Miracle-dz/UniFAR-FTUs huggingface dataset:
Sub-lithic photosynthesis in hot desert habitats.

In hyper-arid soil environments, photosynthetic microorganisms are largely restricted to hypolithic (sub-lithic) habitats: i.e., on the ventral surfaces of translucent pebbles in desert pavements. Here, we combined fluorometric, spectroscopic, biochemical and metagenomic approaches to investigate in situ the light transmission properties of quartz stones in the Namib Desert, and assess the photosynthetic activity of the underlying hypolithic cyanobacterial biofilms. Quartz pebbles greatly reduced the total photon flux to the ventral surface biofilms and filtered out primarily the short wavelength portion of the solar spectrum. Chlorophylls d and f were not detected in biofilm pigment extracts; however, hypolithic cyanobacterial communities showed some evidence of adaptation to sub-lithic conditions, including the prevalence of genes encoding Helical Carotenoid Proteins, which are associated with desiccation stress. Under water-saturated conditions, hypolithic communities showed no evidence of light stress, even when the quartz stones were exposed to full midday sunlight. This initial study creates a foundation for future in-situ and laboratory exploration of various adaptation mechanisms employed by photosynthetic organisms forming hypolithic microbial communities. This article is protected by copyright. All rights reserved.
Fructose malabsorption: causes, diagnosis and treatment

Abstract This review intends to act as an overview of fructose malabsorption (FM) and its role in the aetiology of diseases including, but not limited to, irritable bowel syndrome (IBS) and infantile colic and the relationship between fructose absorption and the propagation of some cancers. IBS results in a variety of symptoms including stomach pains, cramps and bloating. Patients can be categorised into two groups, depending on whether the patients’ experiences either constipation (IBS-C) or diarrhoea (IBS-D). FM has been proposed as a potential cause of IBS-D and other diseases, such as infantile colic. However, our knowledge of FM is limited by our understanding of the biochemistry related to the absorption of fructose in the small intestine and FM’s relationship with small intestinal bacterial overgrowth. It is important to consider the dietary effects on FM and most importantly, the quantity of excess free fructose consumed. The diagnosis of FM is difficult and often requires indirect means that may result in false positives. Current treatments of FM include dietary intervention, such as low fermentable oligo-, di-, monosaccharides and polyols diets and enzymatic treatments, such as the use of xylose isomerase. More research is needed to accurately diagnose and effectively treat FM. This review is designed with the goal of providing a detailed outline of the issues regarding the causes, diagnosis and treatment of FM.
Ammonia Chemiresistor Sensor Based on Poly(3-Hexylthiophene) Film Oxidized by Nitrosonium Hexafluorophosphate

An ammonia chemiresistor sensor is demonstrated on the basis of poly(3-hexylthiophene) film oxidized by nitrosonium hexafluorophosphate. The sensor gave a good response to ammonia, while was insensitive to a variety of organic vapors. The detection limit was estimated to be ca. 0.22 ppm. The redox reaction between ammonia and bipolarons was attributed to the sensor signals.
Development of a Surrogate Sediment To Study the Mechanisms Responsible for Adsorption/Desorption Hysteresis

A surrogate sediment was developed to reduce some of the complexity in the structural aspects of the adsorbed organic carbon phase. Layers of an anionic surfactant were sorbed to colloidal anatase to produce an organic carbon phase that had hydrophobic regions and resisted desorption. The surrogate is verified as a model sediment by comparing the results of contaminant [2,2‘,5,5‘- tetrachlorobiphenyl (PCB) and naphthalene] adsorption and desorption batch experiments to the results of similar experiments performed on a well-studied natural sediment. The surrogate exhibited adsorption of contaminants via a hydrophobic interaction in the same magnitude as to a natural sediment for the two different hydrophobic organic contaminants in 0.1 or 0.15 M NaCl solution. The sorption of PCB to the surrogate at varied organic carbon contents was observed to follow a linear adsorption isotherm. Desorption experiments were conducted by successive dilutions. Both the surrogate and natural sediment were observed to exhibi.
Effects of one- and two-dimensional carbon hybridization of PEDOT:PSS on the power factor of polymer thermoelectric energy conversion devices

