nils-herrmann/clean_scirepeval huggingface dataset:
A Chinese man with chronic recalcitrant hidradenitis suppurativa successfully treated with infliximab

Yaumatei Dermatology Clinic, 12/F Yau Ma Tei Specialist Clinic Extension, 143 Battery Street, Yaumatei, Kowloon, Hong Kong A 31-year-old Chinese male suffered from recalcitrant hidradenitis suppurativa for seven years causing disfiguring scars over the face and intertriginous areas, particularly the axillae and groins. Multiple medical treatments and surgical operation were tried but in vain. Infliximab infusion led to significant improvement. To our best knowledge, this is the first Chinese patient with hidradenitis suppurativa treated with infliximab in Hong Kong.
Cerebellar and Motor Cortical Transcranial Stimulation Decrease Levodopa-Induced Dyskinesias in Parkinson’s Disease

Transcranial direct current stimulation (tDCS) is a non-invasive technique for inducing prolonged functional changes in the human cerebral cortex. This simple and safe neurostimulation technique for modulating motor functions in Parkinson’s disease could extend treatment option for patients with movement disorders. We assessed whether tDCS applied daily over the cerebellum (cerebellar tDCS) and motor cortex (M1-tDCS) improves motor and cognitive symptoms and levodopa-induced dyskinesias in patients with Parkinson’s disease (PD). Nine patients (aged 60–85 years; four women; Hoehn & Yahr scale score 2–3) diagnosed as having idiopathic PD were recruited. To evaluate how tDCS (cerebellar tDCS or M1-tDCS) affects motor and cognitive function in PD, we delivered bilateral anodal (2 mA, 20 min, five consecutive days) and sham tDCS, in random order, in three separate experimental sessions held at least 1 month apart. In each session, as outcome variables, patients underwent the Unified Parkinson’s Disease Rating Scale (UPDRS III and IV) and cognitive testing before treatment (baseline), when treatment ended on day 5 (T1), 1 week later (T2), and then 4 weeks later (T3), at the same time each day. After patients received anodal cerebellar tDCS and M1-tDCS for five days, the UPDRS IV (dyskinesias section) improved (p < 0.001). Conversely, sham tDCS, cerebellar tDCS, and M1-tDCS left the other variables studied unchanged (p > 0.05). Despite the small sample size, our preliminary results show that anodal tDCS applied for five consecutive days over the motor cortical areas and cerebellum improves parkinsonian patients’ levodopa-induced dyskinesias.
Knowledge Transfer with Jacobian Matching

Classical distillation methods transfer representations from a “teacher” neural network to a “student” network by matching their output activations. Recent methods also match the Jacobians, or the gradient of output activations with the input. However, this involves making some ad hoc decisions, in particular, the choice of the loss function. In this paper, we first establish an equivalence between Jacobian matching and distillation with input noise, from which we derive appropriate loss functions for Jacobian matching. We then rely on this analysis to apply Jacobian matching to transfer learning by establishing equivalence of a recent transfer learning procedure to distillation. We then show experimentally on standard image datasets that Jacobian-based penalties improve distillation, robustness to noisy inputs, and transfer learning.
Robust Markov Decision Processes

Markov decision processes (MDPs) are powerful tools for decision making in uncertain dynamic environments. However, the solutions of MDPs are of limited practical use due to their sensitivity to distributional model parameters, which are typically unknown and have to be estimated by the decision maker. To counter the detrimental effects of estimation errors, we consider robust MDPs that offer probabilistic guarantees in view of the unknown parameters. To this end, we assume that an observation history of the MDP is available. Based on this history, we derive a confidence region that contains the unknown parameters with a pre-specified probability 1− β. Afterwards, we determine a policy that attains the highest worst-case performance over this confidence region. By construction, this policy achieves or exceeds its worst-case performance with a confidence of at least 1 − β. Our method involves the solution of tractable conic programs of moderate size. Notation For a finite set X = {1, . . . , X}, M(X ) denotes the probability simplex in R . An X -valued random variable χ has distribution m ∈ M(X ), denoted by χ ∼ m, if P(χ = x) = mx for all x ∈ X . By default, all vectors are column vectors. We denote by ek the kth canonical basis vector, while e denotes the vector whose components are all ones. In both cases, the dimension will usually be clear from the context. For square matrices A and B, the relation A B indicates that the matrix A − B is positive semidefinite. We denote the space of symmetric n × n matrices by S. The declaration f : X c 7→ Y (f : X a 7→ Y ) implies that f is a continuous (affine) function from X to Y . For a matrix A, we denote its ith row by Ai· (a row vector) and its jth column by A·j .
Adaptive Unsupervised Multi-view Feature Selection for Visual Concept Recognition

