Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential gene expression profile analysis in corticosterone-treated PC 12 cells

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that has been ranked as the 4th leading cause of disability worldwide. Past clinical and laboratory evidence has confirmed that abnormalities of the hypothalamic-pituitary-adrenal (HPA)-axis are involved in MDD development. In this study, we took advantage of corticosterone treatment of PC12 cells as a model to identify genes regulated by HPA-axis hormones. Next-generation RNA-Seq technology was utilized to explore genome-wide differentially expressed gene profiles between control and corticosterone treated PC12 cells. 1,274 genes with at least two-fold expression level change were identified. Gene ontology analysis showed that the top enriched biological processes included response to glucocorticoid signaling, apoptosis, cell division/DNA replication, and neuron projection/axon guidance, highly consistent with phenotypes of PC12 cells treated with corticosterone. Taken together, RNA-seq data is reliable and comprehensive, thus providing a valuable resource for understanding underlying mechanisms of glucocorticoid-induced neuron malfunction.
The Dynamic Interplay among EFL Learners' Ambiguity Tolerance, Adaptability, Cultural Intelligence, Learning Approach, and Language Achievement.

A key objective of education is to prepare individuals to be fully-functioning learners. This entails developing the cognitive, metacognitive, motivational, cultural, and emotional competencies. The present study aimed to examine the interrelationships among adaptability, tolerance of ambiguity, cultural intelligence, learning approach, and language achievement as manifestations of the above competencies within a single model. The participants comprised one hundred eighty BA and MA Iranian university students studying English language teaching and translation. The instruments used in this study consisted of the translated versions of four questionnaires: second language tolerance of ambiguity scale, adaptability taken from emotional intelligence inventory, cultural intelligence (CQ) inventory, and the revised study process questionnaire measuring surface and deep learning. The results estimated via structural equation modeling (SEM) revealed that the proposed model containing the variables under study had a good fit with the data. It was found that all the variables except adaptability directly influenced language achievement with deep approach having the highest impact and ambiguity tolerance having the lowest influence. In addition, ambiguity tolerance was a positive and significant predictor of deep approach. CQ was found to be under the influence of both ambiguity tolerance and adaptability. The findings were discussed in the light of the yielded results.
Model-based Visualization of Instationary Geo-Data with Application to Volcano Ash Data

Driven by today’s supercomputers, larger and larger sets of data are created during numerical simulations of geoscientific applications. Such data often describes instationary processes in three-dimensional domains in terms of multi-dimensional data. Due to limited computer resources, it might be impossible or unpractical to store all data created during one simulation, which is why several data reduction techniques are often applied (e.g. only every nth time-step is stored). Intuitive scientific visualization techniques can help to better understand the structures described by transient data. Adequate reconstruction techniques for the time-dimension are needed since standard techniques (e.g. linear interpolation) are insufficient for many applications. We describe a general formalism for a wide class of reconstruction techniques and address aspects of quality characteristics. We propose an approach that is able to take arbitrary physical processes into account to enhance the quality of the reconstruction. For the eruption of the volcano Eyjafjallajkull in Iceland in the spring of 2010, we describe a suitable reduced model and use it for model-based visualization. The original data was created during a COSMO-ART simulation. We discuss the reconstruction errors, related computational costs, and possible extensions. A comparison with linear interpolation clearly motivates the proposed model-based reconstruction approach.
INTELLIGENT SEGMENTATION & YIELD ANALYSIS OF MANGO FRUIT USING BIO-INSPIRED MHS APPROACH

The most time consuming manual process in fruit harvesting is the analysis of fruit state and yield analysis of different fruits like mango, apple, orange, banana and pomegranate. Locating fruits in trees made it easier for analysis and efficient fruit harvesting. An intelligent segmentation and automatic yield calculation of fruits using the Mosquito host seeking approach has been proposed in this paper. Images of the tree are acquired from real time using digital camera under different lighting conditions. Fruit images are first filtered to remove noise from environment and then fruit region is separated from its background. Then the resultant image is segmented based on the color and shape. The resultant image can be classified and used for further analysis with help of features selected through mosquito swarm approach. The images are then labeled for detection and counting of fruits.
Authorship Attribution in Multi-author Documents

Authorship attribution, the task of identifying the author of a document, has been applied to works of historical value such as Shakespeare’s plays or the political Federalist papers but is still highly significant today. We introduce the novel problem of authorship attribution in multi-authored documents and focus on scientific publications. Multi-authored documents present hard challenges for authorship attribution. We propose several ideas how these can be addressed and evaluate when such models perform well. To this end we present a sentencebased prediction model that also allows to estimate which sentences were contributed by which author. We demonstrate using a stylometric approach that paper authors can be predicted with significant accuracy by exploiting authors’ stylistic idiosyncrasies. This challenges the assumption that simply removing names from a paper submission ensures anonymity in a double-blind process.
LARGE-SCALE WEAKLY SUPERVISED SOUND EVENT DETECTION ( DCASE CHALLENGE 2017 )

