Miracle-dz/UniFAR-FTUs huggingface dataset:
Induction of Oxidative Stress Through Inhibition of Thioredoxin Reductase 1 Is an Effective Therapeutic Approach for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide which lacks effective treatment. Cancer cells experience high levels of oxidative stress due to increased generation of reactive oxygen species (ROS). Increased antioxidant‐producing capacity is therefore found in cancer cells to counteract oxidative stress. The thioredoxin system is a ubiquitous mammalian antioxidant system which scavenges ROS, and we demonstrate that it is vital for HCC growth as it maintains intracellular reduction‐oxidation (redox) homeostasis. Transcriptome sequencing in human HCC samples revealed significant overexpression of thioredoxin reductase 1 (TXNRD1), the cytosolic subunit and key enzyme of the thioredoxin system, with significant correlations to poorer clinicopathological features and patient survival. Driven by the transcriptional activation of nuclear factor (erythroid‐derived 2)–like 2, the master protector against oxidative stress, TXNRD1 counteracts intracellular ROS produced in human HCC. Inhibition of TXNRD1 through genetic inhibition hindered the proliferation of HCC cells and induced apoptosis in vitro. Administration of the pharmacological TXNRD1 inhibitor auranofin (AUR) effectively suppressed the growth of HCC tumors induced using the hydrodynamic tail vein injection and orthotopic implantation models in vivo. Furthermore, AUR sensitized HCC cells toward the conventional therapeutic sorafenib. Conclusion: Our study highlights the reliance of HCC cells on antioxidants for redox homeostasis and growth advantage; targeting TXNRD1 resulted in dramatic accumulation of ROS, which was found to be an effective approach for the suppression of HCC tumor growth.
PiCL: A Software-Transparent, Persistent Cache Log for Nonvolatile Main Memory

Software-transparent crash consistency is a promising direction to immediately reap the benefits of nonvolatile main memory (NVMM) without encumbering programmers with error-prone transactional semantics. Unfortunately, proposed hardware write-ahead logging (WAL) schemes have high performance overhead, particularly for multi-core systems with many threads and big on-chip caches and NVMs with low random-access performance. This paper proposes PiCL, a new WAL checkpointing mechanism that provides a low overhead, software-transparent crash consistency solution for NVMM. PiCL introduces multi-undo logging, cache-driven logging, and asynchronous cache-scan to reduce random accesses and enable good row locality at the NVM. The key idea is that: by relaxing the durability timing of checkpoints, crash consistency can be provided with less than 1% performance overhead where 1.5x to 5.0x slowdown was typical with prior work. To demonstrate the feasibility of software-transparent crash consistency, we fully implemented PiCL as an FPGA prototype in Verilog using the OpenPiton framework.
Internet addiction among social networking sites users: Emerging mental health concern among medical undergraduates of Karachi

Objective: To determine the frequency and intensity of Internet Addiction (IA) among medical undergraduates, using Social Networking Sites (SNS), in Karachi. Methods: A cross-sectional survey was conducted in March-June ‘16 in a private and government medical college of Karachi. Self-administered, Young’s Internet Addiction Test was implemented by 340 medical students to assess the frequency and intensity of IA among SNS profile users for past three years. The structured questionnaire further enquired regarding the social and behaviour patterns relevant to IA and SNS use. Data was analyzed using SPSS 16. Results: Internet Addiction (IA) was found in 85% (n=289) of all study participants. Among them, 65.6% (n=223) were ‘minimally addicted’, 18.5% (n=63) were ‘moderately addicted’, whereas 0.9% (n=3) were found to be ‘severely addicted’. Burden of IA was relatively higher among female medical students as compared to male medical students (p=0.02). There was no significant difference between type of medical college attended and IA (p=0.45). However, statistically significant differences were observed in certain behavioural patterns among addicted and non-addicted medical students. Conclusion: Internet Addiction (IA) is an emerging mental health concern affecting social behaviour patterns of medical undergraduates. However, the burden of IA is relatively higher among female medical students.
Statistical Impact Evaluation of Stochastic Parameters Enhancing Solar Power Inherent Smoothing

The characteristic variability of solar power brings potential threats to power system frequency stability by introducing short-term power fluctuations. A nationwide virtual PV (photovoltaic) system of 14 locations in Finland with interplant distances between 10 km to 1065 km has been analyzed at 1 Hz resolution by introducing stochastic variables i.e. power fluctuation, maximum power fluctuation and smoothing potential factor. The paper presents a multifold smoothing analysis based on geographical dispersion, ensemble size, temporal resolutions and interplant distances of PV plants. Empirical expressions are proposed using regression analysis, which represents a decay of smoothing potential factor for a decreasing time resolution. Aggregated strength of maximum power fluctuation of $N$ number of PV plants decreases by a converging factor 1/-N and an ensemble of 14 PV plants follows the strict ramp rate limits for 97.5% of the total production time. Spatial correlation w.r. t interplant distances is also focused.
LSTM Based Short-term Electricity Consumption Forecast with Daily Load Profile Sequences

