Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Term Skeletal Outcomes of Primary Hyperparathyroidism Patients After Treatment with Parathyroidectomy: A Systematic Review and Meta-Analysis

Abstract The aim of the study was to assess and define the association between parathyroidectomy (PTX) and long-term skeletal outcomes in primary hyperparathyroidism (PHPT) patients. PubMed, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials were systematically searched up to June 31, 2017, without language restriction. Any study comparing skeletal outcomes [fracture risk or bone mineral density (BMD)] of PHPT patients after more than 12 months of PTX treatment versus non-PTX treatment was included. Pooled relative risks or odds ratios with 95% confidence intervals and weighted mean difference were calculated using random-effects models irrespective of statistical heterogeneity assessed by I2 statistic. Finally, 5 randomized controlled trials (RCTs, n=584) and 10 cohort studies (CSs, n=12202) were included. CSs suggest PTX treatment versus non-PTX treatment is significantly associated with 36% reduction in the risk of fracture, with no heterogeneity, and an increase in the lumbar spine change by 0.55 WMD, with no heterogeneity. RCTs indicate PTX treatment versus non-PTX treatment is significantly associated with BMD change of 0.97 WMD at the lumbar spine with substantial heterogeneity, and 1.23 WMD at the femoral neck with no heterogeneity. The existing CSs indicate PTX-treatment versus non-PTX-treatment might reduce the risk of fracture in PHPT patients. The existing RCTs do not provide sufficient or precise evidence that PTX-treatment affects the fracture risk of PHPT patients, but offer data that subsets of patients who could potentially benefit from PTX-treatment can be identified.
Sleep insights from the finger tip: How photoplethysmography can help quantify sleep

Sleep is essential for a healthy and productive life, yet its importance is largely overlooked, allowing populations to sleep less and to develop sleep disturbances. This trend results into an epidemic of poor quality and insufficient sleep that is turn jeopardizes health, performance, mood, memory, social relationships and productivity. A first step to overcome this epidemic relies on uncovering it at the individual and societal levels. The availability of different wearable devices that can track physiological signals represents a great opportunity to define and quantify the problem. Many such devices incorporate a photoplethysmography (PPG) sensor. This triggers interest in studies aiming to get insight into human sleep structure based on information obtained from PPG sensors. This study aimed to validate a new automated sleep analysis which is simply based on the inter-beat-interval series obtained from PPG, and that uses features of heart rate variability. The candidate algorithm was tested against gold standard scoring of whole night state-of-the-art sleep studies. The PPG-based sleep scoring performs very well in differentiating sleep stages, however, the sleep/wake separation is not sufficient and requires improvement. This last task is facilitated by the fact that the majority of devices with PPG capabilities, are equipped with accelerometers providing additional information for better separation. Combining accelerometers and PPG signals from wearable devices in a sleep analyser is likely to provide a reliable and accurate automated detection of sleep and wakefulness, including sleep macro- and micro-architecture.
Haplotype Distribution and Evolutionary Pattern of miR-17 and miR-124 Families Based on Population Analysis

Background MicroRNAs (miRNAs) are small, endogenously expressed non-coding RNAs that regulate mRNAs post-transcriptionally. Previous studies have explored miRNA evolutionary trend, but evolutionary history and pattern in the miRNA world are still not fully clear. In the paper, we intended to analyze miRNA haplotype distribution and evolutionary network by analyzing miRNA sequences of miR-17 and miR-124 families across animal species as special populations. Principal Findings 31 haplotypes were detected in miR-17 family while only 9 haplotypes were defined in miR-124 family. The complex miR-17 family was mainly distributed in vertebrates, but miR-124 was shared by more animal species from Caenorhabditis to Homo and had a wide distribution spectrum. Some haplotypes of the two miRNA families appeared discontinuous distributions across animals. Compared with a simple phylogenetic network in miR-124 family, miR-17 family indicated a complex network with some median vectors that might be lost ancestral or potential miRNA haplotypes. By analyzing different miRNAs across 12 animal species, we found these small RNAs showed different haplotype diversities, haplotype distributions and phylogenetic networks. Conclusions Different miRNAs had quite different haplotype distributions and evolutionary patterns. Discontinuous distributions of miRNAs and median vectors in phylogenetic networks implied more members in the miRNA world. miRNA may be an excellent phylogenetic marker to discover its evolutionary history and pattern across the animal kingdom.
Distributed Representation in Information Retrieval - AMRITA_CEN_NLP@IRLeD 2017