State-of-the-art audio event detection (AED) systems fully rely on supervised-learning based on strongly labeled data. The dependence on strong labels severely limits the scalability of AED work. Large-scale manually annotated datasets are difficult and expensive to collect [1], whereas weakly labeled data could be much easier to acquire. In weakly labeled data, we only need to determine whether an event in the recording is present or absent. This not only makes manual labelling significantly easier but also makes automatically inferring labels from online multimedia or audio meta-information (titles, tags, etc) possible [2]. This work employs a subset of Google’s AudioSet [3], which is a large number of weakly labeled YouTube video excerpts. The subset focuses on transportation and warning sounds and consists of 17 sound events divided into two categories: Warning and Vehicle. We perform experiments on 3 sets of features, including standard Mel-frequency cepstral coefficients (MFCC) and log-Mel spectrograms and pre-trained embeddings extracted from a deep convolutional network. Our system employs multiple instance learning (MIL) [4] approaches to deal with weak labels by bagging them to positive or negative bags. We apply 4 models, Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Convolutional Deep Neural Network. Using the late-fusion approach, we improve the performance of the baseline audio tagging (Subtask A) F1 score 13.1% by 18.1%. The embeddings extracted by the convolutional neural networks significantly boosts the performance of all the models.
Sparse Kernel Machines for motor imagery EEG classification

Brain-computer interfaces (BCIs) make humancomputer interaction more natural, especially for people with neuro-muscular disabilities. Among various data acquisition modalities the electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. In this work, a method based on sparse kernel machines is proposed for the classification of motor imagery (MI) EEG data. More specifically, a new sparse prior is proposed for the selection of the most important information and the estimation of model parameters is performed using the bayesian framework. The experimental results obtained on a benchmarking EEG dataset for MI, have shown that the proposed method compares favorably with state of the art approaches in BCI literature.
Screening of Endophytic Bacteria towards the Development of Cottage Industry: An in Vitro Study

Abstract Discovery of novel technology which use beneficial endophytic bacteria associated with the root of Sorghum bicolor, Spinacia oleracea, and Zea mays was researched. Total of 23 endophytic bacteria were characterized on the basis biochemical analysis and plant growth-promoting traits. Results showed that Gramnegative (60.8%) were isolated more frequently than Gram-positive bacteria (39.1%). Approximately 65.2% were motile and the remaining 34.7% were non-motile. Eleven isolates were able to produce indole acetic (IAA) (0.15-2.84 mgl-1). Eleven isolates showed the ability to produce ammonium. Hydrogen cyanide (HCN) production was observed in 10 isolates. It was observed that 16 isolates solubilized insoluble phosphates in Pikovskya plates (8-60.5%). All the isolates tested were active against Fusarium oxysporum. Therefore, following these tests it can be concluded that 11 isolates exhibited differences and they were subjected to partial 16S-rDNA gene sequencing using polymerase chain reaction for phylogenetic analysis. MEGA 5.10 package was used to identify the following isolated bacteria: Pseudomonas sp. (KC010520), Ochrobactrum intermedium (KC010521), Ochrobactrum intermedium (KC010522), Ochrobactrum anthropi (KC010523), Ochrobactrum anthropi (KC010524) Ochrobactrum sp TOA62, and Ochrobactrum sp TOA64. Inoculation of Zea mays seeds with the identified bacterial showed a good level of germination (66%) compared to the control (44%).
Inventory Behavior and the Cost Channel of Monetary Transmission

I develop a New Keynesian model with inventories and convex costs of labor adjustment. For each of the three empirically observed responses to monetary policy shocks: (1) the slow adjustment of inventories compared to changes in sales; (2) the delayed and gradual response of inflation; and (3) the transitory movement in the aggregate price level in the same direction as the interest rate, also known as the “price puzzle,” my model has important implications. First, adjustment costs counteract the financing-cost effect of interest rate changes on inventory holdings, but are still inadequate for the calibrated model to generate countercyclical inventoryto-sales ratios. I find that this financing-cost effect needs to be reduced by 80 percent for the model to predict inventory behavior correctly. Second, firm-specific adjustment costs in production increase the degree of real rigidity for price adjustment, so the response of inflation in the presence of high aggregate marginal costs is still slow-moving and persistent. Finally, the motive of cost smoothing for holding inventories implies that marginal costs should move in the opposite direction as the interest rate, which casts doubt on the use of the cost channel to explain the “price puzzle. ” JEL classifications: E31 F12
Prey selection by larval haddock and cod on copepods with species-specific behavior: an individual-based model analysis

