nils-herrmann/clean_scirepeval huggingface dataset:
A rare 47 XXY/46 XX mosaicism with clinical features of Klinefelter syndrome

47 XXY/46 XX mosaicism with characteristics suggesting Klinefelter syndrome is very rare and at present, only seven cases have been reported in the literature.



We report an Indian boy diagnosed as variant of Klinefelter syndrome with 47 XXY/46 XX mosaicism at age 12 years. He was noted to have right cryptorchidism and chordae at birth, but did not have surgery for these until age 3 years. During surgery, the right gonad was atrophic and removed. Histology revealed atrophic ovarian tissue. Pelvic ultrasound showed no Mullerian structures. There was however no clinical follow up and he was raised as a boy. At 12 years old he was re-evaluated because of parental concern about his 'female' body habitus. He was slightly overweight, had eunuchoid body habitus with mild gynaecomastia. The right scrotal sac was empty and a 2mls testis was present in the left scrotum. Penile length was 5.2 cm and width 2.0 cm. There was absent pubic or axillary hair. Pronation and supination of his upper limbs were reduced and x-ray of both elbow joints revealed bilateral radioulnar synostosis. The baseline laboratory data were LH < 0.1 mIU/ml, FSH 1.4 mIU/ml, testosterone 0.6 nmol/L with raised estradiol, 96 pmol/L. HCG stimulation test showed poor Leydig cell response. The karyotype based on 76 cells was 47 XXY[9]/46 XX[67] with SRY positive. Laparoscopic examination revealed no Mullerian structures.



Insisting on an adequate number of cells (at least 50) to be examined during karyotyping is important so as not to miss diagnosing mosaicism.
The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets

In an increasing number of scientific disciplines, large data collections are emerging as important community resources. In domains as diverse as global climate change, high energy physics, and computational genomics, the volume of interesting data is already measured in terabytes and will soon total petabytes. The communities of researchers that need to access and analyze this data (often using sophisticated and computationally expensive techniques) are often large and are almost always geographically distributed, as are the computing and storage resources that these communities rely upon to store and analyze their data . This combination of large dataset size, geographic distribution of users and resources, and computationally intensive analysis results in complex and stringent performance demands that are not satisfied by any existing data management infrastructure. A large scientific collaboration may generate many queries, each involving access to—or supercomputer-class computations on—gigabytes or terabytes of data. Efficient and reliable execution of these queries may require careful management of terabyte caches, gigabit/s data transfer over wide area networks, coscheduling of data transfers and supercomputer computation, accurate performance estimations to guide the selection of dataset replicas, and other advanced techniques that collectively maximize use of scarce storage, networking, and computing resources. The literature offers numerous point solutions that address these issues (e.g., see [17, 14, 19, 3]). But no integrating architecture exists that allows us to identify requirements and components common to different systems and hence apply different technologies in a coordinated fashion to a range of dataintensive petabyte-scale application domains. Motivated by these considerations, we have launched a collaborative effort to design and produce such an integrating architecture. We call this architecture the data grid, to emphasize its role as a specialization and extension of the “Grid” that has emerged recently as an integrating infrastructure for distributed computation [10, 20, 15]. Our goal in this effort is to define the requirements that a data grid must satisfy and the components and APIs that will be required in its implementation. We hope that the definition of such an architecture will accelerate progress on petascale data-intensive computing by enabling the integration of currently disjoint approaches, encouraging the deployment of basic enabling technologies, and revealing technology gaps that require further research and development. In addition, we plan to construct a reference implementation for this architecture so as to enable large-scale experimentation.
WEALTHY – a wearable healthcare system : new frontier on e-textile

A comfortable health monitoring system named WEALTHY is presented. The system is based on a wearable interface implemented by integrating fabric sensors, advanced signal processing techniques and modern telecommunication systems, on a textile platform. Conducting and piezoresistive materials in form of fibre and yarn are integrated in a garment and used as sensors, connectors and electrode elements. Simultaneous recording of vital signs allows extrapolation of more complex parameters and inter-signal elaboration that contribute to produce alert messages and patient table. The purpose of this publication is to evaluate the performance of the textile platform and the possibility of the simultaneous acquisition of several biomedical signals.  fabric sensors, fabric electrodes, physiological
Parasocial interaction and identification: social change processes for effective health interventions.

