Miracle-dz/UniFAR-FTUs huggingface dataset:
MedRank: Discovering Influential Medical Treatments from Literature by Information Network Analysis

Medical literature has been an important information source for clinical professionals. As the body of medical literature expands rapidly, keeping this knowledge up-to-date becomes a challenge for medical professionals. One question is that for a given disease how can we find the most influential treatments currently available from online medical publications? In this paper we propose MedRank, a new network-based algorithm that ranks heterogeneous objects in a medical information network. The network is extracted from MEDLINE, a large collection of semi-structured medical literature. Different types of objects such as journal articles, pathological symptoms, diseases, clinical trials, treatments, authors, and journals are linked together through their relationships. The experimental results are compared with the expert rankings collected from doctors and two baseline methods, namely degree centrality and NetClus. The evaluation shows that our algorithm is effective and efficient. The success of categorized entity ranking in medical literature domain suggests a new methodology and a potential success in ranking semi-structured data in other domains.
The Microstructure and Mechanical Properties of Al-7%SiMg Alloy Treated with a Exothermic Modifier Contained Na and B

Results of studies on the modification of Al-7%SiMg alloy with an exothermic modifier on base Na2B4O7 are presented in the paper. The alloy was modified in a casting mold. Its structure, tensile strength and percentage elongation were determined. The results of the study indicate that in addition to modifier quantity and the type of reducing agent applied to produce an exothermic reaction, the reaction's thermal effect influences the quality of the modification process, leading to changes in the microstructure and mechanical properties of the alloy.
Is gender a factor in the reduction of cardiovascular risks with exercise training?

BACKGROUND This study compared older men and women with cardiovascular (CV) risk factors in terms of the effects of a 6-month exercise intervention on high-sensitivity C-reactive protein (hsCRP) levels, blood pressure (BP) and other risk factors. METHODS AND RESULTS Sixty older (age 61-79) overweight men and 71 such women with 2 or more risk factors (ie, systolic BP 130-179 mmHg, non-fasting blood glucose 110-139 mg/dl, and low-density lipoprotein cholesterol 120-219 mg/dl) participated in a 6-month exercise intervention. The exercise program consisted of moderate-intensity bicycle exercise for ∼40 min, performed on average 2.5 times per week. Systolic and diastolic BP reductions were found to be greater in women than in men (SBP, -10.6 vs. -5.5 mmHg; DBP, -6.2 vs. -3.3 mmHg; both P<0.05). Decreases in body mass index were larger in women than in men (P<0.05). There was no significant gender difference in the changes in blood glucose and lipid profiles and hsCRP levels. After adjustment for confounders (exercise frequency, weight loss, age, and baseline values), there were still significant gender differences in the SBP and DBP reductions. hsCRP reduction were similar in both genders even after adjusting for weight loss. CONCLUSIONS There could be gender differences in the beneficial effects of exercise training on the potent CV risk factors of BP and body weight, but not on hsCRP.
Towards Search-Based Testing for Event-B Models

This position paper discusses the challenges and opportunities of applying search-based techniques to a formal environment of abstract state machines defined using a language called Event-B. Event-B is based on a formal abstract machine notation that has a mature tool support and gets continuous feedback from industry. Although search-based techniques recently developed for extended finite state machines may be adapted to this context, new challenges such as implicit states, non-determinism, non-numerical data types and hierarchical models are still to be solved for test data generation for Event-B models.
Send Orders of Reprints at Reprints@benthamscience.org Napping and Less Disturbed Nighttime Sleep Associated with Reduced Carotid Intima-media Thickness in Elderly Alzheimer Caregivers