Coupled biological-physical models of larval fish have become a widely used tool for studying recruitment variability. Within these models, foraging components include prey selection as a determinant of food availability but have not yet considered species-specific escape behaviors of prey, which can be important in determining capture success. Furthermore, there has been extensive work on some species (e.g. cod Gadus morhua), but less on others (e.g. haddock Melanogrammus aeglefinus). We collected information from the literature on the escape behaviors of the copepods Calanus finmarchicus, Pseudocalanus spp. , Oithona similis, and Centropages typicus, the dominant prey of larval haddock and cod on Georges Bank (NW Atlantic), for use in a mechanistic foraging model. The foraging model was coupled to a bioenergetics model to simulate larval haddock and cod feeding and growth. Larval haddock and cod demonstrated positive selection of Pseudocalanus spp. and negative selection of C. finmarchicus based on modeled Chesson's preference index. Species- specific differences in escape abilities affected selection more than encounter rate. Prey escape behavior explained why larval cod rarely feed on C. typicus, although these prey items are numerous in the water column. Disparities between larval haddock and cod in their simulated prey selection and growth rates were the result of different mouth sizes and metabolisms. Simulated haddock foraging agreed with gut content observations of the size and species composition of ingested prey. These models are the first to describe haddock foraging and to include species-specific prey behaviors.
Implementasi Pembelajaran Fisika Berbasis Laboratorium Virtual terhadap Keterampilan Proses Sains dan Persepsi Mahasiswa

Penelitian ini bertujuan untuk mengetahui penerapan pembelajaran fisika berbasis media laboratorium virtual , Keterampilan Proses Sains (KPS), dan persepsi mahasiswa pada mata kuliah fisika dasar. Metode penelitian menggunakan pre-eksperimental design dan teknik purposive sample digunakan untuk memilih sampel yaitu kelas pendidikan fisika dan pendidikan matematika Universitas Papua. Instrumen dalam penelitian ini yaitu lembar observasi dan penilaian KPS serta angket persepsi mahasiswa yang dinilai di akhir pembelajaran. Hasil penelitian menunjukkan persentase KPS yang diobservasi selama perkuliahan 86,4% ± SD 12,7 meliputi aspek keterampilan menggunakan alat, mengambil data, dan mengkomunikasikan hasil kegiatan. KPS berdasarkan hasil analisis lembar kerja mahasiswa diperoleh 77,5% ± SD 5,9 meliputi keterampilan merumuskan masalah, menyatakan praduga sementara, mengidentifikasi variabel, mengungkapkan pengertian variabel, mengklasifikasi data, menginterpretasikan data, memformulasikan model, menghubungkan antar data, dan menarik kesimpulan. KPS keseluruhan yaitu 81,95% atau kategori sangat baik. Sedangkan persepsi mahasiswa yaitu 78,53% ± SD 4,9 menunjukkan bahwa mahasiswa sangat setuju terhadap kegiatan pembelajaran yang dilakukan. This study aims to determine the learning application of physics based virtual laboratory, Science Process Skills (SPS), and the perception of students on basic physics course. The research method using pre-experimental design and purposive sample technique used to select sample of physics and mathematics education classes at the University of Papua. Instruments in this research are observation sheet and SPS assessment and student perception questionnaire which assessed at the end of learning. The results showed the percentage of SPS were observed during the lecture 86.4% ± SD 12.7 covering aspects of skills using the tool, retrieve data, and communicate the results of activities. SPS is based on the analysis worksheet students obtained 77.5% ± SD 5.9 includes the skills to formulate the problem, stating the presumption whilst, identifying variables, expressing understanding of variables, classifying the data, interpret the data, formulate a model, linking between data and draw conclusions. SPS students is 81.95% or category very well. While the perception of students that is 78.53% ± SD 4.9 indicates that the student could not agree on the learning undertaken.
Process Variable Optimization in the Manufacture of Resorcinol–Formaldehyde Gel Materials

Influence of process parameters of resorcinol–formaldehyde xerogel manufacture on final gel structure was studied, including solids content, preparation/drying temperature, solvent exchange, and drying method. Xerogels produced using a range of solids content between 10 and 40 w/v% show improved textural character up to 30 w/v% with a subsequent decrease thereafter. Preparation/drying temperature shows a minimal threshold temperature of 55 °C is required to obtain a viable gel structure, with minimal impact on gel properties for further thermal increase. Improving the solvent exchange method by splitting the same amount of acetone used in this phase over the period of solvent exchange, rather than in a single application, shows an increase in total pore volume and average pore diameter, suggesting less shrinkage occurs during drying when using the improved method. Finally, comparing samples dried under vacuum and at ambient pressure, there seems to be less shrinkage when using vacuum drying compared to ambient drying, but these changes are insubstantial. Therefore, of the process parameters investigated, improved solvent exchange seems the most significant, and it is recommended that, economically, gels are produced using a solids content of 20 w/v% at a minimum temperature of 55 °C, with regular solvent replenishment in the exchange step, followed by ambient drying.
Inactivation of speci c β cell transcription factors in type 2 diabetes

1Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 2Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Vanderbilt University, Nashville, Tennessee, USA. 3Departments of Pediatrics and Cellular and Molecular Medicine, Pediatric Diabetes Research Center, University of California at San Diego, La Jolla, California, USA. 4University of Washington, Division of Metabolism, Endocrinology, and Nutrition, Departments of Medicine and Pharmacology, and the Pacific Northwest Diabetes Research Institute, Seattle, Washington, USA. 5VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA.
RECOGNITION OF COMPLEX HUMAN ACTIVITY USING MOBILE PHONES : A SYSTEMATIC LITERATURE REVIEW

Using mobile phones for Human Activities Recognition (HAR) is very helpful in providing a personalized support system for healthcare management and general wellbeing of the user. Many studies have been published which have investigated the HAR with the help of mobile phones. But, in these studies, the researchers briefly mentioned the complex HARs and did not provide any discussion or comparison with the models used. In our study, we have carried out a systematic review of the currently used models in the Complex HAR. We have been carrying out an automatic search in 4 digital libraries since 2012, to address four research questions in our study. We found 11 primary studies after applying the included excluded criteria. Further studies need to be carried out in this area, especially for solving the issue of a trade-off between the recognition accuracy and the computational load.
A Meta-Analysis of School-Based Depression Prevention Programs for Children and Adolescents

A Meta-Analysis of School-Based Depression Prevention Programs for Children and Adolescents Sherry L. Cowen School of Family Life, BYU Doctor of Philosophy School-based depression prevention programs are being implemented in schools across the world in efforts to inoculate children and adolescents from depressive symptoms. This metaanalysis examined 56 manuscripts with a total of 82 studies which focused on school-based programs to determine how they affect depression, anxiety, cognitive skills, self-esteem, coping, and internalizing behaviors. For these specific outcomes, effect sizes ranged from . 08 to . 25. All combined outcomes yielded a significant effect size of . 15. Moderator analyses revealed key differences that identified characteristics of the most effective programs. Targeted programs servicing at-risk students yielded an effect size of . 31, while universal programs produced a significant but small effect size of . 07. However, the program facilitator seemed to impact the effectiveness of all types of programs. Non-school personnel produced a . 39 effect size with targeted samples, and . 17 with universal samples, while school personnel produced about one half to a third of the effect.
Big Data in Natural Disaster Management: A Review

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.
Tissue-specific differential DNA methylation at Igf 2 / H 19 locus in a mouse model for embryonal ethanol exposure

Objective: Maternal ethanol consumption during pregnancy could induce birth defects. Fetal heart, brain and placenta are usually targeted organs. Differential methylation regions (DMRs) in the Igf2/H19 locus are sensitive to environmental conditions. However, the effects of prenatal ethanol consumption on fetal heart, brain and placenta remain largely unknown. The aim of our study is to investigate tissue-specific effects of ethanol consumption during pregnancy on DNA methylation within Igf2/H19 DMRs and the expression of Igf2 in fetal mouse heart, brain, and placenta. Methods: We established a pregnant mouse ethanol gavage model (0.025 ml/g/day; 25% vol:vol). Ethanol was delivered between gestational day (GD) 0.5 and 15.5. Using a MassARRAY EpiTYPER assay, methylation patterns of 4 DMRs, including Igf2 DMR0, Igf2 DMR1, Igf2 DMR2, and H19 DMR, within the Igf2/H19 locus were evaluated in fetal tissues. In parallel, the expression of insulin-like growth factor (Igf2) gene was measured by qRT-PCR and immunohistochemistry. Results: We found decreased DNA methylations at Igf2 DMR1, Igf2 DMR2, and H19 DMR in response to ethanol exposure in murine fetal heart, brain, and placenta, respectively. However, no significant methylation changes were observed at Igf2 DMR0 in all above examined tissues. Importantly, we detected increased Igf2 expression at mRNA and protein levels in corresponding fetal mice brain in response to ethanol exposure. Moreover, for ethanol-treated group, decreased Igf2 expression in fetal mice heart and increased Igf2 expression in fetal mice placenta at mRNA and protein levels were observed compared to controls, although the changes showed no significant differences. Conclusion: Our data indicates that the underlying mechanisms by which fetal developmentally important imprinting genes are dysregulated in a tissue-specific fashion in response to maternal ethanol exposure.