Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of vacuum and high-oxygen modified atmosphere packaging on physico-chemical and microbiological properties of minced water buffalo meat

Objective In this study, the effects of vacuum (VP) and high-oxygen modified atmosphere (80% O2+20% CO2) packaging (HiOx-MAP) on physico-chemical and microbiological properties of minced water buffalo meat were investigated. Methods After minced meat preparation, samples were packaged under VP and HiOx-MAP and stored at 2°C±0.5°C for 14 days. Samples taken on certain days were subjected to total aerobic mesophilic bacteria, total aerobic psychrotrophic bacteria, lactic acid bacteria, Pseudomonas, Enterobacteriaceae and yeast-mold counts as well as pH, color (L*, a*, and b*) and thiobarbituric acid reactive substances (TBARS) analyses. Results In minced water buffalo meat packaged under HiOx-MAP, TBARS value exceeded 1 mg malondialdehyde/kg on the 4th day of the storage. In VP samples, TBARS value remained close to initial TBARS value during storage. According to the findings, a* value was determined to be high in the HiOx-MAP samples within initial days of the storage. However, no significant changes in a* value were observed in VP samples during storage. In contrast, the mean value of L* was detected as higher in HiOx-MAP sample than VP samples. The count of psychrotrophic bacteria increased more than that of mesophilic bacteria during storage. The growth of Enterobacteriaceae and Pseudomonas was delayed in both the packaging methods. However, lactic acid bacteria exhibited more growth in VP samples compared to MAP samples. Conclusion The lipid oxidation proceeded faster than expected in minced water buffalo meat packed with HiOx-MAP method. This situation adversely affected the a* value. On the other hand, similar microbiological results were obtained in both packing methods.
Clinicopathological evaluation and survival of patients with squamous cell carcinoma of the tongue

Background Early detection of oral cancer is the most effective means of reducing morbidity, complexity, and extent of treatment. This study evaluated the clinicopathological profile of epidermoid carcinoma of the tongue, including treatment and survival. Material and Methods This observational, retrospective cross-sectional study evaluated patients with squamous cell carcinoma of the tongue treated at the Dr. Luiz Antônio Hospital, Natal, Brazil, from January 2001 to December 2011. Survival variables were calculated using the Kaplan-Meier method and compared by log rank tests. Results Of the 412 patients diagnosed in this period, 298 (72.3%) were men; their mean age was 60.5 years, and 69.2% were diagnosed with stage III/IV tumours. Improved survival was associated with early stage diagnosis, absence of affected lymph nodes at diagnosis, and treatment with surgery alone. Conclusions Late stage diagnosis of oral cancer negatively affects patient survival. In addition, the general public should be made aware of the prognostic factors for oral SCC of the tongue and of the importance of periodic examinations of the oral cavity. Key words:Mouth neoplasms, tongue neoplasms, carcinoma, squamous cell, survival analysis.
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations

Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, practical solutions remain elusive due to large training data requirements, algorithmic instability, and lack of standard tools. In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks. While prior work has frequently explored applications in simulations, LIFT centers on utilizing human expertise to learn from demonstrations, thus lowering online training times. We further introduce TensorForce, a TensorFlow library for applied deep reinforcement learning exposing a unified declarative interface to common RL algorithms, thus providing a backend to LIFT. We demonstrate the utility of LIFT in two case studies in database compound indexing and resource management in stream processing. Results show LIFT controllers initialized from demonstrations can outperform human baselines and heuristics across latency metrics and space usage by up to 70%.
The Development of Metabolic Derangement in Male Offspring after Perinatal Exposure to Di-(2-Ethylhexyl) Phthalate.

doi: 10.3967/bes2018.071 1. Department of Preventive Medicine, College of Medicine, Chung-Ang University, Seoul 06974, Korea; 2. Institute of Biomedical Science, Catholic Kwandong University International St. Mary’s Hospital, Incheon 22711, Korea; 3. Institute for Catholic Integrative Medicine, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Incheon 21431, Korea Letter to the Editor
Tocilizumab modulates serum levels of adiponectin and chemerin in patients with rheumatoid arthritis: potential cardiovascular protective role of IL-6 inhibition.

