Miracle-dz/UniFAR-FTUs huggingface dataset:
A suction-type, pneumatic microfluidic device for rapid DNA extraction

This current study presents a new miniature, integrated system capable of rapid extraction of genomic DNA (gDNA) from saliva samples. Three major components of the DNA extraction chip, including six symmetrical, normally-closed microvalves, a sample transport/mixing unit and a waste unit are integrated in this device. The microfluidic DNA extraction chip was made of CNC machining and PDMS casting processes to integrate a liquid channel layer, an air chamber layer and a glass layer on a single chip. Liquid samples can be transported and mixed by the suction-type sample transport/mixing unit by using micropumps, micromixers and normally-closed microvalves. Experimental results showed the pumping rate can be as high as 400 µL/min at an applied pressure of −70 KPa for an air chamber with a depth of 1.5 mm. A mixing efficiency as high as 96.5% can be achieved within 3 sec. Experimental data also showed that gDNA with an average concentration of 45 ± 3 ng/µL and an optical intensity (OD) value (260/280) of 1.5 ± 0.2 from ten measurements can be successfully achieved within 25 minutes. Consequently, the proposed miniature system can provide a powerful platform for automatic, rapid DNA extraction.
Bidirectional-GRU Based on Attention Mechanism for Aspect-level Sentiment Analysis

Aspect-level sentiment analysis is a fine-grained natural language processing task. For traditional deep learning models, they cannot accurately construct the aspect-level sentiment features. Such as, for the sentence of "the movie is very funny, but the seats in the theater is uncomfortable. " For the movie, the polarity is positive, but it is negative for seats. To deal with this problem, we propose a bidirectional gated recurrent units neural network model that integrates the attention mechanism to solve the task of aspect-level sentiment analysis. The attention mechanism can focus on the different parts of a sentence when the sentence has several different aspects. Because we use a bidirectional gated recurrent unit, we can get independent context semantic information and get the deeper aspect sentiment information from the front and back, so that we can deal with the specific aspect sentiment polarity. Finally, we experiment on SemEval-2014 dataset and twitter dataset, the result of experiments verified the effectiveness of attention-based bidirectional gated recurrent unit on the aspect sentiment analysis. The model achieves good performance at different datasets and has further improvement comparing to previous models.
Using parallel text for the extraction of German multiword expressions

A procedure for the identification of semantically opaque (i.e. idiomatic) German multiwords is presented. We focus on verb + PP combinations that are lexicographically relevant (extracted via dependency parsing [Schiehlen 2003]) of the kind ins Leben rufen – “to initiate”, lit. : “to call into life”. Starting from [Villada Moiron and Tiedemann 2006], the method exploits the fact that opaque combinations are translated as a whole, whereas compositional uses would show regular, individual translations of the words involved. The translations into other languages are obtained by applying GIZA++ [Och and Ney 2003] word alignment to the EUROPARL corpus [Koehn 2005]. Numerous experiments are performed to further optimise the original method: several parameters are analysed individually as well as in combination with each other. This leads to the following results: depending on the actual parameter settings, values between 0.800 and 0.936 (in terms of uninterpolated average precision) are reached amongst the highest scoring 200 multiword candidates, as opposed to a baseline of 0.584, using the 200 most frequent multiwords in decreasing order of their occurrence frequency.
A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming

In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA in vivo using an iterative method to meet the minimum energy or other constraints. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future.
The Reliability and Validity of the Center for Epidemiologic Studies Depression Scale (CES-D) for Chinese University Students

