Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of central nervous system depressant drug overdose on cognitive functions and driving

Self-poisoning with pharmaceutical agents is very common across the world. Central nervous system depressant drugs (CNS-Ds) are among the most common substances taken in overdose in hospital-treated episodes of self-poisoning in Australia, the UK and the US. The majority of the patients with CNS-D overdose treated in hospitals in Australia and the UK are discharged within 24-48 hours of their admission, when they still could potentially have subclinical effects of those drugs. This thesis systematically reviews published evidence on the effects of CNS-Ds on cognitive functions (Chapter 2), automobile driving and traffic accidents (Chapter 3, Paper 1), and presents original research conducted to examine the effects of CNS-D overdose on cognitive functions underpinning daily activities (Chapter 5, Paper 2), surrogate bedside tests of driving skills (Chapter 6, Paper 3) and risk of traffic accidents (Chapter 7, Paper 4) of patients discharged from hospital following treatment. Comprehensive neuropsychological assessment shows that patients discharged after treatment for CNS-D overdose have significant residual impairments in multiple cognitive functions including visual attention and visuomotor skills, decision-making, and executive functions and working memory (Chapter 5). The impairments, as estimated by regression models, were equivalent to a ‘cognitive ageing’ of 10-20 years depending on the domain tested. Furthermore, executive dysfunction of the patients tends to worsen with increasing task demands. Converging evidence from the neuropsychological assessment and epidemiological approach indicates that CNS-D overdose has deleterious effects on driving. In particular, the performance of Trail-Making Test B, when interpreted with respect to its correlation with driving performance and traffic accident risk, suggests that nearly two-thirds of the patients with CNS-D overdose may be grossly impaired (equal or less than 10th percentile) at the time of discharge from hospital (Chapter 6, Paper 3). The epidemiological evidence (Chapter 7, Paper 4) shows that the traffic accident risk of these individuals increases by 3-4 times in the immediate post-discharge period, and remains nearly twice their baseline risk after one week following overdose. In the concluding chapter (Chapter 8), we examine the impact of these impairments on daily activities that the discharged patients are expected and likely to carryout during the post-discharge period, and discuss the clinical implications in post-discharge management of patients treated for CNS-D overdose. © Copyright 2012 Waidyaratne Dassanayake Mudiyanselage Tharaka Lagath Dassanayake
Adding fuel to the fire: Immunogenic intensification

The durable long term clinical benefits seen for certain patients treated with immunotherapy agents has suggested there is significant therapeutic potential to be derived from these agents, as shown by the increasing prominence of this treatment strategy in upcoming clinical trials. There has been a renewed interest and focus on the drivers of tumoral antigen recognition, and the pathways by which various cells of the immune system can stimulate, propagate and execute an effective anti-tumor response. Various challenges lie ahead in the further development of these treatments, including induction of an endogenous anti-tumor response, tumor microenvironment modulation, and T-cell response amplification. Novel treatment combinations may prove of significant added benefit by immunogenic intensification.
Presence of apolipoprotein E ε4 allele in cerebral palsy

The apolipoprotein E gene, which is located on chromosome 19, has three alleles (epsilon2, epsilon3, and epsilon4). Several recent publications associate the presence of the apolipoprotein E epsilon4 allele with the occurrence of neurologic diseases, and consider it a risk factor for the development of central nervous system affections. A group of 40 patients with cerebral palsy was studied and compared to a control group of 40 subjects, and higher occurrence of the allele epsilon4 in the group of subjects with cerebral palsy was observed. A significantly higher risk of developing cerebral palsy was demonstrated among those subjects with the apolipoprotein E epsilon4 allele.
Iniciação e consumo de substâncias psicoativas entre adolescentes e adultos jovens de Centro de Atenção Psicossocial Antidrogas/CAPS-AD