OBJECTIVES Adipokines play an important role in the pathophysiology of rheumatoid arthritis (RA), provide a link between the disease and overweight, contributing to explain the enhanced cardiovascular (CV) risk and influence the response to disease-modifying anti-rheumatic drugs. The aim of this study was to determine the possible effects of intravenous (IV) tocilizumab (TCZ), an interleukin-6 receptor antagonist, on serum levels of leptin, adiponectin, resistin, visfatin, and chemerin. METHODS Forty-four RA patients with active disease (DAS28-ESR ≥3.2) were treated with IV TCZ (8 mg/kg) once every 4 weeks for six months: 20 patients received TCZ as monotherapy and 24 in association with methotrexate (MTX). At baseline and monthly, before each infusion, body mass index, DAS28-ESR and Health Assessment Questionnaire (HAQ) were recorded. The laboratory parameters, including the adipokines serum levels were collected at baseline and after six months. RESULTS At the end of the follow-up, ESR, CRP, DAS28-ESR and HAQ resulted significantly improved in patients received TCZ as monotherapy or combined with MTX. Lipid profile showed only a significant increase of total cholesterol. A significant reduction of chemerin and an increase of adiponectin were observed in the whole population and in the subgroups of the patients analysed (TCZ mono or combined therapy) without any significant correlations with clinical and biochemical parameters. No changes in the leptin and resistin levels were detected. CONCLUSIONS TCZ is able to regulate serum levels of chemerin and adiponectin in RA patients, independently of the disease treatment response, which contributes to explain the CV safety of TCZ.
Effects of Binge Drinking on the Developing Brain

Binge drinking is a pattern of alcohol drinking that raises a person’s blood alcohol concentration to at least . 08%, which amounts to consuming five alcoholic drinks for men and four alcoholic drinks for women in about 2 hours. It is the most common form of alcohol misuse in adolescents and young adults. Heavy drinking includes the same criterion as binge drinking, but with higher frequency (i.e., 5 or more days in the past 30 days). Although binge drinking or heavy drinking alone is insufficient to meet the criteria for an alcohol use disorder (AUD) diagnosis, there are neurobiological changes, as well as an increased risk of developing an AUD later in life, associated with this form of alcohol misuse. This review describes the recent neuroimaging findings in binge drinking and heavy-drinking adolescents and young adults, a developmental period during which significant neuromaturation occurs.
Proximal Policy Optimization and its Dynamic Version for Sequence Generation

In sequence generation task, many works use policy gradient for model optimization to tackle the intractable backpropagation issue when maximizing the non-differentiable evaluation metrics or fooling the discriminator in adversarial learning. In this paper, we replace policy gradient with proximal policy optimization (PPO), which is a proved more efficient reinforcement learning algorithm, and propose a dynamic approach for PPO (PPO-dynamic). We demonstrate the efficacy of PPO and PPO-dynamic on conditional sequence generation tasks including synthetic experiment and chit-chat chatbot. The results show that PPO and PPO-dynamic can beat policy gradient by stability and performance.
Molecular Detection of Eukaryotic Diets and Gut Mycobiomes in Two Marine Sediment-Dwelling Worms, Sipunculus nudus and Urechis unicinctus

