Miracle-dz/UniFAR-FTUs huggingface dataset:
Handoffs in the Intensive Care Unit

Operating room (OR) to intensive care unit (ICU) handoffs are complex and known to be associated with adverse events and patient harm. The authors hypothesized that handoff quality diminishes during nights/weekends and that bedside handoff practices are similar between ICUs of the same health system. Bedside OR-to-ICU handoffs were directly observed in 2 surgical ICUs with different patient volumes. Handoff quality measures were compared within the ICUs on weekdays versus nights/weekends as well as between the high- and moderate-volume ICUs. In the high-volume ICU, transmitter delivery scores were significantly better during off hours, while other measures were not different. High-volume ICU scores were consistently better than those in the moderate-volume ICU. Bedside handoff practices are not worse during off hours and may be better in ICUs used to a higher patient volume. Specific handoff protocols merit evaluation and training to ensure consistent practices in different ICU models and at different times.
Prognostic impact of the level of neck metastasis in oral cancer patients

Neck staging in oral cancer depends on the number of compromised nodes, their size and side of occurrence. Objective This paper aims to evaluate risk factors for metastatic nodes in levels IV/V and their prognostic impact on patients with oral carcinoma. Method Retrospective study. Inclusion criteria: pathologist's diagnosis of squamous cell carcinoma, primary tumor in the lower oral cavity, no extension into extraoral sites, no previous treatment, synchronous neck dissection and presence of metastatic nodes. Risk factors for metastasis were evaluated through logistic regression and disease-specific survival and recurrence by survival analysis. Classificatory analysis was performed through recursive partitioning. Results 307 patients met the inclusion criteria. Univariate logistic regression identified pN stage, vascular invasion, and multiple metastatic nodes as risk factors for metastases in levels IV/V. Multivariate analysis found vascular invasion and multiple metastatic nodes were significant. Survival analysis revealed pT, pN, neural infiltration, vascular invasion, number of metastatic nodes, metastases in levels IV/V, and node ratio were significant factors. In multivariate survival analysis, pT, pN, vascular invasion and metastases in levels IV/V were significant. Classificatory analysis showed that pN is non-significant in patients with level IV/V metastases. Conclusion The occurrence of metastases in levels IV/V was significant for disease-specific survival.
A study of thread-local garbage collection for multi-core systems

With multi-processor systems in widespread use, and programmers increasingly writing programs that exploit multiple processors, scalability of application performance is more of an issue. Increasing the number of processors available to an application by a factor does not necessarily boost that application's performance by that factor. More processors can actually harm performance. One cause of poor scalability is memory bandwidth becoming saturated as processors contend with each other for memory bus use. More multi-core systems have a non-uniform memory architecture and placement of threads and the data they use is important in tackling this problem. Garbage collection is a memory load and store intensive activity, and whilst well known techniques such as concurrent and parallel garbage collection aim to increase performance with multi-core systems, they do not address the memory bottleneck problem. One garbage collection technique that can address this problem is thread-local heap garbage collection. Smaller, more frequent, garbage collection cycles are performed so that intensive memory activity is distributed. This thesis evaluates a novel thread-local heap garbage collector for Java, that is designed to improve the effectiveness of this thread-independent garbage collection.
A Cognitive-Based Semantic Approach to Deep Content Analysis in Search Engines

This paper presents an innovative cognitive-based semantic approach that uses rule-based Natural Language Processing (NLP) in conjunction with a world model and cognitive frames to semantically analyze, understand, and rank digital text in search engines. The goal is to improve the relevance, accuracy, and efficiency of information search. The world model represents things existing in the real world (e.g., subject-related ontologies or classifications essential for understanding the topics to be analyzed) whereas cognitive frames specify possible users’ interactions with the world, including things that people should know or do (e.g., tasks, methods, procedures, cognitive processes) in such interactions. Using a rule-based semantic approach in conjunction with a subject-related world model and task-relevant cognitive frames to understand and evaluate text is an advancement in search engine technology. It addresses three limitations of the existing approaches: the inadequate measure of the meaningful content in web pages; a poor understanding of users’ intention and tasks in their search and, the irrelevance and inaccuracy of search results. This method has led to the successful implementation of a fullscale semantic search engine in medicine (available at Seenso.com). The method is applicable and adaptable to other disciplines and other types of computer applications.
3D imaging of cleared human skin biopsies using light‐sheet microscopy: A new way to visualize in‐depth skin structure

