Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of Mind in Substance Users: A Systematic Minireview

ABSTRACT Background: Theory of mind concerns the sociocognitive ability to infer others' thoughts. It has been theorized to be impaired in substance use and abuse, as its alterations might explain negative social and interpersonal outcomes noted in the course of disorders. In addition, the brain structures involved in Theory of Mind (ToM) have been found to be disrupted in drug use conditions. Objective: We undertook a systematic review of ToM functioning in drug use conditions. Methods: Four electronic databases (MEDLINE, Web of Science, PsycINFO, and Embase) were searched to find studies that have addressed ToM and conditions related to actual or previous drug use. Results: The search found 147 papers, of which 14 fulfilled our review eligibility criteria. Different methods were used, but overall, results indicated that drugs are related to ToM deficits, particularly related to alcohol and amphetamines use. These impairments correlate with other clinical and cognitive functions. Conclusion: Despite the lack of studies and the methodological limitations of the existing ones Theory of Mind seems to play a role in drug use conditions, which requires further investigation.
Temporal-Difference Learning With Sampling Baseline for Image Captioning

The existing methods for image captioning usually train the language model under the cross entropy loss, which results in the exposure bias and inconsistency of evaluation metric. Recent research has shown these two issues can be well addressed by policy gradient method in reinforcement learning domain attributable to its unique capability of directly optimizing the discrete and non-differentiable evaluation metric. In this paper, we utilize reinforcement learning method to train the image captioning model. Specifically, we train our image captioning model to maximize the overall reward of the sentences by adopting the temporal-difference (TD) learning method, which takes the correlation between temporally successive actions into account. In this way, we assign different values to different words in one sampled sentence by a discounted coefficient when back-propagating the gradient with the REINFORCE algorithm, enabling the correlation between actions to be learned. Besides, instead of estimating a "baseline" to normalize the rewards with another network, we utilize the reward of another Monte-Carlo sample as the "baseline" to avoid high variance. We show that our proposed method can improve the quality of generated captions and outperforms the state-of-the-art methods on the benchmark dataset MS COCO in terms of seven evaluation metrics.
Monte Carlo profile confidence intervals for dynamic systems

Monte Carlo methods to evaluate and maximize the likelihood function enable the construction of confidence intervals and hypothesis tests, facilitating scientific investigation using models for which the likelihood function is intractable. When Monte Carlo error can be made small, by sufficiently exhaustive computation, then the standard theory and practice of likelihood-based inference applies. As datasets become larger, and models more complex, situations arise where no reasonable amount of computation can render Monte Carlo error negligible. We develop profile likelihood methodology to provide frequentist inferences that take into account Monte Carlo uncertainty. We investigate the role of this methodology in facilitating inference for computationally challenging dynamic latent variable models. We present examples arising in the study of infectious disease transmission, demonstrating our methodology for inference on nonlinear dynamic models using genetic sequence data and panel time-series data. We also discuss applicability to nonlinear time-series and spatio-temporal data.
Investigation into the underlying molecular mechanisms of hypertensive nephrosclerosis using bioinformatics analyses

Hypertensive nephrosclerosis (HNS) is a major risk factor for end-stage renal disease. However, the underlying pathogenesis of HNS remains to be fully determined. The gene expression profile of GSE20602, which consists of 14 glomeruli samples from patients with HNS and 4 normal glomeruli control samples, was obtained from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were performed in order to investigate the functions and pathways of differentially expressed genes (DEGs). Pathway relation and co-expression networks were constructed in order to identify key genes and signaling pathways involved in HNS. In total, 483 DEGs were identified to be associated with HNS, including 302 upregulated genes and 181 downregulated genes. Furthermore, GO analysis revealed that DEGs were significantly enriched in the small molecule metabolic process. In addition, pathway analysis also revealed that DEGs were predominantly involved in metabolic pathways. The tricarboxylic acid (TCA) cycle was identified as the hub pathway in the pathway relation network, whereas the sorbitol dehydrogenase (SORD) and cubulin (CUBN) genes were revealed to be the hub genes in the co-expression network. The present study revealed that the SORD, CUBN and albumin genes as well as the TCA cycle and metabolic pathways are involved in the pathogenesis of HNS. The results of the present study may contribute to the determination of the molecular mechanisms underlying HNS, and provide insight into the exploration of novel targets for the diagnosis and treatment of HNS.
Machine Learning-Based Coding Unit Depth Decisions for Flexible Complexity Allocation in High Efficiency Video Coding

