Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquisition of a Lexicon from Semantic Representations of Sentences

A system, Wolfie, that acquires a mapping of words to their semantic representation is presented and a preliminary evaluation is performed. Tree least general generalizations (TLGGs) of the representations of input sentences are performed to assist in determining the representations of individual words in the sentences. The best guess for a meaning of a word is the TLGG which overlaps with the highest percentage of sentence representations in which that word appears. Some promising experimental results on a non-artiicial data set are presented.
Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation

There is a groundswell of interest in using genetically engineered sensor bacteria to study gut microbiota pathways, and diagnose or treat associated diseases. Here, we computationally identify the first biological thiosulfate sensor and an improved tetrathionate sensor, both two‐component systems from marine Shewanella species, and validate them in laboratory Escherichia coli. Then, we port these sensors into a gut‐adapted probiotic E. coli strain, and develop a method based upon oral gavage and flow cytometry of colon and fecal samples to demonstrate that colon inflammation (colitis) activates the thiosulfate sensor in mice harboring native gut microbiota. Our thiosulfate sensor may have applications in bacterial diagnostics or therapeutics. Finally, our approach can be replicated for a wide range of bacterial sensors and should thus enable a new class of minimally invasive studies of gut microbiota pathways.
Using Deep Learning to predict the mortality of Leukemia patients

ii Acknowledgements . iv List of Figures . iix List of Tables . xi List of Abbreviations . xii Chapter 1: Introduction . 1 1.1 Motivation . 1 1.2 Problem . 2 1.2.1 Problem Description . 2 1.2.2 Current Techniques . 3 1.3 Method . 5 1.3.1 Plan . 5 1.3.2 Background of some biological terms and the datasets . 5 1.3.3 Technique . 7 1.4 Thesis Statement . 8 1.5 Research questions . 8 1.6 Organization of thesis . 9 Chapter 2: Background . 11 2.1 Gene Expression Analytics . 12 2.1.1 SNP (genotypic) dataset . 12 2.1.2 Affymetrix (phenotypic) dataset . 14 2.2 Deep Learning . 17 2.2.1 Progression towards Deep Learning . 20 2.2.1.1 The Perceptron algorithm . 20 2.2.1.2 Neural Network.22 2.2.1.2.1 Weights and Biases, Feedforward Neural Network . 22 2.2.1.2.2 Activation function.24 2.2.1.2.3 Error function, Gradient, Stochastic Gradient Descent and Backpropagation . 23 2.2.1.3 Vanishing gradient problem.26 2.2.1.4 Autoencoders.26 vi 2.2.1.5 Restricted Boltzmann Machine.27 2.2.1.6 Deep Belief Net.30 2.3 Why employ the Deep Learning technique on our dataset? 32 2.4 Related Work . 34 2.4.1 Deep Learning on gene expression data . 34 2.4.2 Deep Learning for medical imaging . 35 2.5 H2O . 36 2.5. 1 Role of H2O in the thesis . 36 2.6 Summary . 39 Chapter 3: Design of the Learning Network . 40 3.1 The datasets . 40 3.1.1 Source of the datasets and its Description . 40 3.1.2 Normalization and Preprocessing . 42 3.2 Setup . 43 3.3 Sequence of steps for the prediction task . 44 Step 0 Adjust the network given the properties of H2O . 45 Step 1 Train the network . 46 Step 1.1 Using two sets of fixed train-test splits . 48 Step 1.1.1 Prediction for n = 2, 3 . 48 Step 1.1.2 Prediction for n > 5 . 50 Step 1.1.2.1 Optimal value of retention factor (r) . 50 Step 1.1.2.2 Minimal value of n (number of hidden layers) . 51 Step 1.2 Using Out-Of-Bag (OOB) Sampling . 53 Step 2 Bagging and Voting . 56 3.4 Summary . 57 Chapter 4: Testing the hyper-parameters of the Learning Network . 59 4.1 Number of votes for stabilized outputs . 59 4.1.1 SNP . 60 4.1.2 Affy . 61 4.2 Optimal parameters and H2O hyper-parameters . 63 4.2.1 Number of layers . 63 4.2.2 Input dropout ratio (IDR) . 64 vii 4.2.3 Number of epochs . 66 4.2.4 General observations on the values of hyper-parameters . 67 4.2.4.1 Activation function . 67 4.2.4.2 L1 and L2 . 69 4.2.4.3 train_samples_per_iteration, shuffle_training_data, adaptive_rate . 70 4.2.4.4 Correlation between time taken and epochs . 71 4.3 H2O commands . 71 4.3.1 Final SNP parameters while bagging for predictions . 71 4.3.2 Final Affy parameters while bagging for predictions . 72 4.3.3 Final SNP parameters for autoencoder .
MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles

