Miracle-dz/UniFAR-FTUs huggingface dataset:
Sodium arsenite-induced detriment of cell function in Leydig and Sertoli cells: the potential relation of oxidative damage and antioxidant defense system

Abstract Arsenic is commonly found in the natural environment and is toxic agent for living organism in many countries in the world. Studies on animal models suggest that exposure to arsenic may cause reproductive toxicity; however, effect of arsenic on reproductive toxicity has still not been clearly described. This study was focused on cytotoxicity, oxidative stress, and the antioxidant defense system induced with exposure to sodium arsenite in Mus musculus Leydig and Sertoli cells. The cells were exposed to two different concentrations of sodium arsenite of 50 ppb (0.4 μM) and 1000 ppb (7.7 μM) for 24, 48, and 72 h. Following the exposure time, cell viability, cell proliferation, and lactate dehydrogenase (LDH) activity were determining using colorimetric method. Also, we evaluated oxidative stress markers such as glutathione (GSH), lipid peroxidation, hydroxyl radical, hydrogen peroxide levels, and cellular enzymatic antioxidants such as catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione-s-transferase, and γ-glutamyl transpeptidase (γ-GT). As a result, sodium arsenite exposure in Leydig and Sertoli cells caused cellular cytotoxicity and downregulated the antioxidant defense system by inducing oxidative stress depending on concentration and time. Furthermore, this study demonstrated that when compared with Sertoli cells, Leydig cells were more affected by arsenite toxicity.
Effect of Topical Chamomile Oil on Postoperative Bowel Activity after Cesarean Section: A Randomized Controlled Trial

Objective: Postoperative ileus (POI) is a common complication after surgery that requires a multifactorial therapeutic approach. This study aims to assess the effect of topical chamomile oil on postoperative bowel activity after cesarian section. Methods: This randomized controlled trial was carried out in 2015 at Chamran Hospital in Iran. A block randomization list was generated for 142 parturient divided into three groups. In the intervention group (arm A) (n = 47), chamomile oil was applied topically on abdominal region after the stability of the patient. Placebo group (arm B) (n = 47) received placebo oil and control group (arm C) (n = 48) had no intervention. A recovery program was used after surgery for all participants. The primary outcome was time to first flatus. Secondary outcomes were time to bowel sounds, defecation, return of appetite, hospital stay, and rate of nausea and vomiting, abdominal pain. Findings: Times to first flatus were significantly shorter in Group A (arm A vs. B, P < 0.001 and arm A vs. C, P < 0.001). In addition, time to first bowel sounds (arm A vs. B, P < 0.001 and arm A vs. C, P < 0.001) and return of appetite (arm A vs. B, P < 0.001 and arm A vs. C, P < 0.001) were significantly shorter in arm A. The times from surgery to first defecation were shorter in Group A versus B and C. However, there were no statistically significant differences between three groups. Conclusion: These results suggest that topical chamomile oil has a potential therapeutic effect on gastrointestinal motility and can reduce the duration of POI.
Employing Phonetic Information in DNN Speaker Embeddings to Improve Speaker Recognition Performance

The recent speaker embeddings framework has been shown to provide excellent performance on the task of text-independent speaker recognition. The framework is based on a deep neural network (DNN) trained to directly discriminate between speakers from traditional acoustic features such as Mel frequency cepstral coefficients. Prior studies on speaker recognition have found that phonetic information is valuable in the task of speaker identification, with systems being based on either bottleneck features (BFs) or tied-triphone state posteriors from a DNN trained for the task of speech recognition. In this paper, we analyze the role of phonetic BFs for DNN embeddings and explore methods to enhance the BFs further. Experimental results show that exploiting phonetic information encoded in BFs is very valuable for DNN speaker embeddings. Enriching the BFs using a cascaded DNN multi-task architecture is also shown to provide further improvements to the speaker embed- ding system.
What to Do When Cochlear Implant Users Plateau in Performance: a Pilot Study of Clinician-guided Aural Rehabilitation.

