Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the effectiveness and usability of an educational portion size tool, ServARpreg, for pregnant women.

BACKGROUND Elevated blood glucose levels in pregnancy increases the risk of adverse pregnancy outcomes. Modifying consumption of carbohydrate-rich foods is important for blood glucose regulation; however, the tools commonly used to assist in guiding portion control are impractical. The present study aimed to evaluate usability of ServARpreg, a mobile phone-based nutrition tool, and its effectiveness with respect to improving carbohydrate and standard serve size knowledge in pregnant women. METHODS A baseline survey assessed knowledge of carbohydrates and standard serve sizes of pregnant women. A subset of women living in Newcastle were invited to use ServARpreg, containing pregnancy nutrition information and augmented reality guidance on portion control. A follow-up survey was sent to all women 4 weeks after baseline and women who received ServARpreg also received a process evaluation survey after 10 weeks. RESULTS Responses were received from 186 pregnant women for the baseline survey, with 97 completing the follow-up (52.2%). Of the 56 women eligible to receive ServARpreg in the sub-study, 47 accepted (83.9%) and, of these, 40 completed the process evaluation survey (85.1%). At follow-up, there was a significant group × time interaction in favour of the ServARpreg group for carbohydrate quantification knowledge (F1,279 = 9.705, P = 0.002). Standard serve size knowledge did not change between groups. In the process evaluation survey, 80% strongly agreed/agreed that ServARpreg made them more aware of how much they ate and 72.5% found ServARpreg easy to use. CONCLUSIONS ServARpreg has shown potential to educate pregnant women about carbohydrate quantification and increase portion size awareness. Further refinement of the tool and evaluation is needed to improve standard serve size knowledge.
Wnt3a involved in the mechanical loading on improvement of bone remodeling and angiogenesis in a postmenopausal osteoporosis mouse model

Osteoporosis is a major health problem, making bones fragile and susceptible to fracture. Previous works showed that mechanical loading stimulated bone formation and accelerated fracture healing. Focusing on the role of Wnt3a (wingless/integrated 3a), this study was aimed to assess effects of mechanical loading to the spine, using ovariectomized (OVX) mice as a model of osteoporosis. Two‐week daily application of this novel loading (4N, 10Hz, 5 min/d) altered bone remodeling with an increase in Wnt3a. Spinal loading promoted osteoblast differentiation, endothelial progenitor cell migration, and tube formation and inhibited osteoclast formation, migration, and adhesion. A transient silencing of Wnt3a altered the observed loading effects. Spinal loading significantly increased bone mineral density, bone mineral content, and bone area per tissue area. The loaded OVX group showed a significant increase in the number of osteoblasts and reduction in osteoclast surface/bone surface. Though expression of osteoblastic genes was increased, the levels of osteoclastic genes were decreased by loading. Spinal loading elevated a microvascular volume as well as VEGF expression. Collectively, this study supports the notion that Wnt3a‐mediated signaling involves in the effect of spinal loading on stimulating bone formation, inhibiting bone resorption, and promoting angiogenesis in OVX mice. It also suggests that Wnt3a might be a potential therapeutic target for osteoporosis treatment. —Li, X. , Liu, D. , Li, J., Yang, S. , Xu, J., Yokota, H. , Zhang, P. Wnt3a involved in the mechanical loading on improvement of bone remodeling and angiogenesis in a postmenopausal osteoporosis mouse model. FASEB J. 33, 8913–8924 (2019). www.fasebj. org
An Attentional Neural Network Architecture for Folk Song Classification

In this paper we present an attentional neural network for folk song classification. We introduce the concept of musical motif embedding, and show how using melodic local context we are able to model monophonic folk song motifs using the skipgram version of the word2vec algorithm. We use the motif embeddings to represent folk songs from Germany, China, and Sweden, and classify them using an attentional neural network that is able to discern relevant motifs in a song. The results show how the network obtains state of the art accuracy in a completely unsupervised manner, and how motif embeddings produce high quality motif representations from folk songs. We conjecture on the advantages of this type of representation in large symbolic music corpora, and how it can be helpful in the musicological analysis of folk song collections from different cultures and geographical areas.
Reducing the Device Delay Mismatch Can Improve Sound Localization in Bimodal Cochlear Implant/Hearing-Aid Users

In users of a cochlear implant (CI) together with a contralateral hearing aid (HA), so-called bimodal listeners, differences in processing latencies between digital HA and CI up to 9 ms constantly superimpose interaural time differences. In the present study, the effect of this device delay mismatch on sound localization accuracy was investigated. For this purpose, localization accuracy in the frontal horizontal plane was measured with the original and minimized device delay mismatch. The reduction was achieved by delaying the CI stimulation according to the delay of the individually worn HA. For this, a portable, programmable, battery-powered delay line based on a ring buffer running on a microcontroller was designed and assembled. After an acclimatization period to the delayed CI stimulation of 1 hr, the nine bimodal study participants showed a highly significant improvement in localization accuracy of 11.6% compared with the everyday situation without the delay line (p < .01). Concluding, delaying CI stimulation to minimize the device delay mismatch seems to be a promising method to increase sound localization accuracy in bimodal listeners.
Fungal evolution: major ecological adaptations and evolutionary transitions