We introduce a novel semi-automated metric, MEANT, that assesses translation utility by matching semantic role fillers, producing scores that correlate with human judgment as well as HTER but at much lower labor cost. As machine translation systems improve in lexical choice and fluency, the shortcomings of widespread n-gram based, fluency-oriented MT evaluation metrics such as BLEU, which fail to properly evaluate adequacy, become more apparent. But more accurate, non-automatic adequacy-oriented MT evaluation metrics like HTER are highly labor-intensive, which bottlenecks the evaluation cycle. We first show that when using untrained monolingual readers to annotate semantic roles in MT output, the non-automatic version of the metric HMEANT achieves a 0.43 correlation coefficient with human adequacy judgments at the sentence level, far superior to BLEU at only 0.20, and equal to the far more expensive HTER. We then replace the human semantic role annotators with automatic shallow semantic parsing to further automate the evaluation metric, and show that even the semi-automated evaluation metric achieves a 0.34 correlation coefficient with human adequacy judgment, which is still about 80% as closely correlated as HTER despite an even lower labor cost for the evaluation procedure. The results show that our proposed metric is significantly better correlated with human judgment on adequacy than current widespread automatic evaluation metrics, while being much more cost effective than HTER.
Association of Thallium with Diabetes Risk among Patients with Hearing Loss: Result from NHANES 2013 to 2018

To evaluate the correlation between thallium and diabetes risk among participants with hearing loss. This retrospective cohort study extracted related data such as demographic characteristics, lifestyle factors, and laboratory findings from the National Health and Nutrition Examination Survey (NHANES) database (2013–2018). Logistic regression analysis and interaction analysis were adopted to analyze the correlation between thallium and diabetes risk among patients with hearing loss. Then, the restricted cubic spline was employed to assess the nonlinear relationship between thallium and diabetes risk. The receiver operating characteristic curve and decision curve analysis were used to assess the predictive values of 3 multivariate models with or without thallium for diabetes risk. The Delong test was adopted to assess the significant change of the area under the curves (AUCs) upon thallium addition. A total of 425 participants with hearing loss were enrolled in the study: without diabetes group (n = 316) and diabetes group (n = 109). Patients with hearing loss in the diabetes group had significantly lower thallium (P < .05). The thallium was an independent predictor for diabetes risk after adjusting various covariates (P < .05). The restricted cubic spline (RCS) result showed that there was a linear correlation between thallium and diabetes risk (P nonlinear > .05). Finally, the receiver operating characteristic and decision curve analysis results revealed that adding thallium to the models slightly increased the performance in predicting diabetes risk but without significance in AUC change. Thallium was an independent predictor of diabetes risk among patients with hearing loss. The addition of thallium might help improve the predictive ability of models for risk reclassification. However, the conclusions should be verified in our cohort in the future due to the limitations inherent in the NHANES database.
Assessment of Pharmacokinetic Effects of Herbal Medicines on Escitalopram

