Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of growth hormone supplementation on improving oocyte competence in unexplained poor embryonic development patients of various ages

Abstract Objectives To evaluate the effectiveness of Growth hormone (GH) co-treatment during in vitro fertilization (IVF) cycles in women of different ages who manifest unexplained poor embryonic development. Method This cohort study included a total of 2647 patients with unexplained poor embryonic development in their previous IVF procedures: 872 women received GH co-treatment and 1775 untreated women served as a control group. Patients were divided into 6 groups according to treatment and stratified by age (<35 years of age, A-GH group and A-control group; 35–40 years, B-GH group and B-control group; and ≥40 years, C-GH group and C-control group). The primary outcome was the oocyte-cleavage rate and the clinical pregnancy rate (CPR). Results The oocyte-cleavage rates among the three age groups were significantly higher in the GH group compared to the same-aged control group. In both group A and group B, there was no significant difference in clinical pregnancy rate between the GH group and controls. However, in patients ≥40 years of age, the clinical pregnancy rate in the GH group was significantly higher than in the control group (31.8% vs. 13.7%, p = 0.019). In the three age groups, there was no significant difference in the live birth rate between the GH group and controls. In the multivariate logistic regression analysis model, in both group A and group B, the number of cleaved embryos was independent predictors for CPR (OR = 1.464, 95% CI: 1.311–1.634; respectively, OR = 1.336, 95% CI: 1.126–1.586); Besides, in both group B and group C, age was independent predictors for CPR (OR = 0.657, 95%CI: 0.555–0.778; respectively, OR = 0.622, 95%CI: 0.391–0.989). However, only in group C, supplementation GH increased CPR as compared with not supplementation GH (OR = 2.339, 95%CI: 1.182–6.670). Conclusions For patients with unexplained poor embryonic development, supplementation with GH increased the oocyte-cleavage rates in all three age groups, and the clinical pregnancy rate gradually improved commensurate with increasing age. There was no difference in the clinical pregnancy rate in group A and group B, but group C improved significantly. Therefore, compared with patients under 40 years of age, patients ≥40 may benefit more from GH supplementation.
Multiscale modeling of physical and biological systems

In this work I present recent developments on the Dissipative particle Dynamics (DPD) -a method that bridges the gap between continuum and atomistic scales. In particular, I will first discuss theoretical foundations of DPD and its relation to molecular dynamics (MD) and other coarse-graining approaches, and subsequently I will describe the triple-decker algorithm [1] that interfaces continuum flow regimes with atomistic and mesoscopic regimes. Applications include functionalized surfaces (e.g., polymer brushes, glycocalyx) as well as microflows in physical and biological systems.
Coinfection of cattle in Virginia with Theileria orientalis Ikeda genotype and Anaplasma marginale

Theileria orientalis Ikeda is a newly identified agent of bovine infectious anemia in the United States. Although T. orientalis Ikeda is transmitted by ticks other than the tick that transmits Anaplasma marginale—a bacterial etiology of bovine infectious anemia—the geographic distributions of these 2 infectious organisms overlap, with coinfection reported in some cattle. Only anaplasmosis has an approved effective treatment in the United States. To provide rapid diagnostic information for producers with anemic animals, we developed a duplex real-time PCR (rtPCR) for A. marginale and T. orientalis. With a cutoff of 38 cycles, the duplex assay has a sensitivity of 97.0% and a specificity of 100% for A. marginale; with a cutoff of 45 cycles, the duplex assay has a sensitivity and a specificity of 100% for T. orientalis, compared to existing tests. In addition to providing a tool for improved clinical decision-making for veterinarians and producers, our rtPCR facilitates the study of coinfection of cattle in Virginia. Of 1,359 blood samples analyzed, 174 were positive for T. orientalis, 125 were positive for A. marginale, and 12 samples were positive for both T. orientalis and A. marginale. Hence, coinfection by these 2 agents of bovine infectious anemia does occur within Virginia. It is likely that this pattern of infection will be seen in other regions where T. orientalis and A. marginale infections are endemic, despite the difference in tick vectors.
Correlates of real-world goal-directed behavior in schizophrenia

