Miracle-dz/UniFAR-FTUs huggingface dataset:
Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography.

OBJECTIVES This study aimed to develop a dual-input convolutional neural network (CNN)-based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fracture in conventional radiography, and assess its feasibility and diagnostic performance. MATERIALS AND METHODS To develop the deep-learning model, 1266 pairs of AP and lateral elbow radiographs examined between January 2013 and December 2017 at a single institution were split into a training set (1012 pairs, 79.9%) and a validation set (254 pairs, 20.1%). We performed external tests using 2 types of distinct datasets: one temporally and the other geographically separated from the model development. We used 258 pairs of radiographs examined in 2018 at the same institution as a temporal test set and 95 examined between January 2016 and December 2018 at another hospital as a geographic test set. Images underwent preprocessing, including cropping and histogram equalization, and were input into a dual-input neural network constructed by merging 2 ResNet models. An observer study was performed by radiologists on the geographic test set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model and human readers were calculated and compared. RESULTS Our trained model showed an AUC of 0.976 in the validation set, 0.985 in the temporal test set, and 0.992 in the geographic test set. In AUC comparison, the model showed comparable results to the human readers in the geographic test set; the AUCs of human readers were in the range of 0.977 to 0.997 (P's > 0.05). The model had a sensitivity of 93.9%, a specificity of 92.2%, a PPV of 80.5%, and an NPV of 97.8% in the temporal test set, and a sensitivity of 100%, a specificity of 86.1%, a PPV of 69.7%, and an NPV of 100% in the geographic test set. Compared with the developed deep-learning model, all 3 human readers showed a significant difference (P's < 0.05) using the McNemar test, with lower specificity and PPV in the model. On the other hand, there was no significant difference (P's > 0.05) in sensitivity and NPV between all 3 human readers and the proposed model. CONCLUSIONS The proposed dual-input deep-learning model that interprets both AP and lateral elbow radiographs provided an accurate diagnosis of pediatric supracondylar fracture comparable to radiologists.
The entropy of RR intervals is associated to gestational age in full-term newborns with adequate weight for gestational age

Abstract Introduction The variability of successive RR intervals has been pointed out as an indicator of systemic homeostasis. The entropy of successive RR intervals is associated with a greater adaptive capacity, which is essential after childbirth, characterized by a change from an intrauterine environment that constantly adapts to the fetal demands, to an extrauterine environment that requires constant biological adaptations by the neonate. Objectives To analyze the association between gestational age (GA) and the entropy of RR intervals in term infants with adequate birth weight in the first hours of extrauterine life. Methods In a cross-sectional study design maternal, labor and neonatal characteristics were collected from the obstetric records. Successive RR intervals were recorded from neonates up to 72 hours (i.e. 3 days) of birth. Subjects Fifty term infants, healthy and with adequate birth weight. Outcome measures: the variability of RR intervals was analyzed obtaining the entropy of 1000 successive RR intervals. Pearson’s correlation was used to evaluate the association between GA and the entropy of successive RR intervals, while linear regression was used to obtain the coefficient of determination (r 2) as well as a prediction model. The adequacy of the prediction model was evaluated using the Komolgorov-Smirnov test to evaluate the residuals distribution. Results There was a positive and significant association between the studied variables (r = 0.437; p = .002). The coefficient of determination allowed us to infer that approximately 19.3% of the RR interval entropy from the studied sample can be explained by the GA (r 2 = 0.193; p = .002). The analysis of the residuals distribution confirmed that the regression model obtained here was adequate. Conclusion Our results indicate that, even within a normal range of GA (≥37 a < 42 weeks) and birth weight, a longer intrauterine life allows a greater entropy of successive RR intervals, indicating a greater maturation of biological systems and adaptive capacity.
In vitro metabolism of imidacloprid and acetamiprid in rainbow trout and rat

