Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method.
Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus

Type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD) commonly exist together. It has been regarded as a manifestation of the metabolic syndrome. The presentations of NAFLD range from simple steatosis (NAFL), nonalcoholic steatohepatitis (NASH), and cirrhosis. NAFLD has a prevalence of 70% among T2DM patients. Overweight/obesity and insulin resistance (IR) have been strongly linked with NAFLD. Noninvasive assessment and staging of disease are based on clinical parameters such as age, sex, liver function test, platelet count, lipid profile, BMI, and imaging modalities such as USG, transient elastography (TE), and magnetic resonance imaging mass spectroscopy. Such clinical scoring systems and TE are useful in the early detection of NAFLD and predicting fibrosis. The principle behind the management of NAFLD with T2DM involves an indirect effect through improvement in IR and glycemia and thus is used for the treatment of T2DM as well.
Degenerate String Comparison and Applications

A generalised degenerate string (GD string) S^ is a sequence of n sets of strings of total size N, where the ith set contains strings of the same length k_i but this length can vary between different sets. We denote the sum of these lengths k_0, k_1,. ,k_{n-1} by W. This type of uncertain sequence can represent, for example, a gapless multiple sequence alignment of width W in a compact form. Our first result in this paper is an O(N+M)-time algorithm for deciding whether the intersection of two GD strings of total sizes N and M, respectively, over an integer alphabet, is non-empty. This result is based on a combinatorial result of independent interest: although the intersection of two GD strings can be exponential in the total size of the two strings, it can be represented in only linear space. A similar result can be obtained by employing an automata-based approach but its cost is alphabet-dependent. We then apply our string comparison algorithm to compute palindromes in GD strings. We present an O(min{W,n^2}N)-time algorithm for computing all palindromes in S^. Furthermore, we show a similar conditional lower bound for computing maximal palindromes in S^. Finally, proof-of-concept experimental results are presented using real protein datasets.
Application of CRISPR/Cas9 to Tragopogon (Asteraceae), an evolutionary model for the study of polyploidy

Tragopogon (Asteraceae) is an excellent natural system for studies of recent polyploidy. Development of an efficient CRISPR/Cas9‐based genome editing platform in Tragopogon will facilitate novel studies of the genetic consequences of polyploidy. Here, we report our initial results of developing CRISPR/Cas9 in Tragopogon. We have established a feasible tissue culture and transformation protocol for Tragopogon. Through protoplast transient assays, use of the TragCRISPR system (i.e. the CRISPR/Cas9 system adapted for Tragopogon) was capable of introducing site‐specific mutations in Tragopogon protoplasts. Agrobacterium‐mediated transformation with Cas9‐sgRNA constructs targeting the phytoene desaturase gene (TraPDS) was implemented in this model polyploid system. Sequencing of PCR amplicons from the target regions indicated simultaneous mutations of two alleles and four alleles of TraPDS in albino shoots from Tragopogon porrifolius (2x) and Tragopogon mirus (4x), respectively. The average proportions of successfully transformed calli with the albino phenotype were 87% and 78% in the diploid and polyploid, respectively. This appears to be the first demonstration of CRISPR/Cas9‐based genome editing in any naturally formed neopolyploid system. Although a more efficient tissue culture system should be developed in Tragopogon, application of a robust CRISPR/Cas9 system will permit unique studies of biased fractionation, the gene‐balance hypothesis and cytonuclear interactions in polyploids. In addition, the CRISPR/Cas9 platform enables investigations of those genes involved in phenotypic changes in polyploids and will also facilitate novel functional biology studies in Asteraceae. Our workflow provides a guide for applying CRISPR/Cas9 to other nongenetic model plant systems.
Use of Uterine Characteristics to Improve Fertility-Sparing Diagnosis of Adenomyosis.