HYPOTHESIS For experienced adult cochlear implant (CI) users who have reached a plateau in performance, a clinician-guided aural rehabilitation (CGAR) approach can improve speech recognition and hearing-related quality of life (QOL). BACKGROUND A substantial number of CI users do not reach optimal performance in terms of speech recognition ability and/or personal communication goals. Although self-guided computerized auditory training programs have grown in popularity, compliance and efficacy for these programs are poor. We propose that CGAR can improve speech recognition and hearing-related QOL in experienced CI users. METHODS Twelve adult CI users were enrolled in an 8-week CGAR program guided by a speech-language pathologist and audiologist. Nine patients completed the program along with pre-AR and immediate post-AR testing of speech recognition (AzBio sentences in quiet and in multitalker babble, Consonant-Nucleus-Consonant words in quiet), QOL (Nijmegen Cochlear Implant Questionnaire, Hearing Handicap Inventory for Adults/Elderly, and Speech, Spatial and Qualities of Hearing Scale), and neurocognitive functioning (working memory capacity, information-processing speed, inhibitory control, speed of lexical/phonological access, and nonverbal reasoning). Pilot data for these nine patients are presented. RESULTS From pre-CGAR to post-CGAR, group mean improvements in word recognition were found. Improvements were also demonstrated on some composite and subscale measures of QOL. Patients who demonstrated improvements in word recognition were those who performed most poorly at baseline. CONCLUSIONS CGAR represents a potentially efficacious approach to improving speech recognition and QOL for experienced CI users. Limitations and considerations in implementing and studying aural rehabilitation approaches are discussed.
A novel clinical protocol for the greater palatine compression suture: A case report

Bleeding from the palatal donor site is a worrisome complication of palatal soft tissue harvesting procedures and leads to considerable stress to both the patient and the surgeon. Several methods to control palatal hemorrhage have been suggested in literature, including a palatal suture, but a precise protocol has not been suggested. The present case report aims to suggest a protocol for the greater palatine compression suture (GPCS). Five patients who experienced profuse bleeding from the palatal free gingival graft donor site were treated with the GPCS. The palatal midline was used as a landmark and the location of the greater palatine foramen was estimated. The suture was placed anterior to the estimated location of the foramen and was passed deep into the palatal tissue with the aim of looping and compressing the vascular bundle. In all the patients, an immediate reduction and arrest of bleeding from the palatal wound was observed with blanching of the soft tissues around the suture. The proposed technique is a predictable method to control palatal hemorrhage and can be used if other less invasive techniques fail to stem the blood flow from an injured palatal vessel.
Cycle-Consistency Based Hierarchical Dense Semantic Correspondence

This work aims to estimate dense correspondences between the images from same visual class but with different geometries and visual similarities. This task is particularly challenging because (i) most image pairs have large intra-class variations, and (ii)their visual content is similar only on the high-level structure. To address these problems, this paper proposed a multilevel method to estimate per-pixel correspondences from high-level semantic to low-level structural details by the guidance of cycle-consistency. We utilize CNN feature pyramid to represent images level by level. Meanwhile, we introduce cycle-consistency to measure the reliability of flow vector, which further affects the guidance from higher level to lower level. The proposed method has been extensively evaluated on various challenging benchmarks. The results show that our method significantly outperforms the state-of-the-arts in terms of semantic flow accuracy.
Stem cell in urology—are we at the cusp of a new era?

Stem cell therapy can potentially disrupt conventional medicine as we practice it today. Stem cells can self-renew and differentiate and by this, repair and in certain conditions regenerate damaged tissue. In the past two decades, there has been significant research into its value in several chronic urological conditions for which conventional therapy is unsatisfactory. Stem cell therapy has been tried on animal models of bladder dysfunction, stress urinary incontinence (SUI), erectile dysfunction and urethral injury and certain preclinical studies have had very encouraging results. Yet despite this explosion of knowledge about the nature and value of stem cells, translation of research into the clinical domain has been slow. In addition, lack of regulation of stem cell therapy has resulted in indiscriminate, unscientific administration of stem cell therapy to patients. This review looks into the advances in the use of stem cells in urological practice, the recent regulatory guidelines that have been introduced and what the future holds for this exciting branch.
Effects of dexmedetomidine on inflammatory mediators after tourniquet-induced ischemia-reperfusion injury: a randomized, double-blinded, controlled study.