To reveal and leverage the correlated and complemental information between different views, a great amount of multi-view learning algorithms have been proposed in recent years. However, unsupervised feature selection in multiview learning is still a challenge due to lack of data labels that could be utilized to select the discriminative features. Moreover, most of the traditional feature selection methods are developed for the single-view data, and are not directly applicable to the multi-view data. Therefore, we propose an unsupervised learning method called Adaptive Unsupervised Multi-view Feature Selection (AUMFS) in this paper. AUMFS attempts to jointly utilize three kinds of vital information, i.e., data cluster structure, data similarity and the correlations between different views, contained in the original data together for feature selection. To achieve this goal, a robust sparse regression model with the l2,1-norm penalty is introduced to predict data cluster labels, and at the same time, multiple view-dependent visual similar graphs are constructed to flexibly model the visual similarity in each view. Then, AUMFS integrates data cluster labels prediction and adaptive multi-view visual similar graph learning into a unified framework. To solve the objective function of AUMFS, a simple yet efficient iterative method is proposed. We apply AUMFS to three visual concept recognition applications (i.e., social image concept recognition, object recognition and video-based human action recognition) on four benchmark datasets. Experimental results show the proposed method significantly outperforms several state-of-the-art feature selection methods. More importantly, our method is not very sensitive to the parameters and the optimization method converges very fast.
A review on distance based time series classification

Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to its simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.
Using Active Queue Management to Assist IoT Application Flows in Home Broadband Networks

Internet of Things (IoT) applications such as telehealth, smart appliances, and smart energy are becoming more common within the home. However, they must compete for bandwidth with traditional applications such as video streaming, video conferencing, and bulk file transfers. Such competition can be detrimental to the IoT applications when home gateways use traditional first-in-first-out (FIFO) queue management. Simply increasing bandwidth between the home gateway and the Internet Service Provider (ISP), even when possible, provides no guarantee of bandwidth for IoT applications since many traditional applications will consume as much bandwidth as is available. In this paper, we explore whether active queue management (AQM), now being implemented in home gateways, can provide protection for IoT flows. We investigate the effect of different AQM algorithms deployed at the home gateway in scenarios with multiple concurrent application flows. We find that deploying multiqueue FlowQueue Controlled Delay (FQ-CoDel) or the hybrid FlowQueue Proportional Integral Controller Enhanced (FQ-PIE) at the home gateway can provide excellent capacity sharing, flow isolation, and good protection in terms of throughput and queuing delays for IoT flows and other applications, which cannot be achieved with traditional FIFO or other single-queue AQMs such as Proportional Integral Controller Enhanced (PIE).
Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty

In this research, we develop a fuzzy multi-objective mathematical model to identify and rank the candidate suppliers and find the optimal number of new and refurbished parts and final products in a reverse logistics network configuration. This modeling approach captures the inherent uncertainty in customers’ demand, suppliers’ capacity, and percentage of returned products as well as existence of conflicting objectives in reverse logistics systems. The objective functions in this study are defined as total profit, total defective parts, total late delivered parts, and economic risk factors associated with the candidate suppliers whereas the uncertainties are treated in a fuzzy environment. In order to avoid the subjective weighting from decision makers when solving the multi-objective model, a Monte Carlo simulation integrated with fuzzy goal programming is developed to determine the entire set of Paretooptimal solutions of the proposed model. The effectiveness of the mathematical model and the proposed solution method in obtaining Pareto-optimal solutions is demonstrated in a numerical example from a real case study.
Atlanto-occipital ligament calcification: a novel sign in nevoid basal cell carcinoma syndrome.

The nevoid basal cell carcinoma syndrome (NBCCS), first described by Gorlin and Goltz in 1960, is a hereditary autosomal dominant disease with high penetrance and variable expressivity. Almost 70% of patients with NBCCS have some degree of craniofacial anomaly. Among these, the presence of ectopic calcification have been reported but Atlanto-occipital ligament calcification has never been described. Therefore this investigation was carried out to determine the prevalence of atlanto-occipital ligament calcification on lateral x-ray of NBCCS patients aiming to assess the effectiveness of this sign in NBCCS diagnosis.



Lateral and frontal cephalometric radiographs of 18 patients (11 males and 7 females), aged 8-61 years, with the diagnosis of NBCCS were evaluated for the presence of intracranial calcifications (diaphragma sellae and falx cerebri) and or calcification of the atlanto-occipital ligament.



A total of 11 patients presented calcification of atlanto-occipital ligament to various degrees and in three cases this represented the only sign of ectopic calcification. When compared to the other two sites of ossification, atlanto-occipital ligament calcification had a similar prevalence.



