Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.
Cytoprotective activity of mitochondrial uncoupling protein‐2 in lung and spleen

Mitochondrial uncoupling protein‐2 (UCP2) mediates free fatty acid (FA)‐dependent H+ translocation across the inner mitochondrial membrane (IMM), which leads to acceleration of respiration and suppression of mitochondrial superoxide formation. Redox‐activated mitochondrial phospholipase A2 (mt‐iPLA2γ) cleaves FAs from the IMM and has been shown to acts in synergy with UCP2. Here, we tested the mechanism of mt‐iPLA2γ‐dependent UCP2‐mediated antioxidant protection using lipopolysaccharide (LPS)‐induced pro‐inflammatory and pro‐oxidative responses and their acute influence on the overall oxidative stress reflected by protein carbonylation in murine lung and spleen mitochondria and tissue homogenates. We provided challenges either by blocking the mt‐iPLA2γ function by the selective inhibitor R‐bromoenol lactone (R‐BEL) or by removing UCP2 by genetic ablation. We found that the basal levels of protein carbonyls in lung and spleen tissues and isolated mitochondria were higher in UCP2‐knockout mice relative to the wild‐type (wt) controls. The administration of R‐BEL increased protein carbonyl levels in wt but not in UCP2‐knockout (UCP2‐KO) mice. LPS further increased the protein carbonyl levels in UCP2‐KO mice, which correlated with protein carbonyl levels determined in wt mice treated with R‐BEL. These results are consistent with the UCP2/mt‐iPLA2γ antioxidant mechanisms in these tissues and support the existence of UCP2‐synergic mt‐iPLA2γ‐dependent cytoprotective mechanism in vivo.
Ego Depletion Does Not Interfere With Working Memory Performance

Ego depletion happens if exerting self-control reduces a person’s capacity to subsequently control themselves. Previous research has suggested that ego depletion not only interferes with subsequent self-control but also with working memory. However, recent meta-analytical evidence casts doubt onto this. The present study tackles the question if ego depletion does interfere with working memory performance. We induced ego depletion in two ways: using an e-crossing task and using a Stroop task. We then measured working memory performance using the letter-number sequencing task. There was no evidence of ego depletion interfering with working memory performance. Several aspects of our study render this null finding highly robust. We had a large and heterogeneous sample of N = 1,385, which provided sufficient power. We deployed established depletion tasks from two task families (e-crossing task and Stroop), thus making it less likely that the null finding is due to a specific depletion paradigm. We derived several performance scores from the working memory task and ran different analyses to maximize the chances of finding an effect. Lastly, we controlled for two potential moderators, the implicit theories about willpower and dispositional self-control capacity, to ensure that a possible effect on working memory is not obscured by an interaction effect. In sum, this experiment strengthens the position that ego depletion works but does not affect working memory performance.
Overlapping representations of the neck and whiskers in the rat motor cortex revealed by mapping at different anaesthetic depths

The primary motor cortex of mammals has an orderly representation of different body parts. Within the representation of each body part the organization is more complex, with groups of neurons representing movements of a muscle or a group of muscles. In rats, uncertainties continue to exist regarding organization of the primary motor cortex in the whisker and the neck region. Using intracortical microstimulation (ICMS) we show that movements evoked in the whisker and the neck region of the rat motor cortex are highly sensitive to the depth of anaesthesia. At light anaesthetic depth, whisker movements are readily evoked from a large medial region of the motor cortex. Lateral to this is a small region where movements of the neck are evoked. However, in animals under deep anaesthesia whisker movements cannot be evoked. Instead, neck movements are evoked from this region. The neck movement region thus becomes greatly expanded. An analysis of the threshold currents required to evoke movements at different anaesthetic depths reveals that the caudal portion of the whisker region has dual representation, of both the whisker and the neck movements. The results also underline the importance of carefully controlling the depth of anaesthesia during ICMS experiments.
Association between polymorphism rs12722 in COL5A1 and musculoskeletal soft tissue injuries: a systematic review and meta-analysis

