Miracle-dz/UniFAR-FTUs huggingface dataset:
Vitamin B12 and Depression

Depression is a real illness that impacts the brain: It is a serious illness caused by changes in brain chemistry. Research tells us that other factors contribute to the onset of depression, including genetics, changes in hormone levels, certain medical conditions, stress, grief or difficult life circumstances. Any of these factors alone or in combination can precipitate changes in brain chemistry that lead to depression’s many symptoms. Preliminary results suggest a relationship between vitamin B12 deficiency and increased levels of fatigue and depression in stroke patients. If these findings could be replicated in a larger and general stroke sample, this would open treatment options and may improve quality of life after stroke. High vitamin B-12 status may be associated with better treatment outcome of depression. One possible connection is the effect of vitamin B-12 on the levels of serotonin in your brain, in addition to other chemicals. Serotonin helps to regulate your mood. Low levels of serotonin may be linked to depression. However, recent research has suggested that serotonin may play a smaller role in depression than previously thought. Background/Objective: Recent literature has identified links between vitamin B12 deficiency and depression. We compared the clinical response of SSRI-monotherapy with that of B12-augmentation in a sample of depressed patients with low normal B12 levels who responded inadequately to the first trial with the SSRIs. Methods: Patients with depression and low normal B12 levels were randomized to a control arm (antidepressant only) or treatment arm (antidepressants and injectable vitamin B12 supplementation). The researchers suggest people with depression take a 1milligram supplement of B-12 daily. More research is needed to fully understand the link between B-12 and depression. Results: A total of 100 depressed patients were screened. Out of 76 patients with low normal B12 levels (76%) were randomized to the treatment group while 24 (24%) were randomized to the control arm. At three months follow up 100% of the treatment group showed at least a 20% reduction in HAM-D score, while only 69% in the control arm showed at least a 20% reduction in HAM-D score (p<0.001). The findings remained significant after adjusting for baseline HAM-D score (p=0.001). Hamilton Rating Scale for Depression the Hamilton Rating Scale for Depression (HRSD), also called the Hamilton Depression Rating Scale (HDRS), abbreviated HAM-D, is a multiple item questionnaire used to provide an indication of depression, and as a guide to evaluate recovery.
Posterior Probabilities: Nonmonotonicity, Log-concavity, and Turán’s Inequality

In the standard Bayesian framework the data are assumed to be generated by a distribution parametrized by θ in a parameter space Θ, over which a prior distribution π is defined. A Bayesian statistician quantifies the belief that the true parameter is θ0 in Θ by its posterior probability given the observed data. We investigate the behavior of the posterior belief in θ0 when the data are generated under some parameter θ1, which may or may not be be the same as θ0. Starting from stochastic orders, specifically, likelihood ratio dominance, that obtain for resulting distributions of posteriors, we consider monotonicity properties of the posterior probabilities as a function of the sample size when data arrive sequentially. While the θ0-posterior is monotonically increasing (i.e., it is a submartingale) when the data are generated under that same θ0, it need not be monotonically decreasing in general, not even in terms of its overall expectation, when the data are generated under a different θ1; in fact, it may keep going up and down many times. In the framework of simple iid coin tosses, we show that under certain conditions the overall expected posterior of θ0 eventually becomes monotonically decreasing when the data are generated under θ1 6= θ0. Moreover, we prove that when the prior is uniform this expected posterior is a log-concave function of the sample size, by developing an inequality that is related to Turán’s inequality for Legendre polynomials.
Biochemical Characterization and Antibiotic Susceptibility of Staphylococcus aureus from Lame Broiler Chicken and Mastitic Cows of Sylhet City, Bangladesh

Lameness & mastitis are recognized as one of the most important disease affecting the broiler chicken and dairy cattle respectively. The study was performed to observe the biochemical characters and antibiotic susceptibility of Staphylococcus aureus (S. aureus) causing lameness in broiler chickens and mastitis in cows. Characters of the isolates were determined by biochemical tests such as mannitol fermentation, catalase, coagulase test. To investigate the antibiogram profiling, frequently used antibiotic discs were tested. From this study, it was observed that at least 84% isolates of S. aureus from lame broiler chicken and mastitic cow’s milk sample showed positive result in mannitol fermentation test, catalase and coagulase test respectively. But all isolates showed negative result in oxidase and motility test. Interestingly all the bacterial isolates from both broiler and milk sample showed different antibiogram profiling. Antibiotic susceptibility testing suggested that approximately more than 45-65% of the isolates were resistant to penicillin and about 25% resistant to amoxicillin in all isolates. The study reveals that more than 32% isolates were susceptible to methicillin resistant S. aureus (MRSA) and 35% chicken isolates were vancomycin resistant. Maximum resistance was observed against azithromycin (68%) of mastitic cow isolates. The present study also showed that S. aureus was most sensitive to ciprofloxacin (86-100%). Based on results of this study we conclude that drug resistance of S. aureus may vary with the antibiotics being used and with different population.
SPS Newsletter 2 pdfsubject=Stochastic Programming Society Newsletter pdfkeywords=Stochastic Optimization, Optimization under Uncertainty pdfauthor=Güzin Bayraksan and Francesca Maggioni, Editors