The calcification of the atlanto-occipital ligament should be considered in addition to the other major criteria for NBCCS diagnosis.
Discrimination Increases Suicidal Ideation in Black Adolescents Regardless of Ethnicity and Gender

Discrimination is a common experience for Blacks across various developmental periods. Although much is known about the effect of discrimination on suicidal ideation of adults, less is known about the same association in Black youth.



We examined the association between discrimination and suicidal ideation in a national sample of Black youth. We also explored gender and ethnic differences in this association.



We used data from the National Survey of American Life-Adolescents (NSAL-A), 2003-2004. In total, 1170 Black adolescents entered the study. This number was composed of 810 African American and 360 Caribbean Black youth (aged 13 to 17 years). Demographic and socioeconomic factors were controls, perceived discrimination was the predictor, and lifetime suicidal ideation was the outcome. Logistic regression was used to test the association between perceived discrimination and suicidal ideation in the pooled sample, as well as based on ethnicity and gender.



In the pooled sample of Black youth, higher perceived discrimination was associated with higher odds of suicidal ideation (Odds Ratio (OR) = 1.09; 95% Confidence Interval (CI) = 1.02-1.17). This association was significant net of age, ethnicity, gender, and socioeconomic status. We did not find interactions between perceived discrimination and ethnicity or gender on suicidal ideation. Perceived discrimination was associated with suicidal ideation in African Americans (CI = 1.09; 95% CI = 1.01-1.17) and Caribbean Blacks (CI = 1.16; 95% CI = 1.03-1.32), males (CI = 1.11; 95% CI = 1.00-1.25), and females (CI = 1.08; 95% CI = 1.00-1.16).



Discrimination jeopardizes the mental health of Black youth. In a universal pattern, discrimination is associated with suicidal ideation in Black youth. More research is needed on this topic.
An Exceptional Case of Acute Respiratory Failure Caused by Intra-Thoracic Gastric Perforation Secondary to Overeating.

An 18-year-old female patient arrived at the emergency department complaining of abdominal pain and fullness after a heavy meal. Physical examination revealed she was filthy and cover in feces, and she experienced severe abdominal distension. She died in ED and a diagnostic autopsy examination was requested. At external examination, the pathologist observed a significant dilation of the anal sphincter and suspected sexual assault, thus alerting the Judicial Authority who assigned the case to our department for a forensic autopsy. During the autopsy, we observed anal orifice expansion without signs of violence; food was found in the pleural cavity. The stomach was hyper-distended and perforated at three different points as well as the diaphragm. The patient was suffering from anorexia nervosa with episodes of overeating followed by manual voiding of her feces from the anal cavity (thus explaining the anal dilatation). The forensic pathologists closed the case as an accidental death.
Dijkstra's Algorithm for Solving the Shortest Path Problem on Networks Under Intuitionistic Fuzzy Environment

In this paper, a well known problem called the Shortest Path Problem (SPP) has been considered in an uncertain environment. The cost parameters for traveling each arc have been considered as Intuitionistic Fuzzy Numbers (IFNs) which are the more generalized form of fuzzy numbers involving a degree of acceptance and a degree of rejection. A heuristic methodology for solving the SPP has been developed, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low cost solution corresponding to the minimum-cost path or the shortest path. The Modified Intuitionistic Fuzzy Dijkstra’s Algorithm (MIFDA) has been proposed in this paper for solving Intuitionistic Fuzzy Shortest Path Problem (IFSPP) using the Intuitionistic Fuzzy Hybrid Geometric (IFHG) operator. A numerical example illustrates the effectiveness of the proposed method.
Import of bacterial pathogenicity factors into mitochondria.

Recent research on the mechanism underlying the interaction of bacterial pathogens with their host has shifted the focus to secreted microbial proteins affecting the physiology and innate immune response of the target cell. These proteins either traverse the plasma membrane via specific entry pathways involving host cell receptors or are directly injected via bacterial secretion systems into the host cell, where they frequently target mitochondria. The import routes of bacterial proteins are mostly unknown, whereas the effect of mitochondrial targeting by these proteins has been investigated in detail. For a number of them, classical leader sequences recognized by the mitochondrial protein import machinery have been identified. Bacterial outer membrane beta-barrel proteins can also be recognized and imported by mitochondrial transporters. Besides an obvious importance in pathogenicity, understanding import of bacterial proteins into mitochondria has a highly relevant evolutionary aspect, considering the endosymbiotic, proteobacterial origin of mitochondria. The review covers the current knowledge on the mitochondrial targeting and import of bacterial pathogenicity factors.
Androgenic potential in coconut (Cocos nucifera L.)