Human skin is composed of the superimposition of tissue layers of various thicknesses and components. Histological staining of skin sections is the benchmark approach to analyse the organization and integrity of human skin biopsies; however, this approach does not allow 3D tissue visualization. Alternatively, confocal or two‐photon microscopy is an effective approach to perform fluorescent‐based 3D imaging. However, owing to light scattering, these methods display limited light penetration in depth. The objectives of this study were therefore to combine optical clearing and light‐sheet fluorescence microscopy (LSFM) to perform in‐depth optical sectioning of 5 mm‐thick human skin biopsies and generate 3D images of entire human skin biopsies.
Level Hash Table Resource Discovery Mechanism in Grid Environment

Resource discovery is important in Grid. In this paper, a novel resource discovery mechanism based on level hash table is presented. The Grid resources are hashed into keys, and each LHT Node stores keys that could be mapped into resources registered on LHT Nodes of current sub-tree.We give the detail implementation of it and the simulation results show it works efficiently.
Effects of low-fat or full-fat fermented and non-fermented dairy foods on selected cardiovascular biomarkers in overweight adults

The association between consumption of full-fat dairy foods and CVD may depend partly on the nature of products and may not apply to low-fat dairy foods. Increased circulating levels of inflammatory biomarkers after consumption of dairy product-rich meals suggest an association with CVD. In the present study, we tested the effects of low-fat and full-fat dairy diets on biomarkers associated with inflammation, oxidative stress or atherogenesis and on plasma lipid classes. Within full-fat dairy diets, we also compared fermented v. non-fermented products. In a randomised cross-over study, twelve overweight/obese subjects consumed during two 3-week periods two full-fat dairy diets containing either yogurt plus cheese (fermented) or butter, cream and ice cream (non-fermented) or a low-fat milk plus yogurt diet, with the latter being consumed between and at the end of the full-fat dairy dietary periods. The concentrations of six inflammatory and two atherogenic biomarkers known to be raised in CVD were measured as well as those of plasma F2-isoprostanes and lipid classes. The concentrations of six of the eight biomarkers tended to be higher on consumption of the low-fat dairy diet than on that of the fermented dairy diet and the concentrations of two plasmalogen lipid classes reported to be associated with increased oxidisability were also higher on consumption of the low-fat dairy diet than on that of the fermented dairy diet (P< 0·001), although plasma F2-isoprostane concentrations did not differ on consumption of any of the diets. On the other hand, the concentrations of plasma sphingomyelin and IL-6 were significantly higher on consumption of the non-fermented dairy diet than on that of the low-fat dairy diet (P< 0·02). In conclusion, short-term diets containing low-fat dairy products did not lead to a more favourable biomarker profile associated with CVD risk compared with the full-fat dairy products, suggesting that full-fat fermented dairy products may be the more favourable.
Fuzzy Boundary Control Design for a Class of Nonlinear Parabolic Distributed Parameter Systems

This paper deals with the problem of fuzzy boundary control design for a class of nonlinear distributed parameter systems which are described by semilinear parabolic partial differential equations (PDEs). Both distributed measurement form and collocated boundary measurement form are considered. A Takagi-Sugeno (T-S) fuzzy PDE model is first applied to accurately represent the semilinear parabolic PDE system. Based on the T-S fuzzy PDE model, two types of fuzzy boundary controllers, which are easily implemented since only boundary actuators are used, are proposed to ensure the exponential stability of the resulting closed-loop system. Sufficient conditions of exponential stabilization are established by employing the Lyapunov direct method and the vector-valued Wirtinger's inequality and presented in terms of standard linear matrix inequalities. Finally, the advantages and effectiveness of the proposed control methodology are demonstrated by the simulation results of two examples.
Pancreatic neuroendocrine tumors (PNETs): incidence, prognosis and recent trend toward improved survival.

