Miracle-dz/UniFAR-FTUs huggingface dataset:
Image segmentation by a robust generalized fuzzy c-means algorithm

Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it lacks of sufficient robustness to image noise. In this paper, we propose a simple and effective method to make the traditional FCM more robust to noise, with the help of generalized mean. Traditional FCM can be considered as a linear combination of membership and distance (function) from the expression of its mathematical formula. The proposed generalized FCM (GFCM) is generated by applying generalized mean on these two items. We impose generalized mean on membership to incorporate local spatial information and cluster information, and on distance function to incorporate local spatial information and observation information (image intensity value). Thus, our GFCM is more robust to image noise with the spatial constraints: the generalized mean. The performance of our proposed algorithm, compared with state-of-the-art technologies including modified FCM, HMRF and their hybrid models, demonstrates its improved robustness and effectiveness.
Considerations for managing chronic obstructive pulmonary disease in the elderly

Chronic obstructive pulmonary disease (COPD) is common in older people, with an estimated prevalence of 10% in the US population aged ≥75 years. Inhaled medications are the cornerstone of treatment for COPD and are typically administered by one of three types of devices, ie, pressurized metered dose inhalers, dry powder inhalers, and nebulizers. However, age-related pulmonary changes may negatively influence the delivery of inhaled medications to the small airways. In addition, physical and cognitive impairment, which are common in elderly patients with COPD, pose special challenges to the use of handheld inhalers in the elderly. Health care providers must take time to train patients to use handheld inhalers and must also check that patients are using them correctly on a regular basis. Nebulizers should be considered for patients unable to use handheld inhalers properly. What follows is a review of issues associated with COPD and its treatment in the elderly patient.
Three-year changes in bone mineral density around the knee after a six-month course of oral alendronate following total knee arthroplasty. A prospective, randomized study.

BACKGROUND Bone mineral density decreases after total knee arthroplasty and is believed to affect prosthetic fixation. Treatment with alendronate has been shown to improve short-term bone mineral density after total knee arthroplasty; however, the long-term effects of this therapy are unknown. The purpose of this study was to evaluate the long-term effects of a six-month course of alendronate on bone mineral density after total knee arthroplasty. METHODS Sixty patients were randomly assigned to receive either oral alendronate at a dosage of 10 mg/day for six months or no alendronate. The bone mineral density in the distal aspect of the femur and the proximal aspect of the tibia was measured preoperatively and at six, twelve, and thirty-six months after total knee arthroplasty. RESULTS Fifty-four patients (twenty-nine in the alendronate group and twenty-five in the control group) completed the study. The alendronate group showed significant increases in bone mineral density in the distal aspect of the femur compared with the controls at six months (+4.8% and -14.2%, respectively; p < 0.01) and twelve months (+1.6% and -11.5%, respectively; p < 0.01). No significant difference in bone mineral density was detected between the groups at thirty-six months (-3.9% and -12.2%, respectively; p = 0.08). Similar trends in bone mineral density changes were also observed in the proximal aspect of the tibia. CONCLUSIONS A six-month course of alendronate initially increased bone mineral density at six and twelve months after total knee arthroplasty, but no difference was noted after thirty-six months. The effect of alendronate on bone mineral density after total knee arthroplasty may be limited after discontinuation of therapy.
ERRATUM: “THE CONTRIBUTION OF AGN AND STAR-FORMING GALAXIES TO THE MID-INFRARED AS REVEALED BY THEIR SPECTRAL ENERGY DISTRIBUTIONS” (2008, ApJ, 684, 136)

We present the broad-band Spectral Energy Distributions (SEDs) of the largest available highly (72%) complete spectroscopic sample of mid-infrared (MIR) selected galaxies and AGN at intermediate redshift. The sample contains 203 extragalactic sources from the 15-micron survey in the ELAIS-SWIRE field S1, all with measured spectroscopic redshift. Most of these sources have full multi-wavelength coverage from the far-UV to the far-infrared and lie in the redshift range 0.1 10 mJy, while that obtained from optical spectroscopy never being >30%, even at the higher flux densities. The results of this work will be very useful for updating all the models aimed at interpreting the deep infrared survey data and, in particular, for constraining the nature and the role of dust-obscured systems in the intermediate/high-redshift Universe.
Synthesis and Characterization of New Thiazolidinones Containing Coumarin Moieties and Their Antibacterial and Antioxidant Activities

