Miracle-dz/UniFAR-FTUs huggingface dataset:
The Spectroscopic Variability of GRB 021004

We present spectra of the optical transient (OT) associated with GRB 021004. The spectra show a blue continuum with superposed absorption features and one emission line. We confirm two intervening metal-line systems at z = 1.380 and z = 1.602 and one very strong absorption system at a redshift of z = 2.323. Lyα emission is also seen at this redshift. While the spectrum of the OT overall cannot be simply described with a power law, the spectral index over the range 5500-8850 Å is steep, Fν ∝ ν-0.96±0.03. Comparison of spectra from multiple epochs shows a distinct color evolution with the OT becoming redder with time over the first 3 days. This is the first clear example of color change in an OT detected spectroscopically.
Automated Biomedical Text Classification with Research Domain Criteria

Research Domain Criteria (RDoC) is a recently introduced framework for accurate diagnosis of mental illness. Developing a method to automate the process of labeling biomedical articles with RDoC constructs is highly useful to advance research efforts in the area of mental illness. Therefore, this study focuses on exploring the feasibility of developing a tool for this purpose. Using a gold-standard dataset of about 40,000 Medline abstracts annotated with 26 RDoC constructs, we model this both as a binary and a multilabel classification problem, to perform document classification using several supervised learning algorithms. We use a simple bag-of-words model with standard preprocessing steps such as stemming and stop words removal. According to a performance evaluation obtained through 5-fold cross validation, we observe that overall, multilabel Artificial Neural Networks classifier performs best with an excellent average AUROC of 96% across all the constructs. Interestingly, all the binary classifiers also show a very high-level of performance. However, the cohort of binary classifiers take significantly longer times to train compared to their multilabel counterparts, showing the utility of modeling this as a multilabel problem. This is the first study that focuses on predicting RDoC constructs for biomedical literature. keywords: Research Domain Criteria, mental illness, machine learning, document classification, Artificial Neural Networks, Support Vector Machines.
Term Structure of Interest Rates with Regime Shifts 1

We develop a term structure model where the underlying short term interest rate and the market price of risk are modeled as square-root processes which are subject to discrete regime shifts. Our model captures the idea that the aggregate economy is subject to risks from discrete and persistent changes in the business cycle. We provide a solution for the nominal default-free yield curve in this economic environment. Exploiting these analytical solutions we estimate our model via the Efficient Method of Moments (EMM) using short and long interest rate data from 1964 to 1995. Our empirical evidence provides considerable support to the regime shifts model. Standard term structure models, which includes affine models with up to three factors, are sharply rejected in the data. Our diagnostics, based on computing the model-based transition density for yields, show that only the regime switching model can reasonably justify the observed conditional volatility and conditional correlation of yields. Further, the estimated regime-switching model is capable of justifying the well documented violations of the expectations hypothesis. In addition, this model also produces the smallest absolute pricing error in fitting the yield curve. We find that regimes are correlated with business cycles—differences across regimes are reflected in the short rate volatility and the yield spread.
Joint regression and classification via relational regularization for Parkinson’s disease diagnosis

It is known that the symptoms of Parkinson’s disease (PD) progress successively, early and accurate diagnosis of the disease is of great importance, which slows the disease deterioration further and alleviates mental and physical suffering. In this paper, we propose a joint regression and classification scheme for PD diagnosis using baseline multi-modal neuroimaging data. Specifically, we devise a new feature selection method via relational learning in a unified multi-task feature selection model. Three kinds of relationships (e.g., relationships among features, responses, and subjects) are integrated to represent the similarities among features, responses, and subjects. Our proposed method exploits five regression variables (depression, sleep, olfaction, cognition scores and a clinical label) to jointly select the most discriminative features for clinical scores prediction and class label identification. Extensive experiments are conducted to demonstrate the effectiveness of the proposed method on the Parkinson’s Progression Markers Initiative (PPMI) dataset. Our experimental results demonstrate that multi-modal data can effectively enhance the performance in class label identification compared with single modal data. Our proposed method can greatly improve the performance in clinical scores prediction and outperforms the state-of-art methods as well. The identified brain regions can be recognized for further medical analysis and diagnosis.
The Use of Participant-Observers in Group Therapy: A Critical Exploration in Light of Foucauldian Theory