Abstract Background Deficits in goal-directed behavior (i.e. behavior conducted to achieve a specific goal or outcome) are core to schizophrenia, difficult to treat, and associated with poor functional outcomes. Factors such as negative symptoms, effort-cost decision-making, cognition, and functional skills have all been associated with goal-directed behavior in schizophrenia as indexed by clinical interviews or laboratory-based tasks. However, little work has examined whether these factors relate to the real-world pursuit of goal-directed activities in this population. Methods This study aimed to fill this gap by using Ecological Momentary Assessment (four survey prompts per day for 1 week) to test hypotheses about symptom, effort allocation, cognitive, and functional measures associated with planned and completed goal-directed behavior in the daily lives of 63 individuals with schizophrenia. Results Individuals with schizophrenia completed more goal-directed activities than they planned [t(62) = −4.01, p < 0.001]. Motivation and pleasure (i.e. experiential) negative symptoms, controlling for depressive symptoms, negatively related to planned goal-directed behavior [odds ratio (OR) 0.92, p = 0.005]. Increased effort expenditure for high probability rewards (planned: OR 1.01, p = 0.034, completed: OR 1.01, p = 0.034) along with performance on a daily functional skills task (planned: OR 1.04, p = 0.002, completed: OR 1.03, p = 0.047) negatively related to both planned and completed goal-directed activity. Conclusions Our results present correlates of real-world goal-directed behavior that largely align with impaired ability to make future estimations in schizophrenia. This insight could help identify targeted treatments for the elusive motivated behavior deficits in this population.
Iliopsoas Bursitis Managed with Endoscopic Bursectomy and Lesser Trochanter Decompression

Case: A 32-year-old woman presented with worsening right anterior hip pain, reduced hip flexion strength, and passive range of motion during hip flexion. Magnetic resonance imaging of the hip demonstrated a prominent lesser trochanter and localized fluid signal intensity at the iliopsoas bursa. The patient underwent endoscopic iliopsoas bursectomy and lesser trochanterplasty, reporting improvement in all clinical outcome scores at 1-year follow-up. Conclusion: Lesser trochanter morphology should be evaluated in patients presenting with iliopsoas bursitis. In patients failing to respond to conservative management, endoscopic iliopsoas bursectomy and lesser trochanterplasty may address pain and functional limitations.
Chaotic behaviour and non-linear prediction of airborne radar sea clutter data

The potential to model sea clutter radar returns using chaos theory is examined. Chaotic systems display qualitative similarities to sea clutter returns such as broad flat spectra, boundedness and irregular temporal behaviour. In this report several key parameters of chaotic systems, namely correlation dimension, Lyapunov spectrum and Lyapunov dimension are calculated from real sea clutter returns and found to be consistent with a chaotic interpretation. The airborne high resolution data (less than one metre) produces a correlation coefficient with an average value of 4.63 and an embedding dimension of 6-7. Lyapunov dimensions are consistent with correlation values. A local linear technique and a radial basis function (RBF) are used to construct a one step non-linear predictor. A mean square error (MSE) of approximately 0.0032 between the predicted and normalized (i.e. maximum +/-1 range) real time series is measured.
M ar 2 01 6 Probing hybrid modified gravity by stellar motion around Galactic Centre