Fungi are a highly diverse group of heterotrophic eukaryotes characterized by the absence of phagotrophy and the presence of a chitinous cell wall. While unicellular fungi are far from rare, part of the evolutionary success of the group resides in their ability to grow indefinitely as a cylindrical multinucleated cell (hypha). Armed with these morphological traits and with an extremely high metabolical diversity, fungi have conquered numerous ecological niches and have shaped a whole world of interactions with other living organisms. Herein we survey the main evolutionary and ecological processes that have guided fungal diversity. We will first review the ecology and evolution of the zoosporic lineages and the process of terrestrialization, as one of the major evolutionary transitions in this kingdom. Several plausible scenarios have been proposed for fungal terrestralization and we here propose a new scenario, which considers icy environments as a transitory niche between water and emerged land. We then focus on exploring the main ecological relationships of Fungi with other organisms (other fungi, protozoans, animals and plants), as well as the origin of adaptations to certain specialized ecological niches within the group (lichens, black fungi and yeasts). Throughout this review we use an evolutionary and comparative‐genomics perspective to understand fungal ecological diversity. Finally, we highlight the importance of genome‐enabled inferences to envision plausible narratives and scenarios for important transitions.
Edge Detection With Chroma Components of Video Frame Based on Local Autocorrelation

Edge feature extraction is an important basis part for subsequent processing of video frame. The region with a small color difference is transformed into a luminance value for edge detection, which increases the difficulty of edge detection and reduces the detection accuracy. In order to improve the precision of edge detection in the regions with small color difference, local autocorrelation is proposed to enhance the edge feature. Edge detection is completed by using chroma information, and the detection rate is used to measure the extent of the detected edge of the ideal edge to be extracted. The input video frame is converted to YCbCr color mode, and local autocorrelation is applied to the luminance information ${Y}$ and chroma information Cb for edge detection. Complementary processing is applied to the resulting image of edge detection based on local autocorrelation. In order to further improve the detection rate, supplementary processing is carried out. The standard edge images are defined for quantitative analysis and evaluation of proposed methods. Two kinds of edge correction processing based on the local autocorrelation comparison method and average method are proposed. Through experimental verification, the comparison method has a higher detection rate than the common method on the basis of local autocorrelation.
Scalable Modeling of Transient Self-Heating of GaN High-Electron-Mobility Transistors Based on Experimental Measurements

This paper details an extraction procedure to fully model the transient self-heating of transistors from a GaN HEMT technology. Frequency-resolved gate resistance thermometry (f-GRT) is used to extract the thermal impedance of HEMTs with various gate widths. A fully scalable analytical model is developed from the experimental results. In the second stage, transient thermoreflectance imaging (TTI) is used to bring deeper insights into the HEMTs’ temperature distribution by individually extracting the transient self-heating of each finger. TTI results are further used to successfully validate the f-GRT results and the modeling of the thermal impedance. Overall, f-GRT is demonstrated to be a fast and robust method for characterizing the transient thermal characteristics of a GaN HEMT. For the first time to the authors’ knowledge, a scalable model of the thermal impedance is extracted fully from experimental results.
Control of a Time Delay System Arising From Linearized Conservation Laws

A structure of energy cogeneration with distributed parameters is considered, modeled by an initial boundary value problem for hyperbolic conservation laws with a nonlinear and nonstandard boundary condition. Under a standard simplifying assumption leading to linear partial differential equations, the model is studied by associating a system of functional differential and difference equations of neutral type: considered are basic theory (existence, uniqueness, and continuous data dependence), invariant sets (positiveness of some state variables), equilibria, and their inherent stability (without control). These properties are illustrated by simulation results. Furthermore, a control Lyapunov functional is constructed, and feedback stabilizing structure is designed. This paper ends with a conclusion section, where open problems, such as stability by the first approximation/robustness and stability preservation under singular perturbations, are pointed out.
The Australian Journal of Mathematical Analysis and Applications

The farthest point mapping in a p-normed space X is studied in virtue of the Gateaux derivative and the Frechet derivative. Let M be a closed bounded subset of X having the uniformly p-Gateaux differentiable norm. Under certain conditions, it is shown that every maximizing sequence is convergent, moreover, if M is a uniquely remotal set then the farthest point mapping is continuous and so M is singleton. In addition, a Hahn–Banach type theorem in p-normed spaces is proved.
Cancer Chemoprevention: Preclinical in vivo Alternate Dosing Strategies to Reduce Drug Toxicities.