Abstract Providing an alternative to pyrethroids, organophosphates, and carbamates, the neonicotinoids are now the most widely used insecticides in the world. They are water soluble and relatively stable in soil and water which allows for run-off through surface waters and thus potentially impacting aquatic species and environments. While the mammalian metabolism of neonicotinoids has been studied extensively, there is a lack of understanding of their metabolism in fish species. The current study constitutes the first report of the metabolism of imidacloprid (IMI) and acetamiprid (AC) in rainbow trout. Formation of respective metabolites 5-hydroxy-imidacloprid and N-desmethyl-acetamiprid was conserved across orders of biological organization in both microsomal and liver slice assays. Michaelis–Menten kinetics were determined for the microsomal conversion of IMI to 5-hydroxy-imidacloprid in rainbow trout (Km = 79.2 µM; Vmax = 0.75 pmole/min/mg) and rat (Km = 158.7 µM; Vmax = 38.4 pmole/min/mg). Kinetics for the microsomal demethylation of AC to N-desmethyl-acetamiprid were determined in the rat (Km = 70.9 µM; Vmax = 10 pmoles/min/mg). N-desmethyl-acetamiprid was found in detectable but below quantifiable levels across the range of test concentrations which precluded a calculation of kinetic rate constants in rainbow trout (RBT). Ultimately, the formation of the metabolites 5-hydroxy-imidacloprid and N-desmethyl-acetamiprid was conserved across RBT and rat species.
Endosomal escape enhancing compounds facilitate functional delivery of extracellular vesicle cargo.

Aim: Extracellular vesicles (EVs) are desirable delivery vehicles for therapeutic cargoes. We aimed to load EVs with Cre recombinase protein and determine whether functional delivery to cells could be improved by using endosomal escape enhancing compounds. Materials and methods: Overexpressed CreFRB protein was actively loaded into EVs by rapalog-induced dimerization to CD81FKBP, or passively loaded by overexpression in the absence of rapalog. Functional delivery of CreFRB was analysed using a HEK293 Cre reporter cell line in the absence and presence of endosomal escape enhancing compounds. Results: The EVs loaded with CreFRB by both active and passive mechanisms were able to deliver functional CreFRB to recipient cells only in the presence of endosomal escape enhancing compounds chloroquine and UNC10217832A. Conclusion: The use of endosomal escape enhancing compounds in conjunction with EVs loaded with therapeutic cargoes may improve efficacy of future EV based therapeutics.
Factors influencing subclinical atherosclerosis in patients with biopsy-proven nonalcoholic fatty liver disease

Although the presence of nonalcoholic fatty liver disease (NAFLD) is known to be related to subclinical atherosclerosis, the relationship between the severity of NAFLD and subclinical atherosclerosis is not clear. This study aimed to clarify the factors related to subclinical arteriosclerosis, including the histopathological severity of the disease and PNPLA3 gene polymorphisms, in NAFLD patients. We measured brachial-ankle pulse wave velocity (baPWV) as an index of arterial stiffness in 153 biopsy-proven NAFLD patients. The baPWV values were significantly higher in the advanced fibrosis group than in the less advanced group (median, 1679 cm/s vs 1489 cm/s; p = 5.49×10−4). Multiple logistic regression analysis revealed that older age (≥55 years) (p = 8.57×10−3; OR = 3.03), hypertension (p = 1.05×10−3; OR = 3.46), and advanced fibrosis (p = 9.22×10−3; OR = 2.94) were independently linked to baPWV ≥1600 cm/s. NAFLD patients were categorized into low-risk group (number of risk factors = 0), intermediate-risk group (= 1), and high-risk group (≥2) based on their risk factors, including older age, hypertension, and biopsy-confirmed advanced fibrosis. The prevalence of baPWV ≥1600 cm/s was 7.1% (3/42) in the low-risk group, 30.8% (12/39) in the intermediate-risk group, and 63.9% (46/72) in the high-risk group. Non-invasive liver fibrosis markers and scores, including the FIB-4 index, NAFLD fibrosis score, hyaluronic acid, Wisteria floribunda agglutinin positive Mac-2-binding protein, and type IV collagen 7s, were feasible substitutes for invasive liver biopsy. Older age, hypertension, and advanced fibrosis are independently related to arterial stiffness, and a combination of these three factors may predict risk of arteriosclerosis in NAFLD patients.
Critical Role of p38 in Spinal Cord Injury by Regulating Inflammation and Apoptosis in a Rat Model.