The present study aimed to reveal the eukaryotic diets of two economically important marine sediment-inhabiting worms, Sipunculus nudus (peanut worm) and Urechis unicinctus (spoon worm), using clone libraries and phylogenetic analyses of 18S rRNA genes. Fungal rDNA was also targeted and analyzed to reveal mycobiomes. Overall, we detected a wide range of eukaryotic phylotypes associated with the larvae of S. nudus and in the gut contents of both worms. These phylotypes included ciliates, diatoms, dinoflagellates, eustigmatophytes, placidids, oomycetes, fungi, nematodes, flatworms, seaweeds, and higher plants. Oomycetes were associated with the planktonic larvae of S. nudus. The composition of eukaryotic diets shifted greatly across the larval, juvenile, and adult stages of S. nudus, and among different gut sections in U. unicinctus, reflecting lifestyle changes during the ontogeny of the peanut worm and progressive digestion in the spoon worm. Malassezia-like fungi were prevalent in mycobiomes. Epicoccum and Trichosporon-related phylotypes dominated mycobiomes associated with larval individuals and in the gut contents of adults, respectively. The gut mycobiome of S. nudus was successively characterized through the midgut, aspiratory intestines, hindgut, and rectum as having a high proportion of Climacodon-Rhizochaete, Ceriporiopsis, Cladosporium-Pseudomicrostroma, and Malassezia-related species in the libraries. These results emphasize the dynamics of diets and gut mycobiomes in marine benthic animals.
Approximate Distribution Matching for Sequence-to-Sequence Learning

Sequence-to-Sequence models were introduced to tackle many real-life problems like machine translation, summarization, image captioning, etc. The standard optimization algorithms are mainly based on example-to-example matching like maximum likelihood estimation, which is known to suffer from data sparsity problem. Here we present an alternate view to explain sequence-to-sequence learning as a distribution matching problem, where each source or target example is viewed to represent a local latent distribution in the source or target domain. Then, we interpret sequence-to-sequence learning as learning a transductive model to transform the source local latent distributions to match their corresponding target distributions. In our framework, we approximate both the source and target latent distributions with recurrent neural networks (augmenter). During training, the parallel augmenters learn to better approximate the local latent distributions, while the sequence prediction model learns to minimize the KL-divergence of the transformed source distributions and the approximated target distributions. This algorithm can alleviate the data sparsity issues in sequence learning by locally augmenting more unseen data pairs and increasing the model's robustness. Experiments conducted on machine translation and image captioning consistently demonstrate the superiority of our proposed algorithm over the other competing algorithms.
Single women and stock investment in individual retirement accounts

ABSTRACT This study uses data from the 2013 Survey of Consumer Finances to investigate factors related to stock investment in individual retirement accounts and focuses on the role of gender and marital status in particular. This study finds that single women are less likely to own stocks and invest in fewer stocks in their IRAs. Personal characteristics, such as stock ownership in other financial assets, education level, and risk tolerance may play a key role in shaping individuals’ decisions regarding stock investment in IRAs. However, these determinants are different for single women than for other demographic groups.
Mechanisms Underlying the Spacing Effect in Learning: A Comparison of Three Computational Models

The spacing effect is one of the most widely replicated results in experimental psychology: Separating practice repetitions by a delay slows learning but enhances retention. The current study tested the suitability of the underlying, explanatory mechanism in three computational models of the spacing effect. The relearning of forgotten material was measured, as the models differ in their predictions of how the initial study conditions should affect relearning. Participants learned Japanese–English paired associates presented in a massed or spaced manner during an acquisition phase. They were tested on the pairs after retention intervals ranging from 1 to 21 days. Corrective feedback was given during retention tests to enable relearning. The results of 2 experiments showed that spacing slowed learning during the acquisition phase, increased retention at the start of tests, and accelerated relearning during tests. Of the 3 models, only 1, the predictive performance equation (PPE), was consistent with the finding of spacing-accelerated relearning. The implications of these results for learning theory and educational practice are discussed.
Context-Dependent Developmental Effects of Parental Shade Versus Sun Are Mediated by DNA Methylation