In this paper, we propose a machine learning-based fast coding unit (CU) depth decision method for High Efficiency Video Coding (HEVC), which optimizes the complexity allocation at CU level with given rate-distortion (RD) cost constraints. First, we analyze quad-tree CU depth decision process in HEVC and model it as a three-level of hierarchical binary decision problem. Second, a flexible CU depth decision structure is presented, which allows the performances of each CU depth decision be smoothly transferred between the coding complexity and RD performance. Then, a three-output joint classifier consists of multiple binary classifiers with different parameters is designed to control the risk of false prediction. Finally, a sophisticated RD-complexity model is derived to determine the optimal parameters for the joint classifier, which is capable of minimizing the complexity in each CU depth at given RD degradation constraints. Comparative experiments over various sequences show that the proposed CU depth decision algorithm can reduce the computational complexity from 28.82% to 70.93%, and 51.45% on average when compared with the original HEVC test model. The Bjøntegaard delta peak signal-to-noise ratio and Bjøntegaard delta bit rate are -0.061 dB and 1.98% on average, which is negligible. The overall performance of the proposed algorithm outperforms those of the state-of-the-art schemes.
PEDIATRIC AND CONGENITAL HEART DISEASE Original Studies Multicenter Midterm Follow-Up Results Using the Gore Septal Occluder for Atrial Septal Defect Closure in Pediatric Patients

Objectives: To assess the safety and efficacy of the Gore Septal Occluder (GSO) used for device-closure of significant secundum-type atrial septal defects (ASD II) focusing on pediatric patients. Background: The GSO is a patch-like double disc device. Due to its design, it is assumed to be safe, even when implanted in ASDs with deficient retroaortic rims. Methods: Multicenter retrospective analysis of consecutive children and adolescents with a GSO in situ for at least 12 months according to a 1to 4-year midterm follow-up. Results: Hundred and seventy three pediatric patients were enrolled. At implantation, median age was 6 years (range 0.7–17.9), median body weight and length were 21 kg (6.4–95) and 119 cm (65–193). Median follow-up period was 20 months (range 12–51). ASD anatomy was comprised of single defects in 131 patients (76%), multifenestrated defects in 42 (24%), and deficient retro-aortic rims in 33 (19%). Follow-up confirmed an overall closure-rate of 95.4%. Small residual shunts were reported in eight patients (4.6%) without need for any re-intervention. Complications were classified as minor events both during the initial procedure (9 patients, 5.2%) and on follow-up (another 9 patients), including transient AV block II in three patients (1.8%) and four snare-retrievals (2.4%) during the initial procedure. Conclusions: Periprocedural and midterm follow-up data have shown the GSO to be effective and safe for ASD device closure in children and adolescents. GSO may be considered the first-choice device in deficient retroaortic rims and multi-fenestrated defects, when covering most of the atrial septum is necessary. VC 2016 Wiley Periodicals, Inc.
Binding Space Concept: A New Approach To Enhance the Reliability of Docking Scores and Its Application to Predicting Butyrylcholinesterase Hydrolytic Activity

Docking simulations are very popular approaches able to assess the capacity of a given ligand to interact with a target. Docking simulations are usually focused on a single best complex even though many studies showed that ligands retain a significant mobility within a binding pocket by assuming different binding modes all of which may contribute to the monitored ligand affinity. The present study describes an innovative concept, the binding space, which allows an exploration of the ligand mobility within the binding pocket by simultaneously considering several ligand poses as generated by docking simulations. The multiple poses and the relative docking scores can then be analyzed by taking advantage of the same concepts already used in the property space analysis; hence the binding space can be parametrized by (a) mean scores, (b) score ranges, and (c) score sensitivity values. The first parameter represents a very simple procedure to account for the contribution of the often neglected alternative binding modes, while the last two descriptors encode the degree of mobility which a given ligand retains within the binding cavity (score range) as well as the ease with which a ligand explores such a mobility (score sensitivity). Here, the binding space concept is applied to the prediction of the hydrolytic activity of BChE by synergistically considering multiple poses and multiple protein structures. The obtained results shed light on the remarkable potential of the binding space concept, whose parameters allow a significant increase of the predictive power of the docking results as revealed by extended correlative analyses. Mean scores are the parameters affording the largest statistical improvement, and all the here proposed docking-based descriptors show enhancing effects in developing predictive models. Finally, the study describes a new score function (Contacts score) simply based on the number of surrounding residues which appears to be particularly productive in the framework of the binding space.
Influence of thermo-derivative analysis conditions on microstructure of the Al-Si-Cu alloy

change of the metals and alloys as a result of variable crystallisation conditions also by mind of cooling rate change influence the mechanical properties. In this work there are presented the interdependences between the cooling rate, chemical composition and microstructure of the cast aluminium alloy Al-Si-Cu as a result of the thermo-derivative analysis, using the UMSA (Universal Metallurgical Simulator and Analyzer) device. An important tool for the microstructure evaluation of the Al type AC-AlSi7Cu3Mg alloy was the light and electron scanning microscopy technique.
A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.
Modelling of the behaviour of free water in sapwood during drying

Abstract A stochastic three-dimensional model for the behaviour of free water in wood, which was described in Part I, is used for the simulation and analysis of some further features of wood drying. The kiln brown stain mechanism is studied and the connection to the condition of the wood surface is pointed out. Several drying rate questions and related aspects are analysed and compared with experimental results. Finally the risk of surface checking in an early stage of sapwood drying is shown theoretically.
Do the benefits of using calcitriol and other vitamin D receptor activators in patients with chronic kidney disease outweigh the harms?

