Miracle-dz/UniFAR-FTUs huggingface dataset:
DRUG PRESCRIBING PRACTICE AMONG ANAMEIC PATIENTS IN A TERTIARY CARE TEACHING HOSPITAL

Introduction Anaemia is a major public health problem in India affecting people in all age group with major consequences for human health as well as economic development. The epidemiology of anaemia in rural setting is not well known. Hence the study aims to access prevalence and prescribing pattern among inpatients attending teritary care teaching hospital in a rural setting. Methods: A prospective and observational study was carried out in 200inpatientsadmitted to Medicine, Pediatrics and OBG department diagnosed with anemia from September 2015 to March 2016.The ethical clearance was obtained from Institutional Ethical Committee of AHRC, B G Nagara. Results: Among 200 inpatients 171 patients (85.5%) were from medicine unit, 25 patients (12.5%) from OBG and 4 patients (2%) from pediatrics. The maximum patients in the study population were females (68.5%) and males (31.5%). Majority of the subjects were diagnosed with microcytic hypochromic anemia (58%) which was categorized to be mild in nature (40%) according to the WHO categorization of hemoglobin. As part of the therapy, Ferrous Sulphate-folic acid combinations (56 prescriptions) and folic acid-vitamin combinations (76 prescriptions) were most prescribed drugs. We found 41 drugdrug interactions (20.5%) in which28 drug drug interactions were accepted by physicians (68.2%). About 95% of the subject‟s condition improved as result of the therapy. Conclusions: This study clearly shows that prescribing pattern of drugs in anemia patients needs to be continuously evaluated in order to promote the more rational prescribing so as to decrease morbidity and cost of therapy to the patient. World Journal of Pharmaceutical Research SJIF Impact Factor 7.523 Volume 6, Issue 4, 858-887. Research Article ISSN 2277– 7105 *Corresponding Author Dr. Chippy Sara Eldho Sri Adichunchanagiri College of Pharmacy. B.G Nagara Karnataka. India 571448. Article Received on 23 Jan. 2017, Revised on 13 Feb. 2017, Accepted on 04 March 2017 DOI: 10.20959/wjpr20174-7997
Event-Triggered Reinforcement Learning; An Application to Buildings' Micro-Climate Control

Smart buildings have great potential for shaping an energyefficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. However, most learning methods for micro-climate control in buildings are based on Markov Decision Processes with fixed transition times that suffer from high variance in the learning phase. Furthermore, ignoring its continuing-task nature the micro-climate control problem is often modeled and solved as an episodic-task problem with discounted rewards. This can result in a wrong optimization solution. To overcome these issues we propose an eventtriggered learning control and formulate it based on SemiMarkov Decision Processes with variable transition times and in an average-reward setting. We show via simulation the efficacy of our approach in controlling the micro-climate of a single-zone building.
Surfzone Topography-informed Deep Learning Techniques to Nearshore Bathymetry with Sparse Measurements

Nearshore bathymetry, the knowledge of water depth in coastal zones, has played a vital role in a wide variety of applications including shipping operations, coastal management, and risk assessment. However, direct high resolution surveys of nearshore bathymetry are relatively difﬁcult to perform due to budget constraints and logistical restrictions. One possible approach to nearshore bathymetry without such limitations is the use of spatial interpolation with sparse measurements of water depth by using, for example, geostatis-tics. However, it is often difﬁcult for traditional methods to recognize patterns with a sharp gradient often shown on coastal sand bars, especially in the case of sparse measurements. In this work, we use a conditional Generative Adversarial Neural Network (cGAN) to generate abruptly changing bathymetry samples while being consistent with our sparse, multi-scale measurements. We train our neural network based on synthetic data generated from nearshore surveys provided by the U.S. Army Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We compare our method with Kriging on real surveys as well as ones with artiﬁcially added patterns of sharp gradient. Results show that our conditional Generative Adversarial Network provides estimates with lower root mean squared errors than Kriging in both cases.
Linear and nonlinear prediction techniques for short‐term forecasting of HF fading signals

