Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship of body mass index with bone mineral density in postmenopausal women: an Indian perspective

Background: Osteoporosis is a common health problem that affects postmenopausal females, leading to increased susceptibility to fractures. Body mass index (BMI) has been shown to be an important predictor of bone mineral density (BMD) with increased body weight correlating with positive influence on bone metabolism. Low BMI predisposes postmenopausal females to rapid bone loss and low bone mass, crucial in the pathogenesis of osteoporosis. However, a specific BMI value chart to accurately predict osteoporosis remains to be fully established. The present study aimed to investigate the relationship of BMI and BMD in postmenopausal Indian females.Methods: 90 healthy postmenopausal females with 1-5 years of menopause were enrolled in the study. Subjects were categorized according to their BMI into normal, overweight and obese. BMD was assessed using dual energy X-ray absorptiometry (DEXA) scan at L1-L4 vertebrae and femoral neck and expressed as T-scores. Quantitative variables were compared using ANOVA/Kruskal Wallis Test.Results: Bone mineral density was significantly higher in the obese group as compared to normal BMI group at both lumbar spine (p=0.001) and femoral neck (p=0.001). BMD at lumbar spine was lower than that at femoral neck across all the three groups of BMI.Conclusions: BMI and body weight are important factors affecting BMD. Postmenopausal females with low BMI are more likely to have osteopenia and osteoporosis and are thus at an increased risk of pathological fractures. Routine BMD monitoring in postmenopausal females with low BMI may be necessary to initiate early clinical interventions for osteoporosis.
FACULTY OF NATURAL SCIENCES CONSTANTINE THE PHILOSOPHER UNIVERSITY IN NITRA ACTA MATHEMATICA 17 115 PRE-SERVICE TEACHERS’ PROBLEM POSING IN COMBINATORICS

Combinatorics is seen as one of the more difficult areas of mathematics to teach and to learn. Mathematical knowledge for teaching combinatorics of 14 pre-service teachers for primary school developed during session integrating mathematical and pedagogical activities was assessed through the problem posing. Knowledge of combinatorics of some was enhanced but not in satisfactory extend. Lack of subject matter knowledge influenced students’ ability to pose and subsequently solve combinatorial problems.
The Effect of Electron Beam Irradiation on PE-g-MA Compatibilised Linear Low-density Polyethylene/Soya Powder Blends

In current investigation, the linear low-density polyethylene (LLDPE)/soya powder blends were compatibilised by polyethylene grafted maleic anhydride (PE-g-MA). The blends were prepared using internal mixer at a rotor speed of 50 rpm and temperature 150°C. The effect of LLDPE/soya powder composition and irradiation dose on the tensile properties of the blends was investigated. The results showed that an increase in soya powder content in the blends reduced the tensile strength and elongation at break. However, the tensile strength of LLDPE/soya powder blends was enhanced with the addition of the PE-g-MA and further improved after irradiated with electron beam (EB) irradiation. The tensile strength of the blends increased with increasing irradiation dose, but the elongation at break decreased. From the morphological study done with scanning electron microscopy (SEM), the LLDPE formed continuous matrix after irradiation.
Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization

In this paper, a novel algorithm called opposition-based coral reefs optimization (OCRO) is introduced. The algorithm is built as an improvement for coral reefs optimization (CRO) using opposition-based learning (OBL). For efficient modeling as the main part of this work, a novel time series forecasting model called OCRO-multi-layer neural network (MLNN) is proposed to explore hidden relationships in the non-linear time series data. The model thus combines OCRO with MLNN for data processing, which enables reducing the model complexity by faster convergence than the traditional back-propagation algorithm. For validation of the proposed model, three real-world datasets are used, including Internet traffic collected from a private internet service provider (ISP) with distributed centers in 11 European cities, WorldCup 98 contains request numbers to the server in football world cup season in 1998, and Google cluster log dataset gathered from its data center. Through the carried out experiments, we demonstrated that with both univariate and multivariate data, the proposed prediction model gains good performance in accuracy, run time and model stability aspects as compared with other modern learning techniques like recurrent neural network (RNN) and long short-term memory (LSTM). In addition, with used real datasets, we intend to concentrate on applying OCROMLNN to distributed systems in order to enable the proactive resource allocation capability for e-infrastructures (e.g. clouds services, Internet of Things systems, or blockchain networks).
Certificate Transparency in the Wild: Exploring the Reliability of Monitors

