nils-herrmann/clean_scirepeval huggingface dataset:
Cognitive correlates of borderline intellectual functioning in borderline personality disorder.
Venue: Journal of psychiatric research
Year: 2020

Borderline intellectual functioning (BIF) is highly prevalent in patients with borderline personality disorder (BPD) but their relationship remains poorly understood. This retrospective study aimed to investigate the cognitive profile of BIF among people diagnosed with BPD. Clinical, demographic, and neuropsychological data of fifty five outpatients with BPD were analyzed. The sample split into two groups: BPD with BIF (BIF+ n 25; intelligence quotient IQ range: 71 84) and BPD without BIF (BIF n 30; IQ range: 86 124) Between group comparisons employed either parametric and non parametric descriptive statistics, as necessary. Neuropsychological measures (Wechsler Adult Intelligence Scale Revised WAIS R IQ, factor index, subtest scores) and cognitive performance deficits in the two groups were likewise compared aside, followed by Spearman's correlation test conducted on relevant metrics. The cognitive, but not the clinical and demographic profiles differed significantly between the two groups. BIF+ was associated with a specific pattern of verbal, attentive, and planning dysfunctions. The verbal comprehension index had the highest discriminative value for the presence of BIF in patients with BPD, and it was tightly associated with adaptive and social functioning. The neuropsychological assessment of BPD may be relevant to plan targeted interventions based on measures of cognitive functioning which could also serve to evaluate treatment efficacy and outcomes. Clinical implications and future directions are discussed.
A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan
Venue: Nature Chemical Biology
Year: 2019

Marine seaweeds increasingly grow into extensive algal blooms, which are detrimental to coastal ecosystems, tourism and aquaculture. However, algal biomass is also emerging as a sustainable raw material for the bioeconomy. The potential exploitation of algae is hindered by our limited knowledge of the microbial pathways and hence the distinct biochemical functions of the enzymes involved that convert algal polysaccharides into oligo and monosaccharides. Understanding these processes would be essential, however, for applications such as the fermentation of algal biomass into bioethanol or other value added compounds. Here, we describe the metabolic pathway that enables the marine flavobacterium Formosa agariphila to degrade ulvan, the main cell wall polysaccharide of bloom forming Ulva species. The pathway involves 12 biochemically characterized carbohydrate active enzymes, including two polysaccharide lyases, three sulfatases and seven glycoside hydrolases that sequentially break down ulvan into fermentable monosaccharides. This way, the enzymes turn a previously unexploited renewable into a valuable and ecologically sustainable bioresource.Elucidation of a multi enzyme pathway for degradation of the polysaccharide ulvan by Formosa agariphila provides tools to use ulvan biomass from marine algal blooms as feedstock for renewable sources of carbohydrates.
An Efficient Learning Procedure for Deep Boltzmann Machines
Venue: Neural Computation
Year: 2012

We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer by layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine tuned.
Shape preserving design of vibrating structures using topology optimization
Year: 2018

In several engineering components, the shape of some functional surfaces needs to be preserved in order to avoid losing performance or even its functionality when subjected to loads. This is particularly important when tight tolerances are required for operational conditions in some regions. If the deformation significantly affects product functionality, it is interesting to use a shape preserving design technique. This will often reduce deformation in a local region. To achieve that, we deal with topology optimization of elastic, continuum structures with Rayleigh damping, subjected to time harmonic, design independent external dynamic loading with prescribed excitation frequency, amplitude and spatial distribution. In topology optimization for vibrating structures, the obtained design should often have its resonance frequencies driven far away from the given excitation frequency in order to avoid resonance and to reduce vibration levels. In this work, we explore harmonic vibration problems with the excitation frequency lower than the first resonance frequency of the initial structure. Dynamic compliance minimization is used to improve dynamic response of the structure. An additional local dynamic compliance constraint is used to define the shape preserving problem, thus, reducing deformation in specific regions of a part named shape preserving region (SPR) A commercial FE code (ANSYSR) is used to solve the finite element problem. The optimization Method of Moving Asymptotes (MMA) is used with the modified Solid Isotropic Material with Penalization (SIMP) material interpolation scheme. The effectiveness of this technique is presented using 2D plane structures. Coherent results were achieved using the proposed optimization formulation. It is possible to observe significant decrease on local deformation, at expense of little increase on global dynamic compliance.
Minimum Copper Loss Direct Torque Control of Brushless DC Motor Drive in Electric and Hybrid Electric Vehicles
Venue: IEEE Access
Year: 2019

A novel direct torque control (DTC) method with copper loss minimization is proposed for the brushless dc motor (BLDCM) in electric vehicle (EV) application. In order to realize high efficiency and high torque control precision at the same time, a special stator flux linkage trajectory is designed based on pseudo dq transformation for maximum torque per ampere (MTPA) control of BLDCM. The three phase conduction voltage vectors switch table is used to realize the flux linkage tracking control for high dynamic performance of torque control. The torque ripple caused by the non ideality of the back EMF and the commutation in traditional two phase conduction voltage vector based DTC method can be eliminated, which improves the torque control precision. The validity and effectiveness of the proposed DTC scheme are verified through experimental results.
Model based Bayesian signal extraction algorithm for peripheral nerves.
Venue: Journal of neural engineering
Year: 2017