There exist two major mechanisms which are responsible for the fading phenomenon at HF frequencies. They are the multiple‐mode interference and distortions due to the ionospheric irregularities. Fading time series produced by the first of these mechanisms alone should typically represent a multiple‐periodical process. This kind of signal may also be produced by an autonomous dynamical system. The character of the time series produced by the second mechanism depends on the nature of the ionospheric irregularities. Recently, evidence has been accumulated to show that sometimes the ionospheric turbulence on equatorial and middle latitudes represents a low‐dimensional deterministic chaotic process. These facts suggest that for both mechanisms the fading time series may have a deterministic nature and therefore is predictable. Accordingly we apply the nonlinear predicting technique proposed by Farmer and Sidorovich [1987] to the fading time series obtained by the University of Illinois HF sounder. In its application the prediction technique is modified to take into account specifics of the HF data. For comparison, the conventional linear autoregression prediction technique is also tested. It is found that, in general, the nonlinear prediction and the linear autoregressive forecasting allow prediction on a few correlation times and work with roughly the same success.
Identifying Instructional Methods for Development of Clinical Reasoning in Entry-Level Occupational Therapy Education: A Mixed Methods Design

Occupational therapy education must teach using contemporary and evidence-based practices that yield graduates with clinical reasoning skills to successfully practice in dynamic and challenging environments. Researchers used a mixed-methods research design to identify the most frequently used and valued instructional methods for developing clinical reasoning with entry-level occupational therapy students. Researchers recruited full-time educators teaching in entry-level occupational therapy programs throughout the United States. Ninety-two occupational therapy educators completed the survey for the quantitative portion of the study. Subsequently, six occupational therapy educators participated in an interview for the qualitative portion of the study. Participants most frequently used laboratory experiences and least frequently used rotating chair discussion for developing clinical reasoning. Participants perceived experiential learning as the most valuable and rotating chair discussion as the least valuable instructional method for developing clinical reasoning. The three themes of the educator, the student, and the environment emerged from the qualitative data. Outcomes suggest occupational therapy educators must embrace the role of facilitator and continue to explore a variety of effective instructional methods. In order to achieve this role, occupational therapy educators need to engage in personal and professional development. Researchers provide additional strategies for developing the clinical reasoning skills required for successful occupational therapy practice.
Hybrid multi-agent systems: modeling, specification and verification

Specifying behaviors of multi-agent systems (MASs) is a demanding task, especially when applied in safety-critical systems. In the latter systems, the specification of behaviors has to be carried out carefully in order to avoid side effects that might cause unwanted or even disastrous behaviors. Thus, formal methods based on mathematical models of the system under design are helpful. They not only allow us to formally specify the system at different levels of abstraction, but also to verify the consistency of the specified systems before implementing them. The formal specification aims a precise and unambiguous description of the behavior of MASs, whereas the verification aims at proving the satisfaction of specified requirements. A behavior of an agent can be described as discrete changes of its states with respect to external or internal actions. Whenever an action occurs, the agent moves from one state to another one. Therefore, an efficient way to model this type of discrete behaviors is to use a kind of state transition diagrams such as finite automata. One remarkable advantage of such transition diagrams is that they lend themselves formal analysis techniques using model checking. The latter is an automatic verification technique which determines whether given properties are satisfied within a model underlying a particular system. In realistic physical environments, however, it is necessary to consider continuous behaviors in addition to discrete behaviors of MASs. Examples of those type of behaviors include the movement of a soccer agent to kick off or to go to the ball, the process of putting out the fire by a fire brigade agent in a rescue scenario, or any other behaviors that depend on any timed physical law. The traditional state transition diagrams are not sufficient to combine these types of behaviors. Hybrid automata offer an elegant method to capture such types of behaviors. Hybrid automata extend regular state transition diagrams with methods that deal with those continuous actions such that the state transition diagrams are used to model the discrete changes of behaviors, while differential equations are used to model the continuous changes. The semantics of hybrid automata make them accessible to formal verification by means of model checking. The main goal of this thesis is to approach hybrid automata for specifying and verifying behaviors of MASs. However, specifying and and verifying behaviors of MASs by means of hybrid automata raises several issues that should be considered. These issues include the complexity, modularity, and the expressiveness of MASs' models. This thesis addresses these issues and provides possible solutions to tackle them.
Detecting small bowel strangulation using circulating cell-free DNA with machine learning