Aims: Depression is prevalent among university students worldwide, and the prevalence appears to be increasing. As an intermediate stage between being healthy and having depression, students with subthreshold depression could develop worsening depression or recover with intervention to prevent depression. The Center for Epidemiologic Studies Depression Scale (CES-D) is a useful tool to assess subthreshold depression. The primary purpose of the current study was to evaluate the psychometric characteristics of CES-D in Chinese university students. Secondly, we aimed to describe the prevalence of subthreshold depression among the student sample and examine its demographic correlates. Methods: A total of 2,068 university students participated in the study, and they were asked to respond to the Chinese CES-D, Beck Depression Inventory-II (BDI-II), and Positive and Negative Affect Schedule (PANAS). The factor structure was evaluated by conducting exploratory (EFA) and confirmatory factor analysis (CFA) using a structural equation modeling approach. The reliability was assessed by calculating Cronbach’s alpha, inter-item correlation, and item-total correlation coefficients. The prevalence of subthreshold depression was calculated and demographic correlates of gender, grade, and major were examined by multiple regression. Results: The final sample included 1,920 participants. The EFA results suggested extraction of three factors (somatic symptoms, negative affect, and anhedonia) that account for 52.68% of total variance. The CFA results suggested that the newly derived model with 14 items was the best fit for our data. Six items were removed from the original scale (item 9, 10, 13, 15, 17, and 19). The Cronbach’s alpha of the 14-item CES-D was 0.87. The prevalence of subthreshold depression among university students reached 32.7% for the 20-item CES-D and 31% for the 14-item CES-D, although there was no significant difference of prevalence in gender, grade, and major. Conclusions: The CES-D has good reliability and validity for assessing subthreshold depression in Chinese university students.
Prevalence of Toxoplasma gondii in Rats (Rattus norvegicus) in Grenada, West Indies

Cats are important in the natural epidemiology of Toxoplasma gondii, because they are the only hosts that can excrete environmentally resistant oocysts. Cats are infected with T. gondii via predation on infected birds and rodents. During 2005, 238 rats (Rattus norvegicus) were trapped in Grenada, West Indies, and their sera along with tissue samples from their hearts and brains were examined for T. gondii infection. Antibodies to T. gondii were assayed by the modified agglutination test (MAT, titer 1:40 or higher); only 2 (0.8%) of 238 rats were found to be infected. Brains and hearts of all rats were bioassayed in mice. Toxoplasma gondii was isolated from the brain and the heart of only 1 rat, which had a MAT titer of 1:320. All of 5 mice inoculated with the heart tissue, and the 5 mice inoculated with the brain tissue of the infected rat remained asymptomatic, even though tissue cysts were found in their brains. Genetically, the isolates of T. gondii from the heart and the brain were identical and had genotype III by using the SAG1, SAG2, SAG3, BTUB, and GRA6 gene markers. These data indicate that rats are not important in the natural history of T. gondii in Grenada.
Assessing the Diversity of Endogenous Viruses Throughout Ant Genomes

Endogenous viral elements (EVEs) can play a significant role in the evolution of their hosts and have been identified in animals, plants, and fungi. Additionally, EVEs potentially provide an important snapshot of the evolutionary frequency of viral infection. The purpose of this study is to take a comparative host-centered approach to EVE discovery in ant genomes to better understand the relationship of EVEs to their ant hosts. Using a comprehensive bioinformatic pipeline, we screened all nineteen published ant genomes for EVEs. Once the EVEs were identified, we assessed their phylogenetic relationships to other closely related exogenous viruses. A diverse group of EVEs were discovered in all screened ant host genomes and in many cases are similar to previously identified exogenous viruses. EVEs similar to ssRNA viral proteins are the most common viral lineage throughout the ant hosts, which is potentially due to more chronic infection or more effective endogenization of certain ssRNA viruses in ants. In addition, both EVEs similar to viral glycoproteins and retrovirus-derived proteins are also abundant throughout ant genomes, suggesting their tendency to endogenize. Several of these newly discovered EVEs are found to be potentially functional within the genome. The discovery and analysis of EVEs is essential in beginning to understand viral–ant interactions over evolutionary time.
Discontiguous or Contiguous Spread Patterns Affect the Functional Staging in Patients With Sporadic Amyotrophic Lateral Sclerosis

Objective: This study aimed to investigate whether the spread pattern affects functional staging in amyotrophic lateral sclerosis (ALS). We examined the spreading patterns of disease following symptom onset and the affected regions in ALS using electromyography. Methods: This study reviewed the medical records of 103 patients with sporadic ALS in the Second Hospital of Hebei Medical University from 2012 to 2017. According to the clinical manifestation and the distribution of the affected regions on electromyography, spread patterns were classified as discontiguous or contiguous. The patients were graded according to the ALS-Milano-Torino staging (MITOS) system. Results: The clinical spread patterns were contiguous in 91.5% of patients and discontiguous in 8.5% of patients. The electrophysiological spread patterns were contiguous in 87.4% of patients and discontiguous in 12.6% of patients. Sex, age, or delay in diagnosis did not affect the clinical or electrophysiological spread patterns. No significant correlation was observed between the clinical classification and the ALS-MITOS grade, but the electrophysiological spread was significantly correlated with the ALS-MITOS. Conclusion: This study provides evidence that not all ALS patients show contiguous clinical or electrophysiological spread patterns. The electrophysiological spread pattern can affect the functional staging in ALS patients.
The Experimental Oxime K027—A Promising Protector From Organophosphate Pesticide Poisoning. A Review Comparing K027, K048, Pralidoxime, and Obidoxime