BACKGROUND Pancreatic neuroendocrine tumors (PNETs) are uncommon neoplasms that can present with symptoms of hormone overproduction. We evaluated the incidence, prognosis, and temporal trends of PNETs. PATIENTS AND METHODS We analyzed all cases of PNETs registered in the Surveillance, Epidemiology, and End Results database from 1973 to 2000. Age-adjusted incidence and survival rates were calculated and survival trends over time were evaluated. RESULTS We identified 1483 cases of PNETs. The crude annual incidence per 1,000,000 was 1.8 in females and 2.6 in males and increased with advancing age. The incidence increased over the study period. Most patients (90.8%) had nonfunctional tumors. Advanced stage, higher grade, and age were the strongest predictors of worse survival. Patients with functional tumors had better outcomes than patients with nonfunctional tumors in both univariate and multivariate analysis (P = 0.004). Survival time increased over the period from 1973 to 2000. No differences were seen in the distribution of stage or age at diagnosis among time periods. CONCLUSION PNETs are uncommon neoplasms but the incidence may be increasing. Age, grade, stage, and functional status predict survival in patients with PNETs. Survival has improved over time, but this is not explained by earlier diagnosis or stage migration.
Postnatal development of the hippocampus in the Rhesus macaque (Macaca mulatta): A longitudinal magnetic resonance imaging study

Nonhuman primates are widely used models to investigate the neural substrates of human behavior, including the development of higher cognitive and affective function. Due to their neuroanatomical and behavioral homologies with humans, the rhesus macaque monkey (Macaca mulatta) provides an excellent animal model in which to characterize the maturation of brain structures from birth through adulthood and into senescence. To evaluate hippocampal development in rhesus macaques, structural magnetic resonance imaging scans were obtained longitudinally at 9 time points between 1 week and 260 weeks (5 years) of age on 24 rhesus macaque monkeys (12 males, 12 females). In our sample, the hippocampus reaches 50% of its adult volume by 13 weeks of age and reaches an adult volume by 52 weeks in both males and females. The hippocampus appears to be slightly larger at 3 years than at 5 years of age. Male rhesus macaques have larger hippocampi than females from 8 weeks onward by approximately 5%. Interestingly, there was increased variability in hemispheric asymmetry for hippocampus volumes at younger ages than at later ages. These data provide a comprehensive evaluation of the longitudinal development of male and female rhesus macaque hippocampus across development from 1 week to 5 years of age. © 2014 Wiley Periodicals, Inc.
Recognition of Human Computer Operations Based on Keystroke Sensing by Smartphone Microphone

Human computer operations such as writing documents and playing games have become popular in our daily lives. These activities (especially if identified in a non-intrusive manner) can be used to facilitate context-aware services. In this paper, we propose to recognize human computer operations through keystroke sensing with a smartphone. Specifically, we first utilize the microphone embedded in a smartphone to sense the input audio from a computer keyboard. We then identify keystrokes using fingerprint identification techniques. The determined keystrokes are then corrected with a word recognition procedure, which utilizes the relations of adjacent letters in a word. Finally, by fusing both semantic and acoustic features, a classification model is constructed to recognize four typical human computer operations: 1) chatting; 2) coding; 3) writing documents; and 4) playing games. We recruited 15 volunteers to complete these operations, and evaluated the proposed approach from multiple aspects in realistic environments. Experimental results validated the effectiveness of our approach.
CNN-based Landmark Detection in Cardiac CTA Scans

