Miracle-dz/UniFAR-FTUs huggingface dataset:
Four Channel Multivariate Coherence Training: Development and Evidence in Support of a New Form of Neurofeedback

As the field of neurofeedback and neuromodulation grows, trends toward using neurofeedback to treat problems of brain dysfunction have emerged. While the use of connectivity based fMRI guided neurofeedback has shown itself to be efficacious, the expense related to the treatment calls for a more practical solution. The use of QEEG guided neurofeedback in the treatment has shown promise as an emerging treatment. To date, EEG based neurofeedback approaches have used technology with limited sophistication. We designed a new form of neurofeedback that uses four channels of EEG with a multivariate calculation of coherence metrics. Following a mathematical presentation of this model, we present findings of a multi-site study with clinical subjects with various diagnoses. We compared this form of multivariate coherence neurofeedback to the more standard two channel coherence training. Findings showed that there was a significant difference between the groups with four channel multivariate coherence neurofeedback leading to greater changes in EEG metrics. Compared to two channel coherence training, four channel multivariate coherence neurofeedback led to a greater than 50% change in power and 400% in coherence values per session. The significance of these findings is discussed in relation to complex calculations of effective connectivity and how this might lead to even greater enhancements in neurofeedback efficacy.
SHANK3 Downregulation in the Ventral Tegmental Area Accelerates the Extinction of Contextual Associations Induced by Juvenile Non-familiar Conspecific Interaction

Haploinsufficiency of the SHANK3 gene, encoding for a scaffolding protein located in the postsynaptic density of glutamatergic synapse, has been linked to forms of autism spectrum disorders (ASDs). It has been shown that SHANK3 controls the maturation of social reward circuits in the ventral tegmental area (VTA). Whether the impairments in associative learning observed in ASD relate to SHANK3 insufficiency restricted to the reward system is still an open question. Here, we first characterize a social-conditioned place preference (CPP) paradigm based on the direct and free interaction with a juvenile and non-familiar conspecific. In both group- and single-housed C57Bl6/j late adolescence male mice, this CPP protocol promotes the formation of social-induced contextual associations that undergo extinction. Interestingly, the downregulation of Shank3 expression in the VTA altered the habituation to a non-familiar conspecific during conditioning and accelerated the extinction of social-induced conditioned responses. Thus, inspired by the literature on drugs of abuse-induced contextual learning, we propose that acquisition and extinction of CPP might be used as behavioral assays to assess social-induced contextual association and “social-seeking” dysfunctions in animal models of psychiatric disorders.
PVsiRNAdb: a database for plant exclusive virus-derived small interfering RNAs

Abstract Ribonucleic acids (RNA) interference mechanism has been proved to be an important regulator of both transcriptional and post-transcription controls of gene expression during biotic and abiotic stresses in plants. Virus-derived small interfering RNAs (vsiRNAs) are established components of the RNA silencing mechanism for incurring anti-viral resistance in plants. Some databases like siRNAdb, HIVsirDB and VIRsiRNAdb are available online pertaining to siRNAs as well as vsiRNAs generated during viral infection in humans; however, currently there is a lack of repository for plant exclusive vsiRNAs. We have developed `PVsiRNAdb (http://www.nipgr.res.in/PVsiRNAdb)’, a manually curated plant-exclusive database harboring information related to vsiRNAs found in different virus-infected plants collected by exhaustive data mining of published literature so far. This database contains a total of 322 214 entries and 282 549 unique sequences of vsiRNAs. In PVsiRNAdb, detailed and comprehensive information is available for each vsiRNA sequence. Apart from the core information consisting of plant, tissue, virus name and vsiRNA sequence, additional information of each vsiRNAs (map position, length, coordinates, strand information and predicted structure) may be of high utility to the user. Different types of search and browse modules with three different tools namely BLAST, Smith–Waterman Align and Mapping are provided at PVsiRNAdb. Thus, this database being one of its kind will surely be of much use to molecular biologists for exploring the complex viral genetics and genomics, viral–host interactions and beneficial to the scientific community and can prove to be very advantageous in the field of agriculture for producing viral resistance transgenic crops.
Performance improvement of monaural speech separation system using image analysis techniques

This research work proposes an image analysis-based algorithm to enhance the time–frequency (T–F) mask obtained in the initial segmentation of CASA-based monaural speech separation system to improve speech quality and intelligibility. It consists of labelling the initial segmentation mask, boundary extraction, active pixel detection and eliminating the non-active pixels related to noise. In labelling, the T–F mask obtained is labelled as periodicity pixel ( P ) matrix and non-periodicity pixel ( NP ) matrix. Next speech boundaries are created by connecting all the possible nearby P and NP matrix. Some speech boundary may include noisy T–F units as holes; these holes are treated using the proposed algorithm. The proposed algorithm is evaluated with the quality and intelligibility measures such as signal to noise ratio (SNR), perceptual evaluation of speech quality, P EL , P NR , coherence speech intelligibility index (CSII), normalised covariance metric (NCM), and short-time objective intelligibility (STOI). The experimental results show that the proposed algorithm improves the speech quality by increasing the SNR with an average value of 9.91 dB and reduces the P NR by an average value of 25.6% and also improves the speech intelligibility in terms of CSII, NCM, and STOI when compared with the input noisy speech mixture
Influence of preoperative information support on anxiety, pain and satisfaction with postoperative analgesia in children and adolescents after thoracic surgery: a randomized double blind study.