Poisoning with organophosphorus compounds (OPCs) is a major problem worldwide. Standard therapy with atropine and established oxime-type enzyme reactivators (pralidoxime, obidoxime) is unsatisfactory. In search of more efficacious broad-spectrum oximes, new bispyridinium (K-) oximes have been synthesized, with K027 being among the most promising. This review summarizes pharmacokinetic characteristics of K027, its toxicity and in vivo efficacy to protect from OPC toxicity and compares this oxime with another experimental bisquaternary asymmetric pyridinium aldoxime (K048) and two established oximes (pralidoxime, obidoxime). After intramuscular (i.m.) injection, K027 reaches maximum plasma concentration within ∼30 min; only ∼2% enter the brain. Its intrinsic cholinesterase inhibitory activity is low, making it relatively non-toxic. In vitro reactivation potency is high for ethyl-paraoxon-, methyl-paraoxon-, dichlorvos-, diisopropylfluorophosphate (DFP)- and tabun-inhibited cholinesterase. When administered in vivo after exposure to the same OPCs, K027 is comparable or more efficacious than pralidoxime and obidoxime. When given as a pretreatment before exposure to ethyl-paraoxon, methyl-paraoxon, DFP, or azinphos-methyl, it is superior to the Food and Drug Administration-approved compound pyridostigmine and comparable to physostigmine, which because of its entry into the brain may cause unwanted behavioral effects. Because of its low toxicity, K027 can be given in high dosages, making it a very efficacious oxime not only for postexposure treatment but also for prophylactic administration, especially when brain penetration is undesirable.
An in vivo Like Micro-Carcinoma Model

We here present a novel micro-system which allows to reconstitute an in vivo lung carcinoma where the various constituting epithelial and/or stromal structural and/or cellular components can be incorporated at will. In contrast to various “organs on a chip” the model is based on the observation that in nature, epithelial cells are always supported by a connective tissue or stroma. The model is based on acellular micro-scaffolds of microscopic dimensions which enable seeded cells to obtain gases and nutrients through diffusion thus avoiding the need for vascularization. As a proof of concept, we show that in this model, Calu-3 cells can form a well-organized, continuous, polarized, one-layer epithelium lining the stromal derived alveolar cavities, and express a different pattern of tumor-related genes than when grown as standard monolayer cultures on plastic culture dishes. To our knowledge, this model, introduces for the first time a system where the function of carcinogenic cells can be tested in vitro in an environment that closely mimics the natural in vivo situation.
Prognostic Value of an Inflammation-Related Index in 6,865 Chinese Patients With Postoperative Digestive Tract Cancers: The FIESTA Study

Objectives: We sought to determine the optimal cutting points for two inflammatory biomarkers, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), to assess their prognostic value in patients with postoperative digestive tract cancers overall and by cancer sites, and further to construct an inflammation-related index based on the two biomarkers and assess its predictive performance. Methods: Total 6,865 assessable patients with digestive tract cancers who underwent tumor resection were consecutively enrolled from Fujian Cancer Hospital between January 2000 and December 2010, including 2535/3012/1318 patients with esophageal/gastric/colorectal cancer. The latest follow-up (median: 44.9 months) ended in December 2015. Optimal cutting points were determined using survival tree analysis overall and by cancer sites. Results: Among all study patients, the optimal cutting points were 2.07 and 168.50 to define high and low NLR and PLR, respectively. High NLR (hazard ratio [HR]: 1.48, 95% confidence interval [CI]: 1.37–1.61) and high PLR (HR: 1.41, 95% CI: 1.29–1.53) were associated with a significantly increased risk for the mortality of digestive tract cancers as a whole. By cancer sites, effect-size estimates were comparable and statistically significant. Elevation over the selected optimal cutting points for both NLR and PLR was associated with 1.69-fold increased risk of cancer-specific mortality compared to patients with simultaneously low NLR and PLR among all study patients, and this association persisted by cancer sites, especially for gastric cancer. Conclusions: Our findings demonstrate that the preoperative integrated NLR and PLR, as an inflammation-related index, is a significant independent predictor for postoperative mortality in Chinese patients with digestive tract cancers both overall and by cancer sites.
Animating Still Landscape Photographs Through Cloud Motion Creation