BACKGROUND Tourniquet use during total knee arthroplasty (TKA) produces ischemia-reperfusion injury (IRI), with systemic release of inflammatory cytokines and reactive oxygen species upon tourniquet release. We conducted a randomized, placebo-controlled, double-blind trial to examine whether dexmedetomidine (DEX) as an adjunct during general anesthesia in patients undergoing unilateral TKA could attenuate the rise in inflammatory cytokines and oxidative stress. METHODS Sixty-eight patients were randomized to either the control or DEX group. DEX was administered at a loading dose of 0.5 μg/kg, followed by an infusion of 0.4 μg/kg/h. We measured serum levels of malondialdehyde (biomarker of oxidative stress) and proinflammatory cytokines (interleukin-6 [IL-6] and tumour necrosis factor-α [TNF-α]) preinduction (baseline), 60 and 90 min post-tourniquet release. We also assessed hemodynamics, intraoperative remifentanil consumption, and postoperative pain scores and analgesic consumption. RESULTS Malondialdehyde was higher than baseline after tourniquet release in both groups (P≤0.001), but the levels were similar between groups at all times. TNF-α was significantly higher than baseline at 60 min post-tourniquet release only in the control group (P=0.009). Serum IL-6 increased significantly above baseline at 60 and 90 min post-tourniquet release in both groups (P<0.001). At 90 min, IL-6 was significantly lower in the dexmedetomidine group than in the control group (P=0.049). Remifentanil consumption, heart rate, and pain scores were significantly lower in the dexmedetomidine group. CONCLUSIONS Our results suggest that dexmedetomidine as an adjunct to general anesthesia attenuated the rise in proinflammatory cytokines, providing protective effects in tourniquet-induced IRI.
Iterative Support Vector Machine for Hyperspectral Image Classification

In hyperspectral image classification spectral information and spatial information are always integrated to improve the classification accuracy. This paper develops an iterative version of support vector machine, to be called iterative SVM (ISVM) to perform hyperspectral image classification by extracting spatial information iteratively via feedback loops. In processing ISVM an initial hyperspectral data cube is obtained by combining the original image and its first principal component. SVM is then implemented to the resulting data cube to produce an initial classification map. In each feedback loop, a Gaussian filter is applied to obtain the spatial information of the SVM-classification map so that the Gaussian-filtered map is further fed back to combine with the currently processed hyperspectral cube for the next round of iteration. As for terminating the iterative process an automatic stopping rule is also developed. To evaluate the performance of ISVM real image xperiments are conducted in comparison with state-of-the-art spectral-spatial hyperspectral classification methods. The experiment results demonstrate that ISVM performed better by providing higher classification accuracy.
Devising a Co-Creative Digital Content Development Pipeline for Experiential Healthcare Education

In the medical education domain one of the core challenges in content generation is that of massive topical content. Information and Communication Technologies (ICT) have shaped interventions for healthcare and wellness from their beginning. Currently, Technology Enhanced Learning (TEL) in medicine is mostly based on case-based or problem-based learning (CBL/PBL) amongst other instructional models. TEL resources for these models include simulations, scenario narratives and other structured task-based learning episodes. A key challenge for widespread use of such experiential modalities is the rapid deployment of digital content for the extensive breadth and depth of healthcare related knowledge. This is the area where co-creative methodologies are useful. This work describes the design of a co-creative digital content development pipeline for medical education. For this AGILE development paradigms and semantic provisions are required in order to heavily modify current game development engines with specifically defined Visual Data Structures. These tools and paradigms are used to describe a specific but versatile co-creative development pipeline for experiential medical education resources. This pipeline is the precursor for a co-creative environment between healthcare professionals and developers for educationally valid and experientially rich medical education resources.
The comparison of two professional prophylaxis systems in plaque removal and debonding of orthodontic brackets

Introduction: During orthodontic treatment, patients are expected to undergo consistent periodic prophylaxis as a result of complexities in oral hygiene. The aim of this study was to investigate, analyze, and compare the effectiveness of two professional prophylaxis systems of rubber cup polishing (RCP) and air-powder polishing (APP) in patients undergoing orthodontic treatment based on different levels of plaque and pigment on teeth and the extent of attachment of brackets after prophylaxis. Materials and Methods: A total of 50 patients were selected for this clinical trial study. A couple of months after the orthodontic treatment, the patients were placed on professional prophylaxis in terms of the plaque index. Randomized cross-arch prophylaxis (right maxilla with left mandible), RCP (left maxilla with right mandible), and APP were carried out on the patients. The main indexes in the study (number of plaque removal and debonding), the work time duration per second, and convenience of patients using visual analog scale in each of the methods were evaluated as minor factors. Data were recorded in a special checklist. Results: The mean of the plaque index and the time spent on RCP were observed to be higher than APP. The average satisfaction rate of patients with RCP was higher than APP. The debonding of the bracket and the tooth was not observed in any of the methods. Conclusion: APP is more efficient than RCP for performing professional prophylaxis of orthodontic patients.
Sequential Recognition of Manipulation Actions Using Discriminative Superpixel Group Mining

