Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated software test optimization using test language processing

The delivery of error free software has become a major challenge for software practitioner since many past years. In order to deliver an error free software testers spends 40-50 % software design life cycle cost during testing, which further get incremented with changing user demands. The Large existence of test cases for a particular functionality is possible and some of them may cause software fails. Thus it raises a demand to automate existing approach of manual testing which can minimize execution efforts while maintaining the quality of testing. In this paper, a regression framework based on keyword oriented data-driven approach has been proposed for generation and execution of test cases. The methodology for the developed framework is based on Test Language Processing (TLP) which acts as a comprehensive approach to design and execution of test cases. The framework is tested on an open source web application called Vtiger-Customer Relationship Management (CRM) version 5. The framework is compared against manual testing in terms of test suite execution and their optimization. Based on our experiments it is concluded that (1) Test execution time using TLP based framework is significantly low and (2) a test suite optimization of 83.78% is achieved through the proposed TLP framework.
Examination Stress, Salivary Cortisol, and Academic Performance

Immediately before and after participation in a written final examination, 11 graduate students rated their self-perceived stress and provided saliva samples for cortisol assay. Students rated stress higher before the examination, and these ratings were associated with increased salivary cortisol. Students who reported higher stress and had higher cortisol levels before the examination tended to have significantly lower examination scores.
Absence of urinary opioid peptides in children with autism

Objective: It has been claimed for a number of years that the urine of children with autism contains exogenously derived opioid peptides. This finding is said to reflect a disturbance in the integrity of the gut epithelium, act as a diagnostic marker for autism and predict treatment response to a diet excluding gluten and casein. The aim of the present study was to determine whether exogenous or endogenous peptides were present in the urine of children with autism or of control children. Design: Case-control study Setting: Cases were recruited from two tertiary referral centres specialising in autistic spectrum disorders, while controls were recruited from mainstream primary and secondary schools in the same geographical area. Participants: 65 boys with autism, mean age 7.4 years (range 5–11) and 158 control boys, mean age 7.8 years (range 4.2–11). Investigations: Urine samples were examined by high pressure liquid chromatography (HPLC) and matrix assisted laser desorbtion ionisation-time of flight mass spectrometry (MALDI-TOF MS) for the presence of a number of putative opioid peptides. Outcomes: There were no significant differences between the HPLC urinary profiles of the children affected by autism and the typically developing controls. In those cases where HPLC showed peaks in the locations at which opioid peptides might be expected to be found, MALDI-TOF established that these peaks did not, in fact, represent opioid peptides. Conclusions: Given the lack of evidence for any opioid peptiduria in children with autism, opioid peptides can neither serve as a biomedical marker for autism nor be employed to predict or monitor response to a casein- and gluten-free diet.
The TRAPs From Microglial Vesicles Protect Against Listeria Infection in the CNS

Previous studies have demonstrated that T cells and microglia could fight against cerebral Listeria monocytogenes (Listeria); however, their synergistic anti-Listeria mechanisms remain unknown. Following Listeria infection in a culture system, we found that microglia, but not nerve cells, could release extracellular traps (ETs) which originated from microglial vesicles. Specific inhibitor analysis showed that extracellular DNA (eDNA), matrix metallopeptidases (MMP9 and MMP12), citrullinated histone H3, and peptidyl arginine deiminase 2 were the major components of microglial ETs (MiETs) and were also the components of vesicles. Systematic analysis indicated that Listeria-induced MiETs were cytosolic reactive oxygen species (ROS)- and NADPH oxidase (NOX)-dependent and involved ERK. MiETs were exhibited in Listeria-infected mouse brain and might protected against Listeria infection via bacterial killing in a mouse meningitis model, and MiETs existed in cerebrospinal fluid (CSF) from Listeria meningitis patients in vivo and in vitro. Additionally, interferon-γ could induce MiET formation in Listeria-infected microglia in vitro that was mediated by NOX, and there was a positive relationship between the elevated level of IFN-γ and eDNA and nucleosomes in the brain homogenates and CSF of Listeria meningitis model mice and in the CSF before treatment in clinical Listeria meningitis patients. Together, this is the first report of MiET formation, these findings pave the way for deeper exploration of the innate immune response to pathogens in CNS.
Inhibition of Neuronal Apoptosis and Axonal Regression Ameliorates Sympathetic Atrophy and Hemodynamic Alterations in Portal Hypertensive Rats

