Miracle-dz/UniFAR-FTUs huggingface dataset:
Antidepressant Potentials of Components from Trichilia monadelpha (Thonn.) J.J. de Wilde in Murine Models

Trichilia monadelpha is a common medicinal plant used traditionally in treating central nervous system conditions such as epilepsy, depression, pain, and psychosis. In this study, the antidepressant-like effect of crude extracts of the stem bark of T. monadelpha was investigated using two classical murine models, forced swimming test (FST) and tail suspension test (TST). The extracts, petroleum ether, ethyl acetate, and hydroethanolic extracts (30–300 mg/kg, p.o.), standard drug (imipramine; fluoxetine, 3–30 mg/kg, p.o.), and saline (vehicle) were given to mice one hour prior to the acute study. In a separate experiment the components (flavonoids, saponins, alkaloids, tannins, and terpenoids; 30–300 mg/kg, p.o.) from the most efficacious extract fraction were screened to ascertain which components possessed the antidepressant effect. All the extracts and components significantly induced a decline in immobility in the FST and TST, indicative of an antidepressant-like activity. The extracts and some components showed increase in swimming and climbing in the FST as well as a significant enhancement in swinging and/or curling scores in the TST, suggesting a possible involvement of monoaminergic and/or opioidergic activity. This study reveals the antidepressant-like potential of the stem bark extracts and components of T. monadelpha.
Association of Ischemic Cardiovascular Disease with Inadequacy of Liver Store of Retinol in Elderly Individuals

Objective Vitamin A antioxidant role has an important relationship with the metabolic processes of aging and cardiovascular disease (CVD). This study aimed at assessing the liver store of retinol in elderly individuals who died from cardiovascular disease and its relationship with liver weight and body weight. Methods and Results This is a cross-sectional study conducted in necropsied individuals, aged 60 years or over, until 48 hours postmortem. The study assessed 65 elderly individuals who died from ischemic heart diseases (G1), cerebrovascular diseases (G2), other forms of heart disease (G3), or infectious heart diseases (G4). Twenty percent had inadequate liver store of retinol. G1 showed lower median of liver store of retinol when compared to G3 (p < 0.001), and G3 showed the highest median when compared to G2 (p = 0.007). A significant association was observed between inadequate liver store of retinol and death by ischemic CVD (G1) (p = 0.001) with an odds ratio of 10.38. It was observed that individuals with higher body weight and liver weight showed lower liver store of retinol with significant differences (p = 0.027 and p = 0.026). Conclusion Ischemic CVD and increased body weight and liver weight are related to a greater impairment of the liver store of retinol.
Predictors of L2 word learning accuracy: A big data investigation

What makes some words harder to learn than others in a second language? Although some robust factors have been identified based on small scale experimental studies, many relevant factors are difficult to study in such experiments due to the amount of data necessary to test them. Here, we investigate what factors affect the ease of learning of a word in a second language using a large data set of users learning English as a second language through the Duolingo mobile app. In a regression analysis, we test and confirm the well-studied effect of cognate status on word learning accuracy. Furthermore, we find significant effects for both cross-linguistic semantic alignment and English semantic density, two novel predictors derived from large scale distributional models of lexical semantics. Finally, we provide data on several other psycholinguistically plausible word level predictors. We conclude with a discussion of the limits, benefits and future research potential of using big data for investigating second language learning.
The Management of Glaucoma: Structure and Function

Since the introduction of optical coherence tomography (OCT), this procedure has transformed both the diagnosis and the measurement of the treatment efficacy in glaucoma patients. Furthermore, OCT has become one of the most common imaging modes for evaluations of the optic nerve head (ONH) [1], the retinal nerve fiber layer (RNFL) [2], and the macular thickness [3]. When diagnosing and monitoring glaucoma patients, both structural and functional tests are necessary. Thanks to advances in OCT imaging, such as swept-source OCT and enhanced depth imaging OCT, this has made it possible to perform in vivo imaging of the ONH deep tissues [4, 5]. In addition, it is now possible to differentiate healthy eyes, suspected glaucoma, and glaucomatous eyes of varying severities based on the quantitative assessments of the microcirculation in the ONH and peripapillary region. Furthermore, the diagnostic accuracy that is seen with this technology has been demonstrated to be similar to that seen for both the visual fields [6] and the RNFL thickness [7]. The purpose of this special issue is to present several new approaches that use OCT in the management of glaucoma patients. For this special edition, we received nine submissions in total, and based on these valuable review reports, we have accepted seven original high-quality research articles for publication within this issue. The papers contained within cover a variety of topics and approaches, ranging from the observation of the goniostructure to examinations of the bleb wall after trabeculectomy, along with investigations of the RNFL and macular ganglion cell-inner plexiform layer thickness or optic disc, impact adherence, and the morphology of the scotoma. Moreover, the novel and innovative approaches discussed in these articles may help to stimulate further research into the management of glaucoma patients. The team of guest editors chosen to select the papers for inclusion in this special issue believes that the results included here reflect recent and current trends in this exciting research field, in addition to outlining new ideas for further studies into the management of glaucoma patients.
Factors associated with the prognosis and long-term survival of patients with metastatic lung adenocarcinoma: a retrospective analysis.