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Learning object affordances by leveraging the combination of human-guidance and self-exploration

Our work focuses on robots to be deployed in human environments. These robots, which will need specialized object manipulation skills, should leverage end-users to efficiently learn the affordances of objects in their environment. This approach is promising because people naturally focus on showing salient aspects of the objects . We replicate prior results and build on them to create a combination of self and supervised learning. We present experimental results with a robot learning 5 affordances on 4 objects using 1219 interactions. We compare three conditions: (1) learning through self-exploration, (2) learning from supervised examples provided by 10 naïve users, and (3) self-exploration biased by the user input. Our results characterize the benefits of self and supervised affordance learning and show that a combined approach is the most efficient and successful.
Bootstrapping Semantic Parsers from Conversations

Conversations provide rich opportunities for interactive, continuous learning. When something goes wrong, a system can ask for clarification, rewording, or otherwise redirect the interaction to achieve its goals. In this paper, we present an approach for using conversational interactions of this type to induce semantic parsers. We demonstrate learning without any explicit annotation of the meanings of user utterances. Instead, we model meaning with latent variables, and introduce a loss function to measure how well potential meanings match the conversation. This loss drives the overall learning approach, which induces a weighted CCG grammar that could be used to automatically bootstrap the semantic analysis component in a complete dialog system. Experiments on DARPA Communicator conversational logs demonstrate effective learning, despite requiring no explicit meaning annotations.
The Effectiveness of Mindfulness-Based Therapies for ADHD: A Meta-Analytic Review.

Mindfulness-based therapies (MBTs) have been shown to be efficacious in treating internally focused psychological disorders (e.g., depression); however, it is still unclear whether MBTs provide improved functioning and symptom relief for individuals with externalizing disorders, including ADHD. To clarify the literature on the effectiveness of MBTs in treating ADHD and to guide future research, an effect-size analysis was conducted.



A systematic review of studies published in PsycINFO, PubMed, and Google Scholar was completed from the earliest available date until December 2014.



A total of 10 studies were included in the analysis of inattention and the overall effect size was d = -.66. A total of nine studies were included in the analysis of hyperactivity/impulsivity and the overall effect was calculated at d = -.53.



Results of this study highlight the possible benefits of MBTs in reducing symptoms of ADHD.
Nuts-flow/ml: Data Pre-processing for Deep Learning

Data preprocessing is a fundamental part of any machine learning application and frequently the most time-consuming aspect when developing a machine learning solution. Preprocessing for deep learning is characterized by pipelines that lazily load data and perform data transformation, augmentation, batching and logging. Many of these functions are common across applications but require different arrangements for training, testing or inference. Here we introduce a novel software framework named nuts-flow/ml that encapsulates common preprocessing operations as components, which can be flexibly arranged to rapidly construct efficient preprocessing pipelines for deep learning.
Hospital staff corridor conversations: work in passing.

First, to document the prevalence of corridor occupations and conversations among the staff of a hospital clinic, and their main features. Second, to examine the activities accomplished through corridor conversations and their interactional organization.



Despite extensive research on mobility in hospital work, we still know fairly little about the prevalence and features of hospital staff corridor conversations and how they are organized.



We conducted a study combining descriptive statistical analysis and multimodal conversation analysis of video recordings of staff corridor practices in a hospital outpatient clinic in Switzerland.



In 2012, we collected 59 hours of video recordings in a corridor of a hospital clinic. We coded and statistically analysed the footage that showed the clinic staff exclusively. We also performed qualitative multimodal conversation analysis on a selection of the recorded staff conversations.



Corridor occupations by the clinic staff are frequent and brief and rarely involve stops. Talk events (which include self-talk, face-to-face conversations and telephone conversations) during occupations are also brief and mobile, overwhelmingly focus on professional topics and are particularly frequent when two or more staff members occupy the corridor. The conversations present several interactional configurations and comprise an array of activities consequential to the provision of care and work organization.



These practices are related to the fluid work organization of a spatially distributed team in a fast-paced, multitasking environment and should be taken into consideration in any undertaking aimed at improving hospital units' functioning.
Network Forensics Analysis

