drivers of consumer choices in real time.
Are REDD+ community forest projects following the principles for collective action, as proposed by Ostrom?

Forested countries in the global south that have agreed to engage in REDD+, a policy mechanism for addressing climate change, are receiving support to improve laws, policies, systems and structures. As a mechanism initiated at the global level and seeking to use forests to address a global commons crisis (atmospheric carbon concentration), understanding how REDD+ translates into implementation at the local level is essential. Therefore, using a systematic review approach, we examined 15 studies of REDD+ in the context of public and/or community managed forests, drawn from a comprehensive application of inclusion criteria to identify relevant published peer-reviewed empirical research. The common property resources literature was used to highlight the role of local institutions in REDD+ and to distil how REDD+ community forest projects conform to Ostrom’s collective action principles. The review revealed limited sharing of information and decision-making authority with communities; a general absence of FPIC; and a lack of defined benefit sharing and conflict resolution arrangements in many of the REDD+ projects.
Towards Training Person Detectors for Mobile Robots using Synthetically Generated RGB-D Data

We explore how we can use synthetically generated RGB-D training data from a near photo-realistic game engine to train modality-speciﬁc person detectors. We perform ablation studies on a challenging, real-world dataset which we recorded using a Kinect v2 RGB-D sensor in multiple warehouse environments. Through extensive use of domain randomization techniques, we synthesize a realistic and highly varied training set of challenging intralogistics scenarios as observed from a mobile robot, comprising persons in con-ﬁned and cluttered indoor spaces. We then train the detector layers of a YOLOv3 model from scratch on our synthetic RGB and jet-encoded depth images. While for the RGB case, we still observe a domain gap of 6 points in mAP compared to a pretrained COCO model, results indicate that by exploiting simulation, an immense manual labeling effort needed to prepare large-scale datasets such as MS COCO might be unnecessary for the depth modality. We further ﬁnd that ﬁltering of highly occluded groundtruth bounding boxes during training, as well as modeling of time-of-ﬂight sensor noise characteristics has a positive impact on model performance. We also provide an initial set of qualitative results on our real-world dataset.
Different Trade Models, Different Trade Elasticities?

How has the development of new trade models changed our understanding of the welfare gains from trade? Answering this question depends solely on estimates of the trade elasticity obtained using techniques applicable across different models. In this paper we build on the methods of Simonovska and Waugh (2011) and we develop a common estimator for the trade elasticity that is applicable across different models that feature micro-level heterogeneity. The benefit of our approach is that, while the estimation uses the same moment conditions, it allows for different micro structures to matter. We apply the estimator to the models of Eaton and Kortum (2002), Bernard, Eaton, Jensen, and Kortum (2003), and a variant of the framework of Melitz (2003) and Chaney (2008). We find that the trade elasticity estimates differ considerably across models. The results suggest that the Bernard, Eaton, Jensen, and Kortum (2003) model yields the highest, while the Melitz (2003) model yields the lowest welfare gains from trade.
Meta-conﬁdence estimation for stereo matching

—We propose a novel framework to estimate the conﬁdence of a disparity map taking into account, for the ﬁrst time, the uncertainty a ﬀ ecting the conﬁdence estimation process itself. Conversely to other tasks such as disparity estimation, the uncertainty of conﬁdence directly hints that the conﬁdence should be increased if initially low, but with high uncertainty, decreased otherwise. By modelling such a cue in the form of a second-level conﬁdence, or meta-conﬁdence, our solution allows for ﬁnding incorrect predictions inferred by conﬁdence estimator and for learning a correction for them. Our strategy is suited for any state-of-the-art method known in literature, either implemented using random forest classiﬁers or deep neural networks. Especially, for deep neural networks-based models, we present a multi-headed conﬁdence estimator followed by an uncertainty network, so as to predict mean conﬁdence and meta-conﬁdence within a single network without the cost of lower accuracy, a known limitation in literature for uncertainty estimation. Experimental results on a variety of stereo algorithms and conﬁdence estimation models prove that the modeled meta-conﬁdence is meaningful of the reliability of the estimated conﬁdence and allows for reﬁning it.
SEMI-KELLEY COMPACTIFICATIONS OF (0 , 1]

