Miracle-dz/UniFAR-FTUs huggingface dataset:
Skeletal Fragility in Type 2 Diabetes Mellitus

Type 2 diabetes (T2D) is associated with an increased risk of fracture, which has been reported in several epidemiological studies. However, bone mineral density in T2D is increased and underestimates the fracture risk. Common risk factors for fracture do not fully explain the increased fracture risk observed in patients with T2D. We propose that the pathogenesis of increased fracture risk in T2D is due to low bone turnover caused by osteocyte dysfunction resulting in bone microcracks and fractures. Increased levels of sclerostin may mediate the low bone turnover and may be a novel marker of increased fracture risk, although further research is needed. An impaired incretin response in T2D may also affect bone turnover. Accumulation of advanced glycosylation endproducts may also impair bone strength. Concerning antidiabetic medication, the glitazones are detrimental to bone health and associated with increased fracture risk, and the sulphonylureas may increase fracture risk by causing hypoglycemia. So far, the results on the effect of other antidiabetics are ambiguous. No specific guideline for the management of bone disease in T2D is available and current evidence on the effects of antiosteoporotic medication in T2D is sparse. The aim of this review is to collate current evidence of the pathogenesis, detection and treatment of diabetic bone disease.
Learning Universal Sentence Representations with Mean-Max Attention Autoencoder

In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within the encoder-decoder framework. Our autoencoder rely entirely on the MultiHead self-attention mechanism to reconstruct the input sequence. In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input. To enable the information to steer the reconstruction process dynamically, the decoder performs attention over the mean-max representation. By training our model on a large collection of unlabelled data, we obtain high-quality representations of sentences. Experimental results on a broad range of 10 transfer tasks demonstrate that our model outperforms the state-of-the-art unsupervised single methods, including the classical skip-thoughts and the advanced skip-thoughts+LN model. Furthermore, compared with the traditional recurrent neural network, our mean-max AAE greatly reduce the training time.
Cracking the monolith: challenges in data transitioning to cloud native architectures

Application modernization is the process of transforming a monolithic application to cloud native. This involves gradually building a new application consisting of microservices, and running it in conjunction with the monolithic application. Over a period of time, the functionality of the monolith shrinks drastically or transforms into yet another microservice. Solution architects are often faced with the task of ensuring this smooth transition - from monolith to cloud native. For large and complex monoliths, this task is non-trivial as the code base grows non-linearly over a period of time, thus posing multiple challenges. The complexity of a monolith is moved to the interconnections between microservices, leading to multiple points of failure. This also has implications on scalability and the need for replication. One of the biggest challenges is to maintain data consistency and statefulness across the services and enable a smooth transition of the data. Tracing performance issues also becomes complex as a single transaction can encompass multiple service calls, along with increased operational complexity due to increased demand of managing services. In this paper, we envision an automated approach that will enable a smooth transition from the monolith to microservices, thus alleviating the complexities faced by a solution architect. Our system leverages the existing data schema along with details obtained using profiling tools (in production or development environment), to understand the data flow and access patterns and use this information to to propose functional modules (microservices).
Steadfast Standards or Fluctuating Fancies? Stability and Change in People’s Mate Criteria Over 27 Months

Although research on mate preferences has been built on the assumption that the criteria people report at one point in time should predict their future partnering behavior, little is known about the temporal stability of people’s standards. Using survey data collected at four time points from 285 originally unmarried individuals, this study examined the rank-order, mean-level, individual-level and ipsative stability of people’s mate criteria over 27 months. Overall, reported standards exhibited moderate to high baseline stability, with rank-order and ipsative estimates comparable to those reported for personality traits. At the same time, mean- and individual-level analyses revealed small, but significant, increases in participants’ reported criteria over the study, as well as significant variability in individual trajectories. Consistent with theory, the stability of individuals’ standards was moderated by several contextual factors, including age, changes in perceived mate value, and significant relationship events.
Provider’s Perceptions of Barriers and Facilitators for Latinas to Participate in Genetic Cancer Risk Assessment for Hereditary Breast and Ovarian Cancer