Cancer chemopreventive agents inhibit the formation of precursor lesions and/or the progression of these lesions to late stage disease. This approach to disease control has the potential to reduce the physical and financial costs of cancer in society. Several drugs that have been approved by the FDA for other diseases and have been extensively evaluated for their safety and pharmacokinetic/pharmacodynamic characteristics have the potential to be repurposed for use as cancer chemopreventive agents. These agents often mechanistically inhibit signaling molecules that play key roles in the carcinogenic process. The safety profile of agents is a primary concern when considering the administration of drugs for chemoprevention, as the drugs will be given chronically to high-risk, asymptomatic individuals. To decrease drug toxicity while retaining efficacy, several approaches are currently being explored. In this short review, we describe studies that use preclinical in vivo models to assess efficacy of alternative drug dosing strategies and routes of drug administration on chemopreventive drug efficacy. In vivo drug dosing strategies that reduce toxicity while retaining efficacy will pave the way for future cancer prevention clinical trials.
Immunohistochemical analysis of the mouse celiac ganglion: An integrative relay station of the peripheral nervous system

Celiac ganglia are important sites of signal integration and transduction. Their complex neurochemical anatomy has been studied extensively in guinea pigs but not in mice. The goal of this study was to provide detailed neurochemical characterization of mouse celiac ganglia and noradrenergic nerves in two target tissues, spleen and stomach. A vast majority of mouse celiac neurons express a noradrenergic phenotype, which includes tyrosine hydroxylase (TH), vesicular monoamine transporter 2, and the norepinephrine transporter. Over 80% of these neuron also express neuropeptide Y (NPY), and this coexpression is maintained by dissociated neurons in culture. Likewise, TH and NPY were colocalized in noradrenergic nerves throughout the spleen and in stomach blood vessels. Somatostatin was not detected in principal neurons but did occur in small, TH‐negative cells presumed to be interneurons and in a few varicose nerve fibers. Cholinergic nerves provided the most abundant input to the ganglia, and small percentages of these also contained nitric oxide synthase or vasoactive intestinal polypeptide. A low‐to‐moderate density of nerves also stained separately for the latter markers. Additionally, nerve bundles and varicose nerve fibers containing the sensory neuropeptides, calcitonin gene‐related polypeptide, and substance P, occurred at variable density throughout the ganglia. Collectively, these findings demonstrate that principal neurons of mouse celiac ganglia have less neurochemical diversity than reported for guinea pig and other species but receive input from nerves expressing an array of neurochemical markers. This profile suggests celiac neurons integrate input from many sources to influence target tissues by releasing primarily norepinephrine and NPY.
Comparative performance between C4.5 and Naive Bayes classifiers in predicting student academic performance in a Virtual Learning Environment

People now trying to maximizing function of Virtual Learning Environment. Virtual Learning Environment, not only as a place to help learning system but now has become a place of learning itself. But with the change of the learning system, teacher now have difficulty to monitor the activity of the student and the learning material. Although there is data that is considered capable become a benchmark for students and the interaction with Virtual Learning Activity. This paper will make a data prediction using Naïve bayes and C4.5 Algorithm using the Web History data and the sum of webpage interaction of the students in Virtual Learning Environment.
Stability and Optimization Error of Stochastic Gradient Descent for Pairwise Learning

In this paper, we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking, metric learning, area under ROC curve (AUC) maximization and minimum error entropy (MEE) principle. Our contribution is twofolded. Firstly, we establish the stability results for SGD for pairwise learning in the convex, strongly convex and non-convex settings, from which generalization errors can be naturally derived. Secondly, we establish the trade-off between stability and optimization error of SGD algorithms for pairwise learning. This is achieved by lower-bounding the sum of stability and optimization error by the minimax statistical error over a prescribed class of pairwise loss functions. From this fundamental trade-off, we obtain lower bounds for the optimization error of SGD algorithms and the excess expected risk over a class of pairwise losses. In addition, we illustrate our stability results by giving some specific examples of AUC maximization, metric learning and MEE.
The Australian Journal of Mathematical Analysis and Applications

In this paper a new iterative algorithm for approximating a common solution of split equality monotone inclusion problem and split equality fixed point problem is introduced. Using our algorithm, we state and prove a strong convergence theorem for approximating an element in the intersection of the set of solutions of a split equality monotone inclusion problem and the set of solutions of a split equality fixed point problem for right Bregman strongly nonexpansive mappings in the setting of p-uniformly convex Banach spaces which are also uniformly smooth. We also give some applications.
Epidemiology and Antifungal Susceptibility Profile of Aspergillus Species: Comparison between Environmental and Clinical Isolates from Patients with Hematologic Malignancies

