Miracle-dz/UniFAR-FTUs huggingface dataset:
Revitalized and synovialized allograft for intrasynovial flexor tendon reconstruction in an in vivo canine model

This study was to test our hypothesis that flexor tendon reconstruction with an allograft revitalized with bone marrow stromal cells (BMSCs) and synovialized with carbodiimide derivatized autologous synovial fluid (cd‐SYN) would result in better digit functional restoration than the conventional allograft tendon. A total of 32 flexor digital profundus tendons from the second and fifth digit of 16 dogs were created a repair failure model first. Then, failed‐repaired tendons were reconstructed with either a revitalized‐synovialized allograft tendon or a clinical standard autograft tendon (control group). The allograft tendon was seeded with autologous BMSCs in multiple slits and the graft surface was coated with cd‐SYN. A 6 weeks after tendon reconstruction, the digits were harvested and evaluated for digit function, adhesion status, tendon gliding resistance, attachment strength, cell viability, and histologic factors. The allograft group had significantly improved digit function compared with the control group through decreased work of flexion, increased digit range of motion under 2‐Newton force, and less adhesion score (p < .05). However, the distal attachment‐site strength and stiffness in the allograft tendon were significantly weaker than the autografts (p < .05). No significant difference was found for gliding resistance. Histologically, allograft tendons coated with allograft had smoother surfaces and showed tendon‐to‐bone and tendon‐to‐tendon incorporation. Viable BMSCs were found in the tendon slits 6 weeks after the graft. In conclusion, cellular lubricant‐based modification of allograft tendons improved digit function and reduced the adhesions compared with autograft for flexor tendon reconstruction. However, improvement of graft‐to‐host tendon healing is still challenging. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2218–2227, 2018.
Calpain 3 and CaMKIIβ signaling are required to induce HSP70 necessary for adaptive muscle growth after atrophy

Abstract Mutations in CAPN3 cause autosomal recessive limb girdle muscular dystrophy 2A. Calpain 3 (CAPN3) is a calcium dependent protease residing in the myofibrillar, cytosolic and triad fractions of skeletal muscle. At the triad, it colocalizes with calcium calmodulin kinase IIβ (CaMKIIβ). CAPN3 knock out mice (C3KO) show reduced triad integrity and blunted CaMKIIβ signaling, which correlates with impaired transcriptional activation of myofibrillar and oxidative metabolism genes in response to running exercise. These data suggest a role for CAPN3 and CaMKIIβ in gene regulation that takes place during adaptation to endurance exercise. To assess whether CAPN3- CaMKIIβ signaling influences skeletal muscle remodeling in other contexts, we subjected C3KO and wild type mice to hindlimb unloading and reloading and assessed CaMKIIβ signaling and gene expression by RNA-sequencing. After induced atrophy followed by 4 days of reloading, both CaMKIIβ activation and expression of inflammatory and cellular stress genes were increased. C3KO muscles failed to activate CaMKIIβ signaling, did not activate the same pattern of gene expression and demonstrated impaired growth at 4 days of reloading. Moreover, C3KO muscles failed to activate inducible HSP70, which was previously shown to be indispensible for the inflammatory response needed to promote muscle recovery. Likewise, C3KO showed diminished immune cell infiltration and decreased expression of pro-myogenic genes. These data support a role for CaMKIIβ signaling in induction of HSP70 and promotion of the inflammatory response during muscle growth and remodeling that occurs after atrophy, suggesting that CaMKIIβ regulates remodeling in multiple contexts: endurance exercise and growth after atrophy.
Numerical analysis of granular bed behavior in thermocline storage tank and bed/wall interactions

Thermal Energy Storage (TES) systems are central elements of various types of power plants operated using renewable energy sources. Packed bed TES can be considered as a cost–effective solution in concentrated solar power plants (CSP). Such a device is made up of a tank filled with a granular bed through which heat-transfer fluid circulates. However, in such devices, the thermal stresses may accumulate from cycle to another due to differential thermal dilatation between filler and tank walls, leading to plastic deformations in the tank shell and the failure of the tank might happen. This paper aims at studying the evolution of tank wall stresses over granular bed thermal cycles, taking into account both thermal and mechanical loads, with a numerical model based on the discrete element method (DEM). Simulations were performed to study two different thermal configurations: (i) the tank is heated homogenously along its height or (ii) with a vertical gradient of temperature. Then, the resulting loading stresses applied on the tank are compared as well the response of the internal granular material.Thermal Energy Storage (TES) systems are central elements of various types of power plants operated using renewable energy sources. Packed bed TES can be considered as a cost–effective solution in concentrated solar power plants (CSP). Such a device is made up of a tank filled with a granular bed through which heat-transfer fluid circulates. However, in such devices, the thermal stresses may accumulate from cycle to another due to differential thermal dilatation between filler and tank walls, leading to plastic deformations in the tank shell and the failure of the tank might happen. This paper aims at studying the evolution of tank wall stresses over granular bed thermal cycles, taking into account both thermal and mechanical loads, with a numerical model based on the discrete element method (DEM). Simulations were performed to study two different thermal configurations: (i) the tank is heated homogenously along its height or (ii) with a vertical gradient of temperature. Then, the resulting loading stress.
Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations

We extend the work of Wieting et al. (2017), back-translating a large parallel corpus to produce a dataset of more than 51 million English-English sentential paraphrase pairs in a dataset we call ParaNMT-50M. We find this corpus to be cover many domains and styles of text, in addition to being rich in paraphrases with different sentence structure, and we release it to the community. To show its utility, we use it to train paraphrastic sentence embeddings using only minor changes to the framework of Wieting et al. (2016b). The resulting embeddings outperform all supervised systems on every SemEval semantic textual similarity (STS) competition, and are a significant improvement in capturing paraphrastic similarity over all other sentence embeddings We also show that our embeddings perform competitively on general sentence embedding tasks. We release the corpus, pretrained sentence embeddings, and code to generate them. We believe the corpus can be a valuable resource for automatic paraphrase generation and can provide a rich source of semantic information to improve downstream natural language understanding tasks.
Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus and the risk for post-transplant diabetes. Artificial neural network (ANN) and logistic regression (LR) models were used to predict the bioavailability of tacrolimus and risk for post-transplant diabetes, respectively. The five-fold cross-validation of ANN model showed good correlation with the experimental data of bioavailability (r2 = 0.93–0.96). Younger age, male gender, optimal body mass index were shown to exhibit lower bioavailability of tacrolimus. ABCB1 1236 C>T and 2677G>T/A showed inverse association while CYP3A5*3 showed a positive association with the bioavailability of tacrolimus. Gender bias was observed in the association with ABCB1 3435 C>T polymorphism. CYP3A5*3 was shown to interact synergistically in increasing the bioavailability in combination with ABCB1 1236 TT or 2677GG genotypes. LR model showed an independent association of ABCB1 2677 G>T/A with post transplant diabetes (OR: 4.83, 95% CI: 1.22–19.03). Multifactor dimensionality reduction analysis (MDR) revealed that synergistic interactions between CYP3A5*3 and ABCB1 2677 G>T/A as the determinants of risk for post-transplant diabetes. To conclude, the ANN and MDR models explore both individual and synergistic effects of variables in modulating the bioavailability of tacrolimus and risk for post-transplant diabetes.
Superhuman AI for heads-up no-limit poker: Libratus beats top professionals

Libratus versus humans Pitting artificial intelligence (AI) against top human players demonstrates just how far AI has come. Brown and Sandholm built a poker-playing AI called Libratus that decisively beat four leading human professionals in the two-player variant of poker called heads-up no-limit Texas hold'em (HUNL). Over nearly 3 weeks, Libratus played 120,000 hands of HUNL against the human professionals, using a three-pronged approach that included precomputing an overall strategy, adapting the strategy to actual gameplay, and learning from its opponent. Science, this issue p. 418 An artificial intelligence program called Libratus played 120,000 hands of a two-player variant of poker and beat four leading human professionals. No-limit Texas hold’em is the most popular form of poker. Despite artificial intelligence (AI) successes in perfect-information games, the private information and massive game tree have made no-limit poker difficult to tackle. We present Libratus, an AI that, in a 120,000-hand competition, defeated four top human specialist professionals in heads-up no-limit Texas hold’em, the leading benchmark and long-standing challenge problem in imperfect-information game solving. Our game-theoretic approach features application-independent techniques: an algorithm for computing a blueprint for the overall strategy, an algorithm that fleshes out the details of the strategy for subgames that are reached during play, and a self-improver algorithm that fixes potential weaknesses that opponents have identified in the blueprint strategy.
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

Our first contribution in this paper is a theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the least squares loss function. We prove that the rate of convergence of an ideal curriculum learning method in monotonically increasing with the difficulty of the examples, and that this increase in convergence rate is monotonically decreasing as training proceeds. In our second contribution we analyze curriculum learning in the context of training a CNN for image classification. Here one crucial problem is the means to achieve a curriculum. We describe a method which infers the curriculum by way of transfer learning from another network, pre-trained on a different task. While this approach can only approximate the ideal curriculum, we observe empirically similar behavior to the one predicted by the theory, namely, a significant boost in convergence speed at the beginning of training. When the task is made more difficult, improvement in generalization performance is observed. Finally, curriculum learning exhibits robustness against unfavorable conditions such as strong regularization.
2,6‐Difluorobenzamide Inhibitors of Bacterial Cell Division Protein FtsZ: Design, Synthesis, and Structure–Activity Relationships