Abstract:This article examines the role of participant-observers in group therapy by focusing on a case study of an educational therapy group. In analyzing the findings, I not only apply classics of psychotherapy and group theory but also borrow concepts from Foucauldian theory to elucidate the delicate effect of observation. This research uses a full participation method, together with interviews and document analysis. The study finds that observations have both functional and normative effects, such as providing feedback, regulating the group's dynamics, and socializing.
Towards Recommendation Using Interest-Based Communities in Attributed Social Networks

Social networks can be modeled as attributed networks whose nodes represent users, edges represent relationships among users (e.g. friendship/follow) and attribute vectors hold properties of nodes and/or edges. In this paper, we consider friends' recommendation based on interest-based communities generated from topic based attributed social networks (TbASN). In our model, an attribute vector is not just a container for explicit users' profile data that is stored in social network's dataset, but rather holds topic vectors that are derived from analyzing the implicit interest of users' that are aggregated from his/her posts on the social network (e.g. tweets in Twitter, posts in Facebook). In our framework, topics of interest are represented as a hierarchy of topics (Topics/Subtopics) forming hierarchical interest-based communities. Users within each interest-based community are clustered according to their profile features (age, location, education etc.). Those clusters are later used in recommendations where recommendations target members of the same cluster to guarantee the quality and coherence of recommendations. In addition, we propose a recommendation selection approach to handle the large number of recommended candidates. The main advantage of the proposed approach is that it considers multiple criteria for candidate selection including the number of common communities, the resemblance in basic features, as well as network proximity. In addition to recommending friends of similar interests, frequent pattern mining is used to discover frequently occurring interests in order to be used in recommending communities for users to join. Although our approach is generic and can be applied to most of the existing social networks, we used Twitter as our target social network.
A Case Study of Applying Decision Theory in the Real World: POMDPs and Spoken Dialog Systems

Spoken dialog systems present a classic example of planning under uncertainty. Speech recognition errors are ubiquitous and impossible to detect reliably, so the state of the conversation can never be known with certainty. Despite this, the system must choose actions to make progress to a long term goal. As such, decision theory, and in particular partiallyobservable Markov decision processes (POMDPs), present an attractive approach to building spoken dialog systems. Initial work on “toy” dialog systems validated the benefits of the POMDP approach; however, it also found that straightforward application of POMDPs could not scale to real-world problems. Subsequent work by a number of research teams has scaled up planning and belief monitoring, incorporated high-fidelity user simulations, and married commercial development practices with automatic optimization. Today, statistical dialog systems are being fielded by research labs for public use. This chapter traces the history of POMDPbased spoken dialog systems, and sketches avenues for future work.
Effect of GnRH, hCG and P4 therapy on pregnancy rate of crossbred cattle.

To elucidate the effect of GnRH, hCG and P4 on conception and pregnancy rate in crossbred cows after artificial insemination (AI), a total of 40 lactating crossbred (Friesian x Sahiwal) cows were randomly divided into 4 groups (A, B, C, D) such that each group comprised of 10 animals. At 7 day after AI, group A was given GnRH (100 μg/animal IM), group B was given hCG (3300 IU/animal IM), group C were given P4 (0.5 mg/animal IM) and group D was given no treatment and served as control group. P4 estimation of all cows was done at the start of treatments and 7 day after treatment. All cows were examined for pregnancy by rectal palpation and uterus contents for detection of an embryonic vesicle on 40 ± 1 day after AI, and pregnant cows were re-examined 4 week later on 68 ± 1 day after AI. The mean pregnancy rate was significantly higher (P < 0.05) in cows of group B and C as compared to control whereas non-significant differences (P > 0.05) were observed between group A and D. There was non-significant difference in serum progesterone concentrations between pregnant animals of group A, B, C and D during 7 and 14 days of AI, whereas 7 days after treatment, there were significant differences in serum progesterone concentrations between pregnant and non-pregnant animals of all groups (A, B, C and D). It is concluded that hCG or progesterone could be used as a method for improving pregnancy rate in crossbred cattle. © 2018 Friends Science Publishers
Determination of Simvastatin in Pharmaceutical Dosage Forms by Optimized and Validated Method Using HPLC/UV

