nils-herrmann/clean_scirepeval huggingface dataset:
Characterisation and antimicrobial resistance of sepsis pathogens in neonates born in tertiary care centres in Delhi, India: a cohort study.
Venue: The Lancet. Global health

Sepsis is one of the most common causes of neonatal deaths globally. Most sepsis related deaths occur in low income and middle income countries, where the epidemiology of neonatal sepsis remains poorly understood. Most of these countries lack proper surveillance networks, hampering accurate assessment of the burden of sepsis, implementation of preventive measures, and investment in research. We report results of neonates born in hospital from a multicentre collaboration on neonatal sepsis.  In this cohort study, dedicated research teams prospectively followed up neonates born in one of three tertiary care centres in Delhi, India (Vardhaman Mahavir Medical College, Maulana Azad Medical College, and All India Institute of Medical Sciences [coordinating centre] and subsequently admitted to the intensive care unit. Neonates were followed up daily until discharge or death. On clinical suspicion, neonates underwent sepsis work up including blood cultures. The isolated organisms were identified and tested for antimicrobial susceptibility. We defined Gram negative isolates resistant to any three of five antibiotic classes (extended spectrum cephalosporins, carbapenems, aminoglycosides, fluoroquinolones, and piperacillin tazobactam) as multidrug resistant.  530 neonates of 88 636 livebirths were enrolled between July 18, 2011, and Feb 28, 2014. The incidence of total sepsis was 14*3% (95% CI 13*8 14*9) and of culture positive sepsis was 6*2% (5*8 6*6) Nearly two thirds of total episodes occurred at or before 72 h of life (defined as early onset; 1351 [83% of 1980) Two thirds (645 [64% of 1005 isolates were Gram negative including, Acinetobacter spp (22% Klebsiella spp (17% and Escherichia coli (14% The pathogen mix in early onset sepsis did not differ from that of late onset sepsis (ie, after 72 h) High rates of multidrug resistance were observed in Acinetobacter spp (181/222, 82% Klebsiella spp (91/169, 54% and Escherichia coli (52/137, 38% isolates. Meticillin resistance prevailed in 61% (85/140) of coagulase negative staphylococci and 38% (43/114) of Staphylococcus aureus isolates. Nearly a quarter of the deaths were attributable to sepsis. The population attributable risks of mortality were 8*6% in culture negative sepsis, 15*7% in culture positive sepsis by multidrug resistant organisms, and 12*0% in culture positive sepsis by non multidrug resistant organisms.  The high incidence of sepsis and alarming degree of antimicrobial resistance among pathogens in neonates born in tertiary hospitals underscore the need to understand the pathogenesis of early onset sepsis and to devise measures to prevent it in low income and middle income countries.  Indian Council of Medical Research.
Revisiting the texture zero neutrino mass matrices

In the light of refined and large reactor mixing angle \theta_{13} we have revisited the texture three and two zero neutrino mass matrices in the flavor basis. For Majorana neutrinos, it has been explicitly shown that all the texture three zero mass matrices remain ruled out. Further, for both normal and inverted mass ordering, for the texture two zero neutrino mass matrices one finds interesting constraints on the Dirac like CP violating phase \delta$ and Majorana phases \rho$ and \sigma$
Grain size effects on the thermal conductivity of nanosolids

A theoretical model has been developed for the calculation of the thermal conductivity of nanomaterials with different shapes, such as spherical nanosolids, nanowires and nanofilms, based on size dependent atomic cohesive energy. Thermal conductivity of nanosolids with different shapes decreased as the grain size decreased. The obtained results are compared with the available experimental data. A close agreement between theory and experiment confirmed the validity of the discussed method.
A framework for efficacy in lane discipline management using hybrid approach
Venue: 2016 Second International Conference on Science Technology Engineering and Management (ICONSTEM)