Objective: To describe patient demographics, determine accuracy of clinical diagnosis, and evaluate reliability of laparoscopic uterine characteristics in the diagnosis of adenomyosis. Materials and Methods: Enrollment included 117 patients undergoing laparoscopic hysterectomy for benign indications. Intraoperatively, the attending surgeon predicted uterine weight; evaluated the presence of fibroids; and commented on the uterus' shape, color, and consistency while probing it with a blunt instrument. A prediction was also made about whether final pathology would reveal adenomyosis. Standardized video recordings were obtained at the start of the case. Each video was viewed retrospectively twice by three expert surgeons in a blinded fashion. Uterine characteristics were reported again with a prediction of whether or not there would be a pathologic diagnosis of adenomyosis. These data were used to calculate inter-and intrarater reliability of diagnosis. Results: Women with adenomyosis were more likely to complain of midline pain as opposed to lateral or diffuse pain (p = 0.048) with no difference in the timing of the pain (p = 0.404), compared to patients without adenomyosis. Uterine tenderness on examination was not an accurate predictor of adenomyosis (p = 0.566). Preoperative diagnosis of adenomyosis by clinicians was poor, with an accuracy rate of 51.7%. None of the intraoperative uterine characteristics were significant for predicting adenomyosis on final pathology, nor was any combination of the features (p = 0.546). Retrospective video reviews failed to reveal any uterine characteristics that generated consistent inter- or intrarater reliability (Krippendorff's α < 0.7) in making the diagnosis of adenomyosis. Conclusions: Clinical and video diagnosis of adenomyosis have low accuracy with no uterine characteristics consistently or reliably predicting adenomyosis on final pathology. (J GYNECOL SURG 34:183).
Data augmentation using synthetic data for time series classification with deep residual networks

Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warping distance, for a deep learning model for TSC. To evaluate the potential of augmenting the training set, we performed extensive experiments using the UCR TSC benchmark. Our preliminary experiments reveal that data augmentation can drastically increase deep CNN's accuracy on some datasets and significantly improve the deep model's accuracy when the method is used in an ensemble approach.
Word-Level Loss Extensions for Neural Temporal Relation Classification

Unsupervised pre-trained word embeddings are used effectively for many tasks in natural language processing to leverage unlabeled textual data. Often these embeddings are either used as initializations or as fixed word representations for task-specific classification models. In this work, we extend our classification model’s task loss with an unsupervised auxiliary loss on the word-embedding level of the model. This is to ensure that the learned word representations contain both task-specific features, learned from the supervised loss component, and more general features learned from the unsupervised loss component. We evaluate our approach on the task of temporal relation extraction, in particular, narrative containment relation extraction from clinical records, and show that continued training of the embeddings on the unsupervised objective together with the task objective gives better task-specific embeddings, and results in an improvement over the state of the art on the THYME dataset, using only a general-domain part-of-speech tagger as linguistic resource.
CTLA-4 Mediates Inhibitory Function of Mesenchymal Stem/Stromal Cells

Mesenchymal stem/stromal cells (MSCs) are stem cells of the connective tissue, possess a plastic phenotype, and are able to differentiate into various tissues. Besides their role in tissue regeneration, MSCs perform additional functions as a modulator or inhibitor of immune responses. Due to their pleiotropic function, MSCs have also gained therapeutic importance for the treatment of autoimmune diseases and for improving fracture healing and cartilage regeneration. However, the therapeutic/immunomodulatory mode of action of MSCs is largely unknown. Here, we describe that MSCs express the inhibitory receptor CTLA-4 (cytotoxic T lymphocyte antigen 4). We show that depending on the environmental conditions, MSCs express different isoforms of CTLA-4 with the secreted isoform (sCTLA-4) being the most abundant under hypoxic conditions. Furthermore, we demonstrate that the immunosuppressive function of MSCs is mediated mainly by the secretion of CTLA-4. These findings open new ways for treatment when tissue regeneration/fracture healing is difficult.
Deep Learning with Predictive Control for Human Motion Tracking

We propose to combine model predictive control with deep learning for the task of accurate human motion tracking with a robot. We design the MPC to allow switching between the learned and a conservative prediction. We also explored online learning with a DyBM model. We applied this method to human handwriting motion tracking with a UR-5 robot. The results show that the framework significantly improves tracking performance.
The Endometriotic Tumor Microenvironment in Ovarian Cancer

Women with endometriosis are at increased risk of developing ovarian cancer, specifically ovarian endometrioid, low-grade serous, and clear-cell adenocarcinoma. An important clinical caveat to the association of endometriosis with ovarian cancer is the improved prognosis for women with endometriosis at time of ovarian cancer staging. Whether endometriosis-associated ovarian cancers develop from the molecular transformation of endometriosis or develop because of the endometriotic tumor microenvironment remain unknown. Additionally, how the presence of endometriosis improves prognosis is also undefined, but likely relies on the endometriotic microenvironment. The unique tumor microenvironment of endometriosis is composed of epithelial, stromal, and immune cells, which adapt to survive in hypoxic conditions with high levels of iron, estrogen, and inflammatory cytokines and chemokines. Understanding the unique molecular features of the endometriotic tumor microenvironment may lead to impactful precision therapies and/or modalities for prevention. A challenge to this important study is the rarity of well-characterized clinical samples and the limited model systems. In this review, we will describe the unique molecular features of endometriosis-associated ovarian cancers, the endometriotic tumor microenvironment, and available model systems for endometriosis-associated ovarian cancers. Continued research on these unique ovarian cancers may lead to improved prevention and treatment options.
Cognitive Interpretation Bias: The Effect of a Single Session Moderate Exercise Protocol on Anxiety and Depression