Background Outcome of patients with advanced non-small cell lung cancer (NSCLC) treated with platinum-based doublet chemotherapy is usually poor, with overall survival ranges from 8-13 months. However, the overall survival is improved to 21-28 months in the era of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI). This study aimed to explore the prognostic factors to predict long-term survival in the era of EGFR TKI. Methods A total 1,030 patients were studied who had been diagnosed with metastatic adenocarcinoma of lung from January 2005 to December 2009 at Linkou Chang Gung Memorial Hospital. Long-term survivors (LTS) were defined as patients who survived five years or more after the initial diagnosis of stage IV disease. Data on patient age, gender, smoking status, performance status at diagnosis, TNM stage, metastatic pattern, number of metastases, and organs with tumor involvement, EGFR gene mutation status, types of treatment received, EGFR TKI treatment and longest EGFR TKI duration were retrospectively obtained from medical charts. We calculated the odds ratio (OR) of long-term survival from collected clinical parameters to predict long-term survival. Results In this large retrospective study, we reported a five-year survival of 5.0% among patients with metastatic adenocarcinoma of lung. A total 52 LTS and 978 non-LTS were identiﬁed. Patients had more contralateral or pleural/pericardial metastases in the LTS group than in the non-LTS group (51.9% vs. 19.0%, P<0.001), while less extrathoracic spread than in the non-LTS group (42.3% vs. 79.6%, P<0.001). The mutation of the EGFR gene was more frequent in the LTS group than in the non-LTS group (19.2% vs. 7.1%, P=0.006). Conclusions In conclusion, our results suggest that an age younger than 60 years, absence of extrathoracic spread and EGFR TKI treatment duration of more than one year play an important role in the long-term for survivors who survive for more than 5 years.
Initial depth estimation using adaptive window size with light field image

Light field image generates a set of focusing images at different depth levels, which suggests that one can determine object, foreground, or background regions. Moreover, the image can provide another set of multiple view images with slight shifts (sub-aperture images) which allows disparity computation without the image calibration and rectification steps. Nevertheless, the baseline between sub-aperture images is narrow. It limits the depth estimation range and accuracy. To solve this problem, we present an initial depth estimation method using adaptive window matching on epipolar plane images (EPIs) and structure tensor, which appropriately applied to the sub-aperture light field images. We analyze the sub-aperture images to EPIs and compute depth and corresponding information using the structure tensor on EPIs. An adaptive window size is used to improve the robustness of corresponding information. Finally, an initial depth map of light field image is reconstructed.
Motivated Shield From Chronic Noise Environment: Moderation of the Relationship Between Noise Sensitivity and Work Wellbeing by Need for Closure

Several studies have underlined how chronic exposure to environmental noise may have negative effects on performance, wellbeing, and social relations. The present study (N = 90 employees of a motor factory who are chronically exposed to environmental noise) investigated whether the negative effects of chronic exposure of noise-sensitive individuals to noise in the workplace may be moderated by the need for cognitive closure (i.e., an epistemic tendency to reduce uncertainty; NFCC, Kruglanski, 2004). As NFCC has been shown to enhance protection of the focal goal by reducing interference (Kossowska, 2007; Pica et al., 2013), we hypothesized that people with high NFCC should be able to manage the interference coming from environmental noise and concentrate on their tasks more than their low-NFCC counterparts. The results confirm that the negative effects of noise sensitivity on job satisfaction, state anxiety, and turnover intent were higher among low (vs. high)-NFCC individuals.
Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text

Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.
Examination of Learning Equity among Prospective Science Teachers Who Are Concrete, Formal and Postformal Reasoners after an Argumentation-Based Inquiry Course.