This study tested the hypothesis that disturbed or very short or long sleep durations are associated with subclinical atherosclerosis, as measured by carotid intima-media thickness (CIMT), in a sample of community-dwelling elderly spousal caregivers. Participants were 126 spousal caregivers of a person with Alzheimer's disease who underwent in-home sleep assessment by wrist actigraphy for 72 consecutive hours. Carotid artery ultrasound, also conducted in participants' homes, was used to measure CIMT in millimeters. Linear regression models including covariates related to sleep and CVD indicated that a longer daytime sleep duration was associated with significantly reduced mean common CIMT (B=-0.04, p=0.02). Specifically, for every additional hour of sleep obtained during the day, mean common CIMT was reduced by 0.04mm. Conversely, greater night time wake after sleep onset (WASO) was associated with significantly increased mean common CIMT (B=0.04, p=0.05); for each additional hour of WASO, mean common CIMT increased by 0.04mm. When combinations of sleep parameters were examined, caregivers who slept <30 minutes during the day and had ≥1 hour of WASO throughout the night had significantly greater (B=0.06, p=0.04) mean common CIMT than caregivers who slept <30 minutes during the day and had <1 hour of WASO. These analyses suggest that shorter naps and more disturbed nighttime sleep are associated with increased CIMT. Interventions that enhance sleep quantity and reduce sleep disturbance may decrease the prevalence and extent of subclinical atherosclerosis in older caregivers. Longitudinal studies with objective sleep measures that examine the effects of napping and disturbed sleep on subclinical atherosclerosis are needed.
Manifold Adaptive Kernel Local Fisher Discriminant Analysis for Face Recognition

To efficiently cope with the high dimensionalities and complex nonlinear variations of face images in face recognition task, a novel manifold adaptive kernel local Fisher discriminant analysis algorithm is proposed in this paper. The core idea of this algorithm is as follows: First, the local manifold structure of the face image is modeled by a nearest neighbor graph. Then, an original input kernel function is deformed with respect to the local manifold structure. Finally, the resulting manifold adaptive kernel function is incorporated into the kernel local Fisher discriminant analysis(LFDA) method, which leads to the manifold adaptive kernel LFDA(MAKL) algorithm for face recognition. Experimental results on three popular face databases show that the proposed algorithm performs much better than other related algorithms.
TDoA based indoor visible light positioning systems

In this paper, we propose an indoor positioning system in which the visible light radiated from LEDs is used to locate the position of receiver. Compared to current indoor positioning systems using LED light, our system has the advantages of simple implementation, low cost and high accuracy. In our system, a single photo diode (PD) receives pilot signals from LED panels on the ceiling. Then the time differences of arrival (TDOA) of these pilot signals are used to estimate the position of the receiver. Our proposed sytem is simulated in the room measuring 5×5×3m3 with considering effect of noises such as shot noise, thermal noise, and noise caused by the reflected light from the walls. The simulation result shows that our system can achieve a high estimation accuracy of 3.6cm in average.
Analysis and Design of Iterative Learning Control Strategies for UPS Inverters

In this paper, two iterative-learning-based control methods for uninterruptible power system (UPS) inverters, viz., direct iterative learning controller (direct ILC) and hybrid iterative learning controller (hybrid ILC), are proposed. In both methods, a zero-phase filter that is designed in the frequency domain is applied in order to provide compensation for the resonant peak in the system model to ensure error convergence. Furthermore, a "forgetting factor" is introduced in both control algorithms to increase the robustness of the scheme against measurement noise, initialization error, and/or variation of system dynamics due to any parameter drift. In the direct ILC method, ILC is combined with the feedforward of the reference to improve dynamic performance. This method is shown to be capable of achieving very high steady-state performance although its dynamic response was not very good. In the hybrid ILC method, ILC is combined with a proportional-derivative controller aside from the reference feedforward to improve the dynamic response further. Experimental results show that both proposed controllers can achieve very low total harmonic distortion and fast error convergence under different loads while using only one sensor. The proposed direct ILC is an effective solution for UPS products where high-quality steady-state output voltage is more important than fast dynamic response, while hybrid ILC can result in excellent steady-state performance with a more improved dynamic response than direct ILC
Interactive Video Retrieval based on Multimodal Dissimilarity Representation