In this contemporary research era, the science of retrieving required information from the stored database is extending its applications in the legal and life science domains. With the exponential growth of the digital data available in the legal domain as an electronic media, there is a great demand for efficient and effective ways to retrieve required information from the stored document collection. This paper details our experimented approach in Information Retrieval from Legal Documents (IRLeD 2017) task. The task includes two subtasks, where the subtask 1 deals with information extraction and the subtask 2 deals with document retrieval. Text representation being a core component in any of the text analytics solution, we have experimented on the provided dataset to observe the performance of distributed representation of text in the Information Retrieval task. The distributed representation of text attained 3rd position in subtask 1 and attained satisfactory score in subtask 2.
Estimation of Gene Expression at Isoform Level from mRNA-Seq Data by Bayesian Hierarchical Modeling

mRNA-Seq is a precise and highly reproducible technique for measurement of transcripts levels and yields sequence information of a transcriptome at a single nucleotide base-level thus enabling us to determine splice junctions and alternative splicing events with high confidence. Often analysis of mRNA-Seq data does not attempt to quantify the expressions at isoform level. In this paper our objective would be use the mRNA-Seq data to infer expression at isoform level, where splicing patterns of a gene is assumed to be known. A Bayesian latent variable based modeling framework is proposed here, where the parameterization enables us to infer at various levels. For example, expression variability of an isoform across different conditions; the model parameterization also allows us to carry out two-sample comparisons, e.g., using a Bayesian t-test, in addition simple presence or absence of an isoform can also be estimated by the use of the latent variables present in the model. In this paper we would carry out inference on isoform expression under different normalization techniques, since it has been recently shown that one of the most prominent sources of variation in differential call using mRNA-Seq data is the normalization method used. The statistical framework is developed for multiple isoforms and easily extends to reads mapping to multiple genes. This could be achieved by slight conceptual modifications in definitions of what we consider as a gene and what as an exon. Additionally proposed framework can be extended by appropriate modeling of the design matrix to infer about yet unknown novel transcripts. However such attempts should be made judiciously since the input date used in the proposed model does not use reads from splice junctions.
Hyperspectral image fusion based on non-factorization sparse representation and error matrix estimation

Matrix factorization with non-negative constrains is widely used in hyperspectral image fusion. Nevertheless, the non-negative restriction on the sparse coefficients limits the efficiency of dictionary representation. To solve this problem, a new hyperspectral image fusion method based on non-factorization sparse representation and error matrix estimation is proposed in this paper, for the fusion of remotely sensed high-spatial multi-bands image with low-spatial hyperspectral image in the same scene. Firstly, an efficient spectral dictionary learning method is specifically adopted for the construction of the spectral dictionary, which avoids the procedure of matrix factorization. Then, the sparse codes of the high-spatial multi-bands image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers (ADMM) without non-negative constrains. For improving the quality of final fusion result, an error matrix estimation method is also proposed, exploiting the spatial structure information after non-factorization sparse representation. Experimental results both on simulated and real datasets demonstrate that, compared with the related state-of-the-art methods, our proposed method achieves the highest quality of hyperspectral image fusion, which can improve PSNR over 2.5844 and SAM over 0.3758.
Synthesis of nano-nickel by a wet chemical reduction method in the presence of surfactant (SDS) and a polymer (PVP)

Nano-sized nickel particles have been synthesized by a bottom-up approach, using hydrazine as the reducing agent in the presence of an anionic surfactant - sodium-dodecyl sulphate (SDS). The effect of adding a cationic polymer -polyvinylpyrrolidone (PVP) with an anionic surfactant has been studied at two different temperatures; the rate of reduction increased as the reaction temperature was increased from 60 to 100°C. These nano-aggregated nickel particles were characterized by using SEM with EDX facilities, TEM and XRD. TEM characterization showed the presence of spherical Ni particles as fine as 10 nm in diameter. However, the SEM images showed a very spiky morphology; very small spherical shaped objects were clearly observed within these spiky structures. XRD studies show that the nickel crystals have fcc structure. The combination of SDS/PVP reaction produced nano-sized nickel particles which were much finer than the reactions where SDS was used on its own. PVP has shown some dispersion power and was found to be capable of preventing nickel particles from gradual agglomeration.
Childhood football play and practice in relation to self-regulation and national team selection; a study of Norwegian elite youth players