The rs12722 polymorphism in COL5A1 gene has been implicated in the etiology of musculoskeletal soft tissue injuries in several association studies with limited sample size and conflicting results. The purpose of the present systematic review and meta-analysis was to evaluate and synthesize the currently available data on the association between rs12722 and musculoskeletal soft tissue injuries. Five electronic databases including Pubmed, EMBASE, ISI Web of Science, CNKI and Wanfang were searched to identify relevant studies published before 15 May, 2017. Summary odds ratios (ORs) and corresponding 95% confidence intervals (95% CIs) were estimated using the RevMan 5.3 software. Nine studies comprising 1140 cases and 1410 healthy controls met the eligibility criteria. Recessive model was confirmed to be the optimum model (TT vs TC + CC). The results indicated that rs12722 SNP was significantly associated with musculoskeletal soft tissue injuries (OR 1.58, 95% CI 1.33, 1.89; P < 0.00001). When stratified by injury sites, modest but statistically significant association was found in Achilles tendon pathology (ATP), anterior cruciate ligament injuries (ACLI) and tennis elbow (TE). Subgroup-analysis by ethnicity suggested that TT genotype of rs12722 was associated with tendon and ligament injuries in Caucasians (OR 1.59, 95% CI 1.33, 1.90; P < 0.00001) but not in Asians (OR 1.46, 95% CI 0.46, 4.60; P = 0.52). Our findings indicated that rs12722 of COL5A1 was positively associated with tendon and ligament injuries, especially in Caucasian subjects. Individuals with TT genotype were predisposed to higher risk of ATP, ACLI and TE.
RFID Backscattering in Long-Range Scenarios

This paper presents a 5.8-GHz RFID tag that, by exploiting the quantum tunneling effect, significantly increases the range of backscatter radio links. We present an electronically simple Tunneling RFID Tag characterized by return gains as high as 35 dB with link sensitivity as low as −81 dBm. Without relevant increase in power consumption, the tunneling tag enables a host of new wireless sensors and Internet of Things applications that require both the long range of conventional wireless links and the low power consumption of semi-passive RFID devices. Selected measurements demonstrate a reader-to-tag separation distance 10 times higher than the maximum range of ideal semi-passive tags. Moreover, the collected experimental results allowed to outline a mathematical model demonstrating how the long-range RFID tag prototype can achieve distances unusual for this technology.
Light Alcohol Consumption Does Not Protect Cognitive Function: A Longitudinal Prospective Study

Studies show that light to moderate alcohol consumption is related to better health and higher cognitive performance. However, it has been suggested that this association is caused by a systematic bias in the control group as many people abstain from drinking or quit because of health issues. Therefore, the group of non-drinkers is biased towards poor health and may not be suitable as a control group. The present study examined the effect of alcohol on cognitive performance while addressing this bias by excluding the non-drinkers. Thus, instead of comparing different levels of alcohol consumption to a non-drinking control group, a dose-response association was calculated between all levels of alcohol intake and cognitive performance. The study used information from a sample of people in the Swedish Twin Registry, who in their midlife (1967) participated in a survey on alcohol intake and 25 years later participated in a longitudinal study on cognitive aging (N = 486). The cognitive aging study took place on five occasions, at 2-year intervals, and included the Mini Mental State Examination (MMSE), tests of episodic memory, semantic memory and spatial ability. The association between midlife alcohol consumption and later cognitive performance was analyzed using growth curve models, adjusting for background variables. The findings showed that there was a significant negative dose-response association between alcohol intake in midlife and the MMSE, and the tests of episodic memory, such that higher intake in midlife was related to lower performance in old age. The associations between alcohol and semantic memory, and spatial ability respectively, were not significant. In contrast to findings from other studies, which show that low to moderate alcohol intake promotes cognitive function, the current study showed that alcohol intake was related to lower cognitive performance in a dose-response manner, even at low levels. The results from this study indicate that the observed benefits of moderate alcohol intake for cognitive function reported by others might be solely due to comparisons to an inappropriate control group, a group that is biased towards poor health. Hence, it is concluded that light alcohol intake may not protect cognitive function.
Metallic nanopores for single molecule biosensing

This thesis describes a novel approach to the fabrication and characterisation of metallic nanopores and their application for the detection of single DNA molecules. Metallic nanopores with apparent diameters below 20 nm are produced using electrochemical deposition and real-time ionic current feedback. Beginning with large nanopores (diameter 100-200 nm) milled into gold silicon nitride membranes using a focused ion beam, platinum metal is electrodeposited onto the gold surface, thus reducing the effective pore diameter. By simultaneously observing the ion current feedback, the shrinking of the nanopore can be monitored and terminated at any pre-defined value of the pore conductance in a precisely controlled and reproducible way. The ion transport properties of the metallic nanopore system are investigated by characterising the pore conductance at varying potentials across the nanopore and concentrations of electrolyte. The results are compared to conventional bare silicon nitride nanopore systems. Chemical modification at the nanopore surface is also studied using thiolisation to reduce the capacitive charging effects observed with metallic nanopores. Further to this, impedance measurements are carried out to study the resistive behaviour exhibited in these systems. An equivalent circuit model is proposed to validate the results obtained from the experimental studies. To evaluate the suitability of these nanopores for applications in single-molecule biosensing, translocation experiments using λ-DNA are performed. DNA molecules are electrokinetically driven through the nanopore under an applied electric field, hence as the DNA translocates through the pore, current blockade events are detected. Each event is the result of a single molecular interaction of DNA with the nanopore and is characterised by its dwell time and amplitude. Characterisation studies and noise analysis towards the applicability of metallic nanopores as single molecule detectors are also studied and compared to current bare silicon nitride pore systems.
Determination of Young's Modulus and Poisson Ratio of Lump Ores