We characterize the semi-Kelley compactiﬁcations of (0 , 1] with remainder being an arc or a simple closed curve. We also prove that there are no semi-Kelley compactiﬁcations of (0 , 1] with remainder being a triod. Finally, we prove that if X is a semi-Kelley compactiﬁcation of (0 , 1] with remainder being a Peano continuum G , then G is an arc or a simple closed curve.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Error detection in program code and documentation is a critical problem in computer security. Previous studies have shown promising vulnerability discovery performance by extensive code or document-guided analysis. However, the state-of-the-arts have the following significant limitations: (i) They assume the documents are correct and treat the code that violates documents as bugs, thus cannot find documents’ defects and code’s bugs if APIs have defective documents or no documents. (ii) They utilize majority voting to judge the inconsistent code snippets and treat the deviants as bugs, thus cannot cope with situations where correct usage is minor or all use cases are wrong. In this paper, we present AURC, a static framework for detecting code bugs of incorrect return checks and document defects. We observe that three objects participate in the API invocation, the document, the caller (code that invokes API), and the callee (the source code of API). Mutual corroboration of these three objects eliminates the reliance on the above assumptions. AURC contains a context-sensitive backward analysis to process callees, a pre-trained model-based document classifier, and a container that collects conditions of if statements from callers. After cross-checking the results from callees, callers, and documents, AURC delivers them to the correctness inference module to infer the defective one. We evaluated AURC on ten popular codebases. AURC discovered 529 new bugs that can lead to security issues like heap buffer overflow and sensitive information leakage, and 224 new document defects. Maintainers acknowledge our findings and have accepted 222 code patches and 76 document patches.
he-Short-term feed intake is regulated by macronutrient oxidation in lactating Holstein cows

In addition to plasma metabolites and hormones participating as humoral signals in the control of feed intake, oxidative metabolic processes in peripheral organs also generate signals to terminate feeding. Although the degree of oxidation over longer periods is relatively constant, recent work suggests that the periprandial pattern of fuel oxidation is involved in regulating feeding behavior in the bovine. However, the association between periprandial oxidative metabolism and feed intake of dairy cows has not yet been studied. Therefore, the aim of this study was to elucidate possible associations existing between single feed intake events and whole-body net fat and net carbohydrate oxidation as well as their relation to plasma metabolite concentrations. To this end, 4 late-lactating cows equipped with jugular catheters were kept in respiratory chambers with continuous and simultaneous recording of gas exchange and feed intake. Animals were fed ad libitum (AL) for 24 h and then feed restricted (RE) to 50% of the previous AL intake for a further 24 h. Blood samples were collected hourly to analyze β-hydroxybutyrate (BHBA), glucose, nonesterified fatty acids (NEFA), insulin, and acylated ghrelin concentrations. Cross-correlation analysis revealed an offset ranging between 30 and 42 min between the maximum of a feed intake event and the lowest level of postprandial net fat oxidation (FOX net ) and the maximum level of postprandial net carbohydrate oxidation (COX net ), respectively. During the AL period, FOX net did not increase above −0.2 g/min, whereas COX net did not decrease below 6 g/min before the start of the next feed intake event. A strong inverse cross-correlation was obtained between COX net and plasma glucose concentration. Direct cross-correlations were observed between COXnet and insulin, between heat production and BHBA, between insulin and glucose, and between BHBA and ghrelin. We found no cross-correlation between FOX net and NEFA. During RE, FOX net increased with an exponential slope, exceeded the threshold of −0.2 g/min as indicated by increasing plasma NEFA concentrations, and approached a maximum rate of 0.1 g/min, whereas COX net decayed in an exponential manner, approaching a minimal COX net rate of about 2.5 g/min in all cows. Our novel findings suggest that, in late-lactating cows, postprandial increases in metabolic oxidative processes seem to signal suppression of feed intake, whereas pre-prandially an accelerated FOX net rate and a decelerated COX net rate initiate feed intake.
Clustering emotions in Portuguese

In this paper we present some exploratory studies of emotion words based on large annotated corpora of Portuguese. Those corpora were automatically annotated with emotionality, and each emotion word was assigned one or more groups out of 26 emotion groups. Our goal is to evaluate those groups by applying different statistical approaches to our material, namely based on (a) co-occurrence in a sentence as a sign of closeness of meaning, and (b) word embeddings. After looking at the full material, we turn our attention to two specific emotion groups: Amor (‘love’) and Desespero (‘despair’), investigating whether clustering with those underlying techniques can help improve the shape, or redesign, particular emotion groups. In the paper we suggest some novel forms of measuring semantic coherence on word embedding models. Since computational research on emotion words in Portuguese is still rare, our methods and resources will lay the ground for future investigations.