AIMS/BACKGROUND The proportion of patients who experience anxiety prior to planned surgery, even a minimally invasive one, is estimated at 50-70%. Thoracic surgery, causes significant preoperative anxiety, especially in children. The aim of this study was to determine the effect of an important component of psychological preparation for a surgery as information support on levels of anxiety, pain and satisfaction with postoperative analgesia. METHODS The randomized double blind study including patients aged 9-18 years qualified for lateral thoracotomy or Ravitch procedure. The subjects were randomized to the control group (n=56) provided with a routine preoperative information by a nurse, and the experimental group (n=56) offered additional psychological consultation. Data were collected via the State-Trait Anxiety Inventory. RESULTS In the experimental group, the level of state anxiety at 48 h post-surgery was significantly lower than prior to the procedure, but only in subjects with preoperative trait anxiety <7/10 sten. In the control group, the level of postoperative anxiety was significantly higher than prior to the surgery; this effect was observed irrespectively of preoperative trait anxiety level. Lack of preoperative psychological consultation (β=-0.23), trait anxiety ≥7 sten (β=0.25), and higher level of preoperative state anxiety (β=0.65) were independent predictors of greater state anxiety at 48 h post-surgery. Patients from the experimental group did not differ significantly from the controls in terms of their median pain scores and satisfaction with postoperative analgesia. Greater state anxiety was associated with 22% lesser likelihood of complete satisfaction with the analgesia. CONCLUSION Information support from a psychologist offered prior to a thoracic surgery decrease the level of postoperative state anxiety solely in children with lower levels of trait anxiety. Higher level of postoperative state anxiety negatively affect patients' satisfaction with post-surgical analgesia. TRIAL REGISTRATION ClinicalTrials.gov; Influence of Preoperative Support on Anxiety, Pain and Satisfaction With Postoperative Analgesia; NCT03488459, https://clinicaltrials.gov/ct2/show/record/NCT03488459? cntry=PL&city=Rabka-Zdr%C3%B3j&rank=1.
Formalising Filesystems in the ACL2 Theorem Prover: an Application to FAT32

In this work, we present an approach towards constructing executable specifications of existing filesystems and verifying their functional properties in a theorem proving environment. We detail an application of this approach to the FAT32 filesystem. We also detail the methodology used to build up this type of executable specification through a series of models which incrementally add features of the target filesystem. This methodology has the benefit of allowing the verification effort to start from simple models which encapsulate features common to many filesystems and which are thus suitable for reuse.
Approach to Boolean Controller Design by Algebraic Relaxation for Heating Systems

Abstract The paper proposes a relaxation for a special class of Boolean controller design problems which are relevant in optimization of heating systems for buildings. The controller design problem can be formulated as a discrete optimization problem which is hard to solve because of its exponential complexity. It is shown how Zhegalkin polynomials can be used as algebraic normal forms of the Boolean controller functions to relax the optimization problem to a continuous one. This has been successfully applied to a real heating system with 1.8 MW thermal power and the improvement is discussed.
Prognostic scales in advanced heart failure

Heart transplantation (HT) is the treatment of choice for patients with advanced heart failure (HF) who remain symptomatic despite optimal medical therapy. Due to the shortage of organs for transplantation and constantly increasing number of patients placed on waiting lists, accurate risk stratification is a crucial element of management in this population. Prognostic scales allow one to evaluate the patient prognosis, estimate the potential benefits of therapy and identify those patients most likely to benefit from advanced methods of treatment. In this review, we describe prognostic scales in advanced HF, concentrating on commonly used tools – the Heart Failure Survival Score (HFSS) and Seattle Heart Failure Model (SHFM) – as well as on the new promising scales for evaluating waiting list mortality and post-transplant outcomes.
Terminology Coverage from Semantic Annotated Health Documents

BACKGROUND Unstructured health documents (e.g. discharge summaries) represent an important and unavoidable source of information. METHODS A semantic annotator identified all the concepts present in the health documents from the clinical data warehouse of the Rouen University Hospital. RESULTS 2,087,784,055 annotations were generated from a corpus of about 11.9 million documents with an average of 175 annotations per document. SNOMED CT, NCIt and MeSH were the top 3 terminologies that reported the most annotation. DISCUSSION As expected, the most general terminologies with the most translated concepts were those with the most concepts identified.
Does the empty follicle syndrome occur in cases of low number of maturing follicles in assisted reproduction?