The primary indication for administration of calcitriol or other vitamin D receptor activators (VDRA) in chronic kidney disease (CKD) is secondary hyperparathyroidism (SHPT). Prevention and treatment of SHPT appears important, as imbalances in mineral metabolism are associated with renal osteodystrophy, and higher parathyroid hormone (PTH) levels are associated with increased rates of mortality and morbidity in CKD patients. There is, however, a lack of controlled trial data that show lowering PTH with calcitriol/VDRA equates to improved clinical outcomes. Recent randomized controlled trials have concentrated on potential benefits of calcitriol/VDRA on cardiovascular outcomes and reduction of proteinuria and on possible differences between calcitriol and the various VDRA. Several systematic reviews and meta‐analyses have also been published, evaluating the benefits and harms of calcitriol/VDRA. Concerns have been raised about the effectiveness of calcitriol/VDRA for suppression of SHPT in the CKD stages 3–5 population, as well as potential adverse outcomes such as hypercalcaemia and elevation in FGF23 levels, suggesting their routine use to treat SHPT in the pre‐dialysis CKD population may not be favourable. Conversely, concerns still exist about the wide PTH range in advanced CKD, and that high values may negatively impact bone quality, result in the progression of parathyroid hyperplasia and decrease the effectiveness of treatments to reduce PTH. We discuss the current controversies relating to the challenges in the management of SHPT in patients with CKD stages 3–5 and the need for more evidence to determine the efficacy or harm of using calcitriol/VDRA in this population.
Linear regression through PAC-Bayesian truncation

We consider the problem of predicting as well as the best linear combination of d given functions in least squares regression under L^\infty constraints on the linear combination. When the input distribution is known, there already exists an algorithm having an expected excess risk of order d/n, where n is the size of the training data. Without this strong assumption, standard results often contain a multiplicative log(n) factor, complex constants involving the conditioning of the Gram matrix of the covariates, kurtosis coefficients or some geometric quantity characterizing the relation between L^2 and L^\infty-balls and require some additional assumptions like exponential moments of the output. This work provides a PAC-Bayesian shrinkage procedure with a simple excess risk bound of order d/n holding in expectation and in deviations, under various assumptions. The common surprising factor of these results is their simplicity and the absence of exponential moment condition on the output distribution while achieving exponential deviations. The risk bounds are obtained through a PAC-Bayesian analysis on truncated differences of losses. We also show that these results can be generalized to other strongly convex loss functions.
HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer

Two independent regions within HNF1B are consistently identified in prostate and ovarian cancer genome-wide association studies (GWAS); their functional roles are unclear. We link prostate cancer (PC) risk SNPs rs11649743 and rs3760511 with elevated HNF1B gene expression and allele-specific epigenetic silencing, and outline a mechanism by which common risk variants could effect functional changes that increase disease risk: functional assays suggest that HNF1B is a pro-differentiation factor that suppresses epithelial-to-mesenchymal transition (EMT) in unmethylated, healthy tissues. This tumor-suppressor activity is lost when HNF1B is silenced by promoter methylation in the progression to PC. Epigenetic inactivation of HNF1B in ovarian cancer also associates with known risk SNPs, with a similar impact on EMT. This represents one of the first comprehensive studies into the pleiotropic role of a GWAS-associated transcription factor across distinct cancer types, and is the first to describe a conserved role for a multi-cancer genetic risk factor.
Recovery of ammonia through struvite production using anaerobic digestate of piggery wastewater and leachate of sewage sludge ash

ABSTRACT Anaerobic digestate of piggery wastewater (ADPW) contains high concentrations of ammonia and phosphorus with unbalanced molar ratio. Thus, ammonia remains at a high level even after phosphorus is completely removed through struvite formation. In this study, both ammonia and phosphorus were recovered by adding leachate of sewage sludge ash (SSA) into ADPW. It was demonstrated that 11,600 mg L−1 of total phosphorus and 7266.7 mg L−1 of -P were extracted from SSA by using sulfuric acid at the H2SO4/SSA mass ratio of 0.35. ADPW and the leachate of SSA were mixed at the volumetric ratio of 1:1.29, and then struvite was formed at the molar ratio of 1.2 (Mg2+):1.0 (-P):1.0 (NH3-N). Removal efficiencies of ammonia and phosphorus were 91.95% and 99.65%, respectively. The obtained struvite was analyzed by various methods and was found to meet the Korean fertilizer standards, except for copper.
Origin of the medial circumflex femoral artery--a cadaver study.