Activities such as those involved in food preparation involve interactions between hands, tools and multiple manipulated objects that affect them in visually complex ways making recognition of their constituent actions challenging. We describe a system that classifies action classes in such a setting based on discriminative spatio-temporal superpixel groups. The entire system operates sequentially enabling online action recognition. We obtain state-of-the-art results whilst employing a compact, interpretable representation.
Field-Based and Lab-Based Assisted Jumping: Unveiling the Testing and Training Implications

Purpose: Assisted jumping can supplement resistance training and traditional plyometric training to increase vertical jump performance. However, as coaches may choose to make field-based decisions based on lab-based research, this study determined whether a field-based assisted jumping set-up results in different ground contact times (CT), take off forces (TOF), flight times (FT), and impact forces (IF) compared to a lab-based set-up. Methods: Eighteen active males (24.8 ± 3.0 yr; 178.8 ± 7.8 cm; 77.8 ± 7.8 kg) performed two sessions of assisted jumping: one with each hand holding a commercially available resistance band (1m) that was attached to a pull-up bar (FIELD), and the other with assistance from a custom-built system of ropes, pulleys, and long (3 m) elastic bands (LAB). With each set-up, subjects performed five sets of five countermovement jumps on a force plate. Each set was performed with either bodyweight (BW), 90, 80, 70, or 60% of BW, which was achieved by either grabbing higher or lower on the bands during FIELD, or by being pulled upward via a full-body harness during LAB. The order of each visit was counter-balanced, and the order of jumps within each visit was quasi-randomized. Data from the 90, 80, 70, and 60% trials for each set-up were then expressed relative to the data of BW jumps, and these relative values were then used for analysis. Results: CTFIELD was less than CTLAB at 80, 70, and 60%. FTFIELD was greater than FTLAB at 90 and 80%, but FTLAB became greater at 60%. TOF and IF remained unchanged during LAB, but TOFFIELD was consistently less than TOF during BW, with IFFIELD generally being greater than IFLAB. Conclusion: If the purpose of assisted jumping is to spend less time on the ground without decreasing force, systems with finite adjustments and longer bands like LAB should be used. However, shorter bands similar to FIELD may also be used; but due to the larger variability of assistance throughout the range of motion, such systems may alter the neuromuscular characteristics of the jump in other ways that should be investigated in future research.
Robust Low Rank Approxiamtion via Inliers Selection

Robust low rank approximation is central to many computer version and data mining domains. Although numerous algorithms have been developed to cope with this issue, most of them considered only the setting that the input data matrix is contaminated by sparse noise and ignored the existing of column-outliers. Here, the outliers represent the columns corrupted by noise completely. To recover the low rank component of a data matrix contaminated by sparse noise and outliers simultaneously, in this paper we first implement a novel robust low rank approximation model to recover a low rank matrix $L$, and then utilize the subspace obtained by conducting SVD on $L$ to estimate the low rank component of residual data. Numer-ical experiments on real data and synthetic data demonstrate the superiority of proposed method.
Consolidating user relevance criteria: A meta-ethnography of empirical studies

The purpose of this study is to bring together empirical studies on user-based relevance criteria and to provide a conceptual framework for the organization of factors affecting relevance judgments and information selection. Through systematic analysis applying a meta-ethnographic approach we cluster compatible relevance criteria definitions and resolve semantic differences of the same named criteria with diverse definitions. The consolidated criteria are factored into four dimensions: situational factors pertaining to user's context, task, and cognitive and affective states, user criteria pertaining to users' perceptions and needs, document information elements (DIEs) pertaining to information attributes, and value judgments pertaining to what an information object means to the user). For further investigation, we propose a set of matrices to depict the relationships between dimensions as an effective tool for developing a better understanding of the relationship and interaction between users' relevance factors. These relationships can guide IR system design and improvements.
A Linear Approach to Fault Analysis and Intervention in Boolean Systems

The mutations of a complex systemic disease like cancer can be modeled as stuck-at faults in the Boolean system paradigm. For a class of multiple faults, the fault identification is exceptionally significant under the incomplete access of all the underlying proteins of the system. A comprehensive linear framework has been developed in this manuscript to identify the class of faults under a set of homeostatic input conditions. An algorithm is developed to design new reporters to improve the observability. The other aspect of this manuscript lies in controlling the manifestation of the mutations, which is the essential objective of systems medicine research. The primary goal is to synthesize a cocktail of drug molecules (combination therapy) from a set of existing targeted drugs. The controllability results are included in this paper to understand the problem formally. An improvement of controllability algorithm is discussed to design new target drugs if the available drugs fail to accommodate the underlying fault set. The results are presented for Boolean maps and Boolean control networks. Biological examples are given to highlight the relevant results.
Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data.