Abstract The pathophysiology of the genuine empty follicle syndrome (EFS) is still debated. Ovarian aging has been contested as a cause of this condition. Our aim was to investigate the occurrence of the genuine EFS in cases of a low number of mature follicles in a prospective manner. Ninety-five infertile women were recruited and evaluated following conventional controlled ovarian stimulation (COS) with ≤ six follicles of ≥14 mm diameter on the day of hCG administration. Enrolled women were 37.5 ± 5.2 years of age with basal FSH level of 9.1 ± 3.7 mIU/L, antral follicle count (AFC) of 6.9 ± 4.6, and number of ≥14 mm follicles (on the day of hCG) of 3.4 ± 1.5. Among the 95 women, four were complicated by the genuine EFS (4.2%) with features of the depleted ovarian reserve. Comparison between these four cases and the 91 controls revealed significant differences between age, AFC, number of ≥14 mm follicles, and serum E2 level corresponding to 41.8 ± 1.7 versus 37.4 ± 5.2 years, 1.7 ± 0.6 versus 7.1 ± 4.5, 2.0 ± 0.8 versus 3.4 ± 1.5 follicles, and 356 ± 200 versus 975 ± 557 pg/mL, respectively. Post hoc analysis revealed that 56 among the 95 women fulfilled the Bologna criteria for poor ovarian response and all four cases matched the definition of the genuine EFS raising its incidence to 7.1% in this group. A logistic regression analysis showed that AFC was a significant factor in the development of the genuine EFS. We conclude that the genuine EFS complicates infertile women characterized by a low number of mature follicles. Our findings suggest that the mechanism behind this occurrence is associated with a more exhausted ovarian reserve.
Identification of hospital cost drivers using sparse group lasso

Public hospital spending consumes a large share of government expenditure in many countries. The large cost variability observed between hospitals and also between patients in the same hospital has fueled the belief that consumption of a significant portion of this funding may result in no clinical benefit to patients, thus representing waste. Accurate identification of the main hospital cost drivers and relating them quantitatively to the observed cost variability is a necessary step towards identifying and reducing waste. This study identifies prime cost drivers in a typical, mid-sized Australian hospital and classifies them as sources of cost variability that are either warranted or not warranted—and therefore contributing to waste. An essential step is dimension reduction using Principal Component Analysis to pre-process the data by separating out the low value ‘noise’ from otherwise valuable information. Crucially, the study then adjusts for possible co-linearity of different cost drivers by the use of the sparse group lasso technique. This ensures reliability of the findings and represents a novel and powerful approach to analysing hospital costs. Our statistical model included 32 potential cost predictors with a sample size of over 50,000 hospital admissions. The proportion of cost variability potentially not clinically warranted was estimated at 33.7%. Given the financial footprint involved, once the findings are extrapolated nationwide, this estimation has far-reaching significance for health funding policy.
Physical Design for Non-relational Data Systems

Decades of research have gone into the optimization of physical designs, query execution, and related tools for relational databases. These techniques and tools make it possible for non-expert users to make effective use of relational database management systems. However, the drive for flexible data models and increased scalability has spawned a new generation of data management systems which largely eschew the relational model. These include systems such as NoSQL databases and distributed analytics frameworks such as Apache Spark which make use of a diverse set of data models. Optimization techniques and tools developed for relational data do not directly apply in this setting. This leaves developers making use of these systems with the need to become intimately familiar with system details to obtain good performance. We present techniques and tools for physical design for non-relational data systems. We explore two settings: NoSQL database systems and distributed analytics frameworks. While NoSQL databases often avoid explicit schema definitions, many choices on how to structure data remain. These choices can have a significant impact on application performance. The data structuring process normally requires expert knowledge of the underlying database. We present the NoSQL Schema Evaluator (NoSE). Given a target workload, NoSE provides an optimized physical design for NoSQL database applications which compares favourably to schemas designed by expert users. To enable existing applications to benefit from conceptual modeling, we also present an algorithm to recover a logical model from a denormalized database instance. Our second setting is distributed analytics frameworks such as Apache Spark. As is the case for NoSQL databases, expert knowledge of Spark is often required to construct efficient data pipelines. In NoSQL systems, a key challenge is how to structure stored data, while in Spark, a key challenge is how to cache intermediate results. We examine a particularly common scenario in Spark which involves performing iterative analysis on an input dataset. We show that jobs written in an intuitive manner using existing Spark APIs can have poor performance. We propose ReSpark, which automates caching decisions for iterative Spark analyses. Like NoSE, ReSpark makes it possible for non-expert users to obtain good performance from a non-relational data system.
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation

We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.
Nivolumab for the treatment of hepatocellular carcinoma

ABSTRACT Introduction: T-cell checkpoint inhibition as a cancer treatment approach has been the main breakthrough in cancer treatment during the last years. Since the approval of the first commercial CTLA-4 antibody ipilimumab in 2011 for the treatment of melanoma, research and drug development in this field has accelerated massively. In 2014, the US Food and Drug Administration (FDA) approved the first PD-1 targeting agent, namely pembrolizumab, shortly followed by nivolumab. Areas covered: Nivolumab is a fully human immunoglobulin G4 anti-PD-1 monoclonal antibody which is approved for multiple advanced malignancies, including melanoma, non-small cell lung cancer, renal cell cancer, Hodgkin’s lymphoma, squamous head and neck cancer, and urothelial carcinoma. In September 2017, nivolumab was approved by the FDA for liver cancer as a second line treatment after failure of sorafenib based on the data of the multi-cohort phase 1/2 trial CheckMate-040. This article reviews the concept of immunotherapy in liver cancer with focus on nivolumab. Expert commentary: Immunotherapy in hepatocellular carcinoma is safe and is a new treatment option for patients with advanced stage disease besides sorafenib and regorafenib in the US. Randomized phase III trials of nivolumab, pembrolizumab, atezolizumab, durvalumab and tislelizumab as mono- or combination-therapy are ongoing.
Local activation of mammalian separase in interphase promotes double‐strand break repair and prevents oncogenic transformation

Separase halves eukaryotic chromosomes in M‐phase by cleaving cohesin complexes holding sister chromatids together. Whether this essential protease functions also in interphase and/or impacts carcinogenesis remains largely unknown. Here, we show that mammalian separase is recruited to DNA double‐strand breaks (DSBs) where it is activated to locally cleave cohesin and facilitate homology‐directed repair (HDR). Inactivating phosphorylation of its NES, arginine methylation of its RG‐repeats, and sumoylation redirect separase from the cytosol to DSBs. In vitro assays suggest that DNA damage response‐relevant ATM, PRMT1, and Mms21 represent the corresponding kinase, methyltransferase, and SUMO ligase, respectively. SEPARASE heterozygosity not only debilitates HDR but also predisposes primary embryonic fibroblasts to neoplasia and mice to chemically induced skin cancer. Thus, tethering of separase to DSBs and confined cohesin cleavage promote DSB repair in G2 cells. Importantly, this conserved interphase function of separase protects mammalian cells from oncogenic transformation.
Physical layer spoofing detection based on sparse signal processing and fuzzy recognition

Spoofing attacks is one of the most critical attacks in wireless communication security. Traditional solutions are based on cryptology which is performed in the upper layers, and face many challenges especially in resource-limited application. To overcome this hurdle, physical-layer security has been received a lot of attention recently. In this study, the authors propose a physical-layer spoofing detecting scheme, where signal processing and feature recognition are utilised to improve the detection performance. In this study, they present a pretreatment process based on sparse representation (SR) to reinforce the characteristic of the signal. Furthermore, they formulate the problem of spoofing detection as one of the feature extraction and recognition, and employ a developed fuzzy C-mean algorithm to further increase the recognition accuracy. In addition, in order to verify the proposed method, they conduct experiments and use numerical simulation and analysis to evaluate the detection performance. Results showed that the proposed approach can improve the recognition accuracy significantly (increased by one order of magnitude) and the complexity is acceptable (polynomial complexity). Their findings showed that combining SR and feature extraction and recognition, the proposed method provided a good access to achieve a higher accuracy scheme of spoofing detection.
Efficient harmonic peak detection of vowel sounds for enhanced voice activity detection

Voice activity detection (VAD) involves discriminating speech segments from background noise and is a critical step in numerous speech-related applications. However, distinguishing speech from noise based on the properties of noise is fallible, because it is difficult to predict and characterise the noise occurring in real life. In this study, the authors instead focus on the intrinsic characteristics of speech. The harmonic peaks of vowel sounds have higher energies than the other spectral components of speech and are the speech features most likely to survive in most cases of severe noise. Therefore, the energy differences between harmonic peaks and other spectral features show promise for enabling robust VAD. To exploit this feature, the harmonic peaks must be accurately located. For this purpose, this study proposes an efficient harmonic peak location detection (HPD) method. Based on extensive experiments conducted in the presence of various noise types and signal-to-noise ratios, we found that VAD with the proposed HPD approach outperforms existing VAD methods and does so with reasonable computational cost and higher robustness.