Background and Aim A neuronal pathway participates in the development of portal hypertension: blockade of afferent sensory nerves in portal vein ligated (PVL) rats simultaneously prevents brain cardiovascular regularory nuclei activation, neuromodulator overexpression in superior mesenteric ganglia, sympathetic atrophy of mesenteric innervation and hemodynamic alterations. Here we investigated in PVL rats alterations in neuromodulators and signaling pathways leading to axonal regression or apoptosis in the superior mesenteric ganglia and tested the effects of the stimulation of neuronal proliferation/survival by using a tyrosine kinase receptor A agonist, gambogic amide. Results The neuronal pathway was confirmed by an increased neuronal afferent activity at the vagal nodose ganglia and the presence of semaphorin3A in sympathetic pre-ganglionic neurons at the intermediolateral nucleus of the spinal cord of PVL rats. Expression of the active form of tyrosine kinase receptor A (phosphorylated), leading to proliferation and survival signaling, showed a significant reduction in PVL comparing to sham rats. In contrast, the apoptotic and axonal retraction pathways were stimulated in PVL, demonstrated by a significant overexpression of semaphorin 3A and its receptor neuropilin1, together with increases of cleaved caspase7, inactive poly(ADP-ribose) polymerase and Rho kinase expression. Finally, the administration of gambogic amide in PVL rats showed an amelioration of hemodynamic alterations and sympathetic atrophy, through the activation of survival pathways together with the inhibition of apoptotic cascades and Rho kinase mediated axonal regression. Conclusion The adrenergic alteration and sympathetic atrophy in mesenteric vessels during portal hypertension is caused by alterations on neuromodulation leading to post-ganglionic sympathetic regression and apoptosis and contributing to splanchnic vasodilation.
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Andersson, A-C. 2002. Studies on human endogenous retroviruses (HERVs) with special focus on ERV3. Acta Universitatis Upsaliensis. Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine 1165. 61 pp. Uppsala. ISBN 91-554-5342-2 Human endogenous retroviruses (HERVs) represent approximately 7% of the human genome. This investigation was focused on one particular HERV, ERV3, with the main purpose of characterising its gene expression patterns and genomic distribution of ERV3-like sequences. Furthermore, this careful expression study should provide insights into the biological role of HERVs. The impact of HERVs in health and disease is not yet clarified. ERV3 is expressed as three envelope (env) transcripts, of which two also contain a cellular gene, H-plk (human proviral linked Krüppel). ERV3 env expression was mainly investigated at the RNA level. The gene expression of two other HERVs, HERV-K and HERV-E was analysed and compared with ERV3 activity. Real-time PCRs were developed and in combination with in situ hybridisation, it was found that ERV3 is expressed in a tissueand cell-specific way. High levels of ERV3 mRNA (up to six times over histone3.3) were demonstrated in placenta, sebaceous glands, foetal and adult adrenal glands, brown fat, corpus luteum, pituitary gland, thymus and testis. In monocytic cells including both normal monocytes and malignant U-937 cells, elevated ERV3 mRNA levels were observed after retinoic acid (RA)-induced differentiation. An ERV3-encoded Env protein was detected in selected cases, one following RA-treatment. In addition, several new ERV3–like sequences were discovered in the human genome. ERV3 was found to have conserved open reading frames in contrast to other ERV3-like sequences in the human genome. This suggests that ERV3 may be involved in important cellular processes such as differentiation, cell fusion, immunomodulation and protection against infectious retroviruses. The developed techniques and obtained results will allow further studies of HERV expression to better correlate HERV activity to both normal development and disease.
Hierarchical Machine Learning Model for Mechanical Property Predictions of Polyurethane Elastomers From Small Datasets

Polyurethanes are a broad class of material that finds application in coatings, foams, and solid elastomers. The urethane chemistry allows a diversity of monomers to be used, and prediction of mechanical properties, which are determined by complex interplay between monomer chemistry and chain architecture, is an unresolved challenge. Urethanes are based on aromatic or cyclic isocyanates and linear or branched polyols, and polymerization results in linear chains for bifunctional monomers or branched chains for multifunctional monomers. Strong intermolecular interactions between aromatic groups result in the formation of hard-segment domains that generate physical crosslinks between disorganized rubbery domains and anchor the material microstructure, contributing to resistance to deformation. Here, a general hierarchical machine learning (HML) model for predicting the stress-at-break, strain-at-break, and Tan δ for thermoplastic and thermoset polyurethanes is presented. The algorithm was trained on a library of 18 polymers with different diisocyanates, bifunctional or trifunctional polyols, and NCO:OH index. HML reduces data requirements through robust embedding of domain knowledge and surrogate data in a middle layer that bridges input variables (composition) and output responses (mechanical properties). In this work, the middle layer included information on overall polymer composition, predictions of chain architecture derived from Monte Carlo simulations of polymerization, information on interchain interactions from empirically derived molecular potentials and shifts in infrared (IR) spectroscopy absorbances. The HML predictions are shown to be more accurate than those from a Random Forest model directly relating composition and properties, suggesting that embedding domain knowledge provides significant advantages in predicting the properties of complex material systems based on small datasets.
Functional Analysis of the Chemosensory Protein GmolCSP8 From the Oriental Fruit Moth, Grapholita molesta (Busck) (Lepidoptera: Tortricidae)