We investigated the thermoelectric properties of polymer composites based on a conducting polymer and carbon materials with various dimensionalities. PEDOT:PSS as a conducting polymer matrix was successfully hybridized with graphene sheets and multi-walled carbon nanotubes through in situ polymerization of 3,4-ethlyenedioxythiophene monomers in an aqueous solution in the presence of the carbon materials dispersed by using a polymeric dispersant. The hybrid structures of PEDOT:PSS, graphene, and carbon nanotubes in the composite showed an electrical conductivity, Seebeck coefficient, and power factor of 689 S cm−1, 23.2 μV K−1, and 37.08 μW mK−2, respectively, values that are much higher than those of pristine PEDOT:PSS, PEDOT:PSS/graphene, or PEDOT:PSS/carbon-nanotube composites. The thermoelectric figure of merit increased from 0.017 in the pristine PEDOT:PSS to 0.031 in the composite, corresponding to an 80% enhancement. We believe that the enhanced thermoelectric performance comes from the synergic effects of multi-component systems with excellent electrical bridging and electronic coupling between PEDOT and carbon materials.
Clinical characteristics and molecular genetic analysis of a cohort with idiopathic congenital hypogonadism

Abstract Objectives Hypogonadism is defined as inadequate sex hormone production due to defects in the hypothalamic-pituitary-gonadal axis. In recent years, rare single gene defects have been identified in both hypergonadotropic hypogonadism (Hh), and hypogonadotropic hypogonadism (HH) cases with no chromosomal anomalies. The aim of the present study is to investigate the underlying molecular genetic etiology and the genotype-phenotype relationship of a series of patients with Hh and HH. Methods In total, 27 HH and six Hh cases were evaluated. Clinical and laboratory features are extracted from patients’ hospital files. Whole exome sequencing (WES) analysis was performed. Results A total of 27 HH cases (15 female) (mean age: 15.8 ± 2.7 years) and six Hh patients (six females) (mean age: 14.9 ± 1.2 years) were included. In molecular genetic analysis, a pathogenic/likely pathogenic variant was identified in five (two patients from the same family) of 27 HH cases (two novel) and three of the six Hh. In HH group variants (pathogenic, likely pathogenic and variant of uncertain significance) were identified in KISS1R (n=2), PROK2 (n=1), FGFR1 (n=1), HS6ST1 (n=1), GNRH1 (n=1) genes. In the Hh group, splice-site mutations were detected in DCAF17 (n=1) and MCM9 (n=2) genes. Conclusions HH and Hh cases are genetically heterogeneous diseases due to oligogenic inheritance, incomplete penetrance, and variable expressivity. We found rare variants in CHH related genes in half of our HH cases, whereas they classified as pathogenic/likely pathogenic according to ACMG criteria in only about 15% of HH cases. Using advanced genetic analysis methods such as whole-genome sequencing and long-read sequencing may increase the mutation detection rate, which should always be associated with and expert genetic counseling to interpret the data.
GC/MS determination of Bioactive Components of Bulbophyllum kaitense. Reichib Leaves Estern Ghats in India.

In the bioactive components of Bulbophyllum kaitense leaves were evaluated by using Perkin- Elmer Gas Chromatography - Mass Spectrometry, The mass spectra of the compounds in the extract was matched with the National Institute of standards and Technology (NIST) library. The GC/MS analysis of ethanol extract of Bulbophyllum kaitense revealed that the existence of glycerin (29.52%) cis-z-a- Bisabolene epoxide (23.96%) 3,3, 4, 4, Tetra- methoxystibene (3.69%) 6,10-Dodecadien - 1- yn - 3 - 01, 3, 7, 11- trim ethyl. (3.29%), 3 - tert lox methyl) hex -5-ene -1, 2 - diol. (3.13%), 2h - Pyran, 2- (7- heptade lynyloxy) tetra hydro (2.63%) 1- Heptatrucitabik. (2.16%) Phenol, 2- methoxy -4- propyl (1.90%), 3- (Adamantan- 2- yliden - methoxymethyl)- Phenol(1.66%) Cyclohexane, 1,3,5- trim ethyl - 2- octadegcy (1.36%) Tetradecanoic acid (1.30%) 3,9 - Epoxy - pregnance - 11a, 20 - diol, 3a - methoxy - 18- (N-methyl -N- (2;4 epoxy ethyl) amino). (1.10%). The identification of bioactive components in leaf parts of Bulbophyllum kaitense. Rechib by GC-MS is the first report. (A. Kalaiarasan., P. Kumar. , S. Ahmed john. GC/MS determination of Bioactive Components of Bulbophyllum kaitense. Reichib Leaves Estern Ghats in India. New York Science Journal 2011;4(10):46-49). (ISSN: 1554-0200). http://www.americanscience. org.
Scientific Writing Evaluation Using Ensemble Multi-channel Neural Networks