Over the course of its first year, the new Committee on Stochastic Programming (COSP) has created the Stochastic Programming Society (SPS) Twitter and LinkedIn accounts. Our aim was to increase communication between our members. Little did we know that these would be invaluable during the pandemic. In addition, last summer, we organized a Virtual Seminar Series and, with the permission of speakers, put the videos of the talks on our newly established YouTube channel. I am happy to report that, in addition to hundreds of live attendees, as of this writing, our virtual seminar series have been collectively viewed more than 5500 times on our society’s YouTube channel.
Obesity: Medical Consequences and Treatment Strategies

DOI: 10.21276/haya.2017. 2.8. 2 Abstract: Obesity, a pathologic state characterised by excess fat reserves, is a serious health problem that increases the risk of numerous medical complications and mortality. The disease arises as a direct consequence of alterations in regulating energy balance in the body. The recent global rise in the prevalence of Obesity and unavailability of effective anti-obesity drugs has created an urgent need to understand and identify the important aspects related to the disease. In this review, we provide a perspective on the factors influencing Obesity, Obesity-associated disorders, pharmacological interventions and several other important concepts related to the disease.
Metabolic Physiology, Locomotor Performance, and Thermal Niche Breadth in Neotropical Anurans

The metabolic physiology of five high-elevation anurans in the Colombian Andes (Atelopus sp. nov., Atelopus muisca, Colostethus subpunctatus, Eleutherodactylus bogotensis, and Hyla labialis) was analyzed for correlates with ife in an extreme and variable environment. I compared populations of these species at 3,500 m and 2,900 m and contrasted the results with those from lowland congenerics from Panama (Atelopus varius, Colostethus talamancae, Colostethus flotator, Eleutherodactylus diastema, Hyla microcephala, and Hyla ebraccata). I collected data on the effects of temperature on locomotor performance, the thermal sensitivity of resting and activity metabolism, and the capacity for metabolic acclimation. All five high-elevation species were able to swim through a broad temperature range, whereas low-elevation species showed little ability to move at low temperatures. None of the species studied exhibited a capacity for metabolic acclimation. However, activity metabolic rates and aerobic metabolic scopes at low temperatures were always higher in the high-elevation frogs. Apparently, similar physiological traits that facilitate activity at the low and variable temperatures that characterize the high-elevation environment evolved independently in the four genera studied.
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters

Gaussian processes (GPs) are ﬂexible priors for modeling functions. However, their success depends on the kernel accurately reﬂecting the properties of the data. One of the appeals of the GP framework is that the marginal likelihood of the kernel hyperparameters is often available in closed form, enabling optimization and sampling procedures to ﬁt these hyperparameters to data. Unfortunately, point-wise evaluation of the marginal likelihood is expensive due to the need to solve a linear system; searching or sampling the space of hyperparameters thus often dominates the practical cost of using GPs. We introduce an approach to the identiﬁcation of kernel hyperparameters in GP regression and related problems that sidesteps the need for costly marginal likelihoods. Our strategy is to “amortize” inference over hyperparameters by training a single neural network, which consumes a set of regression data and produces an estimate of the kernel function, useful across diﬀerent tasks. To accommodate the varying dimension and cardinality of diﬀerent regression problems, we use a hierarchical self-attention-based neural network that produces estimates of the hyperparameters which are invariant to the order of the input data points and data dimensions. We show that a single neural model trained on synthetic data is able to generalize directly to several diﬀerent real-world GP use cases. Our experiments demonstrate that the estimated hyperparameters are comparable in quality to those from the conventional model selection procedures, while being much faster to obtain, signiﬁcantly accelerating GP regression and its related applications such as Bayesian optimization and Bayesian quadrature.
Evaluating the Determinants of Irish Inflation

During the period from 1970 to the early 2000s, there was a consensus that Irish inflation was primarily determined by external factors. In this letter, we evaluate the relative importance of domestic factors, external factors, and inflation expectations and specify how these have changed over time. We find that since the crisis, external factors remain the most important determinants of inflation. However, domestic factors such as labour slack have been increasingly important in recent years. Evaluating published forecasts against a benchmark statistical model, the Central Bank performs best at shorter horizons, i.e. less than one year. This validates an approach that combines model-evidence with expert judgement.
Software Defects Prediction Using Support Vector Machine