O estudo tem como objetivo caracterizar a iniciacao e o padrao de consumo de substâncias psicoativas, entre adolescentes e adultos jovens matriculados em Centro de Atencao Psicossocial Alcool e Drogas - CAPS AD. Foram utilizados os registros dos atendimentos, sendo o padrao de consumo classificado segundo a OMS: uso pouco frequente (uso na vida, no ano ou no mes ate cinco dias); uso frequente (de seis ate 19 vezes nos ultimos 30 dias); uso pesado (> 20 vezes nos ultimos 30 dias); na comparacao das faixas etarias utilizou-se teste de proporcoes e para analise das associacoes, calculou-se prevalencia/P e razao de prevalencia/RP, com significância de 5% e intervalo de confianca 95%. No total de adolescentes e jovens atendidos entre 2003 e 2008 (475), a maioria foi masculina, solteira, com baixa escolaridade, coabitando com pais e sintomas psiquicos. Constatou-se significância estatistica para idade de iniciacao ao consumo: adolescentes, em relacao aos jovens iniciaram mais precocemente (< 14 anos): o consumo de tabaco; maconha, cocaina; crack e outras SPA. Entre adolescentes, verificou-se resultados significantes para consumo menos frequente de tabaco; mais frequente de alcool; e consumo pesado de maconha. Esses achados podem contribuir para programas preventivos e terapeuticos do CAPS-AD.
Índice de risco de mortalidade por endocardite infecciosa: um modelo logístico multivariado

Objective: This study aimed at identifying predictive variables for in-hospital mortality, calculating the probability of death and creating a risk index for death by infective endocarditis by comparing two methods using a Receiver Operating Characteristic (ROC) curve. Methods: A retrospective study was conducted of 186 consecutive cases of confirmed infective endocarditis divided into two groups: discharged (137) and in-hospital death (49). Based on the odds ratios obtained by multivariate analysis, the probability of death was calculated and a mortality risk index created. Results: Factors predictive of higher mortality (multivariate analysis) and the risk index, with their respective weights were: age ≥ 40 years (OR = 4.16; 95%CI [1.63-10.80] - 4 points), class IV heart failure or cardiovascular shock (OR = 4.93; 95%CI [1.86-13.05] - 5 points), uncontrolled sepsis (OR = 5.97; 95%CI [1.95-18.35] - 6 points), conduction disorder (OR = 5.07; 95%CI [1.6715.35] - 5 points), arrhythmia (OR = 8.17; 95%CI [2.60-25.71] - 8 points), valve with extensive damage or abscess or prosthesis (OR = 4.77; 95%CI [1.44-15.76] - 5 points) and large and mobile vegetation (OR = 4.36; 95%CI [1.55-12.90] - 4 points). Patients with scores between 0 and 10 had a mortality of 5.26% and scores over 20 of 78.9%. Conclusions: The higher the score, the higher the mortality rate. The mortality risk index may be used to estimate mortality in Infective Endocarditis.
Efeito da estreptozotocina sobre os perfis glicêmico e lipídico e o estresse oxidativo em hamsters

OBJECTIVE: This study evaluated the effects of streptozotocin on glycemic and lipid profiles and oxidative stress status in hamsters. MATERIALS AND METHODS: Male Golden Syrian hamsters were divided in diabetic group (D) which received a streptozotocin single injection (STZ - 50 mg/kg), and control group (C) which received a single injection of the vehicle citrate buffer. Animals were euthanized after 10 days of experiment and blood, liver and kidneys were collected. RESULTS: The diabetic group had higher levels of glucose, triacylglycerols and cholesterol in serum and thiobarbituric acid reactive substances (TBARS) concentration increased in the liver and kidneys. Diabetes induced a significant increase in glutathione concentration in the liver and decreased paraoxonase and superoxide dismutase activities. CONCLUSION: Hamsters provide a novel animal model for diabetes mellitus and oxidative stress, similar to the human syndrome, which may be suitable for the testing of antidiabetic compounds.
[Importance of central corneal thickness when studying ocular hypertensive eyes, glaucoma suspects and preperimetric glaucomatous eyes].

