Miracle-dz/UniFAR-FTUs huggingface dataset:
Acquired pure red cell aplasia in a patient with ankylosing spondylitis- a case report and literature review

Abstract Rationale: Acquired pure red cell aplasia (PRCA) can be a secondary response to some autoimmune disorders. However, there is no data about the possibility of acquired PRCA being a secondary complication to ankylosing spondylitis (AS). Patient concerns: A 42-year-old male who had a history of AS for 14 years. He got serious anemia 17 months ago. Bone marrow smear indicated PRCA. Diagnose: He was diagnosed with acquired PRCA secondary to AS. Intervention: The combination treatment of immunosuppressants with hematopoiesis stimuli was successful. Outcomes: The patient recovered from PRCA, and showed improvement in his AS. Lessons: Acquired PRCA can be secondary to AS. Cyclosporine is effective in controlling AS arthritis syndrome and in addition to immunosuppressants, promotion of erythroid hematopoiesis is equally important.
Metamaterial Lensing Devices

In recent years, the development of metamaterials and metasurfaces has drawn great attention, enabling many important practical applications. Focusing and lensing components are of extreme importance because of their significant potential practical applications in biological imaging, display, and nanolithography fabrication. Metafocusing devices using ultrathin structures (also known as metasurfaces) with superlensing performance are key building blocks for developing integrated optical components with ultrasmall dimensions. In this article, we review the metamaterial superlensing devices working in transmission mode from the perfect lens to two-dimensional metasurfaces and present their working principles. Then we summarize important practical applications of metasurfaces, such as plasmonic lithography, holography, and imaging. Different typical designs and their focusing performance are also discussed in detail.
Expression and Functional Analyses of Dlk1 in Muscle Stem Cells and Mesenchymal Progenitors during Muscle Regeneration

Delta like non-canonical Notch ligand 1 (Dlk1) is a paternally expressed gene which is also known as preadipocyte factor 1 (Pref−1). The accumulation of adipocytes and expression of Dlk1 in regenerating muscle suggests a correlation between fat accumulation and Dlk1 expression in the muscle. Additionally, mice overexpressing Dlk1 show increased muscle weight, while Dlk1-null mice exhibit decreased body weight and muscle mass, indicating that Dlk1 is a critical factor in regulating skeletal muscle mass during development. The muscle regeneration process shares some features with muscle development. However, the role of Dlk1 in regeneration processes remains controversial. Here, we show that mesenchymal progenitors also known as adipocyte progenitors exclusively express Dlk1 during muscle regeneration. Eliminating developmental effects, we used conditional depletion models to examine the specific roles of Dlk1 in muscle stem cells or mesenchymal progenitors. Unexpectedly, deletion of Dlk1 in neither the muscle stem cells nor the mesenchymal progenitors affected the regenerative ability of skeletal muscle. In addition, fat accumulation was not increased by the loss of Dlk1. Collectively, Dlk1 plays essential roles in muscle development, but does not greatly impact regeneration processes and adipogenic differentiation in adult skeletal muscle regeneration.
Factors Associated with Burnout in Medical Academia: An Exploratory Analysis of Romanian and Moldavian Physicians

This study aimed to assess the extent of burnout in Romanian and Moldavian academic physicians and to determine the predictive value of emotional intelligence (EI), coping strategies, work motivation (WM), perceived organizational support (POS), and the socio-demographic characteristics of burnout. Two hundred physicians (40% men, 60% women, mean age = 43.02, SD = 9.91) participated in the study. They were administered the Maslach Burnout Inventory−General Survey, Brief COPE Scale, Multidimensional Work Motivation Scale, Schutte’s Self-Report Emotional Intelligence Test, and Perceived Organizational Support Scale. Mann−Whitney U tests were used to assess the significance of intercountry differences, while hierarchical regressions were performed to investigate the predictive value of the independent variables on burnout. Moldavian participants had significantly lower scores in burnout and amotivation (p < 0.001) and higher scores in EI, POS, and WM (p < 0.001). The main burnout predictors were amotivation (β = 0.388, p < 0.001) and low POS (β = −0.313, p< 0.001) in Moldavian respondents, and WM (intrinsic: β = −0.620, p < 0.001; extrinsic: β = 0.406, p < 0.001) in Romanian participants. Moldavian respondents displayed better adjustment to academic stress. The distribution of burnout predictors suggests better sensitivity of respondents to organizational interventions in Moldova and to individual therapy in Romania. This data could serve to better tailor Public Health interventions addressing burnout in the academic environment.
A computational model of induced pluripotent stem‐cell derived cardiomyocytes incorporating experimental variability from multiple data sources