Animating landscape photographs can create an engaging viewing experience and has long been an active area of multimedia research. Images can be used to create an illusion of movement, thereby providing more vivid and interesting visuals . Among numerous objects in visual scenes, clouds appear as a common element in landscape photos but are challenging to manipulate because clouds are ill-posed structures without concrete forms and present motion with very high degrees of freedom and arbitrary shape deformation. This paper addresses the generation of dynamic imagery from a still photo through automatic motion creation. First, a cloud appearance model is acquired by representing each pixel in the sky region as a combination of two computable image properties , i.e., Cloudiness estimates cloud density and Cloud Structure gives the corresponding cloud shape and texture. Then, based on the obtained Cloudiness and Cloud Structure, a content-aware wind field is generated to synthesize the motion of cloud flow. Experimental results demonstrate that our approach can work well on various types of cloud images and produce visually convincing dynamic imagery of landscape photos. Furthermore , we showcased several extended multimedia applications to validate that the proposed framework is generic and widely extensible to other vapor-like objects like mist and smoke. Please see http://mclab.citi. sinica.edu. tw/demo/dynamicimagery.htm for demos.
Chiral analytical method development of liquiritigenin with application to a pharmacokinetic study.

Pharmacometric characterization studies of liquiritigenin have historically overlooked its chiral nature. To achieve complete characterization, an analytical method enabling the detection and quantification of the individual enantiomers of racemic (±) liquiritigenin is necessary. Resolution of the enantiomers of liquiritigenin was achieved using a simple high-performance liquid chromatographic method. A Chiralpak® ADRH column was employed to perform baseline separation with UV detection at 210 nm.The standard curves were linear ranging from 0.5 to 100 µg/mL for each enantiomer. Limit of quantification was 0.5 µg/mL. The assay was applied successfully to stereoselective serum disposition of liquiritigenin enantiomers in rats. Liquiritigenin enantiomers were detected in serum as both aglycones and glucuronidated conjugates. Both unconjugated enantiomers had a serum half-life of ~15 min in rats. The volume of distribution (V(d) ) for S- and R-liquiritigenin was 1.49 and 2.21 L/kg, respectively. Total clearance (Cl(total) ) was 5.12 L/h/kg for S-liquiritigenin and 4.79 L/h/kg for R-liquiritigenin, and area under the curve (AUC(0-inf) ) was 3.95 µg h/mL for S-liquiritigenin and 4.23 µg h/mL for R-liquiritigenin. The large volume of distribution coupled with the short serum half-life suggests extensive distribution of liquiritigenin into tissues.
Interferon Regulatory Factors 1 and 2 Play Different Roles in MHC II Expression Mediated by CIITA in Grass Carp, Ctenopharyngodon idella

Expression of major histocompatibility complex class II (MHC II) molecules, which determines both the immune repertoire during development and subsequent triggering of immune responses, is always under the control of a unique (MHC class II) transactivator, CIITA. The IFN-γ-inducible MHC II expression has been extensively and thoroughly studied in humans, but not in bony fish. In this study, the characterization of CIITA was identified and its functional domains were analyzed in grass carp. The absence of GAS and E-box in the promoter region of grass carp CIITA, might imply that the cooperative interaction between STAT1 and USF1 to active the CIITA expression, found in mammals, is not present in bony fish. After the transfection of IFN-γ or IFN-γ rel, only IFN-γ could induce MHC II expression mediated by CIITA. Moreover, interferon regulatory factor (IRF) 2, which cooperates with IRF1 to active the CIITA promoter IV expression in mammals, played an antagonistic role to IRF1 in the activation of grass carp CIITA. These data suggested that grass carp, compared with mammals, has both conservative and unique mechanisms in the regulation of MHC II expression.
Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.