This study had two research purposes. First, we examined the scientific reasoning gains of prospective science teachers who are concrete, formal, and postformal reasoners in an argumentationbased physics inquiry instruction. Second, we sought conceptual knowledge and achievement gaps between these student groups before and after the instruction. Results were reported for 114 prospective science teachers. Results showed that concrete reasoners' scientific reasoning gain was higher than those of formal and postformal reasoners. Moreover postformal reasoners outperformed formal and concrete reasoners on a situational conceptual knowledge subscale before and after instruction. In addition, postformal and formal reasoners scored higher than concrete reasoners both on an initial achievement and final achievement measures. However, indepth analyses showed that final achievement differences between postformal and concrete, and formal and concrete reasoners were lower than their respective initial achievement differences. Implications for teacher education programs were discussed according to these findings.
Sociodemographic Characteristics and Health Profile of the Elderly Seeking Health Care in Kampala, Uganda

Aging entails health challenges globally, but pertinent data from low-income countries like Uganda remains scarce. A cross-sectional study was carried out at Mulago National Referral Hospital in Kampala, among 134 patients (38% men and 62% women) aged ≥60 years. Data was collected on sociodemographic characteristics, medical disorders, cognitive function, hearing handicap, and functional status, that is, Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL). The participants had high independency in BADL (89%) and IADL (75%). The most common medical conditions were bone/joint pain (35%), hypertension (24%), and visual problems (20%). More women (54%) than men (37%) reported bone and joint pain. The majority (80%) of the participants did not report any hearing handicap, and half (54%) did not have any cognitive impairment. Dependency in IADL was associated with advanced age, being female, and being financially dependent, and the risk of having a hearing handicap was higher among those above the median age (68 years). In adjusted models, the effects remained similar although statistical significance was only achieved for advanced age versus dependency in IADL (RR: 2.38, 95% CI: 1.12–5.08) and hearing handicap (RR: 2.67, 95% CI: 1.17–6.12). Thus, socioeconomic status and gender are relevant aspects when attempting to understand the health profile of the elderly in Kampala, Uganda.
Human Leukocyte Antigen-A, B, C, DRB1, and DQB1 Allele and Haplotype Frequencies in a Subset of 237 Donors in the South African Bone Marrow Registry

Human leukocyte antigen- (HLA-) A, HLA-B, HLA-C, HLA-DRB1, and HLA-DQB1 allele and haplotype frequencies were studied in a subset of 237 volunteer bone marrow donors registered at the South African Bone Marrow Registry (SABMR). Hapl-o-Mat software was used to compute allele and haplotype frequencies from individuals typed at various resolutions, with some alleles in multiple allele code (MAC) format. Four hundred and thirty-eight HLA-A, 235 HLA-B, 234 HLA-DRB1, 41 HLA-DQB1, and 29 HLA-C alleles are reported. The most frequent alleles were A∗02:02g (0.096), B∗07:02g (0.082), C∗07:02g (0.180), DQB1∗06:02 (0.157), and DRB1∗15:01 (0.072). The most common haplotype was A∗03:01g~B∗07:02g~C∗07:02g~DQB1∗06:02~DRB1∗15:01 (0.067), which has also been reported in other populations. Deviations from Hardy-Weinberg equilibrium were observed in A, B, and DRB1 loci, with C~DQB1 being the only locus pair in linkage disequilibrium. This study describes allele and haplotype frequencies from a subset of donors registered at SABMR, the only active bone marrow donor registry in Africa. Although the sample size was small, our results form a key resource for future population studies, disease association studies, and donor recruitment strategies.
Analysis of Association of Genetic Markers in the LUZP2 and FBXO40 Genes with the Normal Variability in Cognitive Performance in the Elderly

Cognitive performance is an important endophenotype for various neurodegenerative and neuropsychiatric traits. In the present study two genetic variants in the leucine-zipper protein (LUZP2) and the F-box 40 protein (FBXO40) genes, previously reported to be genome-wide significant for Alzheimer's diseases and schizophrenia, were examined for an association with cognitive abilities in normal elderly from the Russian population. Rs1021261 in the LUZP2 and rs3772130 in the FBXO40 were genotyped by multiplex PCR and MALDI-TOF mass spectrometry in a sample of 708 normal elderly subjects tested for cognitive performance using the Montreal Cognitive Assessment (MoCA). Association of genetic variability with the MoCA scores was estimated by parametric and nonparametric analysis of variance and by the frequency comparison between upper and lower quartiles of MoCA distribution. Significantly higher frequency of “TT” genotype of rs1021261 in the LUZP2 gene as well as “A” allele and “AA” genotype of rs3772130 in the FBXO40 gene was found in a subsample of individuals with the MoCA score less than 20 comparing to the fourth quartile's subsample (MoCA > 25). The data of the present study suggests that genetic variability in the LUZP2 and FBXO40 loci associated with neurodegenerative and neuropsychiatric diseases is also contributed to the normal variability in cognitive performance in the elderly.
Machine Learning for Data Management: Problems and Solutions