Parental environment influences progeny development in numerous plant and animal systems. Such inherited environmental effects may alter offspring phenotypes in a consistent way, for instance when resource-deprived parents produce low quality offspring due to reduced maternal provisioning. However, because development of individual organisms is guided by both inherited and immediate environmental cues, parental conditions may have different effects depending on progeny environment. Such context-dependent transgenerational plasticity suggests a mechanism of environmental inheritance that can precisely interact with immediate response pathways, such as epigenetic modification. We show that parental light environment (shade versus sun) resulted in context-dependent effects on seedling development in a common annual plant, and that these effects were mediated by DNA methylation. We grew replicate parents of five highly inbred Polygonum persicaria genotypes in glasshouse shade versus sun and, in a fully factorial design, measured ecologically important traits of their isogenic seedling offspring in both environments. Compared to the offspring of sun-grown parents, the offspring of shade-grown parents produced leaves with greater mean and specific leaf area, and had higher total leaf area and biomass. These shade-adaptive effects of parental shade were pronounced and highly significant for seedlings growing in shade, but slight and generally non-significant for seedlings growing in sun. Based on both regression and covariate analysis, inherited effects of parental shade were not mediated by changes to seed provisioning. To test for a role of DNA methylation, we exposed replicate offspring of isogenic shaded and fully insolated parents to either the demethylating agent zebularine or to control conditions during germination, then raised them in simulated growth chamber shade. Partial demethylation of progeny DNA had no phenotypic effect on offspring of shaded parents, but caused offspring of sun-grown parents to develop as if their parents had been shaded, with larger leaves and greater total canopy area and biomass. These results contribute to the increasing body of evidence that DNA methylation can mediate transgenerational environmental effects, and show that such effects may contribute to nuanced developmental interactions between parental and immediate environments.
An Enhanced SCMA Detector Enabled by Deep Neural Network

In this paper, we propose a learning approach for sparse code multiple access (SCMA) signal detection by using a deep neural network via unfolding the procedure of message passing algorithm (MPA). The MPA can be converted to a sparsely connected neural network if we treat the weights as the parameters of a neural network. The neural network can be trained off-line and then deployed for online detection. By further refining the network weights corresponding to the edges of a factor graph, the proposed method achieves a better performance. Moreover, the deep neural network based detection is a computationally efficient since highly paralleled computations in the network are enabled in emerging Artificial Intelligence (AI) chips.
Performance Limits of Single-Anchor Millimeter-Wave Positioning

The fundamental limits of single-anchor multi-antenna positioning are investigated. Exploiting the structure of the multiple input-multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) channel at millimeter-wave frequencies, we present geometrically intuitive asymptotic expressions for the Fisher information on position, orientation and velocity for large bandwidth and number of antennas. The effects of synchronization errors and mobility are studied and it is shown that non-line-of-sight (NLOS) paths can be used to estimate the synchronization error and drastically improve the positioning performance. We also show that, in the presence of line-of-sight (LOS), mobility has a small impact on the achievable positioning accuracy, but in the NLOS-only scenario it can significantly improve the achievable performance, depending on the variance of the synchronization error. Finally, considering a communication system with device-specific transmission and reception constraints, we compare the positioning accuracy between the downlink and the uplink and show that they are equivalent under the same received signal-to-noise ratio (SNR).
Identification of loci where DNA methylation potentially mediates genetic risk of type 1 diabetes

The risk of Type 1 Diabetes (T1D) comprises both genetic and environmental components. We investigated whether genetic susceptibility to T1D could be mediated by changes in DNA methylation, an epigenetic mechanism that potentially plays a role in autoimmune diabetes. Using data from a non-diabetic population comprising blood samples taken at birth (n=844), childhood (n=911) and adolescence (n=907), we evaluated the associations between 65 top GWAS single nucleotide polymorphisms (SNPs) and genome-wide DNA methylation levels. We identified 159 proximal SNP-cytosine phosphate guanine (CpG) pairs (cis), and 7 distal SNP-CpG associations (trans) at birth, childhood, and adolescence. There was also a systematic enrichment for methylation related SNPs to be associated with T1D across the genome, after controlling for genomic characteristics of the SNPs, implying that methylation could either be on the causal pathway to T1D or a non-causal biomarker. Combining the principles of Mendelian Randomization and genetic colocalization analysis, we provided evidence that at 5 loci, ITGB3BP, AFF3, PTPN2, CTSH and CTLA4, DNA methylation is potentially on the causal pathway to T1D.
Rapid Decoding of Hand Gestures in Electrocorticography Using Recurrent Neural Networks