PURPOSE To compare the central corneal thickness, measured with an ultrasound pachymeter, in normal subjects, those with ocular hypertension, glaucoma suspects and patients with preperimetric glaucoma. METHODS 61 normal eyes (control group), 131 eyes with ocular hypertension, 62 glaucoma suspects (optic nerve head morphology compatible with glaucoma) and 36 patients with preperimetric glaucoma (abnormal short-wavelength automated perimetry) were prospectively and consecutively selected. Scatter plots of central corneal thickness, against the intraocular pressure values were calculated for each of the study groups. Ultrasound pachymetry measurements were compared between those with normal eyes and the other groups. RESULTS Ocular hypertensive subjects had higher pachymetry values than the control group (p=0.009). No differences were found in the central corneal thickness between normal eyes and those who were glaucoma suspects, and between normal and preperimetric glaucomatous eyes. A mild direct logarithmic correlation was evident between central corneal thickness and the Goldmann tonometry result in the ocular hypertensive group. CONCLUSIONS Ocular hypertensive subjects had thicker corneas than the other groups studied. Glaucoma suspects and preperimetric glaucoma patients had similar corneal thickness to the control group.
Uniform error estimates for artificial neural network approximations for heat equations

Recently, artificial neural networks (ANNs) in conjunction with stochastic gradient descent optimization methods have been employed to approximately compute solutions of possibly rather high-dimensional partial differential equations (PDEs). Very recently, there have also been a number of rigorous mathematical results in the scientific literature, which examine the approximation capabilities of such deep learning-based approximation algorithms for PDEs. These mathematical results from the scientific literature prove in part that algorithms based on ANNs are capable of overcoming the curse of dimensionality in the numerical approximation of high-dimensional PDEs. In these mathematical results from the scientific literature, usually the error between the solution of the PDE and the approximating ANN is measured in the $L^p$-sense, with respect to some $p \in [1,\infty )$ and some probability measure. In many applications it is, however, also important to control the error in a uniform $L^\infty $-sense. The key contribution of the main result of this article is to develop the techniques to obtain error estimates between solutions of PDEs and approximating ANNs in the uniform $L^\infty $-sense. In particular, we prove that the number of parameters of an ANN to uniformly approximate the classical solution of the heat equation in a region $ [a,b]^d $ for a fixed time point $ T \in (0,\infty ) $ grows at most polynomially in the dimension $ d \in {\mathbb {N}} $ and the reciprocal of the approximation precision $ \varepsilon> 0 $. This verifies that ANNs can overcome the curse of dimensionality in the numerical approximation of the heat equation when the error is measured in the uniform $L^\infty $-norm.
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem

Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuristics on routing problems has been steadily improving in recent years, but approaches based purely on machine learning are still outperformed by state-of-the-art optimization methods. To close this performance gap, we propose a novel large neighborhood search (LNS) framework for vehicle routing that integrates learned heuristics for generating new solutions. The learning mechanism is based on a deep neural network with an attention mechanism and has been especially designed to be integrated into an LNS search setting. We evaluate our approach on the capacitated vehicle routing problem (CVRP) and the split delivery vehicle routing problem (SDVRP). On CVRP instances with up to 297 customers our approach significantly outperforms an LNS that uses only handcrafted heuristics and a well-known heuristic from the literature. Furthermore, we show for the CVRP and the SDVRP that our approach surpasses the performance of existing machine learning approaches and comes close to the performance of state-of-the-art optimization approaches.
Theory Challenges at Future Lepton Colliders

High energy, high luminosity, future lepton colliders, circular or linear, may possibly give us hint about fundamental laws of Nature governing at very short distances and very short time intervals, the same which have brought our Universe to live. Currently considered projects are on one hand linear electron-positron colliders, which offer higher energy and lower beam intensities and on the other hand circular electron-positron colliders, limited in energy but offering tremendous interaction rates. On the far future horizon, muon circular colliders are the only viable projects which can explore $>10$TeV teritory of the lepton colliders. Experiments in all these future colliders will require theoretical calculations, mainly of Standard Model processes (including QED), at the precision level one or even two orders better than available today. After briefly characterization of theory puzzles in the fundamental interactions we shall overview main challenges in the precision calculations of the Standard Model effects, which have to be removed from data, in order to reveal traces of new unexpected phenomena.
Fast route to equilibration