We present an approach to learn user semantic queries from dissimilarity representations of video audio-visual content. When dealing with large corpora of videos documents, using a feature-based representation calls for the online computation of distances between all documents and the query. Hence, a dissimilarity representation may be preferred because its offline computation speeds up the retrieval process. We show how distances related to visual and audio video features can directly be used to learn complex concepts from a set of positive and negative examples provided by the user. Based on the idea of dissimilarity spaces, we derive a lowdimensional multimodal representation space where an on-line and real-time classification is performed to learn user queries. The classification consists in maximizing a non-linear Fisher criterion to separate positive from negative examples. The evaluation, performed on the complete annotated TRECVid corpus, shows that our technique enables us to improve the precision of retrieval results.
Proneurotensin/neuromedin N secreted from small cell lung carcinoma cell lines as a potential tumor marker

Proteins secreted from specific cancer cells have a high potential for use as tumor markers. We identified secreted proteins produced by 15 different carcinoma cell lines grown in serum‐free medium using MS/MS. Proneurotensin/neuromedin N (proNT/NMN) was found in conditioned medium from four of seven small cell lung carcinoma cell lines but not from eight nonsmall cell lung carcinoma cell lines. These results indicate proNT/NMN has potential as a specific tumor marker of small cell lung carcinoma.
Reappraisal of the Autoclave Expansion Test

This report re-examines the technical basis for the AET as presented in PCA Research Department Bulletin RX45 as well as more recent research. The focus is on the potential for disruptive expansions caused by crystalline MgO in portland cements hydrated under various conditions, including the AET. Much of the data indicates that the AET would be a good test for unsoundness caused by free CaO alone, but that expansions in the AET caused by MgO and C3A do not correlate well with disruptive expansions under ordinary conditions. Consideration of expansive reaction mechanisms and the published data indicate that MgO continues to hydrate slowly under a wide range of conditions and that stress-relief mechanisms and stabilization processes apparently operate to prevent disruptive expansions in moist environments. Since these mechanisms may not operate as well in dry environments, there are undetermined risks with respect to reliance on performance tests that do not cause hydration of MgO. Investigation of these mechanisms is recommended so that portland cement soundness and performance can be assured, perhaps without reliance on the AET.
Applying User-Favorite-Item-Based Similarity into Slope One Scheme for Collaborative Filtering

Collaborative Filtering (CF) is one of the most effective technologies in making rating prediction for recommender systems. Traditional user-based CF methods take user-item matrices as input and compute the prediction value based on user similarity. The computation of similarity between each user pair requires exact matching on item set by finding a minimum number of same items rated by both users. However in real-world scenario, the input matrix is very sparse, therefore the amount of required information is far from enough to compute reliable similarity. To deal with this so-called data sparsity problem, we introduce a novel user similarity measure based on user favorite items (UFI) and apply it into Slope One scheme for rating prediction. The experimental results suggest that UFI user similarity measure is effective in user-based approaches and UFI-based Slope One algorithm can provide better quality of prediction than best available similarity-based CF algorithms.
Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC

In the first offering of Stanford’s Machine Learning Massive Open-Access Online Course (MOOC) there were over a million programming submissions to 42 assignments — a dense sampling of the range of possible solutions. In this paper we map out the syntax and functional similarity of the submissions in order to explore the variation in solutions. While there was a massive number of submissions, there is a much smaller set of unique approaches. This redundancy in student solutions can be leveraged to “force multiply” teacher feedback. Fig. 1. The landscape of solutions for “gradient descent for linear regression” representing over 40,000 student code submissions with edges drawn between syntactically similar submissions and colors corresponding to performance on a battery of unit tests (red submissions passed all unit tests).
Multi-carrier cooperated interference cancelation via macro-BS broadcasting in HetNet