Induced pluripotent stem cell‐derived cardiomyocytes (iPSC‐CMs) capture patient‐specific genotype–phenotype relationships, as well as cell‐to‐cell variability of cardiac electrical activity Computational modelling and simulation provide a high throughput approach to reconcile multiple datasets describing physiological variability, and also identify vulnerable parameter regimes We have developed a whole‐cell model of iPSC‐CMs, composed of single exponential voltage‐dependent gating variable rate constants, parameterized to fit experimental iPSC‐CM outputs We have utilized experimental data across multiple laboratories to model experimental variability and investigate subcellular phenotypic mechanisms in iPSC‐CMs This framework links molecular mechanisms to cellular‐level outputs by revealing unique subsets of model parameters linked to known iPSC‐CM phenotypes
Use of Ages & Stages Questionnaire ™ (ASQ) in a Navajo Population: Comparison With The U.S. Normative Dataset.

BACKGROUND The Ages & Stages Questionnaires (ASQ-3) is a parent-completed screening to identify young children at-risk for developmental delays in the United States and internationally. Federal programs operating on Navajo Nation use the ASQ-3 to determine the need for early intervention services, even though the ASQ-3 national sample used to establish cut-off scores for referral included only 1% Native American children. OBJECTIVES The current study aimed to compare the ASQ-3 results from a sample of Navajo infants to those from a representative national U.S. sample, and to examine the specificity and sensitivity of the ASQ-3 in Navajo population. METHODS The sample included 530 Navajo infants (47.3% males) aged between 1 and 13 months who lived in remote and rural areas across the Navajo Nation. Children's development was assessed during home visits at 2, 6, 9, and 12-month assessment windows. RESULTS Results showed that after 6 months, Navajo children had lower mean scores and higher percentages of children at-risk for developmental delays than those from the national sample. The sensitivities and specificities, estimated using a Bayesian diagnostic approach under both conservative and non-conservative prior range choices, suggested a comparable validity performance to that from other ASQ-3 studies. DISCUSSION The results of this study along with our ongoing comprehensive assessments at 4 years of age inform current programs working with Navajo children to improve early identification of developmental delays.
A Discussion of the Practicality of the Visual Literacy Competency Standards for Higher Education

Development and assessment of critical approaches to information technologies and multimedia practices are inevitable in our modern age, which is characterized by incessant messages conveyed through mass media and by breathtaking technological advances. Multiliteracy is required for accurate use, perception, and interpretation of communication technologies, which constantly penetration into our lives, in educational and instructional processes. The meaning of literacy changed in the 21 st century, and images, which are commonly used now, have led to visual literacy skills. In parallel with the swift changes in the nature of information, higher education institutions have adopted various structural regulations and contemporary methods. In this way, the efficiency of the teaching-learning process is enhanced, and students are prepared for modern life. Within an educational process by which scientific and techno-cultural phenomena are reconstructed, visual literacy enables one to access images and visual materials in all areas. Therefore, individuals are required to have certain competencies so that they can objectively evaluate, subjectively criticize, and accurately reproduce, transform and use images. Success in higher education is becoming more and more dependent on visual literacy skills. The Visual Literacy Competency Standards for Higher Education presents guidelines for educators and provides a scientific framework in which students can acquire visual literacy skills and use visual media in a critical way throughout their professional career. The Visual Literacy Competency Standards for Higher Education was composed by the American Association of College and Research Libraries (ACRL). By means of these standards, the members of the association identified the domains of visual literacy and specified what learning outcomes could enable students to acquire visual literacy skills. The present paper is a discussion of the extent to which the Visual Literacy Competency Standards for Higher Education can be met.
Spectroscopy Transmittance by LED Calibration