A huge number of scientific papers have been authored by non-native English speakers. There is a large demand for effective computer-based writing tools to help writers composing scientific articles. The Automated Evaluation of Scientific Writing (AESW) shared task seeks to promote the use of NLP tools for improving the quality of scientific writing in English by predicting whether a given sentence needs language editing or not. In this study, we propose an ensemble multi-channel BiLSTM-CNN model based on a series of experiments in comparing the number of channels, network architectures
Taiwanese Speech Recognition Based on Hybrid Deep Neural Network Architecture

In this research, we developed the Taiwanese speech recognition system which used the Kaldi toolkit to implement. The Taiwanese corpus was collected by Taiwan Taiwanese National Reading Competition and Classmate Recording, and a total of about 11 hours of audio files were collected. Because the training data is small dataset, two audio augmentation methods are used to increase the training data, so that the acoustic model can be more robust and more effective training. One method is speed perturbation, which speeds up the original data by 1.1 times and slows it down by 0.9 times. Another method is to use multi-condition training data to simulate reverberation of the original speech and add background noise. The background noise includes music, speech, and noise. The acoustic model is trained for different hybrid deep neural network architectures which can use the advantages of each neural network by hybrid different neural networks, including TDNN, CNN-TDNN and CNN-LSTM-TDNN. In the experimental results, the CER in the domain of language modeling reaches 3.95%, and the CER of online decoding test is 3.06%. Compared with other researches on Taiwanese speech recognition of similar dataset size, the recognition results are better than other studies.
Using Clickstream Data Mining Techniques to Understand and Support First-Generation College Students in an Online Chemistry Course

Although online courses can provide students with a high-quality and flexible learning experience, one of the caveats is that they require high levels of self-regulation. This added hurdle may have negative consequences for first-generation college students. In order to better understand and support students’ self-regulated learning, we examined a fully online Chemistry course with high enrollment (N = 312) and a high percentage of first-generation college students (65.70%). Using students’ lecture video clickstream data, we created two indicators of self-regulated learning: lecture video completion and time management. Performing a k-means clustering on these indicators uncovered four distinct self-regulated learning patterns: (1) Early Planning, (2) Planning, (3) Procrastination, and (4) Low Engagement. Early Planning behaviors were especially important for course success—they consistently predicted higher final course grades, even after controlling for important demographic variables. Interestingly, first-generation college students classified as Early Planners achieved at similar levels as their non-first-generation peers, but first-generation students in the Low Engagement group had the lowest average grades among students. Overall, our results show that self-regulation may be an important skill for determining first-generation students’ STEM achievement, and targeting these skills may serve as a useful way to support their specific learning needs.
Impact of learning analytics feedback on self-regulated learning: Triangulating behavioural logs with students’ recall

Learning analytics (LA) has been presented as a viable solution for scaling timely and personalised feedback to support students’ self-regulated learning (SRL). Research is emerging that shows some positive associations between personalised feedback with students’ learning tactics and strategies as well as time management strategies, both important aspects of SRL. However, the definitive role of feedback on students’ SRL adaptations is under-researched; this requires an examination of students’ recalled experiences with their personalised feedback. Furthermore, an important consideration in feedback impact is the course context, comprised of the learning design and delivery modality. This mixed-methods study triangulates learner trace data from two different course contexts, with students’ qualitative data collected from focus group discussions, to more fully understand the impact of their personalised feedback and to explicate the role of this feedback on students’ SRL adaptations. The quantitative analysis showed the contextualised impact of the feedback on students’ learning and time management strategies in the different courses, while the qualitative analysis highlighted specific ways in which students used their feedback to adjust these and other SRL processes.
Quantum of Choice: How learners’ feedback monitoring decisions, goals and self-regulated learning skills are related

Learning analytics dashboards (LADs) are designed as feedback tools for learners, but until recently, learners rarely have had a say in how LADs are designed and what information they receive through LADs. To overcome this shortcoming, we have developed a customisable LAD for Coursera MOOCs on which learners can set goals and choose indicators to monitor. Following a mixed-methods approach, we analyse 401 learners’ indicator selection behaviour in order to understand the decisions they make on the LAD and whether learner goals and self-regulated learning skills influence these decisions. We found that learners overwhelmingly chose indicators about completed activities. Goals are not associated with indicator selection behaviour, while help-seeking skills predict learners’ choice of monitoring their engagement in discussions and time management skills predict learners’ interest in procrastination indicators. The findings have implications for our understanding of learners’ use of LADs and their design.
Seeding Course Forums using the Teacher-in-the-Loop