Simvastatin belongs to the group of anticholesterol agents used in the treatment of hypercholes- teraemia. Analytical methods used to determine the concentration of this active pharmaceutical ingredient (API), in dosage forms in the quality tests, are commonly based on high performance liquid chromatogra- phy (HPLC) and should be fast and reliable. The purpose of this study was to compare and validate two methods of analysis of simvastatin using HPLC and different eluent mixtures: acetonitrile/water vs. me- thanol/water in gradient elution. Several columns were tested at different temperatures. However satisfied peak shapes and validation parameters were obtained for both methods. The one using methanol as an eluent was chosen for the determination of simvastatin in dissolution tests, mostly due to lower price of the eluent. form of simvastatin (β-hydroxyacid) takes place in the liver by ring-opening reaction of the lacton. The inhibi- tion of the HMG-CoA causes a decrease in LDL, low- density lipoprotein (20-40 %), triglycerides (10-20 %), while it increases HDL, high-density lipoprotein (5- 15 %) and LDL receptor expression. 3,6 Due to this fact these compounds are the most commonly prescribed drugs for the prevention of atherosclerosis and heart disease, both as a prodrug or non-modified form. The fact that they can be used after heart attack and in co- existence of diabetes as well as in kidney dysfunction gives statins the status of a first choice drug. However, overdose of statins causes an increase of aminotransfe- rases concentration which can lead to myopathy. 3
On proving inductive properties of abstract data types

The equational axioms of an algebraic specification of a data type (such as finite sequences) often can be formed into a convergent set of rewrite rules; i.e. such that all sequences of rewrites are finite and uniquely terminating. If one adds a rewrite rule corresponding to a data type property whose proof requires induction (such as associativity of sequence concatenation), convergence may be destroyed, but often can be restored by using the Knuth-Bendix algorithm to generate additional rules. A convergent set of rules thus obtained can be used as a decision procedure for the equational theory for the axioms plus the property added. This fact, combined with a "full specification" property of axiomatizations, leads to a new method of proof of inductive properties--not requiring the explicit invocation of an inductive rule of inference.
Brain Activation During Working Memory Is Altered in Patients With Type 1 Diabetes During Hypoglycemia

OBJECTIVE To investigate the effects of acute hypoglycemia on working memory and brain function in patients with type 1 diabetes. RESEARCH DESIGN AND METHODS Using blood oxygen level–dependent (BOLD) functional magnetic resonance imaging during euglycemic (5.0 mmol/L) and hypoglycemic (2.8 mmol/L) hyperinsulinemic clamps, we compared brain activation response to a working-memory task (WMT) in type 1 diabetic subjects (n = 16) with that in age-matched nondiabetic control subjects (n = 16). Behavioral performance was assessed by percent correct responses. RESULTS During euglycemia, the WMT activated the bilateral frontal and parietal cortices, insula, thalamus, and cerebellum in both groups. During hypoglycemia, activation decreased in both groups but remained 80% larger in type 1 diabetic versus control subjects (P < 0.05). In type 1 diabetic subjects, higher HbA1c was associated with lower activation in the right parahippocampal gyrus and amygdala (R2 = 0.45, P < 0.002). Deactivation of the default-mode network (DMN) also was seen in both groups during euglycemia. However, during hypoglycemia, type 1 diabetic patients deactivated the DMN 70% less than control subjects (P < 0.05). Behavioral performance did not differ between glycemic conditions or groups. CONCLUSIONS BOLD activation was increased and deactivation was decreased in type 1 diabetic versus control subjects during hypoglycemia. This higher level of brain activation required by type 1 diabetic subjects to attain the same level of cognitive performance as control subjects suggests reduced cerebral efficiency in type 1 diabetes.
OPA1-anchored PKA phosphorylates perilipin 1 on S522 and S497 in adipocytes differentiated from human adipose stem cells