For energy-related services and researches, not only the energy load data in the past but also the future are essential. In this paper, a short-term electricity consumption prediction method is proposed. The method utilizes Long-Short-Term-Memory (LSTM) network which takes a sequence of past consumption profiles to perform a month-ahead electricity consumption prediction as a sequence. For performance analysis, an experiment with a real dataset is done, and the experimental result validates that the proposed method performs well with the prediction accuracy of about 82.5%. The test accuracy can be improved with a longer period of training time and deliberate hyperparameter setting.
Effects of age, sex and manual task on hand preference in wild Rhinopithecus roxellana

Golden snub-nosed monkeys (Rhinopithecus roxellana), as typical arboreal group-living Old World monkeys, provide an appropriate animal model to research manual laterality and explore the factors affecting hand preference in non-human primates (NHP). This study investigated hand preference based on 63 subjects and four spontaneous manual tasks (including unimanual and bimanual feeding and grooming), and assessed the effects of age, gender and type of task on handedness in R. roxellana. A population-level left-handedness was found not only in the bimanual coordinated tasks (bimanual feeding and grooming), but also in one unimanual reaching task (unimanual feeding). There were no significant differences between the sexes in either direction or strength of hand preference among any task. However, a significant difference between adults and juveniles was found in the unimanual feeding task. This is the first report on handedness in unimanual and bimanual feeding tasks that require bipedal posture in wild R. roxellana. Furthermore, this study demonstrated spontaneous feeding tasks reported previously only in the quadrupedal posture in this species, supporting the importance of factors such as posture and task complexity in the evolution of primate manual lateralization.
The Role of Data in Model Building and Prediction: A Survey Through Examples

The goal of Science is to understand phenomena and systems in order to predict their development and gain control over them. In the scientific process of knowledge elaboration, a crucial role is played by models which, in the language of quantitative sciences, mean abstract mathematical or algorithmical representations. This short review discusses a few key examples from Physics, taken from dynamical systems theory, biophysics, and statistical mechanics, representing three paradigmatic procedures to build models and predictions from available data. In the case of dynamical systems we show how predictions can be obtained in a virtually model-free framework using the methods of analogues, and we briefly discuss other approaches based on machine learning methods. In cases where the complexity of systems is challenging, like in biophysics, we stress the necessity to include part of the empirical knowledge in the models to gain the minimal amount of realism. Finally, we consider many body systems where many (temporal or spatial) scales are at play—and show how to derive from data a dimensional reduction in terms of a Langevin dynamics for their slow components.
Nutritional status and eating disorders among medical students from the Cluj-Napoca University centre

Background and aims The nutritional disorders are modifiable predisposing factors of the current leading causes of morbidity and premature mortality, such as cardiovascular diseases, diabetes and cancers. An early recognition of appropriate interventions could decrease the burden of medical costs and increase the healthy life expectancy. The aims of our study were the description of nutritional status, its associated factors, and the detection of eating disorders among Romanian medical students of the “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca. Methods A cross-sectional study was conducted during the 2015–2016 academic year, including medical students from all study years, by an online questionnaire. The statistics, both descriptive and inferential, were calculated using EpiInfo. Results A total of 222 students from the University Romanian section were included, with a sex-ratio (men:women) of 0.22. The average body mass index of the medical students included was 21.3±3.3 kg/m2, for females it was 20.7±3 kg/m2 and for males 23.9±3.1 kg/m2. For the nutritional disorders such as overweight, the prevalence among males (30%) had a net predominance (p<0.001) over females (4.4%), while for underweight, it was prevalent only for female students (20.3%). Overweight was associated with low academic performance (odds ratio (OR)=7.2; 95% confidence interval (95% CI): 1–53.94), smoking (OR=2.4; 95% CI: 1–5.76) and binge drinking (OR=3; 95% CI: 1.26–7.25). Eating disorders were reported by 25.7% of the students and were significantly more prevalent in students in the second academic study cycle (47.4%; 95% CI: 34.7–60.3%) and in students with jobs (7%; 95% CI: 2.3–16.1%). Conclusions In this survey, two different distribution models of nutritional disorders for both genders were identified, with the predominance of overweight among men. Eating disorders were more prevalent among students in the second study cycle and those with jobs at the same time.
Lipopolysaccharide Stimulated the Migration of NIH3T3 Cells Through a Positive Feedback Between β-Catenin and COX-2