In next generation cellular system, heterogeneous network will be widely used to improve the spatial spectrum efficiency. In this paper, we propose a new interference cancelation scheme to increase the achievable sum-rate of multi-carrier interference channels in heterogeneous networks. The subcarriers are cooperated where one group subcarriers transmit in full-rate as if there is no interference and another group subcarriers transmit redundancy information to help interference cancellation. To improve the transmission efficiency, in mixed interference scenarios we only require macro-BS to broadcast the redundancy information. We will introduce the channel conditions for selecting these two groups of subcarriers, and study an optimized subcarrier allocation method. Finally, simulation results in practical heterogeneous network settings are provided to show the performance improvement.
MALDI-TOF Mass Spectrometry Analysis of Cerebrospinal Fluid Tryptic Peptide Profiles to Diagnose Leptomeningeal Metastases in Patients with Breast Cancer*

Leptomeningeal metastasis (LM) is a devastating complication that occurs in 5% of patients with breast cancer. Early diagnosis and initiation of treatment are essential to prevent neurological deterioration. However, early diagnosis of LM remains challenging because 25% of cerebrospinal fluid (CSF) samples produce false-negative results at first cytological examination. We developed a new, MS-based method to investigate the protein expression patterns present in the CSF from patients with breast cancer with and without LM. CSF samples from 106 patients with active breast cancer (54 with LM and 52 without LM) and 45 control subjects were digested with trypsin. The resulting peptides were measured by MALDI-TOF MS. Then, the mass spectra were analyzed and compared between patient groups using newly developed bioinformatics tools. A total of 895 possible peak positions was detected, and 164 of these peaks discriminated between the patient groups (Kruskal-Wallis, p < 0.01). The discriminatory masses were clustered, and a classifier was built to distinguish patients with breast cancer with and without LM. After bootstrap validation, the classifier had a maximum accuracy of 77% with a sensitivity of 79% and a specificity of 76%. Direct MALDI-TOF analysis of tryptic digests of CSF gives reproducible peptide profiles that can assist in diagnosing LM in patients with breast cancer. The same method can be used to develop diagnostic assays for other neurological disorders.
Microbiota Modulate Anxiety-Like Behavior and Endocrine Abnormalities in Hypothalamic-Pituitary-Adrenal Axis

Intestinal microbes are an important system in the human body, with significant effects on behavior. An increasing body of research indicates that intestinal microbes affect brain function and neurogenesis, including sensitivity to stress. To investigate the effects of microbial colonization on behavior, we examined behavioral changes associated with hormones and hormone receptors in the hypothalamic-pituitary-adrenal (HPA) axis under stress. We tested germ-free (GF) mice and specific pathogen-free (SPF) mice, divided into four groups. A chronic restraint stress (CRS) protocol was utilized to induce external pressure in two stress groups by restraining mice in a conical centrifuge tube for 4 h per day for 21 days. After CRS, Initially, GF restraint-stressed mice explored more time than SPF restraint-stressed mice in the center and total distance of the OFT. Moreover, the CRH, ACTH, CORT, and ALD levels in HPA axis of GF restraint-stressed mice exhibited a significantly greater increase than those of SPF restraint-stressed mice. Finally, the Crhr1 mRNA levels of GF CRS mice were increased compared with SPF CRS mice. However, the Nr3c2 mRNA levels of GF CRS mice were decreased compared with SPF CRS mice. All results revealed that SPF mice exhibited more anxiety-like behavior than GF mice under the same external stress. Moreover, we also found that GF mice exhibited significant differences in, hormones, and hormone receptors compared with SPF mice. In conclusion, Imbalances of the HPA axis caused by intestinal microbes could affect the neuroendocrine system in the brain, resulting in an anxiety-like behavioral phenotype. This study suggested that intervention into intestinal microflora may provide a new approach for treating stress-related diseases.
Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care