Purpose Herbal medicines are occasionally used in combination with conventional antidepressants to mitigate various depression-associated symptoms. However, there is limited information on herb–antidepressant interactions. In this study, we investigated the pharmacokinetic (PK) effects of four herbal medicines (Gami-soyosan, Banhasasim-tang, Ojeok-san, and Bojungikgi-tang) on escitalopram, a commonly used antidepressant. Patients and Methods In this open-label, fixed-sequence, three-period, crossover study, 18 participants were enrolled and divided into two groups. Each group received a 10 mg oral dose of escitalopram in period 1. Participants took escitalopram once daily and their assigned herbal medicines thrice a day for 7 d in periods 2 (group 1: Gami-soyosan, group 2: Ojeok-san) and 3 (group 1: Banhasasim-tang; group 2: Bojungikgi-tang). The primary endpoints were Cmax,ss and AUCtau,ss of escitalopram. Cmax,ss and AUCtau,ss in period 1 were obtained using nonparametric superposition from single-dose data. The PK endpoints were classified according to the CYP2C19 phenotype. Results Of 18 participants, 16 completed the study. Systemic exposure to escitalopram resulted in a minor increase in the presence of each herbal medicine. The geometric mean ratios (GMRs, combination with herbal medicines/escitalopram monotherapy) and their 90% confidence intervals (CIs) for Cmax,ss and AUCtau,ss were as follows: Gamisoyosan– 1.1454 (0.9201, 1.4258) and 1.0749 (0.8084, 1.4291), Banhasasim-tang–1.0470 (0.7779, 1.4092) and 1.0465 (0.7035, 1.5568), Ojeok-san–1.1204 (0.8744, 1.4357) and 1.1267 (0.8466, 1.4996), and Bojungikgi-tang–1.1264 (0.8594, 1.4762) and 1.1400 (0.8515, 1.5261), respectively. Furthermore, no significant differences in the GMRs of Cmax,ss and AUCtau,ss were observed across different CYP2C19 phenotypes in any of the groups. Conclusion The co-administration of escitalopram with Gami-soyosan, Banhasasim-tang, Ojeok-san, or Bojungikgi-tang did not exert significant PK effects on escitalopram. These findings provide valuable insights into the safe use of herbal medicines along with escitalopram.
Racial Disparities in 30-day Readmission After Orthopaedic Surgery: A 5-year National Surgical Quality Improvement Program Database Analysis

Background: Readmission rate after surgery is an important outcome measure in revealing disparities. This study aimed to examine how 30-day readmission rates and causes of readmission differ by race and specific injury areas within orthopaedic surgery. Methods: The American College of Surgeon-National Surgical Quality Improvement Program database was queried for orthopaedic procedures from 2015 to 2019. Patients were stratified by self-reported race. Procedures were stratified using current procedural terminology codes corresponding to given injury areas. Multiple logistic regression was done to evaluate associations between race and all-cause readmission risk, and risk of readmission due to specific causes. Results: Of 780,043 orthopaedic patients, the overall 30-day readmission rate was 4.18%. Black and Asian patients were at greater (OR = 1.18, P < 0.01) and lesser (OR = 0.76, P < 0.01) risk for readmission than White patients, respectively. Black patients were more likely to be readmitted for deep surgical site infection (OR = 1.25, P = 0.03), PE (OR = 1.64, P < 0.01), or wound disruption (OR = 1.45, P < 0.01). For all races, all-cause readmission was highest after spine procedures and lowest after hand/wrist procedures. Conclusions: Black patients were at greater risk for overall, spine, shoulder/elbow, hand/wrist, and hip/knee all-cause readmission. Asian patients were at lower risk for overall, spine, hand/wrist, and hip/knee surgery all-cause readmission. Our findings can identify complications that should be more carefully monitored in certain patient populations.
The relationship between alpha power and heart rate variability commonly seen in various mental states

The extensive exploration of the correlation between electroencephalogram (EEG) and heart rate variability (HRV) has yielded inconsistent outcomes, largely attributable to variations in the tasks employed in the studies. The direct relationship between EEG and HRV is further complicated by alpha power, which is susceptible to influences such as mental fatigue and sleepiness. This research endeavors to examine the brain-heart interplay typically observed during periods of music listening and rest. In an effort to mitigate the indirect effects of mental states on alpha power, subjective fatigue and sleepiness were measured during rest, while emotional valence and arousal were evaluated during music listening. Partial correlation analyses unveiled positive associations between occipital alpha2 power (10–12 Hz) and nHF, an indicator of parasympathetic activity, under both music and rest conditions. These findings underscore brain-heart interactions that persist even after the effects of other variables have been accounted for.
Prognostic value of PRR11 and immune cell infiltration in Ewing sarcoma