Small bowel obstruction is a common acute abdomen condition which can play a role in death. Small bowel strangulation (SBS) refers to a small bowel obstruction associated with bowel ischemia. Patients with SBS need emergency surgery because the ischemic small bowel can become necrotic in a short time, causing sepsis and death. Nowadays the “gold standard” for diagnosing SBS is via computed tomography (CT) scanned images. However, an easier way to detect SBS is desired among emergency medicine physicians. Thus, we tried to develop a rapid test using circulating cell-free DNA (ccfDNA). ccfDNA is part of the DNA from a cell that died because of apoptosis or necrosis. The size of ccfDNA varies depending on the origination of cell death. If a patient has SBS, long-size ccfDNA would appear in the peripheral blood. We used data including the concentration of ccfDNA in the blood of certain patients as training data to make a support vector machine, a decision tree, and a learned random forest. We evaluated these classifiers using leave-one-out cross-validation. These machine-learning methods performed well. In addition, the decision tree and random forest results indicate that long-size ccfDNA is important for classifying SBS. In this paper, we demonstrate that machine learning can be an alternative method for detecting SBS and that the concentration of ccfDNA, especially long ccfDNA, contributes to detecting SBS.
An Efficient Face Recognition Using SIFT Descriptor in RGB-D Images

Automatic face recognition has known a very important evolution in the last decade, due to its huge usage in the security systems. The most of facial recognition approaches use 2D image, but the problem is that this type of image is very sensible to the illumination and lighting changes. Another approach uses the 3D camera and stereo cameras as well, but it’s rarely used because it requires a relatively long processing duration. A new approach rise in this field, which is based on RGB-D images produced by Kinect, this type of cameras cost less and it can be used in any environment and under any circumstances. In this work we propose a new algorithm that combines the RGB image with Depth map which is less sensible to illumination changes. We got a recognition rate of 96, 63% in rank 2.
Reconstruction of Multi-Spectral Images from the AEOS Spectral Imaging Sensor

Spectral images of terrestial objects has provided a wealth of information beyond the tradidtional data in a panchromatic image. What has proven to be successful for terrestial applications, can also be applied to images of objects in space. This research expands on previous work discussing the image processing for data collected by the new AEOS Spectral Imaging Sensor (ASIS). ASIS uses broadband filters to collect AO compensated spectral images of astronomical objects and satellites. The use of the broadband filters spectrally blurs the images collected by ASIS. A post-processing algorithm called Model-Based Spectral Image Reconstruction (MBSIR) can simultaneously remove much of the spatial and spectral blurring introduced by the sensor.
Mapping 22q11.2 Gene Dosage Effects on Brain Morphometry

Reciprocal chromosomal rearrangements at the 22q11.2 locus are associated with elevated risk of neurodevelopmental disorders. The 22q11.2 deletion confers the highest known genetic risk for schizophrenia, but a duplication in the same region is strongly associated with autism and is less common in schizophrenia cases than in the general population. Here we conducted the first study of 22q11.2 gene dosage effects on brain structure in a sample of 143 human subjects: 66 with 22q11.2 deletions (22q-del; 32 males), 21 with 22q11.2 duplications (22q-dup; 14 males), and 56 age- and sex-matched controls (31 males). 22q11.2 gene dosage varied positively with intracranial volume, gray and white matter volume, and cortical surface area (deletion < control < duplication). In contrast, gene dosage varied negatively with mean cortical thickness (deletion > control > duplication). Widespread differences were observed for cortical surface area with more localized effects on cortical thickness. These diametric patterns extended into subcortical regions: 22q-dup carriers had a significantly larger right hippocampus, on average, but lower right caudate and corpus callosum volume, relative to 22q-del carriers. Novel subcortical shape analysis revealed greater radial distance (thickness) of the right amygdala and left thalamus, and localized increases and decreases in subregions of the caudate, putamen, and hippocampus in 22q-dup relative to 22q-del carriers. This study provides the first evidence that 22q11.2 is a genomic region associated with gene-dose-dependent brain phenotypes. Pervasive effects on cortical surface area imply that this copy number variant affects brain structure early in the course of development. SIGNIFICANCE STATEMENT Probing naturally occurring reciprocal copy number variation in the genome may help us understand mechanisms underlying deviations from typical brain and cognitive development. The 22q11.2 genomic region is particularly susceptible to chromosomal rearrangements and contains many genes crucial for neuronal development and migration. Not surprisingly, reciprocal genomic imbalances at this locus confer some of the highest known genetic risks for developmental neuropsychiatric disorders. Here we provide the first evidence that brain morphology differs meaningfully as a function of reciprocal genomic variation at the 22q11.2 locus. Cortical thickness and surface area were affected in opposite directions with more widespread effects of gene dosage on cortical surface area.
RNA-Binding Domains of Heterologous Viral Proteins Substituted for Basic Residues in the RSV Gag NC Domain Restore Specific Packaging of Genomic RNA