AIM To determine the pattern of origin of medial circumflex femoral artery (MCFA), its origin distance from the midpoint of the inguinal ligament and the topographical relations of the origin, which have great significance in clinical work. METHODS Dissections on the autopsy group of 42 thighs were performed, and anatomical relationships of MCFA was analyzed. All data were entered into a scheme of the protocol which was designed for this research containing the case number, age and sex, side, topographical-anatomical relations of MCFA, artery dimensions and variations, and the distance between the place of origin of MCFA and the midpoint of the inguinal ligament. RESULTS Commonly, in 25 (59.5%) limbs MCFA originated from the deep femoral artery. In 14 (33.3%) limbs MCFA arose from the femoral artery (FA), in one case (2.4%) a common source of deep femoral artery (DFA) and MCFA was identified, and in two limbs MCFA was not noticed. The distance between the level of origin of MCFA and the central point of the inguinal ligament was 44.2 mm, in average, when it was emerging from FA, and 57.9 mm, in average, in cases where MCFA originated from DFA. CONCLUSION Surgeons and radiologists should be familiar with the variations of clinically important MCFA, which is commonly involved in peripheral occlusive arterial diseases, to improve effectiveness of diagnosis and treatment of various pathological conditions in the femoral region.
An investigation of the potential effect of sperm nuclear vacuoles in human spermatozoa on DNA fragmentation using a neutral and alkaline Comet assay

Presence of vacuoles and degree of sperm DNA damage are considered to be the basic factors used for the assessment of sperm fertilization capacity. We aimed to investigate the link between these two parameters. According to our knowledge, this is the first study where the Comet assay was used to assess the degree of DNA fragmentation of sperm categorized by Motile Sperm Organelle Morphology Examination (MSOME) Grades. Semen samples from 10 patients were assessed. Spermatozoa were graded into four MSOME groups according to the Vanderzwalmen's criteria. A total of 3930 motile spermatozoa were selected one‐by‐one using an inverted microscope and transferred onto two different slides. The degree of DNA fragmentation was analyzed by alkaline and neutral Comet assay. Results of the neutral Comet assay showed that Grade I spermatozoa (absence of vacuoles) presented significantly lower dsDNA fragmentation level (mean: 3.13 ± 1.17%) than Grade II (maximum of two small vacuoles; mean: 10.34 ± 2.65%), Grade III (more than two small vacuoles or at least one large vacuole; mean: 23.88 ± 8.37%), and Grade IV (large vacuoles associated with abnormal head shapes or other abnormalities; mean: 36.94 ± 7.78%; p < 0.05). Results of the alkaline Comet assay showed that Grade I spermatozoa had significantly lower DNA (ssDNA + dsDNA) fragmentation level (mean: 8.33 ± 3.62%) than Grade III (mean: 25.64 ± 9.15%) and Grade IV (mean: 40.10 ± 9.10%, p < 0.05), but not significantly lower than Grade II (mean: 12.73 ± 5.06%; p > 0.05). Probably, the vacuoles may be responsible for double strand DNA breaks rather than single strand DNA breaks (only 2.39% spermatozoa in MSOME Grade II, 1.76% in III, and 3.16% in IV has single strand breaks). The results demonstrate that lower MSOME grading correlates with lower sperm DNA fragmentation. Therefore, the observation of sperm nuclear vacuoles using real‐time optical microscopy without precise DNA fragmentation examination is not sufficient for optimal sperm selection for intracytoplasmic sperm injection.
Associations Between Supportive Leadership Behavior and the Costs of Absenteeism and Presenteeism: An Epidemiological and Economic Approach

Objective: This study investigates associations between supportive leadership behavior (SLB) and presenteeism/absenteeism, and estimates related costs. Methods: Cross-sectional data from a German industrial sample (n = 17,060) assessing SLB and presenteeism/absenteeism were used. Adjusted interval regressions were performed. The study population was split into tertiles with respect to SLB, and minimum and maximum costs for each tertile were estimated on the basis of national industry averages. Results: Low SLB was associated with higher presenteeism [−0.31, 95% confidence interval (95% CI) −0.33 to −0.28)] and absenteeism (−0.36, 95% CI −0.40 to −0.32). Compared with high SLB, the costs of low SLB for absenteeism are between 534.54 and 1675.16 Euro higher per person and year. For presenteeism, this difference ranges between 63.76 and 433.7 Euro. Conclusions: SLB has the potential to reduce absenteeism, presenteeism, and associated costs. To contribute to workforce health, productivity, and efficiency, SLB merits being fostered by corporate policy.