In the current scenario, accidents are occurring more in number. The occurrence of accidents is more in rural areas when compared to cities. Careless driving and violation of road safety rules are the major causes of such accidents. Single lane roads are common in rural areas. In such roads, the vehicles drivers don't follow lane discipline as there is less population density. Also in case of any accidents, there are fewer chances of people nearby the accident scene to witness the accident and report it to the nearby emergency service. In such cases, helping the victims becomes a difficult task. This increases the number of deaths in accidents. If some safety measures are implemented, such accidents can be avoided. A framework for lane discipline management using hybrid technology is proposed to enhance safer driving on single lane roads and to make people strictly follow the lane discipline. In this framework, the GPS, vehicular ad hoc networks and LTE technologies are used to track the vehicles violating lane discipline and to alert emergency services in case of accidents.
Attention to Scale: Scale Aware Semantic Image Segmentation
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Incorporating multi scale features in fully convolutional neural networks (FCNs) has been a key element to achieving state of the art performance on semantic image segmentation. One common way to extract multi scale features is to feed multiple resized input images to a shared deep network and then merge the resulting features for pixelwise classification. In this work, we propose an attention mechanism that learns to softly weight the multi scale features at each pixel location. We adapt a state of the art semantic image segmentation model, which we jointly train with multi scale input images and the attention model. The proposed attention model not only outperforms averageand max pooling, but allows us to diagnostically visualize the importance of features at different positions and scales. Moreover, we show that adding extra supervision to the output at each scale is essential to achieving excellent performance when merging multi scale features. We demonstrate the effectiveness of our model with extensive experiments on three challenging datasets, including PASCAL Person Part, PASCAL VOC 2012 and a subset of MS COCO 2014.
Off chip bus power minimization using serialization with cache based encoding
Venue: Microelectron. J.

The data bus is a major component of high power consumption in small process high performance systems and in systems on chip (SoC) design. This paper presents an analysis of different state of the art techniques for reducing the power of off chip memory bus interface, with proposing an approach overcoming some limitations existing in the state of art methods. More precisely, the paper introduces a serialization (S) method combined with cache based encoding scheme, aiming at saving the optimal possible power for off chip memory bus. Bus serialization reduces the number of transmission wires, while a Serialization Widening (SW) approach reduces the bus capacitance and the number of transmission wires. Experimental results show that, for off chip data bus, the serialization approach with cache based encoding achieves 31% and 52% power reduction for single core and multi core applications, respectively, when using fixed voltage and frequency with 128 bits data bus.
Chinese Zero Pronoun Resolution with Deep Neural Networks
Venue: ACL

While unsupervised anaphoric zero pronoun (AZP) resolvers have recently been shown to rival their supervised counterparts in performance, it is relatively difficult to scale them up to reach the next level of performance due to the large amount of feature engineering efforts involved and their ineffectiveness in exploiting lexical features. To address these weaknesses, we propose a supervised approach to AZP resolution based on deep neural networks, taking advantage of their ability to learn useful task specific representations and effectively exploit lexical features via word embeddings. Our approach achieves stateof the art performance when resolving the Chinese AZPs in the OntoNotes corpus.
Early, Isolated Duodenal Mucosa Associated Lymphoid Tissue Lymphoma Presenting without Symptoms or Grossly Apparent Endoscopic Lesions and Diagnosed by Random Duodenal Biopsies
Venue: Case Reports in Gastroenterology

Clinical data regarding mucosa associated lymphoid tissue lymphoma (MALToma) solely involving the duodenum are sparse because of the relative rarity of the disease. A comprehensive literature review revealed only 17 cases reported until 2004, and only a moderate number of cases have been reported since. MALToma can be asymptomatic in its very early stages but frequently produces localized or nonspecific symptoms, including early satiety, abdominal pain, vomiting, and involuntary weight loss in later stages. While gastric MALToma is strongly associated with gastric Helicobactor pylori infection, duodenal MALToma is often unassociated with H. pylori infection. A 74 year old female presented with only dysphagia (without symptoms referable to a duodenal lesion) without systemic 'B' symptoms, and with no evident duodenal lesions at esophagogastroduodenoscopy; however, she was diagnosed with duodenal MALToma by pathologic examination of random duodenal biopsies performed to exclude celiac disease. An important clinical feature of this case is that duodenal MALToma was diagnosed by pathologic analysis of duodenal biopsies despite (1) no endoscopically apparent duodenal lesions; (2) duodenal involvement without gastric involvement; (3) lack of symptoms attributable to duodenal MALToma, and (4) absence of evident H. pylori infection. This work shows that early duodenal MALToma can be difficult to diagnose because of absent symptoms, absence of gastric involvement, absence of endoscopic abnormalities, and absence of H. pylori infection; it may require random duodenal biopsies for diagnosis.
Regional Antibiotic Resistance of Helicobacter pylori