Brain-computer interface (BCI) is a direct communication pathway between brain and external devices, and BCI-based prosthetic devices are promising to provide new rehabilitation options for people with motor disabilities. Electrocorticography (ECoG) signals contain rich information correlated with motor activities, and have great potential in hand gesture decoding. However, most existing decoders use long time windows, thus ignore the temporal dynamics within the period. In this study, we propose to use recurrent neural networks (RNNs) to exploit the temporal information in ECoG signals for robust hand gesture decoding. With RNN's high nonlinearity modeling ability, our method can effectively capture the temporal information in ECoG time series for robust gesture recognition. In the experiments, we decode three hand gestures using ECoG signals of two participants, and achieve an accuracy of 90%. Specially, we investigate the possibility of recognizing the gestures in a time interval as short as possible after motion onsets. Our method rapidly recognizes gestures within 0.5 s after motion onsets with an accuracy of about 80%. Experimental results also indicate that the temporal dynamics is especially informative for effective and rapid decoding of hand gestures.
Emerging role of circulating microRNA in the diagnosis of human infectious diseases

The endogenic microRNAs (miRNA) are evolutionary, conserved, and belong to a group of small noncoding RNAs with a stretch of 19–24 nucleotides. The miRNAs play an indispensable role in gene modulation at the posttranscriptional level, inclusive of stem‐cell differentiation, embryogenesis, hematopoiesis, metabolism, immune responses, or infections. The miRNAs secreted from the cells and their presence in the biological fluids signifies the regulatory role of circulating miRNAs in the pathogenesis. The phenomenal expression levels of circulating miRNAs in serum or plasma during infection makes them the potential therapeutic biomarkers for the diagnosis of assorted human infectious diseases. In this article, we have accentuated the methods for the profiling of circulating miRNA as well as the importance of miRNA as biomarkers for the diagnosis of human infectious diseases. To date, numerous biomarkers have been identified for the diagnostic or prognostic purpose; for instance, miR‐182, miR‐486, and miR15a in sepsis; miR‐320 and miR505 in inflammatory bowel disease; miR‐155 and miR‐1260 in influenza; miR‐12, miRVP‐3p, and miR‐184 in arboviruses; and miR‐29b and miR‐125 in hepatitis infection. Nevertheless, the noninvasive diagnostic approach, with the aid of biomarkers, currently plays a decisive role in the untimely diagnosis of human infections. So, in the near future, the exploitation of circulating miRNAs as therapeutic biomarkers for the diagnosis of human infections will help us to cure the associated diseases promptly and effectively.
Lung Sound Analysis and the Respiratory Cycle Dependence of Impulse Oscillometry in Asthma Patients

Objective A lung sound analysis (LSA) is useful for detecting airway inflammation and obstruction in patients with asthma. To elucidate the mechanism of LSA, we investigated the relationship between the exhalation-to-inhalation sound pressure ratio in the low frequency range between 100 and 195 Hz (E/I LF) and the respiratory cycle dependence of impulse oscillometry (IOS) parameters. Methods Asthma patients underwent IOS [resistance of the respiratory system at 5 Hz (R5) and 20 Hz (R20), the reactance area (AX), resonant frequency of reactance (Fres), and reactance of the respiratory system at 5 Hz (X5)], spirography, and an LSA. The correlation between the LSA-derived E/I LF values and the respiratory cycle dependence of the IOS parameters was analyzed. Patients Thirty-four patients with mild to moderate bronchial asthma, who had not received oral or inhaled corticosteroids and who had no episodes of rumbling or wheezing were examined. Results The E/I LF value was significantly correlated with the differences of the R5 and R5-R20 values between exhalation and inhalation (p=0.035 and p=0.050) in a multivariate analysis. Conclusion E/I LF appears to be an index that expresses the respiratory cycle dependence of asthma as well as IOS.