A network’s physical layer is deceptively quiet. Hub lights blink in response to network traffic, but do little to convey the range of information that the network carries. Analysis of the individual traffic flows and their content is essential to a complete understanding of network usage. Many tools let you view traffic in real time, but real-time monitoring at any level requires significant human and hardware resources, and doesn’t scale to networks larger than a single workgroup. It is generally more practical to archive all traffic and analyze subsets as necessary. This process is known as reconstructive traffic analysis, or network  In practice, it is often limited to data collection and packetlevel inspection; however, a network forensics analysis tool (NFAT) can provide a richer view of the data collected, allowing you to inspect the traffic from further up the protocol stack.2 The IT industry’s ever-growing concern with security is the primary motivation for network forensics. A network that has been prepared for forensic analysis is easy to monitor, and security vulnerabilities and configuration problems can be conveniently identified. It also allows the best possible analysis of security violations. Most importantly, analyzing a complete record of your network traffic with the appropriate reconstructive tools provides context for other breach-related events. For example, if your analysis detects a user account and its Pretty Good Privacy (PGP, ) keys being compromised, good practice requires you to review all subsequent activity by that user, or involving those keys. In some industries, laws such as the Health Insurance Portability and Accountability Act (HIPAA, http://cms.hhs.gov/hipaa) regulate monitoring the flow of information. While it is often difficult to balance what is required by law and what is technically feasible, a forensic record of network traffic is a good first step. Security and legal concerns are not the only reasons to want a fuller understanding of your network traffic, however. Forensics tool users have reported many other applications. If your mail server has lost several hours’ or days’ worth of received messages and traditional backup methods have failed, you can recover the messages from the recorded traffic. Similarly, the forensics record allows unhurried analysis of anomalies such as traffic spikes or application errors that might otherwise have remained hearsay.
SDN based Scalable MTD solution in Cloud Network

Software-Defined Networking (SDN) has emerged as a framework for centralized command and control in cloud data centric environments. SDN separates data and control plane, which provides network administrator better visibility and policy enforcement capability compared to traditional networks. The SDN controller can assess reachability information of all the hosts in a network. There are many critical assets in a network which can be compromised by a malicious attacker through a multistage attack. Thus we make use of centralized controller to assess the security state of the entire network and pro-actively perform attack analysis and countermeasure selection. This approach is also known as Moving Target Defense (MTD). We use the SDN controller to assess the attack scenarios through scalable Attack Graphs (AG) and select necessary countermeasures to perform network reconfiguration to counter network attacks. Moreover, our framework has a comprehensive conflict detection and resolution module that ensures that no two flow rules in a distributed SDN-based cloud environment have conflicts at any layer; thereby assuring consistent conflict-free policy implementation and preventing information leakage.
Cellular and molecular regulation of muscle regeneration.

Under normal circumstances, mammalian adult skeletal muscle is a stable tissue with very little turnover of nuclei. However, upon injury, skeletal muscle has the remarkable ability to initiate a rapid and extensive repair process preventing the loss of muscle mass. Skeletal muscle repair is a highly synchronized process involving the activation of various cellular responses. The initial phase of muscle repair is characterized by necrosis of the damaged tissue and activation of an inflammatory response. This phase is rapidly followed by activation of myogenic cells to proliferate, differentiate, and fuse leading to new myofiber formation and reconstitution of a functional contractile apparatus. Activation of adult muscle satellite cells is a key element in this process. Muscle satellite cell activation resembles embryonic myogenesis in several ways including the de novo induction of the myogenic regulatory factors. Signaling factors released during the regenerating process have been identified, but their functions remain to be fully defined. In addition, recent evidence supports the possible contribution of adult stem cells in the muscle regeneration process. In particular, bone marrow-derived and muscle-derived stem cells contribute to new myofiber formation and to the satellite cell pool after injury.
Exercise training and protein metabolism: influences of contraction, protein intake, and sex-based differences.

Muscle contraction during exercise, whether resistive or endurance in nature, has profound affects on muscle protein turnover that can persist for up to 72 h. It is well established that feeding during the postexercise period is required to bring about a positive net protein balance (muscle protein synthesis - muscle protein breakdown). There is mounting evidence that the timing of ingestion and the protein source during recovery independently regulate the protein synthetic response and influence the extent of muscle hypertrophy. Minor differences in muscle protein turnover appear to exist in young men and women; however, with aging there may be more substantial sex-based differences in response to both feeding and resistance exercise. The recognition of anabolic signaling pathways and molecules are also enhancing our understanding of the regulation of protein turnover following exercise perturbations. In this review we summarize the current understanding of muscle protein turnover in response to exercise and feeding and highlight potential sex-based dimorphisms. Furthermore, we examine the underlying anabolic signaling pathways and molecules that regulate these processes.
Polyoma virus infection and urothelial carcinoma of the bladder following renal transplantation