Fast and accurate anatomical landmark detection can benefit many medical image analysis methods. Here, we propose a method to automatically detect anatomical landmarks in medical images. Automatic landmark detection is performed with a patch-based fully convolutional neural network (FCNN) that combines regression and classification. For any given image patch, regression is used to predict the 3D displacement vector from the image patch to the landmark. Simultaneously, classification is used to identify patches that contain the landmark. Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location. The landmark location is obtained by calculating the average landmark location using the computed 3D displacement vectors. The method is evaluated using detection of six clinically relevant landmarks in coronary CT angiography (CCTA) scans: the right and left ostium, the bifurcation of the left main coronary artery (LM) into the left anterior descending and the left circumflex artery, and the origin of the right, non-coronary, and left aortic valve commissure. The proposed method achieved an average Euclidean distance error of 2.19 mm and 2.88 mm for the right and left ostium respectively, 3.78 mm for the bifurcation of the LM, and 1.82 mm, 2.10 mm and 1.89 mm for the origin of the right, non-coronary, and left aortic valve commissure respectively, demonstrating accurate performance. The proposed combination of regression and classification can be used to accurately detect landmarks in CCTA scans.
Effect of human rotavirus vaccine on severe diarrhea in African infants.

BACKGROUND Rotavirus is the most common cause of severe gastroenteritis among young children worldwide. Data are needed to assess the efficacy of the rotavirus vaccine in African children. METHODS We conducted a randomized, placebo-controlled, multicenter trial in South Africa (3166 infants; 64.1% of the total) and Malawi (1773 infants; 35.9% of the total) to evaluate the efficacy of a live, oral rotavirus vaccine in preventing severe rotavirus gastroenteritis. Healthy infants were randomly assigned in a 1:1:1 ratio to receive two doses of vaccine (in addition to one dose of placebo) or three doses of vaccine - the pooled vaccine group - or three doses of placebo at 6, 10, and 14 weeks of age. Episodes of gastroenteritis caused by wild-type rotavirus during the first year of life were assessed through active follow-up surveillance and were graded with the use of the Vesikari scale. RESULTS A total of 4939 infants were enrolled and randomly assigned to one of the three groups; 1647 infants received two doses of the vaccine, 1651 infants received three doses of the vaccine, and 1641 received placebo. Of the 4417 infants included in the per-protocol efficacy analysis, severe rotavirus gastroenteritis occurred in 4.9% of the infants in the placebo group and in 1.9% of those in the pooled vaccine group (vaccine efficacy, 61.2%; 95% confidence interval, 44.0 to 73.2). Vaccine efficacy was lower in Malawi than in South Africa (49.4% vs. 76.9%); however, the number of episodes of severe rotavirus gastroenteritis that were prevented was greater in Malawi than in South Africa (6.7 vs. 4.2 cases prevented per 100 infants vaccinated per year). Efficacy against all-cause severe gastroenteritis was 30.2%. At least one serious adverse event was reported in 9.7% of the infants in the pooled vaccine group and in 11.5% of the infants in the placebo group. CONCLUSIONS Human rotavirus vaccine significantly reduced the incidence of severe rotavirus gastroenteritis among African infants during the first year of life. (ClinicalTrials.gov number, NCT00241644.).
Prognostic Factor Analysis of Overall Survival in Gastric Cancer from Two Phase III Studies of Second-line Ramucirumab (REGARD and RAINBOW) Using Pooled Patient Data

Purpose To identify baseline prognostic factors for survival in patients with disease progression, during or after chemotherapy for the treatment of advanced gastric or gastroesophageal junction (GEJ) cancer. Materials and Methods We pooled data from patients randomized between 2009 and 2012 in 2 phase III, global double-blind studies of ramucirumab for the treatment of advanced gastric or GEJ adenocarcinoma following disease progression on first-line platinum- and/or fluoropyrimidine-containing therapy (REGARD and RAINBOW). Forty-one key baseline clinical and laboratory factors common in both studies were examined. Model building started with covariate screening using univariate Cox models (significance level=0.05). A stepwise multivariable Cox model identified the final prognostic factors (entry+exit significance level=0.01). Cox models were stratified by treatment and geographic region. The process was repeated to identify baseline prognostic quality of life (QoL) parameters. Results Of 1,020 randomized patients, 953 (93%) patients without any missing covariates were included in the analysis. We identified 12 independent prognostic factors of poor survival: 1) peritoneal metastases; 2) Eastern Cooperative Oncology Group (ECOG) performance score 1; 3) the presence of a primary tumor; 4) time to progression since prior therapy <6 months; 5) poor/unknown tumor differentiation; abnormally low blood levels of 6) albumin, 7) sodium, and/or 8) lymphocytes; and abnormally high blood levels of 9) neutrophils, 10) aspartate aminotransferase (AST), 11) alkaline phosphatase (ALP), and/or 12) lactate dehydrogenase (LDH). Factors were used to devise a 4-tier prognostic index (median overall survival [OS] by risk [months]: high=3.4, moderate=6.4, medium=9.9, and low=14.5; Harrell's C-index=0.66; 95% confidence interval [CI], 0.64–0.68). Addition of QoL to the model identified patient-reported appetite loss as an independent prognostic factor. Conclusions The identified prognostic factors and the reported prognostic index may help clinical decision-making, patient stratification, and planning of future clinical studies.
Systematic Review with Meta-Analysis Associations of dietary patterns with the risk of all-cause, CVD and stroke mortality: a meta-analysis of prospective cohort studies