The lifespan of people with severe mental illness (SMI) is shorter compared to the general population. This excess mortality is mainly due to physical illness. We report prevalence rates of different physical illnesses as well as important individual lifestyle choices, side effects of psychotropic treatment and disparities in health care access, utilization and provision that contribute to these poor physical health outcomes. We searched MEDLINE (1966 - August 2010) combining the MeSH terms of schizophrenia, bipolar disorder and major depressive disorder with the different MeSH terms of general physical disease categories to select pertinent reviews and additional relevant studies through cross-referencing to identify prevalence figures and factors contributing to the excess morbidity and mortality rates. Nutritional and metabolic diseases, cardiovascular diseases, viral diseases, respiratory tract diseases, musculoskeletal diseases, sexual dysfunction, pregnancy complications, stomatognathic diseases, and possibly obesity-related cancers are, compared to the general population, more prevalent among people with SMI. It seems that lifestyle as well as treatment specific factors account for much of the increased risk for most of these physical diseases. Moreover, there is sufficient evidence that people with SMI are less likely to receive standard levels of care for most of these diseases. Lifestyle factors, relatively easy to measure, are barely considered for screening; baseline testing of numerous important physical parameters is insufficiently performed. Besides modifiable lifestyle factors and side effects of psychotropic medications, access to and quality of health care remains to be improved for individuals with SMI.
GENIE: estimating demographic history from molecular phylogenies

UNLABELLED GENIE implements a statistical framework for inferring the demographic history of a population from phylogenies that have been reconstructed from sampled DNA sequences. The methods are based on population genetic models known collectively as coalescent theory. AVAILABILITY GENIE is available from http://evolve.zoo. ox.ac. uk. All popular operating systems are supported.
Fabrication of TI/SIC Surface Nano-Composite Layer by Friction Stir Processing

In the present investigation, novel Ti/SiC surface nano-composite layer was successfully fabricated by dispersing nano-sized SiC particles into commercially pure titanium plates employing friction stir processing technique. The process parameters such as tool rotation and advancing speeds were adjusted to produce defect-free surface composite layer, however, uniform distribution of the nano-size SiC particles in a matrix of titanium was achieved after the second pass. The micro hardness value of the Ti/SiC nano-composite surface layer was found to be ~534 HV; this is 3.3 times higher than that of the commercially pure titanium substrate. No reaction was detected between SiC powders and the titanium matrix after friction stir processing.
Towards a Comprehensive Topic Hierarchy for News

To date, a comprehensive, Yahoo-like hierarchy of topics has yet to be offered for the domain of news. The Yahoo approach ofmanaging such a hierarchy—hiring editorial staff to read documents and correctly assign them to topics — is simply not practical in the domain of news. Far too many stories are written and made available online everyday. While many Machine Learning methods exist for organising documents into topics, these methods typically require a large number of labelled training examples before performing accurately. When managing a large and ever-changing topic hierarchy, it is unlikely that there would be enough time to provide many examples per topic. For this reason, it would be useful to identify extra information, within the domain of news, that could be harnessed to minimise the training required to achieve reasonable accuracy. To this end, the notion of a semi-labelled document is introduced. These documents, which are partially labelled by the source which provided them, are common in the domain of news. It is shown that obtaining the labels for semi-labelled documents is relatively straightforward. To make use of the extra information provided by semi-labelled documents, two new architectures are devised. Each architecture employs a different form of hierarchical expectation maximisation, with both forms aimed at providing a boost in accuracy when there is a dearth of training examples. The architectures are evaluated using a collection of 17,000 newsgroup articles. Using semi-labelled documents one architecture achieves a relative increase in accuracy of 39% over a method developed at Carnegie-Mellon University, which uses flat expectation maximisation. Even without using semi-labelled documents, this architecture achieves a relative increase in accuracy of 20% over the method developed at CMU. The increased accuracy achieved by hierarchical expectationmaximisation demonstrates the potential for using this method to manage a comprehensive topic hierarchy for news.