The Rous sarcoma virus Gag polyprotein transiently traffics through the nucleus, which is required for efficient incorporation of the viral genomic RNA (gRNA) into virus particles. Packaging of gRNA is mediated by two zinc knuckles and basic residues located in the nucleocapsid (NC) domain in Gag. To further examine the role of basic residues located downstream of the zinc knuckles in gRNA encapsidation, we used a gain-of-function approach. We replaced a basic residue cluster essential for gRNA packaging with heterologous basic residue motif (BR) with RNA binding activity from either the HIV-1 Rev protein (Rev BR) or the HSV ICP27 protein (ICP27 BR). Compared to wild type Gag, the mutant ICP27 BR and Rev BR Gag proteins were much more strongly localized to the nucleus and released significantly lower levels of virus particles. Surprisingly, both the ICP27 BR and Rev BR mutants packaged normal levels of gRNA per virus particle when examined in the context of a proviral vector, yet both mutants were noninfectious. These results support the hypothesis that basic residues located in the C-terminal region of NC are required for selective gRNA packaging, potentially via initial electrostatic interactions that facilitate specific binding.
Rapid-Release Griffithsin Fibers for Dual Prevention of HSV-2 and HIV-1 Infections

The biologic griffithsin (GRFT) has recently emerged as a candidate to safely prevent sexually transmitted infections (STIs), including human immunodeficiency virus type 1 (HIV-1) and herpes simplex virus 2 (HSV-2). However, to date, there are few delivery platforms that are available to effectively deliver biologics to the female reproductive tract (FRT). The goal of this work was to evaluate rapid-release polyethylene oxide (PEO), polyvinyl alcohol (PVA), and polyvinylpyrrolidone (PVP) fibers that incorporate GRFT in in vitro (HIV-1 and HSV-2) and in vivo (HSV-2) infection models. ABSTRACT The biologic griffithsin (GRFT) has recently emerged as a candidate to safely prevent sexually transmitted infections (STIs), including human immunodeficiency virus type 1 (HIV-1) and herpes simplex virus 2 (HSV-2). However, to date, there are few delivery platforms that are available to effectively deliver biologics to the female reproductive tract (FRT). The goal of this work was to evaluate rapid-release polyethylene oxide (PEO), polyvinyl alcohol (PVA), and polyvinylpyrrolidone (PVP) fibers that incorporate GRFT in in vitro (HIV-1 and HSV-2) and in vivo (HSV-2) infection models. GRFT loading was determined via enzyme-linked immunosorbent assay (ELISA), and the bioactivity of GRFT fibers was assessed using in vitro HIV-1 pseudovirus and HSV-2 plaque assays. Afterwards, the efficacy of GRFT fibers was assessed in a murine model of lethal HSV-2 infection. Finally, murine reproductive tracts and vaginal lavage samples were evaluated for histology and cytokine expression, 24 and 72 h after fiber administration, to determine safety. All rapid-release formulations achieved high levels of GRFT incorporation and were completely efficacious against in vitro HIV-1 and HSV-2 infections. Importantly, all rapid-release GRFT fibers provided potent protection in a murine model of HSV-2 infection. Moreover, histology and cytokine levels, evaluated from collected murine reproductive tissues and vaginal lavage samples treated with blank fibers, showed no increased cytokine production or histological aberrations, demonstrating the preliminary safety of rapid-release GRFT fibers in vaginal tissue.
Mechanistic insights from structure of Mycobacterium smegmatis topoisomerase I with ssDNA bound to both N- and C-terminal domains