Chemosensory proteins (CSPs) belong to a family of small water-soluble proteins that can selectively bind and transport odorant molecules for olfactory communication in insects. To date, their definite physiological functions in olfaction remain controversial when compared with odorant binding proteins (OBPs). To investigate the functions of CSPs in the oriental fruit moth Grapholita molesta, we determined the tissue expression patterns and binding properties of the CSP, GmolCSP8. The key binding sites of GmolCSP8 with a representative ligand were evaluated using molecular flexible docking, site-directed mutagenesis and ligand-binding experiments. Multiple sequence alignment and phylogenetic analysis showed that GmolCSP8 possesses a typical conserved four cysteines motif and shares high sequence identity with some CSP members of other Lepidopteran insects. GmolCSP8 was predominantly expressed in the wings and antennae of both male and female adults and may be involve in contact chemoreception. Recombinant GmolCSP8 (rGmolCSP8) exhibited specific-binding affinities to small aliphatic alcohols (C4–12) and had the strongest binding affinity to 1-hexanol. The three-dimensional structure of GmolCSP8 was constructed using the structure of sgCSP4 as a template. Site-directed mutagenesis and ligand-binding experiments confirmed that Thr27 is the key binding site in GmolCSP8 for 1-hexanol binding, because this residue can form hydrogen bond with the oxygen atom of the hydroxyl group in 1-hexanol, and Leu30 may play an important role in binding to 1-hexanol. We found that pH significantly affected the binding affinities of rGmolCSP8 to ligand, revealing that ligand-binding and -release by this protein is related to a pH-dependent conformational transition. Based on these results, we infer that GmolCSP8 may participate in the recognition and transportation of 1-hexanol and other small aliphatic alcohols.
π-BA: Bundle Adjustment Acceleration on Embedded FPGAs with Co-observation Optimization

Bundle adjustment (BA) is a fundamental optimization technique used in many crucial applications, including 3D scene reconstruction, robotic localization, camera calibration, autonomous driving, space exploration, street view map generation etc. Essentially, BA is a joint non-linear optimization problem, and one which can consume a significant amount of time and power, especially for large optimization problems. Previous approaches of optimizing BA performance heavily rely on parallel processing or distributed computing, which trade higher power consumption for higher performance. In this paper we propose π-BA, the first hardware-software co-designed BA engine on an embedded FPGA-SoC that exploits custom hardware for higher performance and power efficiency. Specifically, based on our key observation that not all points appear on all images in a BA problem, we designed and implemented a Co-Observation Optimization technique to accelerate BA operations with optimized usage of memory and computation resources. Experimental results confirm that π-BA outperforms the existing software implementations in terms of performance and power consumption.
Multi-level improvement for a transcription generated by automatic speech recognition system for arabic

In this paper we will propose a novel approach to improving an automatic speech recognition system. The proposed method constructs a search space based on the relations of semantic dependence of the output of a recognition system. Then, it applies syntactic and phonetic filters so as to choose the most probable hypotheses. To achieve this objective, different techniques are deployed, such as the word2vec or the language model Recurrent Neural Networks Language Models (RNNLM) or ever the language model tagged in addition to a phonetic pruning system. The obtained results showed that the proposed approach allowed to improve the accuracy of the system especially for the recognition of mispronounced words and
A visual programming paradigm for abstract deep learning model development

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a "no-code" paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods.
Self-Assembled Thin-Layer Glycomaterials With a Proper Shell Thickness for Targeted and Activatable Cell Imaging

The construction of targeted and activatable materials can largely improve the precision of disease diagnosis and therapy. However, the currently developed systems either target a transmembrane antigen or are activatable to release imaging and/or therapeutic reagents intracellularly. Here, we develop a simple thin-layer glycomaterial through the self-assembly between fluorescent glycoprobes, in which the carbohydrate-targeting reagent and the fluorophore are linked to each other by polyethylene glycol with a suitable chain length, and thin-layer manganese dioxide. The fluorogenic material developed is both capable of targeting a transmembrane glycoprotein receptor and fluorescently activatable by intracellular biothiols. The shell thickness of the material was determined to be important for achieving the biothiol-induced activation of fluorescence. This research might provide insight into the development of precision-enhanced self-assembled materials for disease theranostics.
Meeting Transcription Using Virtual Microphone Arrays