STUDY DESIGN To evaluate the effect of p38 pathway on spinal cord injury (SCI), a rat model of SCI was performed. OBJECTIVE We determined the effect of p38 on SCI and SCI related inflammation, apoptosis and autophagy. SUMMARY OF BACKGROUND DATA SCI is a severe clinical problem worldwide. It is difficult to prevent cell necroptosis and promote the survival of residual neurons after SCI. p38, a class of mitogen-activated protein kinases (MAPKs), its effect on SCI and SCI related inflammation, apoptosis and autophagy have not been studied very well. METHODS The rats were randomly divided into the following 4 groups: the sham-operated (sham) group, the SCI group, the SCI + vehicle group, and the SCI + SB203580 (10 mg/kg) group. The p38 inhibitor SB203580 was administered by oral (10 mg/kg/d) gavage once per day for 14 d. Neurological recovery was assessed using the Basso, Beattie, and Bresnahan (BBB) locomotion rating scale. Apoptosis, autophagy and inflammation related proteins were measured by ELISA kits or western blotting. RESULTS Our results showed that p38 was upregulated after SCI from day 3, which was paralleled with the levels of its proteins ATF-2, suggesting an increase in p38 activity. Our results showed administration of SB203580 attenuated histopathology and promoted locomotion recovery in rats after SCI. SB203580 administration significantly inhibited inflammatory cytokines levels as well as the inflammation signaling pathway. SB203580 administration also modulated the apoptosis and autophagy signaling pathway. CONCLUSION Our findings suggest that p38 inhibitor SB203580 treatment alleviates secondary SCI by inhibiting inflammation and apoptosis, thereby promoting neurological and locomoter functional recovery, thus suggest the important role of p38 in neuronal protection after SCI. LEVEL OF EVIDENCE N/A.
Inherited Risk for Colorectal Cancer: Practical Approaches for Identification, Referral and Management COLORECTAL CANCER: AN UPDATE FOR DIAGNOSIS AND PREVENTION, SERIES #6

The field of medical genetics is rapidly advancing. Genetic testing is now available for most hereditary colorectal cancer (CRC) syndromes and identification of individuals at risk is necessary for cancer prevention and management. It is essential that medical genetics be implemented by those at the gateway for identification of these syndromes including primary care providers, gastroenterologists, gastrointestinal surgeons and oncologists. Here we review the most common causes of hereditary CRC, present “red flags” for identification of high-risk patients, and discuss the roles providers can play in coordinating care for high-risk individuals and families. Resources to help implement strategies for identifying high-risk into practice are also provided.
Probing the Role of Ion-Pair Strategy in Controlling Dexmedetomidine Penetrate Through Drug-in-Adhesive Patch: Mechanistic Insights Based on Release and Percutaneous Absorption Process

The purpose of present study was to develop a controlled release drug-in-adhesive patch for transdermal delivery of dexmedetomidine (Dex) using ion-pair technique. Based on the in vitro transdermal experiment, the role of ion-pair on the Dex release behavior and percutaneous absorption process was also investigated. Fourier transform infrared spectroscopy (FTIR), molecular modeling, differential scanning calorimetry (DSC), and rheological test were conducted to probe the effect of ion-pair on the Dex release from patch. Besides, the tape stripping test, attenuated total reflectance Fourier transform infrared (ATR-FTIR), and molecular simulation were carried out to elaborate the action of ion-pair on the Dex percutaneous permeation process. Results showed that the optimized patch prepared with Dex-salicylic acid (SA) showed zero-order skin permeation profile within 24 h; Dex-SA had greater hydrogen bonding formation potential with pressure sensitive adhesive (PSA) than Dex, which resulted in the decrease in the formation ability of free volume of PSA and the increase with the improvement of mechanical strength and chain stiffness of PSA and thus controlled the release rate of Dex from transdermal patch. Besides, the physicochemical properties of Dex such as molecular weight and octanol/water partition coefficient were changed after forming ion-pair with SA, which decreased the permeation ability of Dex. In conclusion, a controlled release drug-adhesive patch for Dex was developed and the mechanism study of ion-pair on the Dex release and percutaneous permeation process was proposed at molecular level.
Physical Therapy and Hospice