Ewing’s sarcoma (ES) is the second most common bone and soft tissue malignancy in children and adolescents with a poor prognosis. The identification of genes with prognostic value may contribute to the prediction and treatment of this disease. The GSE17679, GSE68776, GSE63155, and GSE63156 datasets were downloaded from the Gene Expression Omnibus database and qualified. Prognostic value of differentially expressed genes (DEGs) between the normal and tumor groups and immune cell infiltration were explored by several algorithms. A prognostic model was established and validated. Finally, functional analyses of the DEGs were performed. Proline rich 11 (PRR11) and mast cell infiltration were noted as the key indicators for the prognosis of ES. Kaplan–Meier and scatter plots for the training and two validation sets showed that patients in the low-PRR11 expression group were associated with better outcomes than those in the high-PRR11 expression group. The concordance indices and calibration analyses of the prognostic model indicated good predictive accuracy in the training and validation sets. The area under the curve values obtained through the receiver operating characteristic analysis for 1-, 3-, 5-year prediction were ≥ 0.75 in the three cohorts, suggesting satisfactory sensitivity and specificity of the model. Decision curve analyses suggested that patients could benefit more from the model than the other strategies. Functional analyses suggested that DEGs were mainly clustered in the cell cycle pathway. PRR11 and mast cell infiltration are potential prognostic indicators in ES. PRR11 possibly affects the prognosis of patients with ES through the cell cycle pathway.
Frontiers of Biogeography

Lomolino Brown Scheiner 2010). Yet it is likely that the marvels and perils of island life may all ultimately derive from some very general ecological and evolutionary processes and how features of insular environments influence those processes. One of the most promising approaches to unifying island biogeography theory and providing some truly transformative insights into the geog-frontiers Abstract. I describe the set of fundamental principles of biogeography that can serve as an integrative, conceptual framework for unifying and advancing our abilities to explain the geography of life – general-ly. I assert that patterns of variation of biotas among regions and across geographic gradients result from the very regular patterns of variation in environmental conditions across the geographic template. This happens through the influence of that variation on the fundamental biogeographic processes (immigration, extinction and evolution), the influence of those fundamental processes on each other, and ecological feedback in the form of interspecific interactions, which influence the fundamental capacities of other species to immigrate, survive and evolve. I then summarize principal patterns and current theory in island biogeography within the context of the fundamental, unifying principles and show how they can inform a more integrative, conceptual framework for explaining a genuinely comprehensive set of ecological and evolutionary phenomena for insular biotas.
Adipose mesenchymal stem cell-derived exosomes promote skin wound healing in diabetic mice by regulating epidermal autophagy

Abstract Background Adipose mesenchymal stem cell-derived exosomes (ADSC-Exos) have great potential in the field of tissue repair and regenerative medicine, particularly in cases of refractory diabetic wounds. Interestingly, autophagy plays a role in wound healing, and recent research has demonstrated that exosomes are closely associated with intracellular autophagy in biogenesis and molecular signaling mechanisms. Therefore, this study aimed to investigate whether ADSC-Exos promote the repair of diabetic wounds by regulating autophagy to provide a new method and theoretical basis for the treatment of diabetic wounds. Methods Western blot analysis and autophagy double-labelled adenovirus were used to monitor changes in autophagy flow in human immortalized keratinocyte cell line (HaCaT) cells. ADSC-Exos were generated from ADSC supernatants via ultracentrifugation. The effectiveness of ADSC-Exos on HaCaT cells was assessed using a live-cell imaging system, cell counting kit-8 and cell scratch assays. The cells were treated with the autophagy inhibitor bafilomycin A1 to evaluate the effects of autophagy on cell function. The recovery of diabetic wounds after ADSC-Exo treatment was determined by calculating the healing rates and performing histological analysis. High-throughput transcriptome sequencing was used to analyze changes in mRNA expression after the treatment of HaCaT cells with ADSC-Exos. Results ADSC-Exos activated autophagy in HaCaT cells, which was inhibited by high glucose levels, and potentiated their cellular functions. Moreover, ADSC-Exos in combination with the autophagy inhibitor bafilomycin A1 showed that autophagy defects further impaired the biological function of epidermal cells under high-glucose conditions and partially weakened the healing effect of ADSC-Exos. Using a diabetes wound model, we found that ADSC-Exos promoted skin wound healing in diabetic mice, as evidenced by increased epidermal autophagy and rapid re-epithelialization. Finally, sequencing results showed that increased expression of autophagy-related genes nicotinamide phosphoribosyltransferase (NAMPT), CD46, vesicle-associated membrane protein 7 (VAMP7), VAMP3 and eukaryotic translation initiation factor 2 subunit alpha (EIF2S1) may contribute to the underlying mechanism of ADSC-Exo action. Conclusions This study elucidated the molecular mechanism through which ADCS-Exos regulate autophagy in skin epithelial cells, thereby providing a new theoretical basis for the treatment and repair of skin epithelial damage by ADSC-Exos.
Behavioral sensitization and tolerance induced by repeated treatment with ketamine enantiomers in male Wistar rats