Research conducted within the cognitive bias modification (CBM) paradigm has revealed that cognitive biases such as negative cognitive interpretation biases contribute to mental health disorders such as anxiety (Beard, 2011). It has been shown that exercise reduces anxiety (Ensari et al., 2015). Exercise has also been found to reduce negative cognitive attention biases (Tian and Smith, 2011), however, no research to date has investigated the effect of exercise on cognitive interpretation bias. The key aims of the current project is to investigate whether moderate exercise reduces self-reported symptoms of depression and stress. Additionally, to establish which intensity of exercise is required to achieve anxiety reduction and reduce an individual’s negative cognitive interpretation biases. Study 1 recruited a healthy sample of adult participants who were randomly assigned to one of two conditions: a walking exercise protocol or a control condition (n = 2 × 12). Participants completed anxiety and cognitive interpretation bias measures before and after the walking exercise or control condition. Those in the walking exercise condition presented less symptoms of trait anxiety on a measure of state and trait anxiety inventory (STAI), compared to controls relative to baseline measures following the intervention. Study 2 recruited frequent exercisers who were assigned to an exercise or control group (n = 2 × 24). Participants completed anxiety, depression, psychological stress, and cognitive interpretation bias measures before and after the exercise or control condition. Following the intervention, negative interpretation biases decreased in the exercise group and stayed stable in the control group. The exercise group also had significantly decreased anxiety, depression, and stress measures after the exercise condition, while controls did not. The research concludes that CBM holds promise for the management of mood disorders and exercise is an effective accompaniment to psychotherapy.
As Elasticidades Setoriais Das Exportações Brasileiras: Uma Análise Empirica No Curto E Longo Prazo

The study aims to analyze the performance of Brazilian exports of Primary and Industrial products to Asia and Blocs NAFTA and the EU for the period 1999-2013. Therefore, the traditional literature of foreign trade, inferred that the determinants of exports are pegged to an index of income from trading partners, the index of relative price of exports and the exchange rate and, through statistical tools, equations for short and long run for the Brazilian sector exports were estimated. Thus, from an extension of the traditional theoretical model of imperfect substitutes, prepared by Goldstein and Khan (1978), the method of Johansen cointegration and Error Correction Model (ECM) for determining the elasticities was used. The results showed that, compared to sectoral impacts, the elasticities of trade with Asia and Nafta that the class of commodities is more sensitive to income, while that of industrial goods is more sensitive to exchange rate variations. On the other hand, exports to the EU, income is the primary determinant of the class of manufactured goods, while commodities are more sensitive to exchange rates. In reference to average sectorial elasticities, it can be said that income is the main determinant of commodity exports, while industrial products are more sensitive to changes in price levels. Finally, the negative effect of exchange rate volatility, was found to be a dynamic, predominantly short term. Key-Words: Brazilian Exports Sector. Model of Imperfect Substitutes. Elasticities. Área 7: Economia Internacional Classificação JEL: F 13 1 Doutorando PIMES/UFPE e Mestre pelo PPGE&D/UFSM. E-mail: dieisonlenon@yahoo.com. br 2 Professor Adjunto do Departamento de Economia e do Programa de Pós-Graduação em Economia e Desenvolvimento (PPG&D) da Universidade Federal de Santa Maria (UFSM). E-mail: prfeistel@yahoo.com. br 3 Doutor em Economia pela Universidade de São Paulo. Professor do Departamento de Economia e do Programa de PósGraduação em Economia (PIMES) da Universidade Federal de Pernambuco (UFPE) e Pesquisador do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). E-mail: abarrantes@uol.com. br 4 Professor do Mestrado em Economia e do Mestrado Profissional em Gestão e Negócios/ Unisinos e Pesquisador do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). E-mail: aazevedo@unisinos.br
Electroretinography and Rhegmatogenous Retinal Detachment