Hospice, a concept for dying patients and their families, combines human intervention and sophisticated medical pharmacological treatment. With careful planning and nurturance, hospice can be an agent of change in the care of terminally ill patients. For this change to persist, the health-care professional must understand the tenets of hospice and the potential roles for caregivers within this philosophy. The purpose of this article is to provide 1) a definitive description of hospice and the tenets that make it unique within the health-care system, 2) an explanation of the roles the physical therapist can fulfill as a member of a hospice-care team, and 3) a discussion of the implications for educators of health-care professionals that the alternative approach of hospice presents.
Artificial grammar learning and the transfer of sequential dependencies.

Exposure to sequences of elements constrained by an artificial grammar enables observers to classify new sequences as being either wellor ill-formed according to that grammar. Moreover, participants are also able to transfer their knowledge of the grammar to sequences composed of novel vocabulary elements. Two principle theories have been advanced to account for these effects. The first argues that participants learn grammatical rules that are abstract in the sense of being independent of vocabulary; even in a new vocabulary sequences can be classified on the basis of rule-adherence (e. g. A. S. Reber, 1989). The second argues that participants memorise the exemplars and subsequently classify new sequences on the basis of how similar they are to those exemplars (e. g. L. R. Brooks & J. R. Vokey, 1991). ' To assess the relative contributions of these two modes of representation this Thesis re-defines these theories in terms of the information that can be used to classify sequences in a novel vocabulary. The rule-based account of transfer is predicated on the abstraction of sequential dependencies between both repeating and non-repeating elements. These can be applied in a novel vocabulary by inducing the correspondences between vocabularies across sequences. The exemplar-based account of transfer is predicated on memory for dependencies between repeating elements alone. The similarity of new sequences can be determined on the basis of this form of dependency on a sequenceby-sequence basis. A review of the empirical literature suggests that both kinds of information can be transferred to a novel vocabulary. Seventeen experiments confirm this conclusion, but demonstrate that participants are not equally sensitive to the different forms of sequential dependence. Finally, the contributions of these two modes of classification can be dissociated by altering the frequency distribution of the training exemplars. These findings inform both theories and computational models of artificial grammar learning and general cognitive processes.
An automated SOM clustering based on data topology

Self-organizing maps are powerful for cluster extraction due to their ability of obtaining a topologically ordered and adaptive vector quantization of data. Thanks to lower-dimensional representation of high- dimensional data on SOM lattice, clustering is often done interactively from informative SOM visualizations. Yet large volumes of today's data sets necessitate to have automated methods that are as successful as in- teractive ones for fast and accurate knowledge discovery. An automated SOM clustering, based on hierarchical clustering of a topology representing graph, is proposed here. Applications on several data sets indicate that the proposed method can be successfully used for automated partitioning.
Outcome of coronary artery bypass surgery in diabetic and non -diabetic patients: A comparative, retrospective study