ABSTRACT Childhood sport participation is argued to be important to understand differences in self-regulation and performance level in adolescence. This study sought to investigate if football-specific activities in childhood (6–12 years of age) is related to self-regulatory skills and national under 14- and 15-team selection in Norwegian elite youth football. Data of practice histories and self-regulatory skills of 515 youth football players selected at Norwegian regional level were collected and further analysed using multilevel analyses. The results revealed that high self-regulated players were more likely to be selected for national initiatives, and increased their involvement in peer-led football practice and adult-led football practice during childhood, compared to players with lower levels of self-regulation. While national level players reported higher levels of peer-led football play in childhood, the interaction effect suggest that the regional level players increased their involvement in peer-led play during childhood compared to national level players. In conclusion, the findings indicate that childhood sport participation may contribute to later differences in self-regulation, and highlights the importance of childhood engagement in football-specific play and practice in the development of Norwegian youth football players.
Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter

User modeling for inferring user interests from Online Social Networks (OSNs) such as Twitter has received great attention in the user modeling community with the growing popularity of OSNs. The focus of previous works has been on analyzing user-generated content such as tweets to infer user interests. Therefore, these previous studies were limited to active users who have been actively generating content. On the other hand, with the percentage of passive use of OSNs on the rise, some researchers investigated different types of information about followees (i.e., people that a user is following) such as tweets, usernames, and biographies to infer user interests for passive users who use OSNs for consuming information from followees but who do not produce any content. Although different types of information about followees have been exploited, list memberships (a topical list which other Twitter users can freely add a user into) of followees have not yet been investigated extensively for inferring user interests. In this paper, we investigate list memberships of followees, to infer interest profiles for passive users. To this end, we propose user modeling strategies with two different weighting schemes as well as a refined interest propagation strategy based on previous work. In addition, we investigate whether the information from biographies and list memberships of followees can complement each other, and thus improve the quality of inferred interest profiles for passive users. Results show that leveraging list memberships of followees is useful for inferring user interests when the number of followees is relatively small compared to using biographies of followees. In addition, we found that combining the two different types of information (list memberships and biographies) of followees can improve the quality of user interest profiles significantly compared to a state-of-art method in the context of link recommendations on Twitter.
USE OF GRANULATED BLAST FURNACE SLAG AS FINE AGGREGATE IN CEMENT MORTAR

This paper investigates the possibility of utilizing Granulated Blast Furnace Slag (GBFS) as a sand substitute in cement mortar, in order to reduce environmental problems related to aggregate mining and waste disposal. In this investigation, cement mortar mix 1:3 and GBFS at 0, 25, 50, 75 and 100% replacement to natural sand for constant w/c ratio of 0.5 is considered. The work is extended to 100% replacements of natural sand with GBFS for w/c ratios of 0.4 and 0.6. The flow characteristics of the various mixes and their compressive strengths at various ages are studied. From this study it is observed that GBFS could be utilized partially as alternative construction material for natural sand in mortar applications. Reduction in workability expressed as flow can be compensated by adding suitable percentage of super plasticizer.
AmritaNLP@PAN-RusProfiling : Author Profiling using Machine Learning Techniques

This paper illustrates work done on "Gender Identi cation in Russian texts (RusPro ling)" shared task, hosted by PAN in conjunction with FIRE 2017. The task is to predict the author’s gender, based on the Twitter data corpus which is in Russian. We will give a brief introduction to the task at hand, elaborate on the data-set provided by the competition organizers, discuss various feature selection methods, provided experimental analysis that we followed for feature representation and show comparative outcomes of di erent classi ers that we used for validation. We submitted a total of 3 models and their respective prediction for each test data-set with slightly di erent pre-processing technique based upon the test corpus content. As each of the test corpus were sourced from various platforms, this made it challenging to stick to one representation alone. As per the global ranking published for the shared task[6] our team secured 2nd position overall (Concatenating all Data-set) and our 3rd submission model performed the best among the 3 submission models from the overall test data corpus. Further under extended work we discuss in brief how hyper parameter tuning of certain attributes extend our validation accuracy by 6% from baseline.
Diverse Keyword Extraction from Conversations

A new method for keyword extraction from conversations is introduced, which preserves the diversity of topics that are mentioned. Inspired from summarization, the method maximizes the coverage of topics that are recognized automatically in transcripts of conversation fragments. The method is evaluated on excerpts of the Fisher and AMI corpora, using a crowdsourcing platform to elicit comparative relevance judgments. The results demonstrate that the method outperforms two competitive baselines.
Quasi View-Independent Human Motion Recognition in Subspaces