Optic atrophy 1 (OPA1) is the A-kinase anchoring protein targeting the pool of protein kinase A (PKA) responsible for perilipin 1 phosphorylation, a gatekeeper for lipolysis. However, the involvement of OPA1-bound PKA in the downstream regulation of lipolysis is unknown. Here we show up-regulation and relocation of OPA1 from mitochondria to lipid droplets during adipocytic differentiation of human adipose stem cells. We employed various biochemical and immunological approaches to demonstrate that OPA1-bound PKA phosphorylates perilipin 1 at S522 and S497 on lipolytic stimulation. We show that the first 30 amino acids of OPA1 are essential for its lipid droplet localization as is OMA1-dependent processing. Finally, our results indicate that presence of OPA1 is necessary for lipolytic phosphorylation of downstream targets. Our results show for the first time, to our knowledge, how OPA1 mediates adrenergic control of lipolysis in human adipocytes by regulating phosphorylation of perilipin 1.
2-Methyl-2,4-pentanediol (MPD) boosts as detergent-substitute the performance of ß-barrel hybrid catalyst for phenylacetylene polymerization

Covering hydrophobic regions with stabilization agents to solubilize purified transmembrane proteins is crucial for their application in aqueous media. The small molecule 2-methyl-2,4-pentanediol (MPD) was used to stabilize the transmembrane protein Ferric hydroxamate uptake protein component A (FhuA) utilized as host for the construction of a rhodium-based biohybrid catalyst. Unlike commonly used detergents such as sodium dodecyl sulfate or polyethylene polyethyleneglycol, MPD does not form micelles in solution. Molecular dynamics simulations revealed the effect and position of stabilizing MPD molecules. The advantage of the amphiphilic MPD over micelle-forming detergents is demonstrated in the polymerization of phenylacetylene, showing a ten-fold increase in yield and increased molecular weights.
Identification of Risk Factors Associated with Falls in the Long Term Care Setting

Background: Falls threaten the safety of older adults in long term care (LTC). Objectives: To assess environmental, clinical and pharmacological causes of falls as well as compare Fall Risk Assessment Score, Brief Interview of Mental Status (BIMS) score, Activities of Daily Living (ADL) scores, age and gender of residents with one fall, recurrent falls, and no falls. Methods: Using a descriptive-comparative design, we included a convenience sample of 290 adults ≥50 years old at our LTC facility. Fall and recurrent fall groups were matched to those with no falls. We assessed environmental, clinical and pharmacological variables, Fall Risk Assessment Scores, BIMS scores, ADL scores, age, and gender among those with one fall, recurrent falls, and no falls. Results: Among 290 residents, patients who fell had significantly more modifiable environmental (p<.05), clinical (p<.05), and pharmacological (p<.05) causes of falls. Fall risk scores were significantly higher for the initial falls (p=.02) group and the recurrent falls group (p<.001) compared to no fall. BIMS scores were significantly lower for the initial fall group compared to the no fall group (p=.03). For ADL bed mobility (p<.001), transfer (p=.01), eating (p<.001), and toilet use (p<.001), significantly more residents in the no falls group required extensive assistance compared to the recurrent falls group. There was no significant difference in age or gender among fall groups. Conclusions: Many of the significant variables found in the initial fall and recurrent fall groups are modifiable. LTC residents would benefit most from an ongoing multidisciplinary approach to falls risk reduction. IDENTIFICATION OF RISK FACTORS ASSOCIATED WITH 3
BUSINESS ORIENTATION AND GOVERNANCE CHOICES IN CULTURAL FIRMS: A SURVEY RESEARCH IN THE AREA OF NAPLES