Conditions for induction of androgenesis in coconut cv. Sri Lanka Tall were studied. Anthers collected from inflorescences at four maturity stages were given heat (38°C) or cold (4°C) pretreatments for 1, 3, 6 and 14 days, either prior to or post inoculation. Three different basal media and different anther densities were also tested. Androgenesis was observed only in anthers collected from inflorescences 3 weeks before splitting (WBS) and after a heat pretreatment at 38°C for 6 days. Modified Eeuwens Y3 liquid medium supplemented with 100 μM 2,4-dichlorophenoxyacetic acid (2,4-d), 0.1% activated charcoal and 9% sucrose was effective in inducing an androgenic response. The lowest anther density tested, 10 per petri plate, was found to be the optimal density. When androgenic calli or embryos were subcultured to Y3 medium containing 66 μM 2,4-d, followed by transfer to Y3 medium without plant growth regulators and finally to Y3 medium containing 5 μM 6-benzyladenine (BA) and 0.35 μM gibberellic acid (GA3), plantlets regenerated at a frequency of 7%. Histological study indicated that the calli and embryos originated from the inner tissues of the anthers. Ploidy analysis of calli and embryos showed that they were haploid. This is the first report of successful androgenesis yielding haploid plants from coconut anthers.
Big Data, the Internet of Things, and the Revised Knowledge Pyramid

The knowledge pyramid has been used for several years to illustrate the hierarchical relationships between data, information, knowledge, and wisdom.  This paper expands the revised knowledge pyramid to include the Internet of Things and Big Data. The result is a revision of the data aspect of the knowledge pyramid. Previous thought was of data as reflections of reality as recorded by sensors. Big Data and the Internet of Things expand sensors and readings to create two layers of data. The top layer of data is the traditional transaction / operational data and the bottom layer of data is an expanded set of data reflecting massive data sets and sensors that are near mirrors of reality. The result is a knowledge pyramid that appears as an hourglass.
Adult zebrafish as a model organism for behavioural genetics

Recent research has demonstrated the suitability of adult zebrafish to model some aspects of complex behaviour. Studies of reward behaviour, learning and memory, aggression, anxiety and sleep strongly suggest that conserved regulatory processes underlie behaviour in zebrafish and mammals. The isolation and molecular analysis of zebrafish behavioural mutants is now starting, allowing the identification of novel behavioural control genes. As a result of this, studies of adult zebrafish are now helping to uncover the genetic pathways and neural circuits that control vertebrate behaviour.
Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN

Automatic approach for bladder segmentation from computed tomography (CT) images is highly desirable in clinical practice. It is a challenging task since the bladder usually suffers large variations of appearance and low soft-tissue contrast in CT images. In this study, we present a deep learning-based approach which involves a convolutional neural network (CNN) and a 3D fully connected conditional random fields recurrent neural network (CRF-RNN) to perform accurate bladder segmentation. We also propose a novel preprocessing method, called dual-channel preprocessing, to further advance the segmentation performance of our approach. The presented approach works as following: first, we apply our proposed preprocessing method on the input CT image and obtain a dual-channel image which consists of the CT image and an enhanced bladder density map. Second, we exploit a CNN to predict a coarse voxel-wise bladder score map on this dual-channel image. Finally, a 3D fully connected CRF-RNN refines the coarse bladder score map and produce final fine-localized segmentation result. We compare our approach to the state-of-the-art V-net on a clinical dataset. Results show that our approach achieves superior segmentation accuracy, outperforming the V-net by a significant margin. The Dice Similarity Coefficient of our approach (92.24%) is 8.12% higher than that of the V-net. Moreover, the bladder probability maps performed by our approach present sharper boundaries and more accurate localizations compared with that of the V-net. Our approach achieves higher segmentation accuracy than the state-of-the-art method on clinical data. Both the dual-channel processing and the 3D fully connected CRF-RNN contribute to this improvement. The united deep network composed of the CNN and 3D CRF-RNN also outperforms a system where the CRF model acts as a post-processing method disconnected from the CNN.
Endodontic or dental implant therapy: the factors affecting treatment planning.

Clinicians are confronted with difficult choices regarding whether a tooth with pulpal and/or periapical disease should be saved through endodontic treatment or be extracted and replaced with an implant.



The authors examined publications (research, literature reviews and systematic reviews) related to the factors affecting decision making for patients who have oral diseases or traumatic injuries.



The factors to be considered included patient-related issues (systemic and oral health, as well as comfort and treatment perceptions), tooth- and periodontium-related factors (pulpal and periodontal conditions, color characteristics of the teeth, quantity and quality of bone, and soft-tissue anatomy) and treatment-related factors (the potential for procedural complications, required adjunctive procedures and treatment outcomes).



On the basis of survival rates, it appears that more than 95 percent of dental implants and teeth that have undergone endodontic treatment remain functional over time.



Clinicians need to consider carefully several factors before choosing whether to perform endodontic therapy or extract a tooth and place an implant. The result should be high levels of comfort, function, longevity and esthetics for patients.