Multi channel cuff electrodes have recently been investigated for extracting fascicular level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal to noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time.  To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE) is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms.  The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10 20% These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity.  HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.
The use of ict in teaching English grammar in secondary schools
Year: 2020

Nowadays, in every aspect of our life information, communication and technology (ICT) are playing a vital role. Even in the field of education the technological developments have shown great impact on the learners to become innovative and a source for motivating tool towards learning. ICT is often used to talk about computers and other technology use in schools. There are many ways to involve ICT in language teaching. In this article, we discuss the role of ICT and ways of using of ICT in teaching English Grammar in secondary schools.
A simple, robust and rapid approach to detect carbapenemases in Gram negative isolates by MALDI TOF mass spectrometry: validation with triple quadripole tandem mass spectrometry, microarray and PCR.
Venue: Clinical microbiology and infection the official publication of the European Society of Clinical Microbiology and Infectious Diseases
Year: 2014

Carbapenemases should be accurately and rapidly detected, given their possible epidemiological spread and their impact on treatment options. Here, we developed a simple, easy and rapid matrix assisted laser desorption ionization time of flight (MALDI TOF) based assay to detect carbapenemases and compared this innovative test with four other diagnostic approaches on 47 clinical isolates. Tandem mass spectrometry (MS MS) was also used to determine accurately the amount of antibiotic present in the supernatant after 1 h of incubation and both MALDI TOF and MS MS approaches exhibited a 100% sensitivity and a 100% specificity. By comparison, molecular genetic techniques (Check MDR Carba PCR and Check MDR CT103 microarray) showed a 90.5% sensitivity and a 100% specificity, as two strains of Aeromonas were not detected because their chromosomal carbapenemase is not targeted by probes used in both kits. Altogether, this innovative MALDI TOF based approach that uses a stable 10 mg disk of ertapenem was highly efficient in detecting carbapenemase, with a sensitivity higher than that of PCR and microarray.
Family history of schizophrenia as a risk factor for axis I psychiatric conditions.
Venue: Journal of psychiatric research
Year: 2013

People with first degree relatives with schizophrenia are at an elevated risk of developing the disorder themselves. High rates of psychotic symptoms in non psychotic disorders, high rates of comorbidity in psychotic disorders, and diversity of outcomes following psychosis risk states together suggest that this vulnerability may be for psychiatric conditions in general, not limited to schizophrenia. In this study, data from the National Survey of American Life (NSAL) were used to examine the association between having a first degree relative with schizophrenia and the lifetime development of a range of non psychotic axis I psychiatric disorders using adjusted odds ratios. Having a relative with schizophrenia was associated with increased risk for most non psychotic psychiatric conditions examined, including those expected to be associated with schizophrenia (affective, anxiety, and substance use disorders) and those not expected (bulimia, disorders of childhood onset) excluding respondents with lifetime psychotic symptoms and controlling for demographic factors. Family history of schizophrenia among this predominantly African American and Afro Caribbean sample appears to be a risk factor for a range of axis I diagnoses, supporting a continuous rather than categorical nature of psychiatric vulnerability. Future studies should examine whether these associations are due to genetic or environmental factors, or both.
Detecting Spambot as an Antispam Technique for Web Internet BBS
Year: 2013

Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS) BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behaviouralbased Spam Detection (BSD) method. Behavioral based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send emails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.  SPAM, Botnet detection, Network threat detection, Network worm detection.
Determinants of abundance and habitat association of mammals in Barandabhar Corridor Forest, Chitwan, Nepal
Year: 2021

Corridor Forest (BCF) the biologically functional corridor, is surrounded by the large human settlements that exploit the corridor where large mammals such as tigers, leopards and their prey such as ungulates, primates, and rhinoceros occur. This study aimed to evaluate major determinants that affect the distribution of large mammals in BCF, Chitwan, Nepal that connects the biologically significant Chitwan National Park with the Mahabharat range. The status and distribution of large mammals along the habitat and disturbance gradients were determined by using 29 line transects (mean length 4.59 0.38 km) that covered a linear distance of 133.13 km. The chital were the most abundant mammals (density per km2 (D) 8.9095 1.4570 and encounter rate per km (ER) 1.49) followed by rhesus monkey (D 38.896 16.013, ER 0.28) wild boar (D 14.814 3.57, ER 0.62) northern red muntjac (D 9.6566 2.9514, ER 0.62) and sambar (D 5.392 2.319, ER 0.38) Similarly, the sign encounter rate of tiger and leopard was 0.435 and 0.503 respectively. Habitat types, human disturbances, and coverage of invasive and alien plant species (IAPs) played a key role in the distribution of large mammals. The occurrence of mammals was low nearer to the settlements and roads and coverage of IAPs and more nearer to the water resources. However, degradation of foraging grounds such as grasslands by succession and invasion of alien plant species added more threats to the survival of large mammals. Therefore, such a situation can be improved through the scientific management of forests and grasslands.
A Novel Hybrid Deep Learning Model for Sentiment Classification
Venue: IEEE Access
Year: 2020