To detect fraudulent TLS server certificates and improve the accountability of certification authorities (CAs), certificate transparency (CT) is proposed to record certificates in publicly-visible logs, from which the monitors fetch all certificates and watch for suspicious ones. However, if the monitors, either domain owners themselves or third-party services, fail to return a complete set of certificates issued for a domain of interest, potentially fraudulent certificates may not be detected and then the CT framework becomes less reliable. This paper presents the first systematic study on CT monitors. We analyze the data in 88 public logs and the services of 5 active third-party monitors regarding 3,000,431 certificates of 6,000 selected Alexa Top-1M websites. We find that although CT allows ordinary domain owners to act as monitors, it is impractical for them to perform reliable processing by themselves, due to the rapidly increasing volume of certificates in public logs (e.g., on average 5 million records or 28.29 GB daily for the minimal set of logs that need to be monitored). Moreover, our study discloses that (a) none of the third-party monitors guarantees to return the complete set of certificates for a domain, and (b) for some domains, even the union of the certificates returned by the five third-party monitors can probably be incomplete. As a result, the certificates accepted by CT-enabled browsers are not absolutely visible to the claimed domain owners, even when CT is adopted with well-functioning logs. The risk of invisible fraudulent certificates in public logs raises doubts on the reliability of CT in practice.
Curcumin Elevates TFH Cells and Germinal Center B Cell Response for Antibody Production in Mice

Curcumin is a natural product extracted from Curcuma longa. It has been reported as a potent antioxidant and anti-inflammatory compound. Previous studies have demonstrated that curcumin suppresses pro-inflammatory cytokine production via inhibition of NF-κB in macrophages. However, its role in adaptive immune cells such as T cells, in vivo, has not clearly been elucidated. Here, we examined the effects of curcumin in T follicular helper (TFH) cells and on Ab production during NP-ovalbumin immunization in mice. The results revealed that curcumin administered daily significantly increased CXCR5+B-cell lymphoma 6+ TFH cells and CD95+GL-7+ germinal center (GC) B cells in draining lymph nodes. In addition, curcumin treatment in mice induced total Ab production as well as high affinity IgG1 and IgG2b Ab production. Collectively, these results suggest that curcumin has positive regulatory roles in TFH cell functions and GC responses. Thus, this could be an advantageous supplement to enhance humoral immunity against infectious diseases and cancer.
Effects of dietary energy level on appetite and central adenosine monophosphate-activated protein kinase (AMPK) in broilers

Abstract Adenosine monophosphate-activated protein kinase (AMPK) acts as a sensor of cellular energy changes and is involved in the control of food intake. A total of 216 1-d-old broilers were randomly allotted into 3 treatments with 6 replicates per treatment and 12 broilers in each cage. The dietary treatments included 1) high-energy (HE) diet (3,500 kcal/kg), 2) normal-energy (NE) diet (3,200 kcal/kg), and 3) low-energy (LE) diet (2,900 kcal/kg). The present study was conducted to investigate the effects of dietary energy level on appetite and the central AMPK signal pathway. The results showed that a HE diet increased average daily gain (ADG), whereas a LE diet had the opposite effect (P < 0.05, N = 6). The average daily feed intake (ADFI) of the chickens fed the LE diet was significantly higher than that of the control (P < 0.05, N = 6). Overall, the feed conversion rate gradually decreased with increasing dietary energy level (P < 0.05, N = 6). Moreover, the chickens fed the LE and HE diets demonstrated markedly improved urea content compared with the control group (P < 0.0001, N = 8). The triglyceride (TG) content in the LE group was obviously higher than that in the HE group but showed no change compared with the control (P = 0.0678, N = 8). The abdominal fat rate gradually increased with increased dietary energy level (P = 0.0927, N = 8). The HE group showed downregulated gene expression levels of liver kinase B1 (LKB1), neuropeptide Y (NPY), cholecystokinin (CCK), and glucocorticoid receptor (GR) in the hypothalamus compared with the control group (P < 0.05, N = 8). However, LE treatment significantly increased the mRNA level of AMP-activated protein kinase α2 (AMPKα2) compared with other groups (P = 0.0110, N = 8). In conclusion, a HE diet inhibited appetite and central AMPK signaling. In contrast, a LE diet activated central AMPK and appetite. Overall, the central AMPK signal pathway and appetite were modulated in accordance with the energy level in the diet to regulate nutritional status and maintain energy homeostasis in birds.
Individual Specialization in Multi-Task Environments with Multiagent Reinforcement Learners