We describe a system that generates speaker-annotated transcripts of meetings by using a virtual microphone array, a set of spatially distributed asynchronous recording devices such as laptops and mobile phones. The system is composed of continuous audio stream alignment, blind beamforming, speech recognition, speaker diarization using prior speaker information, and system combination. When utilizing seven input audio streams, our system achieves a word error rate (WER) of 22.3% and comes within 3% of the close-talking microphone WER on the non-overlapping speech segments. The speaker-attributed WER (SAWER) is 26.7%. The relative gains in SAWER over the single-device system are 14.8%, 20.3%, and 22.4% for three, five, and seven microphones, respectively. The presented system achieves a 13.6% diarization error rate when 10% of the speech duration contains more than one speaker. The contribution of each component to the overall performance is also investigated, and we validate the system with experiments on the NIST RT-07 conference meeting test set.
Polarization-Modulated, Goos–Hanchen Shift Sensing for Common Mode Drift Suppression

A polarization-modulation-based Goos–Hanchen (GH) sensing scheme leveraging the polarization-dependence of the Bloch surface wave enhanced GH shift is proposed and experimentally demonstrated. Based on a simple setup utilizing a liquid crystal modulator to switch the polarization state of the input beam periodically, the alternating positions of the reflected beam for both polarizations are monitored by a lock-in amplifier to handily retrieve the GH shift signal. The conventional direct measurement of the beam position for the target state of polarization is vulnerable to instabilities in the optomechanical setup and alignment. Our proposed scheme provides a sensitive yet robust GH shift-sensing setup where the common mode drift and noise could be suppressed to ensure better system stability.
The Distinct Roles of Proximal and Distal Utility Values in Academic Behaviors: Future Time Perspective as a Moderator

Utility value for long-term goals, named distal utility value, can be differentiated from utility value for short-term goals, named proximal utility value. The purposes of the present study were (1) to examine the distinct roles of proximal and distal utility value in predicting academic outcomes, (2) to test the mediating role of effort cost in the relationship between these two types of utility value and academic outcomes, and (3) to examine whether future time perspective moderates the role of distal utility value. The results from two independent studies provided compelling evidence for the distinct roles of proximal and distal utility value in predicting academic outcomes, as well as the mediating role of effort cost and the moderating role of future time perspective. Study 1, in which 598 Chinese students participated, demonstrated that proximal utility value negatively predicted effort cost, which in turn negatively predicted academic choice intentions. However, distal utility value did not predict effort cost but did directly predict academic choice intentions. Just as in Study 1, Study 2, in which 891 Korean students participated, found that proximal utility value negatively predicted avoidance intentions and procrastination, directly and indirectly, by lowering effort cost perception. By contrast, distal utility value positively predicted effort cost, which in turn positively predicted avoidance intentions and procrastination. Although distal utility value negatively predicted procrastination directly, the total effects of distal utility value on both academic behaviors were not significant. In Study 2, we also found that future time perspective moderated the relationship between distal utility value and effort cost. The findings of the present study extend the scope of expectancy-value theory, bridge expectancy and value theory with future time perspective theory, and provide guidelines for utility value intervention.
Internet Speed Measurement: Current Challenges and Future Recommendations

Government organizations, regulators, consumers, Internet service providers, and application providers alike all have an interest in measuring user Internet "speed". Access speeds have increased by an order of magnitude in past years, with gigabit speeds available to tens of millions of homes. Approaches must evolve to accurately reflect the changing user experience and network speeds. This paper offers historical and technical background on current speed testing methods, highlights their limitations as access network speeds continue to increase, and offers recommendations for the next generation of Internet "speed" measurement.
Self-Interference (SELFI) Microscopy for Live Super-Resolution Imaging and Single Particle Tracking in 3D

Through the formation of fluorescent self-interference (SELFI), quantitative intensity and phase imaging enables the 3D localization of single fluorescent molecules inside a fixed tissue with an accuracy well beyond the diffraction limit. Here we demonstrate that this concept can be extended to 3D super-resolution microscopy and 3D single particle tracking in various living samples ranging from adherent cells to organotypic brain slices, using diverse fluorescent emitters (fluorescent proteins, organic dyes or quantum dots). This basically covers the most popular single molecule imaging techniques used for live sample studies. We also show that SELFI can be used in combination with different illumination schemes including highly inclined illumination and total internal reflection.