In the field of software engineering, there are a number of prediction methods that are fault estimation, security estimation, effort estimation, correlation cost estimation, re-usability estimation, test effort estimation and quality estimation. These methods are helps to reduce the cost of testing which ultimately reduce the cost of the project. One of the most important stage is testing of software which reduces the defects of the software. Software defects are predicted by a software developers or testers at an early stage of software development life cycle and it reduces the overall time, cost and effort of the software development team. Nowadays, defect prediction methods are based on an adequate quantity of historic project data. Many researchers are using NASA’s PROMISE dataset to develop prediction techniques for various phases of software development. We use the dataset available for defect prediction. This work aims to achieve high prediction accuracy by applying Support Vector Machine based technique. In this research work, our primary target will be to focus on making a MATLAB based interface to predict software defects. This interface will implement SVM as the underlying algorithm and will be trained and tested using ANT-1.7 dataset.
RF Heating of Implants in MRI: Electromagnetic Analysis and Solutions

Recently, a magnetic resonance imaging (MRI) has become an indispensable medical imaging device. Unlike a computed tomography (CT) system, the MRI has an advantage of acquiring an anatomical image using a magnetic field without tissue ionizing. However, as a patient taking MRI scan is exposed to a much stronger electromagnetic field than in everyday life, there is a risk of unwanted electromagnetic reactions in the human body. Except for an interaction with a static magnetic field (B0) generated by a main magnet, RF heating, increase of the tissue temperature due to a RF magnetic field (B1) transmitted by RF coil, is the major concern. In this review article, some studies related to the RF heating in the MRI are extensively analyzed. Especially, researches regarding the artificial products such as implantable medical devices (IMDs) and tattoos which aggravate the RF heating are mainly discussed in the article.
Constrained Video Face Clustering using1NN Relations

In this work, we introduce the Constrained ﬁrst nearest neighbour Clustering (C1C) method for video face clustering. Using the premise that the ﬁrst nearest neighbour (1NN) of an instance is sufﬁcient to discover large chains and groupings, C1C builds upon the hierarchical clustering method FINCH by imposing must-link and cannot-link constraints acquired in a self-supervised manner. We show that adding these constraints leads to performance improvements with low computational cost. C1C is easily scalable and does not require any training. Additionally, we introduce a new Friends dataset for evaluating the performance of face clustering algorithms. Given that most video datasets for face clustering are saturated or emphasize only the main characters, the Friends dataset is larger, contains identities for several main and secondary characters, and tackles more challenging cases as it labels also the ‘back of the head’. We evaluate C1C on the Big Bang Theory, Buffy, and Sherlock datasets for video face clustering, and show that it achieves the new state of the art whilst setting the baseline on Friends.
Project Cost Estimation : Issues and the Possible Solutions

The process of developing a comprehensive project cost estimate is critical for a project to be adjudged successful on completion. Projects’ costing is one of the most critical and most widely used project management tools. The complex nature of Projects and the inherent uncertainty of the financial performance of construction projects, development funding, and the monitoring and controlling of costs and schedules make exact budget needs impossible to forecast accurately. This same characteristic also makes projects to deviate from plans. The main object of this paper is to identify the factors affecting the accuracy of project cost estimation, determine the various methods of carrying out project cost estimation in construction projects within Owerri, Imo State. The study is motivated by the inability of most construction professionals to arrive at a tentative and reliable project cost estimate in project realization which has created obvious problems of project cost overrun and subsequent abandonment. The study sampled the opinion of fifty-three selected project professionals who had worked on related construction outfits in Owerri, Imo State. An objective realization instrument developed using eighteen (18) factors identified in the literature as possible factors affecting the accuracy of project cost estimation were ranked based on a Likert four-point scale. The score of respondents to the factors were analyzed using descriptive and inferential statistics, mean score value and factor analytical approach as the major tool with the aid of SPSS. Firstly, results of the analysis among others shows that the most frequently utilized method for carrying out project cost estimation is the detailed estimating method with a mean score value of 3.491. Secondly, results of the factor analysis among others shows that the type and nature of project is the most critical factor affecting the accuracy of project cost estimation in Owerri. This was followed by in that order; number of bidders tendering, location of project, closure and blockade of borders, scale and scope of construction, materials price availability, contractor’s workload, constraints on site, detailed drawings and specifications, conditions of the market, and buildability.
Sexual Selection or Natural Selection? New Look on the Evolution of Human Morphology, Behavior and Art

The author of the article argues that, contrary to Charles Darwin’s assertion, sexual selection played only a marginal role in the early evolution of the Homo sapiens. Natural selection through the mechanisms of predator control is suggested to be the central reason behind the crucial evolutionary changes of human morphology (appearance of longer legs, head hair, eyebrows, low male voice, reduction of canines) and behavior (bipedalism, singing, dancing, painting).
Performance and Impact of Prostate Specific Membrane Antigen-Based Diagnostics in the Management of Men with Biochemical Recurrence of Prostate Cancer and Its Role in Salvage Lymph Node Dissection