There is a growing interest in Multi-Agent Reinforcement Learning (MARL) as the first steps towards building general intelligent agents that learn to make low and high-level decisions in non-stationary complex environments in the presence of other agents. Previous results point us towards increased conditions for coordination, efficiency/fairness, and common-pool resource sharing. We further study coordination in multi-task environments where several rewarding tasks can be performed and thus agents don't necessarily need to perform well in all tasks, but under certain conditions may specialize. An observation derived from the study is that epsilon greedy exploration of value-based reinforcement learning methods is not adequate for multi-agent independent learners because the epsilon parameter that controls the probability of selecting a random action synchronizes the agents artificially and forces them to have deterministic policies at the same time. By using policy-based methods with independent entropy regularised exploration updates, we achieved a better and smoother convergence. Another result that needs to be further investigated is that with an increased number of agents specialization tends to be more probable.
Detecting Earthquake-Related Borehole Strain Data Anomalies With Variational Mode Decomposition and Principal Component Analysis: A Case Study of the Wenchuan Earthquake

Borehole strain monitoring has high sensitivity and is therefore widely used to study slow earthquakes, volcanic activity, earthquake precursors, and other nature phenomena. However, environmental factors seriously affect the identification of strain changes caused by crustal deformation. This paper proposes a method of anomaly detection based on variational mode decomposition (VMD) and principal component analysis (PCA). The borehole strain signal is decomposed into a number of modes simultaneously using VMD, and a new state-space model used to determine the number of the modes those are decomposed by the VMD algorithm. The influencing factors of each component are determined by spectrum analysis and comparative analysis. An example of the separation process of borehole strain data by the VMD method is presented. Then, we use PCA to calculate eigenvalues, which are used to detect anomalies associated with an earthquake, and eigenvectors, which are applied to show the spatial distribution characteristics of the data. Our method has been applied to detect borehole strain data anomalies associated with the Wenchuan earthquake; the VMD demonstrates excellent separation performance for borehole strain signals, and eigenvalues and eigenvectors together reflect the accelerated deformation of focal faults and adjacent areas before earthquake in time and space.
Comparative evaluation of ALBI, MELD, and Child-Pugh scores in prognosis of cirrhosis: is ALBI the new alternative?