Antibiotic resistance is considered the cause of unsuccessful eradication of Helicobactor pylori (H. pylori) infections. Awareness of regional resistance rates of H. pylori isolates can improve not only empiric antibiotic therapy but also lead to the development of second line treatment and rescue regimens. At present, the most common treatment is empiric eradication. Global regional regimen therapies should be based upon regional in vitro antibiotic resistance rates. This approach is crucial in successfully treating the individual patient. However, in regions where antibiotic susceptibility testing is unavailable, epidemiological data for secondary H. pylori resistance are essential for the judicious use of antibiotics following several treatment failures. Primary H. pylori resistance to clarithromycin is less prevalent worldwide than H. pylori resistance to metronidazole. Secondary resistance that develops in vivo in previously susceptible organisms has been documented in cases of therapeutic failures. All antibiotics used to treat H. pylori are widely used to treat other bacterial infections. Pretreatment exposure of H. pylori to inadequate levels of these drugs as well as the use of inadequate regional antibiotics is associated with secondary resistance. Herein, we review regional resistance rates of H. pylori isolates to clarithromycin, metronidazole and levofloxacin, the main antimicrobial agents used for eradication of H. pylori.
KNOWLEDGE, ATTITUDE AND PRACTICE REGARDING DENGUE AND CHIKUNGUNYA IN SECONDARY SCHOOL CHILDREN IN A CITY OF NORTH INDIA

Many vector borne diseases are prevalent in India and have become serious public health problem. Recurring outbreaks of dengue fever have been reported in various states and union territories of India including Uttar Pradesh. Currently in 2016, big upsurge of chikungunya is being observed in Delhi and nearby cities with reporting of cases from other States/UT's too. Meerut comprises of about 2% of the population of Uttar Pradesh. Despite this there was no documented evidence of knowledge, attitude and practices (KAP) of school children regarding dengue and chikungunya. Therefore current study was planned on secondary and senior secondary school children of 9 to 12 standard who are in adolescent age group.  A cross sectional study was carried out among 738 school children in four schools of Meerut in the month of September 2016. A predesigned, pre tested, semi structured, self administered questionnaire was used to collect information.  The fact that dengue and chikungunya are caused by mosquito bite was known to 96% and 71% children respectively. 43% children knew female mosquito bites and spread diseases. Only 21% knew that they are caused by Aedes mosquito. 47% knew that it breeds in clean stagnant water and 49% knew that it bites during day. Only 50 60% children took personal protective measures against mosquito bites and check for breeding of mosquitoes. 95% children feels that prevention of these diseases should be taught in schools. Findings of the study can help making strategies for children to enhance knowledge on these diseases.
An interventional study to assess knowledge regarding rabies in secondary school students

Despite the tremendous progress in vaccination, rabies is widely prevalent in India. In India, about 15 million people get animal bite, every year and need post exposure prophylaxis. Although all age groups are susceptible, rabies is most common in children. 40% of post exposure immunization is given to children aged 5 14 years. On this background, present study was designed to assess knowledge regarding rabies in secondary school students and to impart health education to them.  An interventional study was carried out in an English medium secondary school. Total 140 students of age 13 15 years were studied. Baseline knowledge regarding rabies was determined by questionnaire method. A health education intervention regarding rabies was given to them. Knowledge was again tested using same questionnaire after 10 days. The data gathered was analysed using appropriate statistical test.  Out of total, 65.71% were male and 34.29% were female students. 85.71% students knew that dog bite can cause rabies.74.28% students knew about antirabies vaccination for human, which rose to 85% after health education. 66.43% students perceived application of turmeric, oil, chuna on wound as beneficial before health education. There was significant difference between knowledge regarding mode of transmission of rabies, animals that transmit rabies in India and immediate steps after animal bite in students before health education intervention.  There was inadequate knowledge regarding rabies, in students which shown significant increase after health education.
Device to device meets LTE unlicensed
Venue: IEEE Communications Magazine

In this article, we look into how the LTE network can efficiently evolve to cater for new data services by utilizing direct communications between mobile devices and extending the direct transmissions to the unlicensed bands, that is, D2D communications in conjunction with LTE Unlicensed. In doing so, it provides an opportunity to solve the main challenge of mutual interference between D2D and CC transmissions. In this context, we review three interconnected major technical areas of multihop D2D: transmission band selection, routing path selection, and resource management. Traditionally, D2D transmissions are limited to specific regions of a cell's coverage area in order to limit the interference to CC primary links. We show that by allowing D2D to operate in the unlicensed bands with protective fairness measures for WiFi transmissions, D2D is able to operate across the whole coverage area and, in doing so, efficiently scale the overall network capacity while minimizing cross tier and cross technology interference.
Tiles Cards: a Card based Design Game for Smart Objects Ecosystems
Venue: SERVE@AVI