We consider possible signatures for the so called hybrid gravitywithin the Galactic Central Parsec. This modified theory of gravity consists of a superposition of the metric E instein-Hilbert Lagrangian with anf (R) term constructed à la Palatiniand can be easily reduced to an equivalent scalar-tensor the ory. Such an approach is introduced in order to cure the shortcomings related to f (R) gravity, in general formulated either in metric or in metri c-affine frameworks. Hybrid gravity allows to disentangle the further gravitati onal degrees of freedom with respect to those of standard General Relativity. The present analysis is based on the S2 s tar orbital precession around the massive compact dark object at the Galactic Centre where the simulated orbits in h ybrid modified gravity are compared with astronomical observations. These simulations result with constraints o the range of hybrid gravity interaction parameter φ0, showing that in the case of S2 star it is between -0.0009 and -0.000 2. At the same time, we are also able to obtain the constraints on the e ffective mass parameter mφ, and found that it is between -0.0034 and -0.0025 AU −1 for S2 star. Furthermore, the hybrid gravity potential induces precess ion of S2 star orbit in the same direction as General Relativity. In previous papers, we considered other types of ext ended gravities, like metric power law f (R) ∝ Rn gravity, inducing Yukawa and Sanders-like gravitational potential s, but it seems that hybrid gravity is the best among these models to explain di fferent gravitational phenomena at di fferent astronomical scales.
The Fiji Education Support Program: An International Development Initiative

The Fiji Education Support Program, (FESP) is an initiative of AusAid and is managed by ACIL. The program which was jointly implemented by the Department of Education and Training, Western Australia, (DETWA) and Curtin University of Technology, (CUT) involved a group of nine senior executives, including the CEO from the Ministry of Education in Fiji, and representatives from Lautoka Teachers College, who undertook a study tour of the Western Australian education system in semester one 2004. The aim of the program was for participants to develop an understanding of the Primary, Secondary and TAFE sector in Western Australia, to form partnerships, strengthen leadership and to build the capacity to reform policy and strategic direction for the improvement and delivery of education in Fiji. This study is an evaluation of the program, the perceptions of participants and the conclusions they reached for prioritising policy in their own country as a result of their experiences.
Pulse charging of lead-acid traction cells

Pulse charging, as a method of rapidly and efficiently charging 300 amp-hour lead-acid traction cells for an electric vehicle application was investigated. A wide range of charge pulse current square waveforms were investigated and the results were compared to constant current charging at the time averaged pulse current values. Representative pulse current waveforms were: (1) positive waveform-peak charge pulse current of 300 amperes (amps), discharge pulse-current of zero amps, and a duty cycle of about 50%; (2) Romanov waveform-peak charge pulse current of 300 amps, peak discharge pulse current of 15 amps, and a duty of 50%; and (3) McCulloch waveform peak charge pulse current of 193 amps, peak discharge pulse current of about 575 amps, and a duty cycle of 94%. Experimental results indicate that on the basis of amp-hour efficiency, pulse charging offered no significant advantage as a method of rapidly charging 300 amp-hour lead-acid traction cells when compared to constant current charging at the time average pulse current value. There were, however, some disadvantages of pulse charging in particular a decrease in charge amp-hour and energy efficiencies and an increase in cell electrolyte temperature. The constant current charge method resulted in the best energy efficiency with no significant sacrifice of charge time or amp-hour output. Whether or not pulse charging offers an advantage over constant current charging with regard to the cell charge/discharge cycle life is unknown at this time.
An Internal Model Control (IMC) Algorithm for Automatic Trajectory Tracking

This paper presented a trajectory tracking algorithm applied for an intelligent driving system. Both the path tracking and longitudinal speed tracking controller are based on IMC algorithm and are carried out respectively. A simulation tool is introduced in this paper and some simulation tests were conducted to verify the proposed trajectory planning algorithm. The simulation results show that the proposed trajectory tracking algorithm is valid and good for the applications of vehicle driving in complex environment.
Research on Shanghai Composite Index Forecasting Model Based on Recurrent Neural

The index data of stocks is a typical time series, so the prediction of it is very uncertain. Traditional recurrent neural networks, such as LSTM and RNN, cannot solve this problem well, so an integrated forecasting model based on the combination of principal component analysis (PCA) and multi-layer gated recurrent network (GRU) is proposed. The training set is the daily data from the first 5800 days of the Shanghai Composite Index from 1993 to 2018, and the test set is the remaining data. Compared with the traditional recurrent neural network, it is found that in the test set, by finding the mean values after several comparative experiments and constructing the fusion model of one-layer, two-layer and three-layer models of PCA and RNN, LSM, GRU, the two-layer GRU has the best effect. The values of root mean square error (RMSE) and the trend prediction accuracy reach 0.015 and 75.9%, respectively.
Workload Prediction of Serverless Computing