The Comprehensive Cancer Network (NCCN) recommends genetic cancer risk assessment (GCRA) referral to women at high risk of hereditary breast and ovarian cancer. Latinas affected by breast cancer have the second highest prevalence of BRCA1/2 mutations after Ashkenazi Jews. Compared to non-Hispanic Whites, Latinas have lower GCRA uptake. While some studies have identified barriers for GCRA use in this population, few studies have focused on health care providers’ perspectives. The purpose of the study was to examine providers’ perceptions of barriers and facilitators for at-risk Latina women to participate in GCRA and their experiences providing services to this population. We conducted semi-structured interviews with 20 healthcare providers (e.g., genetic counselors, patient navigators) recruited nationally through snowballing. Interviews were transcribed. Two coders independently coded each interview and then met to reconcile the codes using Consensual Qualitative Research guidelines. Providers identified several facilitators for GCRA uptake (e.g., family, treatment/prevention decisions) and barriers (e.g., cost, referrals, awareness, stigma). Genetic counselors described important aspects to consider when working with at-risk Latina including language barriers, obtaining accurate family histories, family communication, and testing relatives who live outside the US. Findings from this study can inform future interventions to enhance uptake and quality of GCRA in at-risk Latina women to reduce disparities.
90-Day Hospital Readmissions of Patients in a Geriatric Acute Care Ward in Singapore

The objective of the study is to examine the 90-day readmission rate and identify the predictors for 90-day readmissions at a geriatric ward in a tertiary hospital in Singapore. A secondary analysis of case–control data was performed. Data of patients discharged from a geriatric ward between January 2015 and January 2016 were retrieved from an existing data set. Out of 564 index admissions involving older adults, the 90-day geriatric readmission rate was 10.1%. Activities of daily living dependency (odds ratio [OR]: 0.988, 95% confidence interval [CI]: [0.978, 0.999]) and living with the spouse (OR: 2.988, 95% CI: [1.388, 6.432]) were identified as significant predictors of 90-day geriatric readmissions. The study suggests that rehabilitation to restore the geriatric patient’s ability to perform daily activities and adequate caregiver training for the spouse are essential in reducing geriatric readmissions. Also, postdischarge follow-up with both the patient and caregiver can greatly reduce the risk of readmission in geriatric patients.
LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network

The intrusion detection models (IDMs) based on machine learning play a vital role in the security protection of the network environment, and, by learning the characteristics of the network traffic, these IDMs can divide the network traffic into normal behavior or attack behavior automatically. However, existing IDMs cannot solve the imbalance of traffic distribution, while ignoring the temporal relationship within traffic, which result in the reduction of the detection performance of the IDM and increase the false alarm rate, especially for low-frequency attacks. So, in this paper, we propose a new combined IDM called LA-GRU based on a novel imbalanced learning method and gated recurrent unit (GRU) neural network. In the proposed model, a modified local adaptive synthetic minority oversampling technique (LA-SMOTE) algorithm is provided to handle imbalanced traffic, and then the GRU neural network based on deep learning theory is used to implement the anomaly detection of traffic. The experimental results evaluated on the NSL-KDD dataset confirm that, compared with the existing state-of-the-art IDMs, the proposed model not only obtains excellent overall detection performance with a low false alarm rate but also more effectively solves the learning problem of imbalanced traffic distribution.
A Synthetic Fusion Rule for Salient Region Detection under the Framework of DS-Evidence Theory

Saliency detection is one of the most valuable research topics in computer vision. It focuses on the detection of the most significant objects/regions in images and reduces the computational time cost of getting the desired information from salient regions. Local saliency detection or common pattern discovery schemes were actively used by the researchers to overcome the saliency detection problems. In this paper, we propose a bottom-up saliency fusion method by taking into consideration the importance of the DS-Evidence (Dempster–Shafer (DS)) theory. Firstly, we calculate saliency maps from different algorithms based on the pixels-level, patches-level and region-level methods. Secondly, we fuse the pixels based on the foreground and background information under the framework of DS-Evidence theory (evidence theory allows one to combine evidence from different sources and arrive at a degree of belief that takes into account all the available evidence). The development inclination of image saliency detection through DS-Evidence theory gives us better results for saliency prediction. Experiments are conducted on the publicly available four different datasets (MSRA, ECSSD, DUT-OMRON and PASCAL-S). Our saliency detection method performs well and shows prominent results as compared to the state-of-the-art algorithms.
Computational complexity: a conceptual perspective

This book is rooted in the thesis that complexity theory is extremely rich in conceptual content, and that this contents should be explicitly communicated in expositions and courses on the subject. The desire to provide a corresponding textbook is indeed the motivation for writing the current book and its main governing principle. The book offers a conceptual perspective on complexity theory, and the presentation is designed to highlight this perspective. It is intended mainly for students that wish to learn complexity theory and for educators that intend to teach a course on complexity theory. The book is also intended to promote interest in complexity theory and make it acccessible to general readers with adequate background (which is mainly being comfortable with abstract discussions, definitions and proofs). We expect most readers to have a basic knowledge of algorithms, or at least be fairly comfortable with the notion of an algorithm. The book focuses on several sub-areas of complexity theory (including, e.g., pseudorandomness and probabilistic proof systems). In each case, the exposition starts from the intuitive questions addresses by the sub-area, as embodied in the concepts that it studies. The exposition discusses the fundamental importance of these questions, the choices made in the actual formulation of these questions and notions, the approaches that underly the answers, and the ideas that are embedded in these answers. Our view is that these (\non-technical") aspects are the core of the field, and the presentation attempts to re ect this view.
Identification and diversity of Y-chromosome haplotypes in Qinghai yak populations.