We present Tiles Cards, a tool to foster end user design thinking of smart object applications. Tiles Cards facilitate participatory design by abstracting the complexity of IoT technology into a set of interaction primitives and composition rules accessible to nonexperts. Further, it promotes creative thinking by means of a mission based game in which teams have to fulfill design tasks under constraints. Tiles Cards is a generic tool that can specialized to multiple application domains including learning and games. In this workshop we are presenting results from our pilot study discussing the opportunities of using card games for end user design.
Development of the Functional Mobility Assessment Family Centered Version

Pediatric wheeled mobility devices require periodic updating to accommodate children's physical growth, changing needs, and device maintenance requirements. Valid and reliable instruments are needed to facilitate the WMD assessment process and to evaluate the effect of new WMDs on clients' functional status. While the Functional Mobility Assessment (FMA) is currently available for use with adults, no similar instrument has been developed for children. In this dissertation study, the Functional Mobility Assessment Family Centered Version (FMA FC) was developed to fill this void in assessment technologies. Content validity of the FMA FC was established qualitatively using interviews with parents/caregivers and therapists with expertise in WMD assessment. Using the relevance scale, quantitative content validity of the FMA FC Beta Version 1, as rated by parents/caregivers was 92 percent and as rated by therapists was 99 percent. Parents/caregivers and therapists indicated that the meaning of items was clear and all items were easy to rate. Both test retest reliability (ICC .85) and internal consistency (Cronbach's alpha .87) of FMA FC Beta Version 3 were found to be acceptable.
Hemangioma intraoseo: reto diagnostico. Presentacion de un caso y revision de la literatura. Intraosseous hemangioma: A diagnostic challenge. Case report and review of the literature.

Hemangiomas ar e benign vasoformative neoplasms of endothelial origin. Intraosseous hemangiomas are rare conditions that account for only 0.5 to 1% of all intraosseous tumors. The most common locations are the vertebral column and skull. Intraosseous hemangiomas of the jawbones are very rare. Two thirds of such tumors are located in the mandible, with a female to male ratio of 2:1. The aim of this article is to report a signifi cant case of a central hemangioma of large dimensions in the mandibular ramus on the right hand side, which was successfully treated by surgical resection and ligature of the nutritional vessels.  A 14 year old male patient who presented a unilocular radiolucent area on the mandibular ramus on the righthand side. Clinically, diffuse swelling of the face was observed, which had resulted in asymptomatic facial asymmetry of 3 years' evolution.  The importance of intraosseous hemangiomas of the jaw lies in their proximity to the teeth, which represents a high risk of bleeding in the event of any traumatism as a result of an attempted extraction. Due to their low incidence and unusual presentation, diagnosis of intraosseous hemangioma represents a signifi cant challenge.
Deep Sentence Embedding Using Long Short Term Memory Networks: Analysis and Application to Information Retrieval
Venue: IEEE/ACM Transactions on Audio, Speech, and Language Processing

This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short Term Memory (LSTM) cells. The proposed LSTM RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM RNN is trained in a weakly supervised manner on user click through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM RNN. On a web search task, the LSTM RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms Paragraph Vector method for web document retrieval task.
Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling
Venue: Genome Biology

BackgroundHigh throughput sequencing enables unbiased profiling of microbial communities, universal pathogen detection, and host response to infectious diseases. However, computation times and algorithmic inaccuracies have hindered adoption.ResultsWe present Taxonomer, an ultrafast, web tool for comprehensive metagenomics data analysis and interactive results visualization. Taxonomer is unique in providing integrated nucleotide and protein based classification and simultaneous host messenger RNA (mRNA) transcript profiling. Using real world case studies, we show that Taxonomer detects previously unrecognized infections and reveals antiviral host mRNA expression profiles. To facilitate data sharing across geographic distances in outbreak settings, Taxonomer is publicly available through a web based user interface.ConclusionsTaxonomer enables rapid, accurate, and interactive analyses of metagenomics data on personal computers and mobile devices.