Synopsis: Mechanical properties of iron ores are measured under the compression test. The results obtained are as follows: (1) Young's modulus of iron ores is proportional to the apparent specific gravity respectively. And the Young's modulus of the hematite shows considerably higher than that of the magnetite. (2) Young's modulus of iron ores can be expressed as a straight line, independent of the ore brand, when it is indicated against the porosity. (3) Poisson ratio of hematite and magnetite is 0.24 and 0.33 respectively. (4) There is a remarkable difference of Young's modulus between pellet and iron ore.
Towards Automatic Classification of Privacy Policy Text

Privacy policies notify Internet users about the privacy practices of websites, mobile apps, and other products and services. However, users rarely read them and struggle to understand their contents. Also, the entities that provide these policies are sometimes unmotivated to make them com-prehensible. Recently, annotated corpora of privacy policies have been introduced to the research community. They open the door to the development of machine learning and natural language processing techniques to automate the annotation of these documents. In turn, these annotations can be passed on to interfaces (e.g., web browser plugins) that help users quickly identify and understand relevant privacy statements. We present advances in extracting privacy policy paragraphs (termed segments in this paper) and individual sentences that relate to expert-identiﬁed categories of policy contents, using methods in supervised learning. In particular, we show that relevant segments and sentences can be classiﬁed with average micro-F1 scores of 0.78 and 0.66 respectively, improving over prior work. We discuss how the techniques introduced in this paper have been used to automatically annotate the text of about 7,000 privacy policies. Our discussion highlights opportunities as well as limitations associated with our classiﬁcation approach.
A Comparative Study on Physicochemical Characteristics of Raw Goat Milk Collected from Different Farms in Malaysia

This study was conducted to determine the physical and chemical composition of goat milk produced by eight local farms located in the central region of Malaysia. Farms 1 to 4 (F1-SC, F2-SP, F3-SP, F4-SBC) reared Saanen-type goats while farms 5 to 8 (F5-JK, F6-JPEC, F7-JTC, F8-JC), Jamnapari-type goats. The common feedstuffs used in all farms comprised of fresh or silage from Napier grass, feed pellets, and brans while two farms, F5-JK and F6-JPEC supplemented the feeds with soybean-based product. The total solid content, dry matter, and proximate composition of goat milk and feedstuffs from the different farms were determined and the results analysed using principal component analysis. Total solid content of goat milk from the Jamnapari crossbreed had the highest solid content ranging from 11.81% to 17.54% compared to milk from farms with Saanen and Saanen crossbreed (10.95% to 14.63%). Jamnapari-type goats from F5-JK, F6-JPEC, and F8-JC had significantly higher (p < 0.05) milk fat and protein contents (7.36%, 7.14%, and 6.59% fat; 5.08%, 6.19%, and 4.23% protein, respectively) than milk from other farms but, milk produced by Saanen-type goats from F4-SBC contained similar protein content (4.34%) to that from F8-JC. Total ash and carbohydrate contents in milk ranged between 0.67% to 0.86% and 3.26% to 4.71%, respectively, regardless of goat breed. Feeding soybean-based products appear to have a positive influence on milk fat and protein content in Jamnaparitype goats.
Co-precipitation synthesis, physical and magnetic properties of manganese ferrite powder

Magnetic manganese ferrite powder was synthesized via chemical co-precipitation techniques using metallic chlorides of manganese and iron. Sodium hydroxide (NaOH) base was used as precipitant agent. The calcinations were performed at 600°C for 5 h. The structural investigation of the prepared sample was performed with X-ray diffractometer (XRD) and scanning electron microscope (SEM). The crystallite size was 26.53 nm, calculated using Sherrer formula. Magnetic properties were studied by vibrating sample magnetometer (VSM) techniques at room temperature. Key words: Ferrite, co-precipitation, X-ray diffractometer (XRD), scanning electron microscopy (SEM), vibrating sample magnetometer (VSM).
Strawberries Improve Pain and Inflammation in Obese Adults with Radiographic Evidence of Knee Osteoarthritis