A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services. However, sentiment classification plays an important role in the user's feedback evaluation. At present, deep learning such as long short term memory (LSTM) gated recurrent unit (GRU) bidirectionally long short term memory (BiLSTM) or convolutional neural network (CNN) are prevalently preferred in sentiment classification. Moreover, word embedding such as Word2Vec and FastText is closely examined in text for mapping closely related to the vectors of real numbers. However, both deep learning and word embedding methods have strengths and weaknesses. Combining the strengths of the deep learning models with that of word embedding is the key to high performance sentiment classification in the field of natural language processing (NLP) In the present study, we propose a novel hybrid deep learning model that strategically combines different word embedding (Word2Vec, FastText, character level embedding) with different deep learning methods (LSTM, GRU, BiLSTM, CNN) The proposed model extracts features of different deep learning methods of word embedding, combines these features and classifies texts in terms of sentiment. To verify the performance of the proposed model, several deep learning models called basic models were created to perform series of experiments. By comparing, the performance of the proposed model with that of past studies, the proposed model offers better sentiment classification performance.
Changing preferences for information and participation in the last phase of life: a longitudinal study among newly diagnosed advanced lung cancer patients
Venue: Supportive Care in Cancer
Year: 2011

PurposeThe objective is to explore changes over time in the information and participation preferences of newly diagnosed stage IIIb/IV non small cell lung cancer patients.MethodsPatients were recruited by physicians in 13 hospitals and interviewed every 2 months until the fourth and every 4 months until the sixth interview.ResultsSixty seven patients were interviewed three times. Over a period of 4 months from diagnosis, half of patients changed their information preferences for palliative care and end of life decisions with a possible or certain life shortening effect (ELDs, e.g. non treatment decisions) in both directions, from not wanting to wanting the information, but also and as much from wanting to no longer wanting it. The latter were more likely to be in a better physical condition. Preferences for participation in medical decision making also changed: 50% to 78% depending on the type of decision (general, treatment, transfer or ELD) changed their preference towards wanting more or less participation. Pain seemed to be a trigger for patients wanting more involvement, which contrasts with studies suggesting that patients who are more ill tend to give up more control.ConclusionsDoctors should regularly ask their advanced lung cancer patients how much information and participation they want because preferences do change in unexpected ways.
The challenge of COVID 19 and hematopoietic cell transplantation; EBMT recommendations for management of hematopoietic cell transplant recipients, their donors, and patients undergoing CAR T cell therapy.
Venue: Bone marrow transplantation
Year: 2020

The new coronavirus SARS CoV 2 has rapidly spread over the world causing the disease by WHO called COVID 19. This pandemic poses unprecedented stress on the health care system including programs performing allogeneic and autologous hematopoietic cell transplantation (HCT) and cellular therapy such as with CAR T cells. Risk factors for severe disease include age and predisposing conditions such as cancer. The true impact on stem cell transplant and CAR T cell recipients in unknown. The European Society for Blood and Marrow Transplantation (EBMT) has therefore developed recommendations for transplant programs and physicians caring for these patients. These guidelines were developed by experts from the Infectious Diseases Working Party and have been endorsed by EBMT's scientific council and board. This work intends to provide guidelines for transplant centers, management of transplant candidates and recipients, and donor issues until the COVID 19 pandemic has passed.
La construccion de comunidades ciudadanas inclusivas: papel de las bibliotecas en contextos de crisis y de emergencia social
Year: 2018

Independientemente de la titularidad a la que se circunscriba cualquier biblioteca la tarea fundamental del profesional del siglo 21 es hacer que las bibliotecas pertenezcan a sus comunidades, que los ciudadanos se identifi quen con la biblioteca y la consideren como propia. Esto es aun mas importante en el momento y contexto en el que vivimos, en el que algunas personas empiezan a cuestionar la necesidad de seguir fi nanciando servicios bibliotecario
Observation of the use of songs in teaching of English pronunciation in the students of sixth year of basic education class "D" at Jose Maria Roman school, city of Riobamba, Chimborazo province, during the academic term 2016 2017.
Year: 2017

By means of a previous observation made at the Jose Maria Roman school with the sixth year students of basic education class "D" it was determined as a problem that the students have difficulties when they pronounce the words in English, even though the teacher applies a good teaching strategy students only practice pronunciation as a repetition without recognizing the different sounds that there is in the English language. This study describes how the English teacher uses the songs to teach pronunciation in English. The research was realized during the academic year 2016 2017, with students in the sixth year of basic class "D" the students involved were 33 of whom 15 were men and 18 were women. The purpose of this research is to observe the use of songs in the teaching of English language pronunciation, the type of study is qualitative of exploratory level, the qualitative method used was the ethnographic; the technique used was observation and the instrument was a checklist. The investigation concluded that despite of the methodology used, based on the songs activities, the students continue having problems in pronunciation because the teacher does not perform the activities in a good way.