The aim of the present study was to perform a preliminary analysis of the characterization and diversity of Y-chromosome haplotypes/haplogroups in yak of Qinghai Province, China. A total of 322 male yaks from nine populations belonging to three officially recognized breeds (Gaoyuan, Huanhu and Datong) were sampled. Animals were genotyped using six previously reported Y-SNPs present in the SRY, USP9Y, UTY, AMELY and OFD1Y genes and four new Y-SNPs in the OFD1Y gene (g.569A>C, g.578A>C, g.608G>T and g.653G>C) identified in this study. Seven Y-chromosome haplotypes (H1-H7) were identified according to the combination of the 10 Y-SNPs. H1, H2 and H6 were the most common and shared haplotypes across all yak populations/breeds. Private haplotypes H3 and H7 were detected in the Datong breed; H4 in Guoleimude, Qumalai, Qilian, Tianjun and Ganglong populations; and H5 in Qumalai of Gaoyuan breed. Haplotype clustering and network analyses inferred two haplogroups, Y1 and Y2, indicating two divergent lineages of paternal origins of Qinghai yak. The analysis of molecular variance showed a significant difference among individuals (P < 0.0001) with more than 93% of the total genetic variation present within populations, suggesting a weak genetic structure among Qinghai yak populations. The overall Y-haplotype diversity was 0.538 ± 0.028, showing a relatively high diversity in Qinghai yak. The Gaoyuan and Datong breeds had similar haplotype diversities (0.547 ± 0.030 and 0.553 ± 0.083, respectively), which were higher than that of the Huanhu breed (0.441 ± 0.098). Our results support the conservation and sustainable use of unique yak genetic resources in Qinghai.
Factor Structure of Urdu Version of the Flourishing Scale

Background: A great deal of research has been carried out on the assessment of the eudaimonic perspective of psychological well-being and the hedonic perspective of subjective well-being. The Flourishing Scale (FS) has been extensively used in research and practice, as it assesses the fundamental aspects of social psychological functioning. Nevertheless, the psychometric properties of Urdu versions of eudaimonic measures, such as the FS, have not yet been ascertained. The translation and validation of the FS in the Urdu language was not available, and hence this study was planned with the aim to validate the Urdu version of the FS. Methods: We assessed the psychometric properties of the FS in a sample of adults aged 18 years and above in Pakistan (N = 130) using exploratory factor analysis based on principal component analysis with varimax rotation and confirmatory factor analysis. Results: The exploratory factor analysis confirmed the unidimensional nature of the 8-item FS. We assessed that the Urdu version of the FS showed a high internal consistency reliability (α = 0.914) with a significant intraclass correlation coefficient (ICC), p < 0.001). In our study, the Kaiser–Mayer–Olkin value was 0.915 with a chi-square test value (χ2) of 637.687, and Bartlett's test of sphericity was significant (df = 28, p < 0.001). The intraclass correlation coefficients (ICCs) at test–retest for all domains were statistically significant (p < 0.001) and showed excellent agreement for all the items. The revised confirmatory factor analysis revealed a good-fit model, but with item 8—“People respect me”—removed due to its lower factor loading. Conclusions: The findings suggest that the FS is a psychometrically sound instrument for assessing social psychological functioning among adults in Pakistan. Therefore, the validated Urdu version of the FS may be used in future studies of well-being in clinical psychology and positive psychology.
Understanding the Long-Term Use of Smart Speaker Assistants

Over the past two years the Ubicomp vision of ambient voice assistants, in the form of smart speakers such as the Amazon Echo and Google Home, has been integrated into tens of millions of homes. However, the use of these systems over time in the home has not been studied in depth. We set out to understand exactly what users are doing with these devices over time through analyzing voice history logs of 65,499 interactions with existing Google Home devices from 88 diverse homes over an average of 110 days. We found that specific types of commands were made more often at particular times of day and that commands in some domains increased in length over time as participants tried out new ways to interact with their devices, yet exploration of new topics was low. Four distinct user groups also emerged based on using the device more or less during the day vs. in the evening or using particular categories. We conclude by comparing smart speaker use to a similar study of smartphone use and offer implications for the design of new smart speaker assistants and skills, highlighting specific areas where both manufacturers and skill providers can focus in this domain.
Effects of Chronic NAD Supplementation on Energy Metabolism and Diurnal Rhythm in Obese Mice