Online forums are an integral part of modern day courses, but motivating students to participate in educationally beneficial discussions can be challenging. Our proposed solution is to initialize (or “seed”) a new course forum with comments from past instances of the same course that are intended to trigger discussion that is beneficial to learning. In this work, we develop methods for selecting high-quality seeds and evaluate their impact over one course instance of a 186-student biology class. We designed a scale for measuring the “seeding suitability” score of a given thread (an opening comment and its ensuing discussion). We then constructed a supervised machine learning (ML) model for predicting the seeding suitability score of a given thread. This model was evaluated in two ways: first, by comparing its performance to the expert opinion of the course instructors on test/holdout data; and second, by embedding it in a live course, where it was actively used to facilitate seeding by the course instructors. For each reading assignment in the course, we presented a ranked list of seeding recommendations to the course instructors, who could review the list and filter out seeds with inconsistent or malformed content. We then ran a randomized controlled study, in which one group of students was shown seeds that were recommended by the ML model, and another group was shown seeds that were recommended by an alternative model that ranked seeds purely by the length of discussion that was generated in previous course instances. We found that the group of students that received posts from either seeding model generated more discussion than a control group in the course that did not get seeded posts. Furthermore, students who received seeds selected by the ML-based model showed higher levels of engagement, as well as greater learning gains, than those who received seeds ranked by length of discussion.
A Teacher-facing Learning Analytics Dashboard for Process-oriented Feedback in Online Learning

In online learning, teachers need constant feedback about their students’ progress and regulation needs. Learning Analytics Dashboards for process-oriented feedback can be a valuable tool for this purpose. However, few such dashboards have been proposed in literature, and most of them lack empirical validation or grounding in learning theories. We present a teacher-facing dashboard for process-oriented feedback in online learning, co-designed and evaluated through an iterative design process involving teachers and visualization experts. We also reflect on our design process by discussing the challenges, pitfalls, and successful strategies for building this type of dashboard.
Combining Data-Driven Models and Expert Knowledge for Personalized Support to Foster Computational Thinking Skills

Game-Design (GD) environments show promise in fostering Computational Thinking (CT) skills at a young age. However, such environments can be challenging to some students due to their highly open-ended nature. We propose to alleviate this difficulty by learning interpretable student models from data that can drive personalization of a real-world GD learning environment to the student’s needs. We apply our approach on a dataset collected in ecological settings and evaluate the ability of the generated student models at predicting ineffective learning behaviors over the course of the interaction. We then discuss how these behaviors can be used to define personalized support in GD learning activities, by conducting extensive interviews with experienced instructors.
Undergraduate Grade Prediction in Chinese Higher Education Using Convolutional Neural Networks

Prediction of undergraduate grades before their course enrollments is beneficial to the student’s learning plan on selective courses and failure warnings to compulsory courses in Chinese higher education. This study proposed to use a deep learning-based model composed of sparse attention layers, convolutional neural layers, and a fully connected layer, called Sparse Attention Convolutional Neural Networks (SACNN), to predict undergraduate grades. Concretely, sparse attention layers response to the fact that courses have different contributions to the grade prediction of the target course; convolutional neural layers aim to capture the one-dimensional temporal feature on these courses organized in terms; the fully connected layer is to complete the final classification based on achieved features. We collected a dataset including grade records, student’s demographics and course descriptions from our institution in the past five years. The dataset contained about 54k grade records from 1307 students and 137 courses, where all mentioned methods were evaluated by the hold-out evaluation. The result shows SACNN achieves 81% prediction precision and 85% accuracy on the failure prediction, which is more effective than those compared methods. Besides, SACNN delivers a potential explanation to the reason of the predicted result, thanks to the sparse attention layer. This study provides a useful technique for personalized learning and course relationship discovery in undergraduate education.
Multiple lock-ins in vortex-induced vibration of a filament

Abstract The vortex-induced vibration of a flexible filament attached behind a stationary cylinder is studied in the two-dimensional, laminar flow regime. We explore the response of the filament for a wide range of flexibility and inertia. Lock-in with a large number of normal modes of the filament, each in a different regime of reduced speed, is observed. Reduced speed is the free-stream speed of the incoming flow non-dimensionalized with the first natural frequency of the structure and the diameter of the cylinder. Several branches, based on response of the filament, are identified and the contributions of various structural modes along these branches are quantified. Contribution from a particular structural mode increases significantly during lock-in, accompanied by a large amplitude of vibration. The transition between different branches is found to be hysteretic and intermittent. The flow exhibits a variety of vortex-shedding patterns, including the $\mathsf {2P+2S}$ mode. The modes of shedding show a systematic variation with amplitude and frequency. The map of vortex-shedding patterns in the amplitude–frequency plane resembles the corresponding map for forced vibration of a rigid cylinder. The transformation of wake from one mode of shedding to another is explained phenomenologically. Variation of rate of energy transfer between the fluid and filament with space and time is analysed to determine optimal placement of transducers for harvesting energy.