How β-catenin/COX-2 contribute to inflammation-induced fibroblasts migration remains poorly understood. Therefore, in this study, lipopolysaccharide (LPS) was used as a stimulus to accelerate the migration of NIH3T3 cells, which mimicked the tissue repair process. LPS treatment increased the cell migration in concentration-and time-dependent manner. And NS398, a COX-2 inhibitor, inhibited LPS-induced NIH3T3 cells migration. DKK-1, an antagonist of the Wnt/β-catenin signaling, also inhibited that migration. However, TWS119, an inducer of β-catenin via GSK-3β, increased the cell migration. LPS or TWS119 treatment increased COX-2, β-catenin, TGF-β1, and HMGB-1 expressions, and that could be attenuated by NS398 or DKK-1 addition. LPS induced the PGE2 production, and PGE2 increased the expression and nuclear translocation of β-catenin, while EP2 blocker, AH6809, alleviated those effects. TWS119 increased the luciferase activity in the COX-2 promoter. In conclusion, LPS stimulated the NIH3T3 fibroblasts migration through a positive feedback between β-catenin and COX-2, in which PGE2, EP2, TGF-β1, and HMGB-1 played as signal molecules.
CD19 Chimeric Antigen Receptor Therapy for Refractory Aggressive B-Cell Lymphoma.

PURPOSE Anti-CD19-directed chimeric antigen receptor (CAR) T-cell therapy has had a resounding effect on the treatment of chemotherapy-insensitive aggressive B-cell non-Hodgkin lymphoma (B-NHL). There are now two US Food and Drug Administration (FDA)-approved products available for treating these patients, and a third product is expected to be approved in the coming months. The question remains: Is there a preferred or best product for my patient? However, answering that question is more complicated than simply balancing the reported efficacy and toxicity results. DESIGN This review outlines potential confounding factors involving the three products and their pivotal clinical trials and highlights additional considerations of manufacturing reliability and overall cost that must be considered when weighing one product against another. It will also review the directions in which the field is moving and strategies being examined to improve efficacy as well as toxicity. CONCLUSION Because a randomized three-arm clinical trial is unlikely, a product may have to be chosen on the basis of results from treatment centers that have experience with all three products. But by the time those results are available, they are likely to be outdated because, given the rapid evolution of the field, the next product will probably have been identified.
MiR‐99a‐5p inhibits bladder cancer cell proliferation by directly targeting mammalian target of rapamycin and predicts patient survival

Bladder cancer is a common malignancy and miR‐99a‐5p has been reported to be downregulated in bladder cancer, but its function and the underlying mechanism in bladder cancer development remains largely unclear. Here, we report that miR‐99a‐5p expression was decreased in bladder cancer compared with the adjacent normal tissues. Receiver operating characteristic curve revealed that miR‐99a‐5p expression signature had area under curve value of 0.7989 in differing bladder cancer from the adjacent normal tissues. Bladder cancer patients with low expression of miR‐99a‐5p had a poor survival rate. Gain‐of‐function and loss‐of‐function approaches demonstrated that miR‐99a‐5p inhibited bladder cell proliferation and cell cycle. Furthermore, we identified that mammalian target of rapamycin (mTOR) was a direct target of miR‐99a‐5p and mTOR restore could rescue the proliferative ability of bladder cancer cells. Moreover, miR‐99a‐5p/mTOR axis regulated S6K1 phosphorylation. These suggested that miR‐99a‐5p/mTOR axis might be a therapeutic target for bladder cancer.
Underwater Acoustic Target Recognition Based on Supervised Feature-Separation Algorithm

For the purpose of improving the accuracy of underwater acoustic target recognition with only a small number of labeled data, we proposed a novel recognition method, including 4 steps: pre-processing, pre-training, fine-tuning and recognition. The 4 steps can be explained as follows: (1) Pre-processing with Resonance-based Sparsity Signal Decomposition (RSSD): RSSD was firstly utilized to extract high-resonance components from ship-radiated noise. The high-resonance components contain the major information for target recognition. (2) Pre-training with unsupervised feature-extraction: we proposed a one-dimensional convolution autoencoder-decoder model and then we pre-trained the model to extract features from the high-resonance components. (3) Fine-tuning with supervised feature-separation: a supervised feature-separation algorithm was proposed to fine-tune the model and separate the extracted features. (4) Recognition: classifiers were trained to recognize the separated features and complete the recognition mission. The unsupervised pre-training autoencoder-decoder can make good use of a large number of unlabeled data, so that only a small number of labeled data are required in the following supervised fine-tuning and recognition, which is quite effective when it is difficult to collect enough labeled data. The recognition experiments were all conducted on ship-radiated noise data recorded using a sensory hydrophone. By combining the 4 steps above, the proposed recognition method can achieve recognition accuracy of 93.28%, which sufficiently surpasses other traditional state-of-art feature-extraction methods.
Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine

In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets.
Australian Heterosexual Men’s Experiences of Pregnancy Loss: The Relationships Between Grief, Psychological Distress, Stigma, Help-Seeking, and Support

This study explores experiences of pregnancy loss via a questionnaire completed by a convenience sample of 48 Australian cisgender heterosexual men. The questionnaire included measures of support following pregnancy loss, recognition of loss, perceived utility of help-seeking, perceived stigma attached to help-seeking, perinatal grief, and depression and anxiety. The questionnaire also included open-ended questions focused on help-seeking and support. Higher levels of grief were related to higher levels of both depression and anxiety. Perceptions about stigma were related to the perceived utility of help-seeking. While a majority of participants had accessed formal support services, feeling supported was unrelated to either grief or depression. Participants emphasized the utility of men’s groups where members have experienced pregnancy loss, though barriers to support were also identified in terms of the unavailability of support or the perceived need to focus on a partner’s loss.
Clinically indicated electrical stimulation strategies to treat patients with medically refractory epilepsy

Focal epilepsies represent approximately half of all diagnoses, and more than one‐third of these patients are refractory to pharmacologic treatment. Although resection can result in seizure freedom, many patients do not meet surgical criteria, as seizures may be multifocal in origin or have a focus in an eloquent region of the brain. For these individuals, several U.S. Food and Drug Administration (FDA)–approved electrical stimulation paradigms serve as alternative options, including vagus nerve stimulation, responsive neurostimulation, and stimulation of the anterior nucleus of the thalamus. All of these are safe, flexible, and lead to progressive seizure control over time when used as an adjunctive therapy to antiepileptic drugs. Focal epilepsies frequently involve significant comorbidities such as cognitive decline. Similar to antiepilepsy medications and surgical resection, current stimulation targets and parameters have yet to address cognitive impairments directly, with patients reporting persistent comorbidities associated with focal epilepsy despite a significant reduction in the number of their seizures. Although low‐frequency theta oscillations of the septohippocampal network are critical for modulating cellular activity and, in turn, cognitive processing, the coordination of neural excitability is also imperative for preventing seizures. In this review, we summarize current FDA‐approved electrical stimulation paradigms and propose that theta oscillations of the medial septal nucleus represent a novel neuromodulation target for concurrent seizure reduction and cognitive improvement in epilepsy. Ultimately, further advancements in clinical neurostimulation strategies will allow for the efficient treatment of both seizures and comorbidities, thereby improving overall quality of life for patients with epilepsy.
Risk factors and prevalence of toxocariasis in pregnant women and diabetic patients compared to healthy adults in Ilam province, western Iran

Toxocara is one of the common intestinal nematodes in dogs and cats and is the agent of tissue migratory larvae in humans. Customarily, the prevalence of human toxocariasis hovers around 15.8 % in Iran. Furthermore, other research outcomes demonstrated a tendency for an outbreak of toxocariasis in Iran. Therefore, we carried out a cross-sectional study and assessed the seroprevalence of toxocariasis humans in Ilam Province, western of Iran. A total of 539 serum samples were collected between September 2017 and March 2018 from patients referred to the Health Centers of Ilam province, Iran. Serum samples were investigated for the presence of Toxocara using IgG antibodies, ELISA (Enzyme-Linked Immunosorbent Assay) kit. Risk factors such as contact with cats and dogs, living in rural areas were investigated among the study population. Out of 539 total samples collected, 97 cases (17.99 %) were positive for anti-toxocara IgG antibodies. These antibodies were recovered from serum samples of otherwise healthy adults (15.54 %, 49/296), pregnant women (21.16 %, 40/189) and diabetic patients (14.81 %, 8/54). This study showed significant relationship between toxocariasis and contact with animal pets in all studied groups (P value ≤ 0.05) and a significant relationship between toxocariasis and living in rural areas among pregnant women (P value ≤ 0.05).
Consistent Robust Adversarial Prediction for General Multiclass Classification

We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-continuous multiclass loss metrics against the worst-case conditional label distributions (the adversarial distributions) that (approximately) match the statistics of the training data. Although the optimized loss metrics are non-convex and non-continuous, the dual formulation of the framework is a convex optimization problem that can be recast as a risk minimization model with a prescribed convex surrogate loss we call the adversarial surrogate loss. We show that the adversarial surrogate losses fill an existing gap in surrogate loss construction for general multiclass classification problems, by simultaneously aligning better with the original multiclass loss, guaranteeing Fisher consistency, enabling a way to incorporate rich feature spaces via the kernel trick, and providing competitive performance in practice.