Abstract Type IA topoisomerases interact with G-strand and T-strand ssDNA to regulate DNA topology. However, simultaneous binding of two ssDNA segments to a type IA topoisomerase has not been observed previously. We report here the crystal structure of a type IA topoisomerase with ssDNA segments bound in opposite polarity to the N- and C-terminal domains. Titration of small ssDNA oligonucleotides to Mycobacterium smegmatis topoisomerase I with progressive C-terminal deletions showed that the C-terminal region has higher affinity for ssDNA than the N-terminal active site. This allows the C-terminal domains to capture one strand of underwound negatively supercoiled DNA substrate first and position the N-terminal domains to bind and cleave the opposite strand in the relaxation reaction. Efficiency of negative supercoiling relaxation increases with the number of domains that bind ssDNA primarily with conserved aromatic residues and possibly with assistance from polar/basic residues. A comparison of bacterial topoisomerase I structures showed that a conserved transesterification unit (N-terminal toroid structure) for cutting and rejoining of a ssDNA strand can be combined with two different types of C-terminal ssDNA binding domains to form diverse bacterial topoisomerase I enzymes that are highly efficient in their physiological role of preventing excess negative supercoiling in the genome.
Cytosolic diffusivity and microscopic anisotropy of N‐acetyl aspartate in human white matter with diffusion‐weighted MRS at 7 T

Metabolite diffusion measurable in humans in vivo with diffusion‐weighted spectroscopy (DW‐MRS) provides a window into the intracellular morphology and state of specific cell types. Anisotropic diffusion in white matter is governed by the microscopic properties of the individual cell types and their structural units (axons, soma, dendrites). However, anisotropy is also markedly affected by the macroscopic orientational distribution over the imaging voxel, particularly in DW‐MRS, where the dimensions of the volume of interest (VOI) are much larger than those typically used in diffusion‐weighted imaging. One way to address the confound of macroscopic structural features is to average the measurements acquired with uniformly distributed gradient directions to mimic a situation where fibers present in the VOI are orientationally uniformly distributed. This situation allows the extraction of relevant microstructural features such as transverse and longitudinal diffusivities within axons and the related microscopic fractional anisotropy. We present human DW‐MRS data acquired at 7 T in two different white matter regions, processed and analyzed as described above, and find that intra‐axonal diffusion of the neuronal metabolite N‐acetyl aspartate is in good correspondence to simple model interpretations, such as multi‐Gaussian diffusion from disperse fibers where the transverse diffusivity can be neglected. We also discuss the implications of our approach for current and future applications of DW‐MRS for cell‐specific measurements.
Novel Approach to Automated Flow Titration for the Determination of Fe(III)

A novel approach to automated flow titration with spectrophotometric detection for the determination of Fe(III) is presented. The approach is based on the possibility of strict and simultaneous control of the flow rates of sample and titrant streams over time. It consists of creating different but precisely defined concentration gradients of titrant and analyte in each successively formed monosegments, and is based on using the calculated titrant dilution factor. The procedure was verified by complexometric titration of Fe(III) in the form of a complex with sulfosalicylic acid, using EDTA as a titrant. Fe(III) and Fe(II) (after oxidation to Fe(III) with the use of H2O2) were determined with good precision (CV lower than 1.7%, n = 6) and accuracy (|RE| lower than 3.3%). The approach was applied to determine Fe(III) and Fe(II) in artesian water samples. Results of determinations were consistent with values obtained using the ICP–OES reference method. Using the procedure, it was possible to perform titration in 6 min for a wide range of analyte concentrations, using 2.4 mL of both sample and titrant.