Up to 50% of patients initially treated for prostate cancer in a curative intent experience biochemical recurrence, possibly requiring adjuvant treatment. However, salvage treatment decisions, such as lymph node dissection or radiation therapy, are typically based on prostate specific antigen (PSA) recurrence. Importantly, common imaging modalities (e.g., computed tomography [CT], magnetic resonance imaging, and bone scan) are limited and the detection of recurrent disease is particularly challenging if PSA is low. Prostate specific membrane antigen (PSMA) positron-emission tomography/computed tomography (PET/CT) is a novel and promising imaging modality which aims to overcome the incapability of early identification of distant and regional metastases. Within this review, we summarize the current evidence related to PSMA-PET/CT in prostate cancer men diagnosed with biochemical recurrence after local treatment with curative intent. We discuss detection rates of PSMA-PET/CT stratified by PSA-levels and its impact on clinical decision making. Furthermore, we compare different image-fusion techniques such as PSMA-PET vs. F-/C-Choline-PET scans vs. PSMA-single photon emission computed tomography/CT. Finally, we touch upon the contemporary role of radio-guided-PSMA salvage lymphadenectomy.
The Role of Social Support Networks Could Develop on the Well-Being and Mental Health toward COVID-19 Pandemic: A

Attribution-Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Global Journal of HUMAN-SOCIAL SCIENCE: C Sociology & Culture Volume 20 Issue 6 Version 1.0 Year 2020 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 2249-460x & Print ISSN: 0975-587X
Machine Learning For Predicting Renewable Energy Generation

The increasing need to preserve the environment by reducing the emissions of CO2 and other greenhouse gases highlighted the need work in the energy field. It embraces many other sectors, such as transport, building, ICT and the need for energy management is going to become preponderant, above all with the penetration of renewable energy resources and with the advent of the Smart Grids. This work studies the problem of predicting renewable energy generation focusing on solar energy. The thesis investigates if physical data, such as the Global Solar Irradiance and its components, allow to obtain good performances in predicting solar energy generation. The solution of the problem has been reached by using two types of machine learning techniques, Feed-Forward and Recurrent Neural Network. With them, several training approaches were tested and the Recurrent NN was the one which performed better. Then, it was tested in different locations and time granularity, hourly and half hour. In the end, Neural Networks results extremely effective in predicting renewable energy generation and the work can positively affect several domains, such as pricing strategies, critical situation, emergencies during a blackout, and in energy management and for cost reduction. CHAPTER 0 Introduction IPCC (Intergovernmental Panel on Climate Change) defined climate change as a variation in either the mean state of the climate or its variability for an extended period (decades or longer), and it may be due to whether natural internal processes of the earth or human activities. The idea of anthropogenic changes spread out and recently penetrated popular culture, with a growing political resonance. Indeed, humans are increasingly influencing the climate and the earth’s temperature. The global raising temperature is affected by the enormous amount of emission gases produced by human activities, responsible for 64% of man-made global warming. The most popular activities involve cutting down forests, livestock farming, burning fossil fuel, and Figure 1 shows quantitative values of CO2 emissions due to human activities. Energy efficiency improvements, technological advancement, market dynamics, worries about climate change are leading during last years to a decoupling between CO2 emissions and economic growth. They aim to carry on decarbonization or a huge reduction of CO2 emissions. In parallel, however, technological systems are continuously increasing their environmental impact and from one side it is due to computational improvements which enable the release of new services; on the other side, it is due to the exponential usage from people of these services. For instance, the advent of Internet during the ’90s, and consequently of the Cloud Computing, Internet Of Things (IoT), Social Media and so on, increased the access and the
Second gradient vs. hyper-pre-stress for surface relaxation in diamond-like structures

In this paper, based on the discrete valence force field model for diamond-like crystals we construct and compare predictions of two models with cubic symmetry containing an intrinsic internal length. The first model is constructed using lattice dynamics and the classical polynomial approximation of the acoustic branches near the Γ point. We show that the second-gradient continuum approximation obtained in this way leads to unphysically small internal lengths, enforcing a result obtained previously recently in [1]. The second model follows a line studied in the one-dimensional context in [2]. It takes into account incompatible reference lengths in the discrete setting and is able to model both cohesion, surface energy and defects. We study the ground state of an infinite lattice and we conclude that as expected, for parameters in the range of interest, a boundary-layer effect occur. We explore numerically using the second model : (a) the average lattice parameter of bulk-like, plate-like and beam-like nano-structures and (b) we predict lattice parameter variations as a function of porosity in porous silicon. We conclude that the anisotropic three-dimensional model with hyper-pre-stress provides a realistic model for mechanics of nanostructures.