Local administrations demand real-time and continuous pollution monitoring in sewer networks. Spectroscopy is a non-destructive technique that can be used to continuously monitor quality in sewers. Covering a wide range of wavelengths can be useful for improving pollution characterization in wastewater. Cost-effective and in-sewer spectrophotometers would contribute to accomplishing discharge requirements. Nevertheless, most available spectrometers are based on incandescent lamps, which makes it unfeasible to place them in a sewerage network for real-time monitoring. This research work shows an innovative calibration procedure that allows (Light-Emitting Diode) LED technology to be used as a replacement for traditional incandescent lamps in the development of spectrophotometry equipment. This involves firstly obtaining transmittance values similar to those provided by incandescent lamps, without using any optical components. Secondly, this calibration process enables an increase in the range of wavelengths available (working range) through a better use of the LED’s spectral width, resulting in a significant reduction in the number of LEDs required. Thirdly, this method allows important reductions in costs, dimensions and consumptions to be achieved, making its implementation in a wide variety of environments possible.
Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.
Molecular computations of preferential interaction coefficients of IgG1 monoclonal antibodies with sorbitol, sucrose, and trehalose and the impact of these excipients on aggregation and viscosity.

Preferential interactions of formulation excipients govern their overall interactions with protein molecules, and molecular dynamics simulations allow for the examination of the interactions at the molecular level. We used molecular dynamics simulations to examine the interactions of sorbitol, sucrose, and trehalose with three different IgG1 antibodies to gain insight into how these excipients impact aggregation and viscosity. We found that sucrose and trehalose reduce aggregation more than sorbitol due to their larger size as well as their stronger interactions with high-spatial aggregation propensity residues compared to sorbitol. Two of the antibodies had high viscosity in a sodium acetate buffer, and for these we found that sucrose and trehalose tended to have opposite effects on the viscosity. The data presented here provides further insight into the mechanisms of interactions of these three carbohydrate excipients with the antibody surface and thus their impact on excipient stabilization of antibody formulations.
Cost-Based Automatic Recovery Policy in Data Centers

Today's data centers either provide critical applications to organizations or host computing clouds used by huge Internet populations. Their size and complex structure make management difficult, causing high operational cost. The large number of servers with various different hardware and software components cause frequent failures and need continuous recovery work. Much of the operational cost is from this recovery work. While there is significant research related to automatic recovery, from automatic error detection to different automatic recovery techniques, there is currently no automatic solution that can determine the exact fault, and hence the preferred recovery action. There is some study on how to automatically select a suitable recovery action without knowing the fault behind the error. In this thesis we propose an estimated-total-cost model based on analysis of the cost and the recovery-action-success probability. Our recovery-action selection is based on minimal estimated-total-cost; we implement three policies to use this model under different considerations of failed recovery attempts. The preferred policy is to reduce the recoveryaction-success probability when it failed to fix the error; we also study different reduction coefficients in this policy. To evaluate the various policies, we design and implement a simulation environment. Our simulation experiments demonstrate significant cost improvement over previous research based on simple heuristic models.
Providing transactional behavior to services coordination

Our research project proposes an approach for providing transactional behavior to services coordination. In service composition functional aspects have been successfully addressed. However transactional properties have been poorly addressed using ad-hoc solutions at the back end of the systems. This situation makes current applications not reliable and not adaptable to existing distributed environments (i.e. Internet). The objective of our research is proposing mechanisms for adding transactional behavior to services coordination.
Evaluation of split-window and dual-angle correction methods for land surface temperature retrieval from Envisat / AATSR data

Land surface temperature (LST) can be derived from thermal infrared remote sensing data provided that atmospheric and emissivity effects are corrected fo r. In this paper, two correction methods were evalu ated using a database of ground LST measurements and concurren t Envisat/Advanced Along Track Scanning Radiometer (AATSR) data. They were the split-window (SW) metho d, which uses two channels at 11 and 12 μm, and the dual-angle (DA) method using one single channel (11 μm) at two observation angles (close to nadir and ar ound 55o forward). The ground LST measurements were perf ormed in a large, flat and thermally homogeneous ar ea of rice fields during the summers of 2002-2005, when t he crop showed full vegetation cover. A total of 23 concurrences of ground measurements and AATSR data were obtained. Results showed that the SW algorithm s worked satisfactorily provided that the characteris ics of the area are correctly prescribed, either t rough the classification of the land cover type and vegetatio n c ver fraction, or with the surface emissivity. I n this case, the AATSR derived LSTs agreed with the ground LSTs with in ±1.0 oC for all the data of the comparison, with negligible average bias and standard deviation of 0 . 5 oC. The DA algorithms were less accurate than th e SW algorithms for the data used in this study, yieldin g standard deviations of 1.0 oC.
MODELLING A WETLAND FOR SUSTAINABLE MANAGEMENT : ULUABAT LAKE