New coumarin derivatives, namely (2-(4-methyl-2-oxo-2H-chromen-7-yloxy)-N-(4-oxo-2-phenylthiazolidin-3-yl)acetamide, N-(2-(3-methoxyphenyl)-4-oxothiazolidin-3-yl)-2-(4-methyl-2-oxo-2H-chromen-7-yloxy)acetamide, 2-(4-methyl-2-oxo-2H-chromen-7-yloxy)-N-(4-oxo-2-(2,3,4trimethoxyphenyl)thiazolidin-3-yl)acetamide and N-(2-(4-bromophenyl)-4-oxothiazolidin-3-yl)-2-(4-methyl-2-oxo-2H-chromen-7-yloxy)acetamide) were synthesized starting from 4-methyl-7-hydroxycoumarin. The structures of the obtained compounds were confirmed by analytical IR and NMR spectra to elucidate the different positions of protons and carbons and as well as theoretical studies (DFT/B3LYP). The new compounds were screened for antibacterial activity. Most of them are more active against E. coli S. aureus and B. subtilis than standard references.
Consuming meals prepared at home more frequently appears to reduce the risk of type 2 diabetes, weight gain and obesity

Commentary on : Zong G, Eisenberg DM, Hu FB, et al. Consumption of meals prepared at home and risk of type 2 diabetes: an analysis of two prospective cohort studies. PLoS Med 2016;13:e1002052[OpenUrl][1]. Many public health strategies include promotion of cooking at home as a method to improve diet, and reduce obesity and diseases related to diet and obesity. However, a recent systematic review found that the majority of existing studies on the relationships between home food preparation and diet and health outcomes are cross-sectional, raising the possibility of reverse causation.1 The literature is also confused by poor conceptualisation and operationalisation of ‘home food preparation’, with many studies focusing on the behaviour of cooking, rather than consumption of food cooked at home. This longitudinal analysis of two prospective cohort studies from the USA examined associations between frequency of eating meals prepared at home (MPAH) in 1986, and incidence … [1]: {openurl}? query=rft.jtitle%253DPLoS%2BMed%26rft.volume%253D13%26rft.spage%253De1002052%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx
Title Pulsed Digital Oscillators for Electrostatic MEMS

This paper introduces a new actuation scheme for implementing Pulsed Digital Oscillators (PDOs) for electrostatic MEMS resonators. In this scheme, the capacitance of the device is biased with a voltage and it is periodically sampled. Short pulses of zero voltage are applied depending on the decisions taken by the oscillator loop. The paper discusses in detail the implementation of such electrostatic PDO (e-PDO) through a prototype and links the e-PDO to the conventional PDO theory. As an example, it is shown that with this actuation scheme it is possible to excite different resonances of the mechanical structure simply by changing the parameters of the feedback filter of the oscillator.
Indian Sign Language Character Recognition using Neural Networks

Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to expose themselves to the outside world. This paper proposes a method to convert the Indian Sign Language (ISL) hand gestures into appropriate text message. In this paper the hand gestures corresponding to ISL English alphabets are captured through a webcam. In the captured frames the hand is segmented and the neural network is used to recognize the alphabet. The features such as angle made between fingers, number of fingers that are fully opened, fully closed or semi closed and identification of each finger are used as input to the neural network. Experimentation done for single hand alphabets and the results are summarized.
New clustering of the spikes morphology based on dynamic clouds in partial epilepsy

The acquisition of EEG signals can be done during days. In this paper, the signals of depth Stereo-Electroencephalography (SEEG) are used. In SEEG signal, several inter-ictal paroxystic events (IPE) are found, they appear between the crises. To analyze their distribution, they should initially be detected by separating them from the basic activity. After the detection step, we implement a clustering process to distinguish IPE or spike according to their morphology. Results of detection and clustering would characterize a link between space-time distribution of IPE and the arrival of the crises. It would be thus a contribution on the diagnosis of the partial epilepsies.
International Union of Pharmacology. XLVII. Nomenclature and Structure-Function Relationships of Voltage-Gated Sodium Channels

The family of voltage-gated sodium channels initiates action potentials in all types of excitable cells. Nine members of the voltage-gated sodium channel family have been characterized in mammals, and a 10th member has been recognized as a related protein. These distinct sodium channels have similar structural and functional properties, but they initiate action potentials in different cell types and have distinct regulatory and pharmacological properties. This article presents the molecular relationships and physiological roles of these sodium channel proteins and provides comprehensive information on their molecular, genetic, physiological, and pharmacological properties.
Learning Causal Structure from Overlapping Variable Sets

We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges corresponding to all possible pairwise causal relations between two variables, named Pairwise Causal Graph (PCG). Examples of interesting inferences include the induction of the absence or presence of some causal relation between two variables never measured together. cSAT+ converts the problem to a series of SAT problems, obtaining leverage from the efficiency of state-ofthe-art solvers. In our empirical evaluation, it is shown to outperform ION, the first algorithm solving a similar but more general problem, by two orders of magnitude.
Temporal index sharding for space-time efficiency in archive search