Osteoarthritis (OA), the most common form of arthritis, is a significant public health burden in U.S. adults. Among its many risk factors, obesity is a key player, causing inflammation, pain, impaired joint function, and reduced quality of life. Dietary polyphenols and other bioactive compounds in berries, curcumin, and tea have shown effects in ameliorating pain and inflammation in OA, but few clinical studies have been reported. The purpose of the present study was to examine the effects of dietary strawberries on pain, markers of inflammation, and quality of life indicators in obese adults with OA of the knee. In a randomized, double-blind cross-over trial, adults with radiographic evidence of knee OA (n = 17; body mass index (BMI): (mean ± SD) 39.1 ± 1.5; age (years): 57 ± 7) were randomized to a reconstituted freeze-dried strawberry beverage (50 g/day) or control beverage daily, each for 12 weeks, separated by a 2-week washout phase (total duration, 26 weeks). Blood draws and assessments of pain and quality of life indicators were conducted using the Visual Analog Scale for Pain (VAS Pain), Measures of Intermittent and Constant Osteoarthritis Pain (ICOAP), and Health Assessment Questionnaire-Disability Index (HAQ-DI) questionnaires, which were completed at baseline and at weeks 12, 14, and 26 of the study. Among the serum biomarkers of inflammation and cartilage degradation, interleukin (IL)-6, IL-1β, and matrix metalloproteinase (MMP)-3 were significantly decreased after strawberry vs. control treatment (all p < 0.05). Strawberry supplementation also significantly reduced constant, intermittent, and total pain as evaluated by the ICOAP questionnaire as well as the HAQ-DI scores (all p < 0.05). No effects of treatment were noted on serum C-reactive protein (CRP), nitrite, glucose, and lipid profiles. Dietary strawberries may have significant analgesic and anti-inflammatory effects in obese adults with established knee OA.
BXtend - A Framework for (Bidirectional) Incremental Model Transformations

Model transformations constitute the core essence of model-driven software development (MDSD) – a software engineering discipline, which gained more and more attention during the last decade. While technology for unidirectional batch transformations seems to be fairly well developed, tool support for bidirectional and incremental transformations is still restricted. Results obtained with case studies carried out with popular bidirectional approaches motivated us to set up our own light-weight framework for bidirectional and incremental model transformations based on the Xtend programming language. Our approach provides several advantages, as it reduces the cognitive complexity for transformation developers, and allows for a greater flexibility in transformation specifications by providing procedural language constructs. In addition, it provides a higher expressive power and allows for compact specifications at the same time.
An Integrated Response of Trichodesmium erythraeum IMS101 Growth and Photo-Physiology to Iron, CO2, and Light Intensity

We have assessed how varying CO2 (180, 380, and 720 μatm) and growth light intensity (40 and 400 μmol photons m−2 s−1) affected Trichodesmium erythraeum IMS101 growth and photophysiology over free iron (Fe′) concentrations between 20 and 9,600 pM. We found significant iron dependencies of growth rate and the initial slope and maximal relative PSII electron transport rates (rPm). Under iron-limiting concentrations, high-light increased growth rates and rPm; possibly indicating a lower allocation of resources to iron-containing photosynthetic proteins. Higher CO2 increased growth rates across all iron concentrations, enabled growth to occur at lower Fe′ concentrations, increased rPm and lowered the iron half saturation constants for growth (Km). We attribute these CO2 responses to the operation of the CCM and the ATP spent/saved for CO2 uptake and transport at low and high CO2, respectively. It seems reasonable to conclude that T. erythraeum IMS101 can exhibit a high degree of phenotypic plasticity in response to CO2, light intensity and iron-limitation. These results are important given predictions of increased dissolved CO2 and water column stratification (i.e., higher light exposures) over the coming decades.
Derivative Free Optimization Via Repeated Classification

We develop an algorithm for minimizing a function using $n$ batched function value measurements at each of $T$ rounds by using classifiers to identify a function's sublevel set. We show that sufficiently accurate classifiers can achieve linear convergence rates, and show that the convergence rate is tied to the difficulty of active learning sublevel sets. Further, we show that the bootstrap is a computationally efficient approximation to the necessary classification scheme. The end result is a computationally efficient derivative-free algorithm requiring no tuning that consistently outperforms other approaches on simulations, standard benchmarks, real-world DNA binding optimization, and airfoil design problems whenever batched function queries are natural.
Bringing Alive Blurred Moments

We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during the time of exposure. We first learn motion representation from sharp videos in an unsupervised manner through training of a convolutional recurrent video autoencoder network that performs a surrogate task of video reconstruction. Once trained, it is employed for guided training of a motion encoder for blurred images. This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder. As an intermediate step, we also design an efficient architecture that enables real-time single image deblurring and outperforms competing methods across all factors: accuracy, speed, and compactness. Experiments on real scenes and standard datasets demonstrate the superiority of our framework over the state-of-the-art and its ability to generate a plausible sequence of temporally consistent sharp frames.