This research deals with sustainable management policies in a wetland. The region consists of a shallow lake and its surroundings. Fishing is a major commercial activity. The lake is under high nutrient loads. The goal of this research is to find a balance between ecosystem and economic activities in the region. To this end, a system dynamics model of the wetland is constructed. The results obtained from the model show that there is no threat of eutrophication in the near future. The major problem seems to be a potential decline in the welfare of the inhabitants mainly due to unsustainable population increase. The scenario runs reveal the lake would have become eutrophic, if crayfish population did not collapse due to a fungus disease in 1986. Recovery of crayfish sometime within the model timeframe results increase in crayfish harvest; hence in income from fishing leading to betterment in social conditions. ‘Improved agricultural techniques’ is the only policy that leads to better social conditions through increasing yield per hectare. It is hoped that the dynamic simulation model will serve as a laboratory to analyse different policy alternatives.
The Visualization Pipeline is Broken

Because of the emphasis on sensemaking and synthesis processes in visual analytics, interaction has evolved to take on a newer, deeper meaning in information visualization. Typically algorithmic data visualization and user-generated story synthesis tasks are separated in existing tools, but what if they could be better integrated? Malleable visualization is needed to enable analysts to interactively reorganize data visualizations into stories that capture deep semantics. However, the rigid mathematical nature of the visualization pipeline currently restricts visual representations and interactions, reducing their effectiveness – the visualization pipeline is broken. A new model is needed that better supports sensemaking and human thought. Can a visualization also provide methods for editing data, capturing thoughts, and setting the semantic framework chosen by analysts to complete their task successfully? How should user interactions in visual space propagate backwards up the pipeline through visual mappings and data space? Can we create such mappings that are useful, yet flexible? Or, is there a deeper problem stemming from theories of embodied interaction about the disconnect between human visual interaction and computational data processing? This broader view of visualization could give designers the ability to build new visualizations outside the traditional realm.
New and Efficient Algorithms for Producing Frequent Itemsets with the MapReduce Framework

The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem. In this paper we focus on algorithms producing association rules and frequent itemsets. After reviewing the most recent algorithms that perform this task within the MR framework, we present two new algorithms: one algorithm for producing closed frequent itemsets, and the second one for producing frequent itemsets when the database is updated and new data is added to the old database. Both algorithms include novel optimizations which are suitable to the MR framework, as well as to other parallel architectures. A detailed experimental evaluation shows the effectiveness and advantages of the algorithms over existing methods when it comes to large distributed databases.
From Fake to Junk News, the Data Politics of Online Virality

‘Fake news’ is a key subject of data politics, but also a tricky a one. As this chapter aims to show, various phenomena signified by this misleading label have little in common, except being opposite to the kind of algorithmic intelligence that most other chapters present as the main concern of data politics. This does not mean that ‘fake news’ is not related to computational analytics or political intentions, but it does mean that this relation is not straightforward.
A Study on Manual and Automa c Evalua on Procedures and Produc on of Automa c Post-edi ng Rules for Persian Machine Transla on

Evaluation of machine translation is an important step towards improving MT. One way to evaluate the output of MT is to focus on different types of errors occurring in the translation hypotheses, and to think of possible solutions to fix those errors. An error categorization is a rather beneficent tool that makes it easy to analyze the translation errors and can also be utilized to manually generate post-editing rules to be applied automatically to the product of machine translation. In this work, we define a categorization for the errors occurring in Swedish– Persian machine translation by analyzing the errors that occur in three data-sets from two websites: 1177.se, and Linköping municipality. We define three types of monolingual reference free evaluation (MRF), and use two automatic metrics BLEU and TER, to conduct a bilingual evaluation for Swedish–Persian translation. Later on, based on the experience of working with the errors that occur in the corpora, we manually generate automatic post-editing (APE) rules and apply them to the product of machine translation. Three different sets of results are obtained: (1) The results of analyzing MT errors show that the three most common types of errors that occur in the translation hypotheses are mistranslated words, wrong word order, and extra prepositions. These types of errors are placed in semantic and syntactic categories respectively. (2) The results of comparing the correlation between the automatic and manual evaluation show a low correlation between the two evaluations. (3) Lastly, applying the APE rules to the product of machine translation gives an increase in BLEU score on the largest data-set while remaining almost unchanged on the other two data-sets. The results for TER show a better score on one data-set, while the scores on the two other data-sets remain unchanged.