Time-travel queries that couple temporal constraints with keyword queries are useful in searching large-scale archives of time-evolving content such as the web archives or wikis. Typical approaches for efficient evaluation of these queries involve slicing either the entire collection [20] or individual index lists [10] along the time-axis. Both these methods are not satisfactory since they sacrifice compactness of index for processing efficiency making them either too big or, otherwise, too slow. We present a novel index organization scheme that shards each index list with almost zero increase in index size but still minimizes the cost of reading index entries during query processing. Based on the optimal sharding thus btained, we develop a practically efficient sharding that takes into account the different costs of random and sequential accesses. Our algorithm merges shards from the optimal solution to allow for a few extra sequential accesses while gaining significantly by reducing the number of random accesses. We empirically establish the effectiveness of our sharding scheme with experiments over the revision history of the English Wikipedia between 2001-2005 (approx 700 GB) and an archive of U.K. governmental web sites (approx 400 GB). Our results demonstrate the feasibility of faster time-travel query processing with no space overhead.
Metabolomics: The Principles and Potential Applications to Transplantation

This review provides a summary of the applications and potential applications of metabolite profiling (i.e. metabolomics) in monitoring organ transplants. While the concept of metabolomics is relatively new to organ transplantation, the idea of measuring metabolites as a quick, noninvasive probe of organ function is not. Indeed, metabolite measurements of serum creatinine have long been used to assess pre‐ and post‐operative organ function. Over the past 10 years, a number of lesser‐known, organ‐specific metabolites have also been shown to be good diagnostic indicators of both organ function and viability. In general, metabolomics offers a complementary picture to what can be revealed via techniques based on genomics, proteomics or histology. Because metabolic changes typically happen within seconds or minutes after an ‘event’, whereas some transcript, protein abundance or tissue changes may take place over days or weeks, metabolomic measurements may offer a particularly useful and inexpensive diagnostic tool to monitor donor organ viability or to detect organ rejection. The excitement associated with metabolomics, however, must be tempered by the fact that the technology for rapid metabolite identification is still in its infancy, and that metabolites are but one part of a very complex picture pertaining to organ function.
Regulation of Human Hepatic Drug Transporter Activity and Expression by Diesel Exhaust Particle Extract

Diesel exhaust particles (DEPs) are common environmental air pollutants primarily affecting the lung. DEPs or chemicals adsorbed on DEPs also exert extra-pulmonary effects, including alteration of hepatic drug detoxifying enzyme expression. The present study was designed to determine whether organic DEP extract (DEPe) may target hepatic drug transporters that contribute in a major way to drug detoxification. Using primary human hepatocytes and transporter-overexpressing cells, DEPe was first shown to strongly inhibit activities of the sinusoidal solute carrier (SLC) uptake transporters organic anion-transporting polypeptides (OATP) 1B1, 1B3 and 2B1 and of the canalicular ATP-binding cassette (ABC) efflux pump multidrug resistance-associated protein 2, with IC50 values ranging from approximately 1 to 20 μg/mL and relevant to environmental exposure situations. By contrast, 25 μg/mL DEPe failed to alter activities of the SLC transporter organic cation transporter (OCT) 1 and of the ABC efflux pumps P-glycoprotein and bile salt export pump (BSEP), whereas it only moderately inhibited those of sodium taurocholate co-transporting polypeptide and of breast cancer resistance protein (BCRP). Treatment by 25 μg/mL DEPe was next demonstrated to induce expression of BCRP at both mRNA and protein level in cultured human hepatic cells, whereas it concomitantly repressed mRNA expression of various transporters, including OATP1B3, OATP2B1, OCT1 and BSEP. Such changes in transporter expression were found to be highly correlated to those caused by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a reference activator of the aryl hydrocarbon receptor (AhR) pathway. This suggests that DEPe, which is enriched in known ligands of AhR like polycyclic aromatic hydrocarbons, alters drug transporter expression via activation of the AhR cascade. Taken together, these data established human hepatic transporters as targets of organic chemicals containing in DEPs, which may contribute to their systemic effects through impairing hepatic transport of endogenous compound or drug substrates of these transporters.
Effective Biomarkers for Diagnosis of Neonatal Sepsis.