We present a control scheme for quantum systems coupled to a thermal bath. We demonstrate state-to-state control between two Gibbs states. This scheme can be used to accelerate thermalization and cool the open system. Starting from a microscopic description, we derive the reduced system dynamics, leading to a non-adiabatic master equation. The equation contains non-trivial effects due to the non-adiabatic driving and bath interaction. These special features enable controlling the open system and accelerating the entropy changes. For a two-level system model, we obtain a general solution and introduce a reverse-engineering scheme for control. The control problem is analyzed in the context of the theory of quantum control and the accompanying thermodynamic cost.
EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning

Deep neural networks have been successfully applied to many real-world applications. However, these successes rely heavily on large amounts of labeled data, which is expensive to obtain. Recently, Auto-Encoding Transformation (AET) and MixMatch have been proposed and achieved state-of-the-art results for unsupervised and semi-supervised learning, respectively. In this study, we train an Ensemble of Auto-Encoding Transformations (EnAET) to learn from both labeled and unlabeled data based on the embedded representations by decoding both spatial and non-spatial transformations. This distinguishes EnAET from conventional semi-supervised methods that focus on improving prediction consistency and confidence by different models on both unlabeled and labeled examples. In contrast, we propose to explore the role of self-supervised representations in semi-supervised learning under a rich family of transformations. Experiment results on CIFAR-10, CIFAR-100, SVHN and STL10 demonstrate that the proposed EnAET outperforms the state-of-the-art semi-supervised methods by significant margins. In particular, we apply the proposed method to extremely challenging scenarios with only 10 images per class, and show that EnAET can achieve an error rate of 9.35% on CIFAR-10 and 16.92% on SVHN. In addition, EnAET achieves the best result when compared with fully supervised learning using all labeled data with the same network architecture. The performance on CIFAR-10, CIFAR-100 and SVHN with a smaller network is even more competitive than the state-of-the-art of supervised learning methods based on a larger network. We also set a new performance record with an error rate of 1.99% on CIFAR-10 and 4.52% on STL10. The code and experiment records are released at this https URL.
3D MHD Modeling of the Impact of Subsurface Stratification on the Solar Dynamo

Various models of solar subsurface stratification are tested in the global EULAG-MHD solver to simulate diverse regimes of near-surface convective transport. Sub- and superadiabacity are altered at the surface of the model (r > 0.95R⊙) to either suppress or enhance convective flow speeds in an effort to investigate the impact of the near-surface layer on global dynamics. A major consequence of increasing surface convection rates appears to be a significant alteration of the distribution of angular momentum, especially below the tachocline where the rotational frequency predominantly increases at higher latitudes. These hydrodynamic changes correspond to large shifts in the development of the current helicity in this stable layer (r < 0.72R⊙), significantly altering its impact on the generation of poloidal and toroidal fields at the tachocline and below, acting as a major contributor toward transitions in the dynamo cycle. The enhanced near-surface flow speed manifests in a global shift of the toroidal field (Bϕ) in the butterfly diagram, from a north–south symmetric pattern to a staggered antisymmetric emergence.
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture

Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including both cell-based and node-based approaches, the search process is time consuming and the performance of found architecture may be sub-optimal. To address these problems, we propose AutoShrink, a topology-aware Neural Architecture Search (NAS) for searching efficient building blocks of neural architectures. Our method is node-based and thus can learn flexible network patterns in cell structures within a topological search space. Directed Acyclic Graphs (DAGs) are used to abstract DNN architectures and progressively optimize the cell structure through edge shrinking. As the search space intrinsically reduces as the edges are progressively shrunk, AutoShrink explores more flexible search space with even less search time. We evaluate AutoShrink on image classification and language tasks by crafting ShrinkCNN and ShrinkRNN models. ShrinkCNN is able to achieve up to 48% parameter reduction and save 34% Multiply-Accumulates (MACs) on ImageNet-1K with comparable accuracy of state-of-the-art (SOTA) models. Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1.5 GPU hours, which is 7.2× and 6.7× faster than the crafting time of SOTA CNN and RNN models, respectively.
Prosody Transfer in Neural Text to Speech Using Global Pitch and Loudness Features