217 Electroretinography (ERG) is commonly used to assess the physiological status of the retina. It has been extensively studied in inherited retinal and choroidal diseases, and is a main paraclinical examination in the diagnosis and follow‐up of patients affected by retinal and choroidal dystrophies. [1‐3] Other possible clinical applications of ERG include, but are not limited to, assessing the toxicity of ocular drugs[4] and evaluating the potential for vision in blind eyes. [5] Few studies have evaluated the role of retinal electrophysiology testing in patients with retinal detachment and after retinal reattachment surgery using full‐field[6] and multifocal[7] ERG. Both animal and human studies have shown that retinal detachment causes the loss of the outer segments of photoreceptor cells. [8,9] Both cone and rod photoreceptors are affected in retinal detachment. However, the magnitude of the damage and its likely location in the retina are not clearly known. In this issue of the Journal of Ophthalmic and Vision Research, Lin et al[10] report ERG findings in eyes with rhegmatogenous retinal detachment (RRD) before surgery. The authors found that eyes with RRD had significant decreases in a and b wave amplitudes of both rod and cone responses when compared to fellow normal eyes. Interestingly, the magnitude of change was similar for dark and light adapted responses. The results of the above study indicate that in RRD, outer retinal dysfunction equally affects the rods and cones, and is accompanied by inner retinal dysfunction. This study is unique in that the ERG testing was performed on both photopic and scotopic components before any surgical intervention. Kim et al[1] have reported changes in scotopic ERG in patients with RRD before surgery. They reported that the amplitudes of scotopic a and b waves were significantly decreased in the eye with detached retina when compared to fellow normal eyes. The amplitudes improved after successful surgery. Hayashi and Yamamoto[11] evaluated changes in short‐wavelength (S), mixed long‐wavelength (L), and middle‐wavelength (M) sensitive cone ERG recordings before and after Electroretinography and Rhegmatogenous Retinal Detachment
Development of a Screening Platform to Identify Small Molecules That Modify ELP1 Pre-mRNA Splicing in Familial Dysautonomia

Familial dysautonomia (FD) is an autonomic and sensory neuropathy caused by a mutation in the splice donor site of intron 20 of the ELP1 gene. Variable skipping of exon 20 leads to a tissue-specific reduction in the level of ELP1 protein. We have shown that the plant cytokinin kinetin is able to increase cellular ELP1 protein levels in vivo and in vitro through correction of ELP1 splicing. Studies in FD patients determined that kinetin is not a practical therapy due to low potency and rapid elimination. To identify molecules with improved potency and efficacy, we developed a cell-based luciferase splicing assay by inserting renilla (Rluc) and firefly (Fluc) luciferase reporters into our previously well-characterized ELP1 minigene construct. Evaluation of the Fluc/Rluc signal ratio enables a fast and accurate way to measure exon 20 inclusion. Further, we developed a secondary assay that measures ELP1 splicing in FD patient-derived fibroblasts. Here we demonstrate the quality and reproducibility of our screening method. Development and implementation of this screening platform has allowed us to efficiently screen for new compounds that robustly and specifically enhance ELP1 pre-mRNA splicing.
Injectable Hyaluronic Acid-co-Gelatin Cryogels for Tissue-Engineering Applications

Polymeric scaffolds such as hydrogels can be engineered to restore, maintain, or improve impaired tissues and organs. However, most hydrogels require surgical implantation that can cause several complications such as infection and damage to adjacent tissues. Therefore, developing minimally invasive strategies is of critical importance for these purposes. Herein, we developed several injectable cryogels made out of hyaluronic acid and gelatin for tissue-engineering applications. The physicochemical properties of hyaluronic acid combined with the intrinsic cell-adhesion properties of gelatin can provide suitable physical support for the attachment, survival, and spreading of cells. The physical characteristics of pure gelatin cryogels, such as mechanics and injectability, were enhanced once copolymerized with hyaluronic acid. Reciprocally, the adhesion of 3T3 cells cultured in hyaluronic acid cryogels was enhanced when formulated with gelatin. Furthermore, cryogels had a minimal effect on bone marrow dendritic cell activation, suggesting their cytocompatibility. Finally, in vitro studies revealed that copolymerizing gelatin with hyaluronic acid did not significantly alter their respective intrinsic biological properties. These findings suggest that hyaluronic acid-co-gelatin cryogels combined the favorable inherent properties of each biopolymer, providing a mechanically robust, cell-responsive, macroporous, and injectable platform for tissue-engineering applications.