Adequate nicotinamide adenine dinucleotide (NAD) content in hypothalamic neurons is critical for the maintenance of normal energy balance and circadian rhythm. In this study, the beneficial metabolic effects of chronic NAD supplementation on diet‐induced obesity and obesity‐related disruption of diurnal rhythms were examined.
Decreasing birth asphyxia: utility of statistical process control in a low-resource setting

The neonatal period is a critical time for survival of the child. A disproportionate amount of neonatal deaths occur in low-resource countries and are attributable to perinatal events, especially birth asphyxia. This project aimed to reduce the incidence of birth asphyxia by 20% by June 2014 through training in neonatal resuscitation and improving the availability of resuscitation equipment in the delivery room in the National Hospital Abuja, Nigeria. A prospective, longitudinal study using statistical process control analytical methods was done enrolling babies delivered at the National Hospital Abuja. Low Apgar scores or birth asphyxia (defined a priori as any score <7 at 1, 5 and/or at 10 min) was assessed. To ensure reliability and validity of Apgar scoring, trainings on scoring were held for labour and delivery staff. Interventions included provision of additional equipment and trainings on neonatal resuscitation. Apgar scores were aggregated weekly over 25 months. Control charts with three SE confidence limits were used to monitor the proportion of scores ≤7. The baseline incidence of low Apgar scores, as defined a priori, was 33%, 17% and 10% while postintervention the incidence was 18%, 17% and 6% at 1, 5 and 10 min, respectively—a reduction of 45% and 40% in the 1-min and 10-min low Apgar scores. Increased communication, additional resuscitation equipment and training of delivery personnel on neonatal resuscitation are associated with reductions in measures of birth asphyxia. These improvements have been sustained and efforts are ongoing to spread our interventions to other special care delivery units/nursery in adjoining states. Our study demonstrates the feasibility and utility of using improvement science methods to assess and improve perinatal outcome in low-resource settings.
Association of N6AMT1 rs2254638 Polymorphism With Clopidogrel Response in Chinese Patients With Coronary Artery Disease

Dual antiplatelet treatment with aspirin and clopidogrel is the standard therapy for patients undergoing percutaneous coronary intervention (PCI). However, a portion of patients suffer from clopidogrel resistance (CR) and consequently with recurrence of cardiovascular events. Genetic factors such as loss-of-function variants of CYP2C19 contribute a lot to CR. Recently, the N-6-adenine-specific DNA methyltransferase 1 (N6AMT1) rs2254638 polymorphism is reported to be associated with clopidogrel response. To validate the association between N6AMT1 rs2254638 polymorphism and clopidogrel response, 435 Chinese CAD patients receiving aspirin and clopidogrel were recruited. N6AMT1 rs2254638 and CYP2C19*2/*3 polymorphisms were genotyped. Platelet reaction index (PRI) was measured by VASP-phosphorylation assay after treated with a 300 mg loading dose (LD) clopidogrel or 75 mg daily maintenance dose (MD) clopidogrel for at least 5 days. There was a significant difference in PRI between LD cohort and MD cohort. Carriers of CYP2C19*2 allele showed significantly increased PRI in the entire cohort and in respective of the MD and LD cohorts (p < 0.001, p = 0.003, p < 0.001, respectively). However, carriers of CYP2C19*3 allele exhibited significantly higher PRI only in the entire cohort and LD cohort (p = 0.023, p = 0.023 respectively). PRI value was significantly higher in CYP2C19 PM genotyped patients as compared with those carrying the IM genotypes and EM genotype (p < 0.001). Besides, carriers of the rs2254638 C allele showed significantly higher PRI in entire cohort and in the LD cohort (p = 0.023, p = 0.008, respectively). When the patients were grouped into clopidogrel resistance (CR) and non-clopidogrel resistance (non-CR) groups, CYP2C19*2 was associated with increased risk of CR in the entire cohort, the LD cohort and the MD cohort (p < 0.001, p < 0.001, and p = 0.019, respectively). Carriers of the rs2254638 C allele also showed increased risk of CR in the entire cohort and the LD cohort (p = 0.024, and p = 0.028, respectively). N6AMT1 rs2254638 remained as a strong predictor for CR (TC vs. TT: OR = 1.880, 95% CI = 1.099–3.216,p = 0.021; CC vs. TT: OR = 1.930, 95% CI = 1.056-3.527, p = 0.032; TC + CC vs. TT: OR = 1.846, 95%CI = 1.126–3.026, p = 0.015) after adjustment for confounding factors. Our study confirmed the influence of CYP2C19*2 and rs2254638 polymorphisms on clopidogrel resistance in Chinese CAD patients. Both CYP2C19*2 and N6AMT1 rs2254638 polymorphism may serve as independent biomarkers to predict CR.
An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA’s unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.