Renal transplant recipients are at increased risk of bladder carcinoma. The aetiology is unknown but a polyoma virus (PV), BK virus (BKV), may play a role; urinary reactivation of this virus is common post-renal transplantation and PV large T-antigen (T-Ag) has transforming activity. In this study, we investigate the potential role of BKV in post-transplant urothelial carcinoma by immunostaining tumour tissue for PV T-Ag. There was no positivity for PV T-Ag in urothelial carcinomas from 20 non-transplant patients. Since 1990, 10 transplant recipients in our unit have developed urothelial carcinoma, and tumour tissue was available in eight recipients. Two patients were transplanted since the first case of PV nephropathy (PVN) was diagnosed in our unit in 2000 and both showed PV reactivation post-transplantation. In one of these patients, there was strong nuclear staining for PV T-Ag in tumour cells, with no staining of non-neoplastic urothelium. We conclude that PV infection is not associated with urothelial carcinoma in non-transplant patients, and is uncommon in transplant-associated tumours. Its presence in all tumour cells in one patient transplanted in the PVN era might suggest a possible role in tumorigenesis in that case.
A repetitive high-current pulsed accelerator—TPG700

The design and construction of a repetitive high-current pulsed accelerator-TPG700 is described in this paper. The accelerator consists of a Tesla transformer with 40 ohm build-in coaxial pulse forming line. The triggered high-pressure switch of TPG700 has the capability of conducting current of 17.5kA in 35ns' duration at 100 pps. The transformer was designed to operate at 1.4MV, when its primary capacitors were charged to approximately 1000V. Under the working state of 100pps, the jitter of breakdown of the switch voltages is lower than 1% on average. To enhance the overall efficiency of the pulser, resonant charging technology based on IGBTs was utilized. As the experimental results indicate, the total efficiency of the pulser, when measured on matched dummy load, is close to 75%. The experimental results indicate that in matching case, the output of 700kV, 17kA for 40Ω resistive load is obtained. Moreover, some experiments such as long lifetime cathode testing and high power microwave (HPM) generation using backward oscillator (BWO) have been conducted on TPG700.
Building Correlations Between Filters in Convolutional Neural Networks

In this paper, a new optimization approach is designed for convolutional neural network (CNN) which introduces explicit logical relations between filters in the convolutional layer. In a conventional CNN, the filters’ weights in convolutional layers are separately trained by their own residual errors, and the relations of these filters are not explored for learning. Different from the traditional learning mechanism, the proposed correlative filters (CFs) are initiated and trained jointly in accordance with predefined correlations, which are efficient to work cooperatively and finally make a more generalized optical system. The improvement in CNN performance with the proposed CF is verified on five benchmark image classification datasets, including CIFAR-10, CIFAR-100, MNIST, STL-10, and street view house number. The comparative experimental results demonstrate that the proposed approach outperforms a number of state-of-the-art CNN approaches.
A start-up boosting circuit with 133× speed gain for 2-transistor voltage reference

This work presents a start-up boosting circuit designed for fast stabilization of a 2-transistor voltage reference. A clock injection method is used to induce a large bias on the 2-transistor voltage reference resulting in a fast output voltage settling which is critical to reducing initialization time for analog components, reducing energy consumption. The fast stabilization technique is implemented in 180nm CMOS process and uses 0.404mm2 of area. Measurement from test chips shows 50.4μW power consumption during start-up phase with 133× speed gain.
The extension of TAM: The effects of social media and perceived risk in online purchase

This present study is designed to propose a conceptual framework extended from the previously advanced Theory of Acceptance Model (TAM). The framework makes it possible to examine the effects of social media, and perceived risk as the moderating effects between intention and actual purchase to be able to advance the Theory of Acceptance Model (TAM). 400 samples will be randomly selected among Saudi in Jeddah, Dammam and Riyadh. Data will be collected using questionnaire survey. As the research involves the analysis of numerical data, the assessment is carried out using Structural Equation Model (SEM). The hypothesis will be tested and the result is used to explain the proposed TAM. The findings from the present study will be beneficial for marketers to understand the intrinsic behavioral factors that influence consumers' selection hence avoid trial and errors in their advertising drives.
Hand-eye calibration of SCARA robots using dual quaternions

In SCARA robots, which are often used in industrial applications, all joint axes are parallel, covering three degrees of freedom in translation and one degree of freedom in rotation. Therefore, conventional approaches for the hand-eye calibration of articulated robots cannot be used for SCARA robots. In this paper, we present a new linear method that is based on dual quaternions and extends the work of Daniilid is 1999 (IJRR) for SCARA robots. To improve the accuracy, a subsequent nonlinear optimization is proposed. We address several practical implementation issues and show the effectiveness of the method by evaluating it on synthetic and real data.