A wide variety of drug‐resistant microorganisms are continuously emerging, restricting the therapeutic options for common bacterial infections. Antimicrobial agents that were originally potent are now no longer helpful, due to their weak or null activity toward these antibiotic‐resistant bacteria. In addition, none of the recently approved antibiotics affect innovative targets, resulting in a need for novel drugs with innovative antibacterial mechanisms of action. The essential cell division protein filamentous temperature‐sensitive Z (FtsZ) has emerged as a possible target, thanks to its ubiquitous expression and its homology to eukaryotic β‐tubulin. In the latest years, several compounds were shown to interact with this prokaryotic protein and selectively inhibit bacterial cell division. Recently, our research group developed interesting derivatives displaying good antibacterial activities against methicillin‐resistant Staphylococcus aureus, as well as vancomycin‐resistant Enterococcus faecalis and Mycobacterium tuberculosis. The aim of the present study was to summarize the structure–activity relationships of differently substituted heterocycles, linked by a methylenoxy bridge to the 2,6‐difluorobenzamide, and to validate FtsZ as the real target of this class of antimicrobials.
Litsea cubeba leaf essential oil from Vietnam: chemical diversity and its impacts on antibacterial activity

The threat of bacterial resistance to antibiotics has created an urgent need to develop new antimicrobials. The aim of this study was to characterize the chemical diversity of Litsea cubeba leaf essential oil (EO) and its impacts on the antibacterial activity against pathogenic bacteria. Essential oils collected from seven provinces in North Vietnam (n = 25) were characterized by their high content in either 1,8‐cineole or linalool. Linalool‐type EOs were more effective against the eight bacterial strains tested than 1,8‐cineole‐type. Oil samples, LC19 (50% 1,8‐cineole) and BV27 (94% linalool), were selected to investigate their antibacterial mechanisms against Escherichia coli. A strong bactericidal effect was observed after 4 and 2 h of exposure respectively. Microscopic analysis of treated E. coli cultures clearly showed that EOs caused changes in cell morphology, loss of integrity and permeability of the cell membrane, as well as DNA loss. However, the effects of both EOs were distinct. LC19 mostly affected cell membrane, led to a significant cell filamentation rate and altered cell width, whereas BV27 damaged cell membrane integrity leading to cell permeabilization and altered nucleoid morphology with the appearance of spot and visibly altered compaction.
Clonal Dissemination of OXA-232 Carbapenemase-Producing Klebsiella pneumoniae in Neonates

ABSTRACT Five OXA-232 carbapenemase-producing Klebsiella pneumoniae isolates, belonging to the pandemic clone sequence type 15 (ST15), were isolated from neonates and coproduced blaCTX-M-15 and blaSHV-1 genes. All isolates were resistant to ertapenem (MICs of >32 μg/ml) and meropenem (MICs of 4 to 8 μg/ml) and susceptible or intermediate to imipenem (MICs of 1 to 2 μg/ml). The blaOXA-232 gene was located on a ColE-type transformable plasmid of 6,141 bp. To the best of our knowledge, this is the first report of OXA-232 carbapenemase among clinical isolates in China.
Parental age and attention-deficit/hyperactivity disorder (ADHD).

Background Previous studies have suggested that young mothers more often have children with ADHD. We used sibling comparisons to examine the nature of this association and to investigate if this association is explained by early environment or genetic and socioeconomic factors. Methods A large population-based cohort including all singletons born in Denmark from 1 January 1991 through 31 December 2005 was followed from birth until 30 April 2011. Data were available for 94% ( N = 943 785) of the population. Offspring ADHD was identified by an ICD-10 diagnosis of Hyperkinetic Disorder (HKD). We used sibling-matched Cox regression to control for genetic and socioeconomic factors. Results In the population cohort we found that children born by parents aged 20 years or younger had more than twice the risk of being diagnosed with ADHD compared with children with parents between 26 and 30 years of age. When comparing full siblings the associations were attenuated, but we found a trend of increased risk of ADHD with decreasing maternal age, which was not seen for paternal age. Conclusions Sibling comparisons suggested that the associations between both maternal and paternal age and ADHD are partly explained by common genetic and socioeconomic factors. The trend of increased risk of ADHD with decreasing maternal age, but not with paternal age, may be linked to pregnancy or early-life environmental factors. Even though only a smaller part of the association can be attributed to environmental factors, there is a public health interest to support young parents through their first years of parenthood.
Ethnicity and long-term course and outcome of psychotic disorders in a UK sample: the ÆSOP-10 study