Ketamine has gained significant attention as a fast-acting antidepressant. However, ketamine is also associated with undesirable side effects. In our preclinical study, we explored the behavioral effects of ketamine enantiomers at subanesthetic doses. During repeated intermittent treatment, we examined locomotor stimulation and sensitization, ataxia, and expression of natural behaviors (grooming and rearing). Male Wistar rats were subcutaneously treated repeatedly with either 5 mg/kg of R-ketamine or S-ketamine, 15 mg/kg of R-ketamine, S-ketamine or racemic ketamine, 30 mg/kg of racemic ketamine or saline every third day for three weeks (seven treatments overall). After the first treatment, only 15 mg/kg of S-ketamine induced locomotor stimulation, and both 15 mg/kg of S-ketamine and 30 mg/kg of racemic ketamine induced ataxia. Upon repeated administration, doses of 15 mg/kg of R-ketamine, S-ketamine, and racemic ketamine, as well as 30 mg/kg of racemic ketamine, stimulated locomotion. 15 mg/kg of R-ketamine, S-ketamine, and racemic ketamine additionally resulted in locomotor sensitization. The last administration of 15 mg/kg of S-ketamine, 15 mg/kg of racemic ketamine, and 30 mg/kg of racemic ketamine resulted in ataxia. In the case of 15 mg/kg of S-ketamine, ataxic effects were significantly weaker in comparison to the effects from the first administration, indicating tolerance. Natural behaviors were attenuated after 5 and 15 mg/kg of S-ketamine and 15 and 30 mg/kg of racemic ketamine. Neither of the R-ketamine doses produced such an effect. We conclude that S-ketamine has a stronger behavioral effect than R-ketamine.
Design of the 2015 ChaLearn AutoML challenge

ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing difficulty are introduced throughout the six rounds of the challenge. (Participants can enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen, and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance. This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge.
15 . 4 : People with Visual Impairment Prefer TV Viewing Using a Contrast Enhancement Consumer Product

A device that uses technology previously tested for those with visual impairment to improve video clarity has recently been implemented in a product marketed for home theater viewers with normal sight. We found it to benefit viewers with visual impairment, even with the settings aimed at normally-sighted users, suggesting that the higher enhancement capabilities not accessible from the user interface may benefit this population even more. 1. Background 1.
Truncation point estimation using multiple replications in parallel

In steady-state simulation the output data of the transient phase often causes a bias in the estimation of the steady-state results. A common advice is to cut off this transient phase. Finding an appropriate truncation point is a well-known problem and is still not completely solved. In this paper we consider two algorithms for the determination of the truncation point. Both are based on a technique which takes the definition of the steady-state phase more closely into consideration. The capabilities of the algorithms are demonstrated by comparisons with two methods most often used in practice.
Interaction in VR using High Density Electrotactile Feedback

Inria, the French national research institute for the digital sciences, promotes scientific excellence and technology transfer to maximise its impact. It employs 2,400 people. Its 200 agile project teams, generally with academic partners, involve more than 3,000 scientists in meeting the challenges of computer science and mathematics, o en at the interface of other disciplines. Inria works with many companies and has assisted in the creation of over 160 startups. It strives to meet the challenges of the digital transformation of science, society and the economy.