Global data on the epidemiology and susceptibility of Aspergillus are crucial in the management of invasive aspergillosis. Here, we aimed to determine the characteristics of clinical and environmental Aspergillus isolates, focusing mainly on hematologic malignancy patients. ABSTRACT Global data on the epidemiology and susceptibility of Aspergillus are crucial in the management of invasive aspergillosis. Here, we aimed to determine the characteristics of clinical and environmental Aspergillus isolates, focusing mainly on hematologic malignancy patients. We prospectively collected all consecutive cases and clinical isolates of culture-positive proven/probable invasive aspergillosis patients from January 2016 to April 2018 and sampled the air inside and outside the hospital. Cryptic species-level identification of Aspergillus, antifungal susceptibilities, and cyp51 gene sequencing were performed, and clinical data were analyzed. This study was conducted as part of the Catholic Hematology Hospital Fungi Epidemiology (CAFÉ) study. A total of 207 proven/probable invasive aspergillosis and 102 clinical and 129 environmental Aspergillus isolates were included in this analysis. The incidence of proven/probable invasive aspergillosis was 1.3 cases/1,000 patient-days during the study period. Cryptic Aspergillus species accounted for 33.8%, with no differences in proportions between the clinical and environmental isolates. Section Nigri presented a high proportion (70.5%) of cryptic species, mainly from A. tubingensis and A. awamori: the former being dominant in environmental samples, and the latter being more common in clinical isolates (P < 0.001). Of 91 A. fumigatus isolates, azole-resistant A. fumigatus was found in 5.3% of all A. fumigatus isolates. Three isolates presented the TR34/L98H mutation of the cyp51A gene. Patients with invasive aspergillosis caused by azole-resistant A. fumigatus showed 100% all-cause mortality at 100 days. This study demonstrates the significant portion of cryptic Aspergillus species and clinical implications of azole resistance and underscores the comparison between clinical and environmental isolates.
Research Progress on the Relationship between Gastric Microorganisms and the Occurrence and Development of Gastric Cancer

The changes of composition and function of gastric microflora are closely related to the occurrence and development of gastric cancer (Gastriccancer, GC). Gastric microflora has great potential as a biomarker for diagnosis and risk assessment of gastric cancer. There is a complex flora structure in the stomach. The changes of gastric microenvironment caused by Helicobacter pylori (H. pylori HP) may change the flora structure and mainly participate in the early process of gastric carcinogenesis. Gastric microflora other than Helicobacter pylori may play a role in the last steps of gastric carcinogenesis. Exploring the mechanism of gastric microorganisms in the occurrence and development of gastric cancer is helpful to provide a basis for early diagnosis and treatment of gastric cancer. This article mainly reviews the role of Helicobacter pylori and other microorganisms in the occurrence and development of gastric cancer.
Steady Flow Force Compensation and Test Research on Electrohydraulic Proportional Relief Valve

The pressure control accuracy of electrohydraulic proportional relief valve (EPRV) is affected by steady flow force. Thus, this paper investigates the method of designing a spool groove into the turbine bucket profile to compensate for the steady flow force maximally. The influence of steady flow force on the working pressure of the EPRV is analyzed. The calculation model of the steady flow force is deduced. Moreover, the computational fluid dynamics (CFD) model of the spool is established considering the fitting clearance between the spool and the valve body. The test bench is built to verify the CFD model. The turbine bucket profile is designed in the spool groove and optimized on the basis of the response surface method. Furthermore, the flow impact coefficient is defined and the compensation effect of the optimized spool to the steady flow force is tested. The results show that the absolute error value of the CFD simulation and test is smaller than 6.51%. When the working pressure is 0.5, 2, and 4 MPa, the flow impact coefficients of the optimized spool can be reduced by 100%, 59.15%, and 26.72%, respectively, compared with the original spool, indicating that the optimized spool can obviously compensate the steady flow force and improve the control accuracy of the EPRV.
Glucocorticoids and programming of the microenvironment in heart.

Glucocorticoids are primary stress hormones and can improve neonatal survival when given to pregnant women threatened by preterm birth or to preterm infants. It has become increasingly apparent that glucocorticoids, primarily through interacting with glucocorticoid receptors, play a critical role in late gestational cardiac maturation. Altered glucocorticoid actions contribute to the development and progression of heart disease. The knowledge gained from studies in the mature heart or cardiac damage is insufficient but a necessary starting point for understanding cardiac programming including programming of the cardiac microenvironment by glucocorticoids in the fetal heart. This review aims to highlight potential roles of glucocorticoids in programming of the cardiac microenvironment, especially the supporting cells including endothelial cells, immune cells and fibroblasts. The molecular mechanisms by which glucocorticoids regulate the various cellular and extracellular components and the clinical relevance of glucocorticoid functions in the heart are also discussed.