We demonstrate the efficiency of a proposed feature extraction technique for dimensionality reduction in classifying human motions using MoCap data. We follow a technique introduced by Körner and Denzler [1] for designing feature vectors before and after a transformation of these features into a subspace. This approach uses an average of a skeleton in a video in combination with a specific normalization to form an action descriptor. The major advantage of this approach is that it yields a very small fixed data size regardless of video length. This leads to a very fast computation with any classifiers. In our experiment, we show the robustness of applying this feature extraction technique for various dimensionality reduction approaches under two conditions: i) untrained camera angles in combination with untrained subjects, and ii) data loss; by further extension of i) by subsampling the original test data. Furthermore, we suggest an anatomically in-frame normalization which can improve the classification performance in several scenarios.
Accuracy Rates of Sex Estimation by Forensic Anthropologists through Comparison with DNA Typing Results in Forensic Casework

A common task in forensic anthropology involves the estimation of the biological sex of a decedent by exploiting the sexual dimorphism between males and females. Estimation methods are often based on analysis of skeletal collections of known sex and most include a research‐based accuracy rate. However, the accuracy rates of sex estimation methods in actual forensic casework have rarely been studied. This article uses sex determinations based on DNA results from 360 forensic cases to develop accuracy rates for sex estimations conducted by forensic anthropologists. The overall rate of correct sex estimation from these cases is 94.7% with increasing accuracy rates as more skeletal material is available for analysis and as the education level and certification of the examiner increases. Nine of 19 incorrect assessments resulted from cases in which one skeletal element was available, suggesting that the use of an “undetermined” result may be more appropriate for these cases.
The Expression of TLR2 and TLR3 in Sertoli Cells of Azoospermic Patients

Objective Toll-like receptors (TLRs) on Sertoli cells are thought to have essential roles in sperm protection. This study was conducted to investigate the expression of TLR2 and TLR3 in Sertoli cells of men with azoospermia. Materials and Methods In this experimental study, testicular biopsies were taken from ten azoospermic men. Following enzymatic dissociation, the samples were moved to lectin coated petri dishes. After a few passages, all cells were cultivated and Seroli cells were sorted by flow cytometry. To confirm Sertoli cell purification, alkaline phosphatase activity (ALP) and immunohistochemistry assays were employed. The expression of TLR2 and TLR3 at the transcript and protein levels was examined with real-time quantitative reverse transcription-polymerase chain reaction (RT-QPCR) and western blot, respectively. Results Isolation, purification and cultivation of human Sertoli cells were performed successfully. Efficacy of purification of Sertoli cells by fluorescence-activated cell sorting (FACS) sorter was ~97%. The type of cultured cells was confirmed by vimentin and follicle-stimulating hormone (FSH) receptor markers. Furthermore, the existence of anti- Müllerian hormone in culture was confirmed. RT-PCR showed that both genes were expressed in Sertoli cells. Consistently, proteins of both were also expressed in Sertoli cells. Moreover, QPCR showed that the relative expression of TLR3 transcripts was significantly higher than TLR2 in Sertoli cells. Although both genes are expressed in fibroblast cells, their level of expression was significantly lower than in Sertoli cells. Conclusion This study confirmed expression of TLR2 and TLR3 in human Sertoli cells. This may be an indicator of their roles in developing immunity against pathogens as well as allo- and auto-antigens or viral antigens in seminiferous tubules.
Content Analysis of Smartphone Apps for Smoking Cessation in China: Empirical Study

Background With 360 million smokers, China consumes more cigarettes than any other country in the world. Given that 620 million Chinese own smartphones, smartphone apps for smoking cessation are increasingly used in China to help smokers quit. Objective This study analyzed and evaluated the contents of all smoking cessation apps (iOS and Android) available in China, applying the China Clinical Smoking Cessation Guideline (CCSCG; identical to the US Clinical Practice Guideline for Treating Tobacco Use and Dependence) as a framework for analysis. Methods We conducted a content analysis of Chinese Android and iOS smoking cessation apps (N=64) designed to assist users in quitting smoking. Each app was independently coded by two raters for its approach to smoking cessation and adherence to the CCSCG. We also recorded the features of smoking cessation apps (eg, release date, size, frequency of downloads, user ratings, type, quality scores by raters, and designers). Linear regression was used to test predictors of popularity and user-rated quality. Results Chinese smoking cessation apps have low levels of adherence to guidelines, with an average score of 11.1 for Android and 14.6 for iOS apps on a scale of 0 to 46. There was no significant association between popularity, user rating, and the characteristics of apps. However, there was a positive relationship between popularity, user rating, and adherence score. Conclusions Chinese apps for smoking cessation have low levels of adherence to standard clinical practice guidelines. New apps need be developed and existing apps be revised following evidence-based principles in China.