The aim of the paper is to analyze the level of innovation in terms of market and business orientation within cultural firms, to gain competitiveness in the global market. We conducted a census on the population of cultural organizations in the area of Naples. We applied some univariate and bivariate descriptive statistics and multivariate statistical analysis. Results show that the selected cultural firms are still rather conservative, even if we identified three different clusters, with different levels of business and market orientation. Our paper adds content to ongoing research on the topic. It offers a quantitative analysis within a literature that is mainly based on conceptual works. Both the principles and the proposed methodology can be applied, mutatis mutandis, to other geographical contexts in the same sector for interesting comparisons. However, the analysis is focused on a specific area and the selected sample is heterogeneous in terms of firm’s typology (heritage sites, museums, monuments, etc.), reflecting the typical structure of Italian cultural offer.
Electronic band structure, optical, dynamical and thermodynamic properties of cesium chloride (CsCl) from first-principles

The geometric structural optimization, electronic band structure, total density of states for valence electrons, density of states for phonons, optical, dynamical, and thermodynamical features of cesium chloride have been investigated by linearized augmented plane wave method using the density functional theory under the generalized gradient approximation. Ground state properties of cesium chloride are studied. The calculated ground state properties are consistent with experimental results. Calculated band structure indicates that the cesium chloride structure has an indirect band gap value of 5.46 eV and is an insulator. From the obtained phonon spectra, the cesium chloride structure is dynamically stable along the various directions in the Brillouin zone. Temperature dependent thermodynamic properties are studied using the harmonic approximation model.
Parallel SECONDO: A practical system for large-scale processing of moving objects

Parallel Secondo scales up the capability of processing extensible data models in Secondo. It combines Hadoop with a set of Secondo databases, providing almost all existing SECONDO data types and operators. Therefore it is possible for the user to convert large-scale sequential queries to parallel queries without learning the Map/Reduce programming details. This paper demonstrates such a procedure. It imports the data from the project OpenStreetMap into Secondo databases to build up the urban traffic network and then processes network-based queries like map-matching and symbolic trajectory pattern matching. All involved queries were stated as sequential expressions and time-consuming in single-computer Secondo. However, they can achieve an impressive performance in Parallel Secondo after being converted to the corresponding parallel queries, even on a small cluster consisting of six low-end computers.
OVERVIEW OF THE RECENT DEVELOPMENTS ON GRAIN-SCALE MODELING TO SIMULATE ULTRASONIC SCATTERING WITH A 2 D FINITE ELEMENT CODE

Numerous studies were undertaken by EDF R&D for a few years to improve the NDT proces s on these applications and to help to their qualification. In many cases, the inspected component s may exhibit a coarse grain microstructure which induces severe beam disturbances an d reduces the reliability of the control. Moreover, modeling is more and more used to evaluate the b am to structure or to defect interaction and to perform parametric studies. However, the modeling of the scattering phenomena attenuation and structural noise – must be based on accurate input data for the material description. The methodology proposed by EDF R&D to simulate the inspection of coarse gra in structures is to combine the finite element code ATHENA 2D with a grain scale description of the materials. The method reproduces the beam perturbations, the attenuation and the appearance of struct ural noise due to scattering of the wave at grain boundaries. It enables to compute macroscopic attenuation coefficient. In the case of anisotropic structure like austenitic welds, the anisotr op c evolution of the attenuation in the inspection plane can be simulated. The performances and the limits of the grain-scale modeling approach are illust ated on several configurations. Firstly the influence of the microstructure model on the accuracy of s imulation results have been investigated on isotropic coarse grain INCONEL 600 components. Two specific illustrations are presented and compared to experimental results, which are the model ing of a multilayer isotropic structure (centrifugally cast stainless steel) and a complex anisot ropic structure (austenitic weld).