Background: The existence of reliable prognostic indices is of paramount importance in the management of cirrhosis. Both the model for end-stage liver disease (MELD) score and the older Child-Pugh (CP) scores are widely used. The albumin-bilirubin (ALBI) score, initially used in hepatocellular carcinoma, has not been thoroughly investigated in cirrhosis. The aim of this study was to compare the prognostic accuracy of ALBI, MELD, MELD with sodium (MELD-Na), CP, and the corrected for creatinine CP scores in a genetically homogeneous Cretan cirrhotic population. Methods: One hundred ninety-five outpatients or hospitalized cirrhotics (127 male, median age 66 years) were studied over a period of 2 years and ALBI, platelet-albumin-bilirubin, MELD, MELD-Na, CP score, and 2 types of modified CP score (CP-I and CP-II) with serum creatinine were calculated and correlated with survival. Results: ALBI had an optimum balance between sensitivity and specificity (area under the curve 0.704, 95% confidence interval [CI] 0.630-0.778) compared to the other scores. In the multivariate analysis, the only factors independently associated with death were the ALBI score (hazard ratio [HR] 2.51, 95%CI 1.69-3.73; P<0.001), the MELD-Na score (HR 1.04, 95%CI 1.00-1.09; P=0.045), and age (HR 1.05, 95%CI 1.03-1.07; P<0.001). When only decompensated cirrhosis was evaluated, the multivariate analysis showed that the ALBI score (HR 3.03; 95%CI 1.92-4.78; P<0.001), and age (HR 1.05, 95%CI 1.03-1.07; P<0.001) were independently associated with death. Conclusion: ALBI score might be a better prognostic indicator of mortality in cirrhosis and given its simplicity could substitute for the CP, MELD, and MELD-Na scores.
Device-Free CSI-Based Wireless Localization for High Precision Drone Landing Applications

Unstable drone landing scenarios have caused severe damage to expensive drone-based devices. It is required to provide precision altitude information of the drone during its landing maneuvers. In this paper, we propose a device-free wireless localization algorithm, by adopting channel state information (CSI) to assist drone landing. By adopting our proposed system, the drone will not need to carry any additional sensing devices for precise positioning. The commodity Wi-Fi access points only need to be installed near the landing nest of the drone, which means that multiple drones can use the same set of landing systems. Experimental results show that our proposed system can provide centimeter localization precision for drone landing applications. With the device-free feature, system resources can be effectively utilized and costs are greatly reduced.
AutoFFT: a template-based FFT codes auto-generation framework for ARM and X86 CPUs

The discrete Fourier transform (DFT) is widely used in scientific and engineering computation. This paper proposes a template-based code generation framework named AutoFFT that can automatically generate high-performance fast Fourier transform (FFT) codes. AutoFFT employs the Cooley-Tukey FFT algorithm, which exploits the symmetric and periodic properties of the DFT matrix as the outer parallelization framework. To further reduce the number of floating-point operations of butterflies, we explore more symmetric and periodic properties of the DFT matrix and formulate two optimized calculation templates for prime and power-of-two radices. To fully exploit hardware resources, we encapsulate a series of optimizations in an assembly template optimizer. Given any DFT problem, AutoFFT automatically generates C FFT kernels using these two templates and transfers them to efficient assembly codes using the template optimizer. Experiments show that AutoFFT outperforms FFTW, ARMPL, and Intel MKL on average across all FFT types on ARMv8 and Intel x86-64 processors.
Dealing with a hairy beast–larval morphology and chaetotaxy of the Australian endemic diving beetle genus Spencerhydrus (Coleoptera, Dytiscidae, Cybistrini)

Abstract In this contribution, the larval morphology of Spencerhydrus Sharp, 1882 was studied, an Australian endemic genus in the diving beetle tribe Cybistrini. All instars of the only two species included in the genus (S. latecinctus Sharp, 1882 and S. pulchellus Sharp, 1882) are described and illustrated with the exception of the third instar of S. latecinctus. Detailed morphometric and primary chaetotaxic analyses were performed to discover useful characters for generic diagnosis and species distinction. Spencerhydrus can be distinguished from other Cybistrini genera by the medial projection of frontoclypeus slightly indented apically, with lamellae clypeales directed forward in a characteristic V-shaped pattern, the median process of prementum strongly developed, the presence of a single ventral sclerite on prothorax, the presence of basoventral spinulae on claws, and the reduced sclerotization of the abdominal segment VII which covers only the anterior half. Larvae of the two species of Spencerhydrus can readily be distinguished by the shape of the median process of prementum, which is visibly broader in S. pulchellus than in S. latecinctus.
Prevalence, awareness and factors associated with hypertension among adult residents in Lagos: a population based study.