∗ As enterprises continue to move to the cloud, Reasonable resource scheduling is of great significance to cloud service management. Accurate prediction and reasonable explanation provide important guarantee for resource scheduling management. However, there is no excellent data set to support workload prediction of cloud computing, which has hysteresis. Moreover, due to the lack of interpretation of the model for managers, complex models still have serious limitations, which limit the application of managers in practice. The paper starts from the actual application of Serverless, makes use of the time series data set provided by Huawei Cloud and makes the workload prediction of Serverless based on the business scenario of renting the server. First of all, the optimal feature engineering construction method is mined to increase the prediction ac-curacy. Secondly, the results of different prediction models and the complexity of prediction are compared to explore the comparative advantages between different methods. Finally, through multiple groups of comparative experiments to explore the optimal use of LightGBM in this example, using SHAP method to explain the model black box, to provide a reference for the management and operation of Serverless.
The Pushes and Pulls of New Localism: School-Level Instructional Arrangements, Instructional Resources, and Family-Community Partnerships

In recent decades, the “cocoon of localism” that for so long shielded educational policymaking from broader societal agendas and competing solutions for school improvement has begun to crack. That localism—at times, insular and stultifying—came at great public cost in the form of segregation, creationism, and neglect of the nation’s lowest performing students (Henig & Stone, 2008). The butterfly that has emerged in its place—with its focus on state and national objectives and accountability systems to assure such objectives are met—may itself pose a threat. Recently, Henig and Stone suggested that one of the most serious problems for education today is that centralizing tendencies—and the national clashes among competing orthodoxies which accompany them—are in danger of “generating solutions that fit poorly the complexity of the situation on the ground” (p. 206).
Analyzing engineering surface texture using wavelet filter

Analysis of surface profile generally involves filtering to separate short wavelengths from medium and long wavelengths. This is usually accomplished using digital filters. The wavelet filter is an ideal means to separate the profile into different bands. The space frequency localization and multiscale presentation of different wavelength components is useful in manufacturing process control and in establishing relationship between surface texture and function. The use of wavelet filter to analyze surface is explored in this paper. This paper deals with the evaluation of suitable wavelet basis for analyzing surface texture of machined surfaces using wavelet filter. The multiscale surface features are analyzed using wavelet filter to explore the potential use of wavelet filter in monitoring of manufacturing process and feature detection in engineering surfaces.
ADDRESSING THE "SECOND YEAR SLUMP" PHENOMENON IN AUSTRALIAN SCIENCE UNDERGRADUATES

The noticeable increase in diversity of the student population over the past decade has now highlighted engagement issues that will be compounded by the general dissociative tendencies that Gen Y and other individuals of today's society now commonly participate in. More and more of our students try to complete degrees in an isolated, relatively off-campus, and academic environment. This is somewhat facilitated by the ever-increasing, time-flexible, blended learning approaches that incorporate the new information technologies. However, these strategies are often at the expense of extrinsic and intrinsic social interactions for students. Moreover, when the “Second Year Slump” is experienced by students, their engagement in learning and motivation are often impaired and they potentially find themselves without the supportive social networks that typically would provide both assistance and encouragement during this time and aid in the progression through their degree. In turn, this may negatively impinge upon student retention.
Virus reactivation in a non-cirrhotic HBV patient requiring liver transplantation after cessation of nucleoside analogue therapy