Considerable controversy exists regarding the associations of dietary patterns with the risk of all-cause, CVD and stroke mortality. Therefore, a meta-analysis was conducted to elucidate the potential associations between dietary patterns and the risk of all-cause, CVD and stroke mortality. The PubMed database was searched for prospective cohort studies on the associations between dietary patterns and the risk of all-cause, CVD and stroke mortality published until February 2014. Random-effects models were used to calculate the sum-mary relative risk estimates (SRRE) based on the highest v. the lowest category of dietary pattern scores. Stratiﬁed analyses were conducted based on sex, geographical region, follow-up duration, and adjustment/non-adjustment for energy intake. A total of thirteen prospective cohort studies involving 338787 participants were included in the meta-analysis. There was evidence of inverse associations between the prudent/healthy dietary pattern and the risk of all-cause (SRRE ¼ 0·76, 95% CI 0·68, 0·86) and CVD (SRRE ¼ 0·81, 95% CI 0·75, 0·87) mortality and an absence of association between this dietary pattern and stroke mortality (SRRE ¼ 0·89, 95% CI 0·77, 1·02). However, no signiﬁcant associations were observed between the Western/unhealthy dietary pattern and the risk of all-cause (SRRE ¼ 1·07, 95% CI 0·96, 1·20), CVD (SRRE ¼ 0·99, 95% CI 0·91, 1·08) and stroke (SRRE ¼ 0·94, 95% CI 0·81, 1·10) mortality. In conclusion, the ﬁndings provide evidence that greater adherence to a prudent/healthy dietary pattern is associated with a lower risk of all-cause and CVD mortality and not signiﬁcantly associated with stroke mortality and that the Western/unhealthy dietary pattern is not associated with all-cause, CVD and stroke mortality. Further studies are required to conﬁrm these ﬁndings.
Solving $\ell^p\!$-norm regularization with tensor kernels

In this paper, we discuss how a suitable family of tensor kernels can be used to efficiently solve nonparametric extensions of $\ell^p$ regularized learning methods. Our main contribution is proposing a fast dual algorithm, and showing that it allows to solve the problem efficiently. Our results contrast recent findings suggesting kernel methods cannot be extended beyond Hilbert setting. Numerical experiments confirm the effectiveness of the method.
An HMM for online signature verification based on velocity and hand movement directions

The online signature is a behavioural biometrie trait and forms time series for verifying a persons identity. In this paper, a new online signature verification system using Hidden Markov Model (HMM) is presented. Prior model-based approachs use the combination of HMM and Gaussian Mixture Model, which causes the problem of proper selection of HMM states numbers. The advantage of the proposed HMM is the small number of observations and proper definition of states for time series modelling. The proposed system segments each signature curve based on pen's velocity value. The signature curve, would be decomposed in low or high partition according to velocity's value. For each partition, hand movement direction between two consequent point Extracted. The resulted time sequence of directions is then used for training an HMM. Development and evaluation experiments are reported on SVC2004 dataset. Two partitioning methods based on velocity or pressure are examined. Experiment result suggest that our system, with velocity based partitioning perform better results. FAR = 4.8% and FRR = 5%.