Background: Hypertension is a global public health problem in both developed and developing countries. It is estimated that 75% of the world hypertensive patients in 2025 will be in developing Original Research Article Daniel et al.; BJMMR, 8(10): 874-882, 2015; Article no.BJMMR. 2015.518 875 countries including Nigeria. There is need to understand the nature of the epidemic and institute control measures to reverse the increasing tide of the disease in developing countries. Methodology: A descriptive cross-sectional survey carried out among Lagos residents between May and June 2012 in four Local Government Areas namely Alimosho, Mushin, Ajegunle/Ifelodun and Kosofe. The study variables were age, sex, occupation, education, marital status, religion and BMI while the outcome variable were frequency of hypertension and mean blood pressure. Statistical analysis included the use of proportions, percentages, chi-square, ‘t’ test and logistic regression using SPSS version 21 Results: A total of 4088 adults aged between 20-81 years (male = 1465 and females= 2623) were included in this study. The overall prevalence of hypertension was 40.9%. Hypertension increased with age in both males and females. Of the 1673 respondents identified as having hypertension, only 316 (18.9%) respondents were previously aware of their diagnosis. Of the 316 known hypertensive patients, only 20 (6.3%) had their blood pressure within the normal range. Age, gender, marital status and BMI were socio-demographic factors associated with hypertension. Conclusion: The prevalence of hypertension and obesity were found to be high in Lagos state. The need for public health actions to avert the consequences of the high prevalence of hypertension.cannot be over-emphasized.
Tamoxifen Blocks the Development of Motivational Features of an Addiction-Like Phenotype in Female Rats

Women become addicted sooner after initiating cocaine use as compared to men. Preclinical studies reveal a similar vulnerability in females, with findings from ovariectomized rats suggesting that estradiol mediates the enhanced vulnerability. However, since ovariectomy depletes not only estradiol, but all ovarian hormones, its role in a physiological context is not clear. Thus, the goal of this study was to determine the role of estradiol in the development of an addiction-like phenotype in ovary-intact females treated chronically with the selective estrogen receptor (ER) modulator tamoxifen. We hypothesized that tamoxifen, by antagonizing ERs, would block the development of an addiction-like phenotype as defined by an enhanced motivation for cocaine (assessed under a progressive-ratio schedule), and a heightened vulnerability to relapse (assessed under an extinction/cue-induced reinstatement procedure). Effects were examined following extended access cocaine self-administration (24-h/day; 4-discrete trials/h; 1.5 mg/kg/infusion) and 14-days of abstinence, conditions optimized for inducing an addiction-like phenotype. As predicted, motivation for cocaine was increased following extended-access self-administration and protracted abstinence in the vehicle (sesame oil) and no-injection control groups, but not in the tamoxifen group indicating that ER signaling is critical for the development of this feature of an addiction-like phenotype. Surprisingly, the increase in motivation for cocaine following abstinence was also attenuated in the vehicle group as compared to no-injection controls suggesting that oil/injections also affected its development. Contrary to our hypothesis, tamoxifen did not decrease vulnerability to relapse as this group responded at similar levels during initial extinction sessions and cue-induced reinstatement testing as compared to controls. Tamoxifen did, however, impair extinction learning as this group took longer to extinguish as compared to controls. Taken together, these findings indicate that estradiol is critical for the extinction of drug-associated cues and the development of motivational features of addiction.
Sorting out valuation in the charity shop

Recent work within HCI and CSCW has become attentive to the politics of data and metrics in order to highlight the implications of what counts and how. In this paper, we relate these discussions to the longstanding distinctions made between value and values. We introduce literature on ‘Valuation Studies’ and argue for understanding the politics of data through valuation – an ongoing social practice that transforms socially embedded values into different forms of more abstract value. This theoretical work is developed through an ethnographic study of contemporary UK charity shops, as a site focused on the labour of valuation, but embedded in both local and global values. Through this study, we consider implications for the intervention and design of ‘data-driven innovation’, with a particular focus on distributed ledger technologies. We argue that these technologies inevitably engage in valuation, and require careful attention to the ongoing processes by which value is translated and performed by different stakeholders.