Diabetes mellitus is a risk factor for adverse long-term outcome after medical therapy, PCI, or coronary artery bypass surgery. To assess the validity of this concern, we evaluated the effect of diabetes mellitus on in-hospital mortality and morbidity after coronary artery bypass grafting (CABG). Thirteen thousand eight hundred fifty seven patients (4389 diabetics and 9468 non-diabetics) were retrospectively analyzed after isolated CABG. Demographic and preoperative data and postoperative in-hospital mortality and morbidity were compared in both diabetic and non-diabetic groups. Analysis was performed in both univariate and multivariable logistic regression to compare the results. Diabetic patients were older than the non-diabetic population (mean age 59.2±8.7yrs versus 58.3±9.9yrs), included more women (37.6% vs 19.6%), had more postoperative superficial/deep sternal wound infection (0.6% vs 0.2 %), prolonged ventilation (3.2% vs 1.5%), cardiac arrest (1.3% vs 0.5%), stroke (0.6% vs 0.3%), renal dysfunction (1.5% vs 0.6%), coma (0.5% vs 0.1%), AF (6.5% vs 5.7%), and in- hospital mortality (1.4% versus 0.5%) (all p< 0.05). There were also significant differences for mean aortic cross-clamp and cardiopulmonary bypass times between diabetics and non-diabetics (43.74±14. 37 vs 41.89±15.76 mins and 73.10±24.05 vs 69.97±24.43 mins, respectively) (p<0.05). Logistic regression showed that there was a significant relationship between diabetes and postoperative superficial/deep sternal wound infection as well as prolonged ventilation and cardiac arrest, but it did not show any relationship between diabetes and in-hospital mortality. It seems that diabetes mellitus has a significant impact on postoperative superficial/deep sternal wound infection, prolonged ventilation, and cardiac arrest. But diabetic patients do not necessarily have an increased risk of in-hospital mortality and other complications.
IRIT at INEX 2014: Tweet Contextualization Trac

The paper presents IRIT’s approach used at INEX Tweet Contex-tualization Track 2014. Systems had to provide a context to a tweet from the perspective of the entity. This year we further modified our approach presented at INEX 2011, 2012 and 2013 underlain by the product of different measures based on smoothing from local context, named entity recognition, part-of-speech weighting and sentence quality analysis. We introduced two ways to link an entity and a tweet, namely (1) concatenation of the entity and the tweet and (2) usage of the results obtained for the entity as a restriction to filter results retrieved for the tweet. Besides, we examined the influence of topic-comment relationship on contextualization.
Full-Stack Architecting to Achieve a Billion-Requests-Per-Second Throughput on a Single Key-Value Store Server Platform

Distributed in-memory key-value stores (KVSs), such as memcached, have become a critical data serving layer in modern Internet-oriented data center infrastructure. Their performance and efficiency directly affect the QoS of web services and the efficiency of data centers. Traditionally, these systems have had significant overheads from inefficient network processing, OS kernel involvement, and concurrency control. Two recent research thrusts have focused on improving key-value performance. Hardware-centric research has started to explore specialized platforms including FPGAs for KVSs; results demonstrated an order of magnitude increase in throughput and energy efficiency over stock memcached. Software-centric research revisited the KVS application to address fundamental software bottlenecks and to exploit the full potential of modern commodity hardware; these efforts also showed orders of magnitude improvement over stock memcached. We aim at architecting high-performance and efficient KVS platforms, and start with a rigorous architectural characterization across system stacks over a collection of representative KVS implementations. Our detailed full-system characterization not only identifies the critical hardware/software ingredients for high-performance KVS systems but also leads to guided optimizations atop a recent design to achieve a record-setting throughput of 120 million requests per second (MRPS) (167MRPS with client-side batching) on a single commodity server. Our system delivers the best performance and energy efficiency (RPS/watt) demonstrated to date over existing KVSs including the best-published FPGA-based and GPU-based claims. We craft a set of design principles for future platform architectures, and via detailed simulations demonstrate the capability of achieving a billion RPS with a single server constructed following our principles.
Artificial Intelligence in Education: Building Technology Rich Learning Contexts that Work

The nature of technology has changed since Artificial Intelligence in Education (AIED) was conceptualized as a research community and Interactive Learning Environments were initially developed. Technology is smaller, more mobile, networked, pervasive and often ubiquitous as well as being provided by the standard desktop PC. This creates the potential for technology supported learning wherever and whenever learners need and want it. However, in order to take advantage of this potential for greater flexibility we need to understand and model learners and the contexts with which they interact in a manner that enables us to design, deploy and evaluate technology to most effectively support learning across multiple locations, subjects and times. The AIED community has much to contribute to this endeavor.