Machine learning has made great strides in recent years, and its applications are spreading rapidly. Unfortunately, the standard machine learning formulation does not match well with data management problems. For example, most learning algorithms assume that the data is contained in a single table, and consists of i.i. d. (independent and identically distributed) samples. This leads to a proliferation of ad hoc solutions, slow development, and suboptimal results. Fortunately, a body of machine learning theory and practice is being developed that dispenses with such assumptions, and promises to make machine learning for data management much easier and more effective [1]. In particular, representations like Markov logic, which includes many types of deep networks as special cases, allow us to define very rich probability distributions over non-i.i. d. , multi-relational data [2]. Despite their generality, learning the parameters of these models is still a convex optimization problem, allowing for efficient solution. Learning structure-in the case of Markov logic, a set of formulas in first-order logic-is intractable, as in more traditional representations, but can be done effectively using inductive logic programming techniques. Inference is performed using probabilistic generalizations of theorem proving, and takes linear time and space in tractable Markov logic, an object-oriented specialization of Markov logic [3]. These techniques have led to state-of-the-art, principled solutions to problems like entity resolution, schema matching, ontology alignment, and information extraction. Using tractable Markov logic, we have extracted from the Web a probabilistic knowledge base with millions of objects and billions of parameters, which can be queried exactly in subsecond times using an RDBMS backend [3]. With these foundations in place, we expect the pace of machine learning applications in data management to continue to accelerate in coming years.
Panel Data Hedonics: Rosen'S First Stage as a “Sufficient Statistic”

Traditional cross-sectional estimates of hedonic price functions can recover marginal willingness to pay for characteristics, but face endogeneity problems for estimating non-marginal welfare measures. I show that when panel data on household demands are available, one can construct a second-order approximation to non-marginal welfare measures using only the first-stage marginal prices. With repeated cross sections of product prices, the measure can be set identified or, under a single-crossing restriction, point identified. Bounds also can be constructed when there are mobility costs. Finally, a variant remains valid when individual preferences shift over time.
Pharmacologic activation of peroxisome proliferator-activating receptor-α accelerates hepatic fatty acid oxidation in neonatal pigs

Up-regulation of peroxisome proliferator-activating receptor-α (PPARα) and increasing fatty acid oxidation are important for reducing pre-weaning mortality of pigs. We examined the time-dependent regulatory effects of PPARα activation via oral postnatal clofibrate administration (75 mg/(kg-BW·d) for up to 7 days) on mitochondrial and peroxisomal fatty acid oxidation in pigs, a species with limited hepatic fatty acid oxidative capacity due to low ketogenesis. Hepatic oxidation was increased by 44-147% (depending on fatty acid chain-length) and was attained after only 4 days of clofibrate treatment. Acyl-CoA oxidase (ACO) and carnitine palmitoyltransferase I (CPTI) activities accelerated in parallel. The increase in CPTI activity was accompanied by a rapid reduction in the sensitivity of CPTI to malonyl-CoA inhibition. The mRNA abundance of CPTI and ACO, as well as peroxisomal keto-acyl-CoA thiolase (KetoACoA) and mitochondrial malonyl-CoA decarboxylase (MCD), also were augmented greatly. However, the increase in ACO activity and MCD expression were different from CPTI, and significant interactions were observed between postnatal age and clofibrate administration. Furthermore, the expression of acetyl-CoA carboxylase β (ACCβ) decreased with postnatal age and clofibrate had no effect on its expression. Collectively these results demonstrate that the expression of PPARα target genes and the increase in fatty acid oxidation induced by clofibrate are time- and age-dependent in the liver of neonatal pigs. Although the induction patterns of CPTI, MCD, ACO, KetoACoA, and ACCβ are different during the early postnatal period, 4 days of exposure to clofibrate were sufficient to robustly accelerate fatty acid oxidation.
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications

Simultaneous Localisation And Mapping (SLAM) is a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is particularly true when it comes to evaluate the potential trade-offs between computation speed, accuracy, and power consumption. SLAMBench is a benchmarking framework to evaluate existing and future SLAM systems, both open and closed source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. SLAMBench is a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption across SLAM systems. In this poster we give an overview of SLAMBench and in particular we show how this framework can be used within Design Space Exploration and large-scale performance evaluation on mobile phones.
Treatment modalities in poor responder patients undergoing assisted reproductive techniques

Successful IVF/ICSI treatment depends on obtaining a sufficient oocyte sample and selecting patients carefully. COH (Controlled Overian Hyperstimulation) treatment in patients with poor ovarian response is one of the most important issues in IVF programmes. The ovarian response that follows ovarian stimulation is the most important determiner in ART treatment. Although many other stimulation protocols have been applied on patients with poor ovarian response, low pregnancy rates are currently reported.