Background The incidence of psychotic disorders is elevated in some minority ethnic populations. However, we know little about the outcome of psychoses in these populations. Aims To investigate patterns and determinants of long-term course and outcome of psychoses by ethnic group following a first episode. Method ÆSOP-10 is a 10-year follow-up of an ethnically diverse cohort of 532 individuals with first-episode psychosis identified in the UK. Information was collected, at baseline, on clinical presentation and neurodevelopmental and social factors and, at follow-up, on course and outcome. Results There was evidence that, compared with White British, Black Caribbean patients experienced worse clinical, social and service use outcomes and Black African patients experienced worse social and service use outcomes. There was evidence that baseline social disadvantage contributed to these disparities. Conclusions These findings suggest ethnic disparities in the incidence of psychoses extend, for some groups, to worse outcomes in multiple domains.
A flexible semiparametric modeling approach for doubly censored data with an application to prostate cancer

Doubly censored data often arise in medical studies of disease progression involving two related events for which both an originating and a terminating event are interval-censored. Although regression modeling for such doubly censored data may be complicated, we propose a simple semiparametric regression modeling strategy based on jackknife pseudo-observations obtained using nonparametric estimators of the survival function. Inference is carried out via generalized estimating equations. Simulations studies show that the proposed method produces virtually unbiased covariate effect estimates, even for moderate sample sizes. A prostate cancer study example illustrates the practical advantages of the proposed approach.
An in vitro comparative analysis of sealing ability of bioceramic-based, methacrylate-based, and epoxy resin-based sealers

Aim: The aim of this study is to evaluate and compare the apical sealing ability of new bioceramic-based (TotalFill BC) and methacrylate-based (Hybrid Root SEAL) sealers with epoxy resin-based (AH Plus) sealer using a dye penetration method. Materials and Methods: A total of 74 extracted mandibular premolars were selected and decoronated to the root length of 14 mm. The root canals were prepared using high flex to size 35, 0.06 taper to the working length. After completion of the biomechanical preparation of root canal, samples were divided into four experimental groups (n = 16) and two control groups (n = 5). Experimental groups were obturated as follows - Group A: Obturated with AH Plus + 6% conventional gutta-percha, Group B: Obturated with TotalFill BC + 6% conventional gutta-percha, Group C: Obturated with TotalFill BC + 6% bioceramic gutta-percha, and Group D: Obturated with Hybrid Root SEAL + 6% conventional gutta-percha. Results: The best results were obtained with TotalFill BC with bioceramic gutta-percha followed by TotalFill BC with conventional gutta-percha, AH Plus, and Hybrid Root SEAL. Conclusion: Bioceramic sealer with bioceramic gutta-percha or conventional gutta-percha provides comparable sealing ability to AH Plus and superior to Hybrid Root SEAL.
Markov Logic Networks for Optical Chemical Structure Recognition

Optical chemical structure recognition is the problem of converting a bitmap image containing a chemical structure formula into a standard structured representation of the molecule. We introduce a novel approach to this problem based on the pipelined integration of pattern recognition techniques with probabilistic knowledge representation and reasoning. Basic entities and relations (such as textual elements, points, lines, etc.) are first extracted by a low-level processing module. A probabilistic reasoning engine based on Markov logic, embodying chemical and graphical knowledge, is subsequently used to refine these pieces of information. An annotated connection table of atoms and bonds is finally assembled and converted into a standard chemical exchange format. We report a successful evaluation on two large image data sets, showing that the method compares favorably with the current state-of-the-art, especially on degraded low-resolution images. The system is available as a web server at http://mlocsr.dinfo. unifi.it.
Classification of SchoolNet Data

SchoolNet Data, mainly educational material, was authored by SchoolNet to make it easy for teachers and learners to find educational resources in various subjects. The task of automatically assigning subject categories to learning materials has become one of the key steps for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn classifiers from examples. In this paper, SchoolNet data is tested to facilitate the research on the learning object metadata by applying classification techniques developed. C4.5 is a well known decision-tree learning method and the AdaBoost.M1 algorithm is known as a competitive metatechnique to improve the performance of any base classification algorithm. However, these learning algorithms may not be satisfactory when encountering data with imbalanced class distributions (or imbalanced class performance). To tackle this problem, we have developed a costsensitive boosting algorithm AdaC2.M1 to cater to those important classes. With the SchoolNet data, we apply these classification learning algorithms, C4.5 decision tree classifier, AdaBoost.M1 applied to C4.5 and AdaC2.M1 applied to C4.5, to investigate the classification performance. keywords: SchoolNet data, classification, imbalanced class distribution, AdaBoost, cost-sensitive learning, LORNET project