Nucleos(t)ide analogues (NAs) are a mainstay of therapy for chronic hepatitis B (CHB) infections and have a profound effect on hepatitis B virus (HBV) suppression. We report a rare case of HBV reactivation in a CHB patient without cirrhosis following cessation of NA therapy that resulted in acute liver failure requiring liver transplantation. Investigation of the viral genetics and host immune responses suggest that viral mutations known to promote virus replication are associated with reactivation, whereas adaptive immunity to HBV remained defective in this patient. Viral sequencing may be useful for identifying mutations that are unfavorable for therapy withdrawal.
Using Machine Learning to Advance Early Warning Systems: Promise and Pitfalls

Background/Context Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like course failures, chronic absenteeism, and suspensions can help identify students in need of additional supports. In parallel with the expansion of administrative data that have made early versions of EWI possible, new machine learning methods have been developed. These methods are data-driven and often designed to sift through thousands of variables with the purpose of identifying the best predictors of a given outcome. While applications of machine learning techniques to identify students at-risk of high school dropout have obvious appeal, few studies consider the benefits and limitations of applying those models in an EWI context, especially as they relate to questions of fairness and equity. Focus of Study In this study, we will provide applied examples of how machine learning can be used to support EWI selection. The purpose is to articulate the broad risks and benefits of using machine learning methods to identify students who may be at risk of dropping out. We focus on dropping out given its salience in the EWI literature, but also anticipate generating insights that will be germane to EWI used for a variety of outcomes. Research Design We explore these issues by using several hypothetical examples of how ML techniques might be used to identify EWI. For example, we show results from decision tree algorithms used to identify predictors of dropout that use simulated data. Conclusions/Recommendations Generally, we argue that machine learning techniques have several potential benefits in the EWI context. For example, some related methods can help create clear decision rules for which students are a dropout risk, and their predictive accuracy can be higher than for more traditional, regression-based models. At the same time, these methods often require additional statistical and data management expertise to be used appropriately. Further, the black-box nature of machine learning algorithms could invite their users to interpret results through the lens of preexisting biases about students and educational settings.
A prevalence survey of Wolbachia in butterflies from southern China

Wolbachia is a maternally inherited endosymbiont of the phylum α‐Proteobacteria that is found widely in arthropods and nematodes. Wolbachia is the most common endosymbiont in butterflies (Lepidoptera) and it manipulates host reproduction mainly by inducing cytoplasmic incompatibility, male killing, and feminization. We studied the prevalence patterns of Wolbachia in 52 butterfly species from five families collected in southern China. A substantial infection incidence was determined in the study region, where 18 out of the 52 butterfly species (35%) were infected, including members of the families Hesperiidae, Lycaenidae, Nymphalidae, Papilionidae, and Pieridae. Based on multi‐locus sequence typing (MLST) and Wolbachia cell surface protein (WSP) profiles, seven unique sequence types (STs) and six wsp alleles were detected. One MLST haplotype was detected for the first time (ST‐wPcau). Most of the identified Wolbachia STs belonged to supergroup B, those found in two Ypthima spp. belonged to supergroup A. Many STs were shared among various insect species; the most widespread strain in our samples, ST‐41, was detected in seven butterfly species from four families. We discuss the potential horizontal transmission routes of Wolbachia, as well as the suitability of the Wolbachia strain typing method.
Impact of Primary User Emulation Attacks on Cognitive Radio Networks

In this paper, we study the effects of Primary user emulation attacks (PUEA) in a cognitive radio network consisting of a single primary transmitter and a set of co-operating secondary or cognitive users. The primary transmitter communicates with primary receivers within an area called the primary exclusive region (PER). Inside the PER, no cognitive users may transmit so as to guarantee a certain performance for the primary receivers in the region. Outside the PER, cognitive users are uniformly distributed and allowed to transmit provided they are at a distance from the primary receiver. We present an analysis to calculate the powers and probability density function (PDF) of both malicious users and good secondary users and compare it with the simulated results. The PDF is used in Neyman-Pearsons composite hypothesis test to study the impact of PUEA in the network. Results show that the number of malicious users in the system can significantly increase the probability of false alarm in the network