This paper presents a simple yet effective method to achieve prosody transfer from a reference speech signal to synthesized speech. The main idea is to incorporate well-known acoustic correlates of prosody such as pitch and loudness contours of the reference speech into a modern neural text-to-speech (TTS) synthesizer such as Tacotron2 (TC2). More specifically, a small set of acoustic features are extracted from reference audio and then used to condition a TC2 synthesizer. The trained model is evaluated using subjective listening tests and a novel objective evaluation of prosody transfer is proposed. Listening tests show that the synthesized speech is rated as highly natural and that prosody is successfully transferred from the reference speech signal to the synthesized signal.
The young stellar content of the giant H II regions M 8, G333.6−0.2, and NGC 6357 with VLT/KMOS

Context. The identification and characterisation of populations of young massive stars in (giant) H II regions provides important constraints on (i) the formation process of massive stars and their early feedback on the environment, and (ii) the initial conditions for population synthesis models predicting the evolution of ensembles of stars. Aims. We identify and characterise the stellar populations of the following young giant H II regions: M 8, G333.6−0.2, and NGC 6357. Methods. We have acquired H- and K-band spectra of around 200 stars using the K-band Multi Object Spectrograph on the ESO Very Large Telescope. The targets for M 8 and NGC 6357 were selected from the Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX), which combines X-ray observations with near-infrared (NIR) and mid-infrared data. For G333.6−0.2, the sample selection is based on the NIR colours combined with X-ray data. We introduce an automatic spectral classification method in order to obtain temperatures and luminosities for the observed stars. We analysed the stellar populations using their photometric, astrometric, and spectroscopic properties and compared the position of the stars in the Hertzprung-Russell diagram with stellar evolution models to constrain their ages and mass ranges. Results. We confirm the presence of candidate ionising sources in the three regions and report new ones, including the first spectroscopically identified O stars in G333.6−0.2. In M 8 and NGC 6357, two populations are identified: (i) OB main-sequence stars (M > 5 M⊙) and (ii) pre-main sequence stars (M ≈ 0.5 − 5 M⊙). The ages of the clusters are ∼1−3 Myr, < 3 Myr, and ∼0.5−3 Myr for M 8, G333.6−0.2, and NGC 6357, respectively. We show that MYStIX selected targets have > 90% probability of being members of the H II region, whereas a selection based on NIR colours leads to a membership probability of only ∼70%.
The r-Hunter–Saxton equation, smooth and singular solutions and their approximation

In this work we introduce the r-Hunter–Saxton equation, a generalisation of the Hunter–Saxton equation arising as extremals of an action principle posed in L r . We characterise solutions to the Cauchy problem, quantifying the blow-up time and studying various symmetry reductions. We construct piecewise linear functions and show that they are weak solutions to the r-Hunter–Saxton equation.
Intermediate-Mass Black Holes

We describe ongoing searches for intermediate-mass black holes with M_BH ~ 100-10^5 M_sun. We review a range of search mechanisms, both dynamical and those that rely on accretion signatures. We find that dynamical and accretion signatures alike point to a high fraction of 10^9-10^10 M_sun galaxies hosting black holes with M_BH<10^5 M_sun. In contrast, there are no solid detections of black holes in globular clusters. There are few observational constraints on black holes in any environment with M_BH ~ 100-10^4 M_sun. Considering low-mass galaxies with dynamical black hole masses and constraining limits, we find that the M_BH-sigma_* relation continues unbroken to M_BH~10^5 M_sun, albeit with large scatter. We believe the scatter is at least partially driven by a broad range in black hole mass, since the occupation fraction appears to be relatively high in these galaxies. We fold the observed scaling relations with our empirical limits on occupation fraction and the galaxy mass function to put observational bounds on the black hole mass function in galaxy nuclei. We are pessimistic that local demographic observations of galaxy nuclei alone could constrain seeding mechanisms, although either high-redshift luminosity functions or robust measurements of off-nuclear black holes could begin to discriminate models.