Infection in neonates continues to be a global problem with significant morbidity and mortality. The diagnosis of neonatal sepsis is complicated by nonspecific clinical symptomatology, a high-false negative rate, and a delay in obtaining blood culture results. An ideal biomarker needs to have a high degree of accuracy in recognizing the presence or absence of definite infection at an early stage, to guide the initiation and duration of antibiotic therapy. The diagnostic utility of the following biomarkers seems to be most practical in the early (interleukin [IL]-6, IL-8, tumor necrosis factor-alpha, neutrophil CD64), mid (procalcitonin) and late (C-reactive protein) phases of neonatal sepsis. Future research studies to assess reliability of these biomarkers should be (1) adequately powered for sample size and (2) use the gold-standard definition of blood-culture proven pathogen-specific sepsis. Significant advances in diagnostic accuracy of novel biomarkers to allow early, accurate, and cost-effective identification of pathogens responsible for neonatal sepsis is anticipated in the next 5 years.
Debiasing the lasso: Optimal sample size for Gaussian designs

Performing statistical inference in high-dimension is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of high-dimensional estimators. Here, we consider linear regression in the high-dimensional regime $p\gg n$. In this context, we would like to perform inference on a high-dimensional parameters vector $\theta^*\in{\mathbb R}^p$. Important progress has been achieved in computing confidence intervals for single coordinates $\theta^*_i$. A key role in these new methods is played by a certain debiased estimator $\hat{\theta}^{\rm d}$ that is constructed from the Lasso. Earlier work establishes that, under suitable assumptions on the design matrix, the coordinates of $\hat{\theta}^{\rm d}$ are asymptotically Gaussian provided $\theta^*$ is $s_0$-sparse with $s_0 = o(\sqrt{n}/\log p )$. The condition $s_0 = o(\sqrt{n}/ \log p )$ is stronger than the one for consistent estimation, namely $s_0 = o(n/ \log p)$. We study Gaussian designs with known or unknown population covariance. When the covariance is known, we prove that the debiased estimator is asymptotically Gaussian under the nearly optimal condition $s_0 = o(n/ (\log p)^2)$. Note that earlier work was limited to $s_0 = o(\sqrt{n}/\log p)$ even for perfectly known covariance. The same conclusion holds if the population covariance is unknown but can be estimated sufficiently well, e.g. under the same sparsity conditions on the inverse covariance as assumed by earlier work. For intermediate regimes, we describe the trade-off between sparsity in the coefficients and in the inverse covariance of the design. We further discuss several applications of our results to high-dimensional inference. In particular, we propose a new estimator that is minimax optimal up to a factor $1+o_n(1)$ for i.i. d. Gaussian designs.
The efficacy of glucosamine sulfate in osteoarthritis: financial and nonfinancial conflict of interest.

Glucosamine has attracted a lot of interest as a specific drug for osteoarthritis (OA). Pharmacologic treatment of the disease is in fact dominated by the use of nonspecific analgesics or antiinflammatory agents, including nonsteroidal antiinflammatory drugs (NSAIDs) that have shown modest, although sound, symptomatic efficacy over short-term treatment courses, but no relevant effect during long-term use (1) and a poor risk/benefit ratio (2). Conversely, glucosamine appears to be safe (3). In addition, well-conducted clinical research with a particular form of glucosamine sulfate has shown clinically relevant symptom modification over medium-term treatment of knee OA (4). This symptom modification is sustained for years, and there is an intriguing suggestion of possible disease (structure) modification (5,6). These long-term effects are particularly relevant (7), given the chronic and progressive nature of this degenerative joint disease. Several reviews and meta-analyses have scrutinized the efficacy of glucosamine. At least 3 of these have emerged as being worthy of attention: a most important Cochrane Review first released in 2001 and updated in 2005 (8), a meta-analysis from our group that had the merit of being the first to include the long-term trials of glucosamine sulfate (9), and finally, 2 meta-analyses from a group in Boston. The Boston group’s first meta-analysis was published in 2000 (10), but its usefulness is limited because it was performed before all of the most relevant trials with glucosamine. Vlad and coworkers have now updated their meta-analysis, and it is published in this issue of Arthritis & Rheumatism (11). All these meta-analyses suggest the efficacy of at least 1 particular formulation of glucosamine sulfate, which is a prescription drug in Europe. Actually, the recent European League Against Rheumatism practice guidelines for knee OA assign to glucosamine sulfate the highest level of evidence, 1A, and strength of the recommendation, A, acknowledging the high quality of the studies performed (12). In contrast, Vlad and coworkers dismiss the quality of the most important glucosamine sulfate trials, as well as the major differences in glucosamine forms, products, dosages, and trial designs, and support only a view of bias due to industry involvement (11), a conclusion I find to be inaccurate and misleading. There are therefore 2 aspects of this issue that deserve attention. The first is of course the actual role of industry bias in clinical research, and the second is an understanding of why the findings of the meta-analysis by Vlad et al are of concern.
Distributed private online learning for social big data computing over data center networks

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.