Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the landmark and ultrasound methods in cesarean sections performed under spinal anesthesia on obese pregnants

Abstract Difficulties in the palpation of anatomical landmarks in pregnancy due to obesity may create problems in the regional anaesthesia. Objective of this study was to compare ultrasound examination carried out before the procedure with conventional spinal anaesthesia method with papation of bony landmarks in caesarean sections performed under spinal anaesthesia in obese pregnants. Ninety-seven obese pregnants having pre- and post-pregnancy body mass index > 30 kg/m2 were prospectively examined. Patients were randomised to two groups as landmark group (n = 49) and ultrasound group (n = 48). The needle insertion point was determined at L4–L5 level before the procedure through palpation in the landmark group and with the examination in the ultrasound. The numbers of skin punctures and needle passes, total procedure time (TPT) and spinal block occurrence time (SBOT) were recorded. TPT was significantly longer in the ultrasound than in the landmark group (p < 0.001) (8 ± 2 and 5 ± 1; respectively). Whereas SBOT values were similar (p = 0.063). The numbers of skin punctures and needle passes were significantly fewer in the ultrasound than in the landmark group (p < 0.001). We believe that, accurate determination of the needle introducing site before the procedure by viewing the vertebral structures through ultrasound examination in obese pregnants could increase the success rate.
Molecular Mechanism of Quorum-Sensing in Enterococcus faecalis: Its Role in Virulence and Therapeutic Approaches

Quorum-sensing systems control major virulence determinants in Enterococcus faecalis, which causes nosocomial infections. The E. faecalis quorum-sensing systems include several virulence factors that are regulated by the cytolysin operon, which encodes the cytolysin toxin. In addition, the E. faecalis Fsr regulator system controls the expression of gelatinase, serine protease, and enterocin O16. The cytolysin and Fsr virulence factor systems are linked to enterococcal diseases that affect the health of humans and other host models. Therefore, there is substantial interest in understanding and targeting these regulatory pathways to develop novel therapies for enterococcal infection control. Quorum-sensing inhibitors could be potential therapeutic agents for attenuating the pathogenic effects of E. faecalis. Here, we discuss the regulation of cytolysin, the LuxS system, and the Fsr system, their role in E. faecalis-mediated infections, and possible therapeutic approaches to prevent E. faecalis infection.
Pattern-Based Approach to the Workflow Satisfiability Problem with User-Independent Constraints

The fixed parameter tractable (FPT) approach is a powerful tool in tackling computationally hard problems. In this paper, we link FPT results to classic artificial intelligence (AI) techniques to show how they complement each other. Specifically, we consider the workflow satisfiability problem (WSP) which asks whether there exists an assignment of authorised users to the steps in a workflow specification, subject to certain constraints on the assignment. It was shown by Cohen et al. (JAIR 2014) that WSP restricted to the class of user-independent constraints (UI), covering many practical cases, admits FPT algorithms, i.e. can be solved in time exponential only in the number of steps k and polynomial in the number of users n. Since usually k << n in WSP, such FPT algorithms are of great practical interest. We present a new interpretation of the FPT nature of the WSP with UI constraints giving a decomposition of the problem into two levels. Exploiting this two-level split, we develop a new FPT algorithm that is by many orders of magnitude faster than the previous state-of-the-art WSP algorithm and also has only polynomial-space complexity. We also introduce new pseudo-Boolean (PB) and Constraint Satisfaction (CSP) formulations of the WSP with UI constraints which efficiently exploit this new decomposition of the problem and raise the novel issue of how to use general-purpose solvers to tackle FPT problems in a fashion that meets FPT efficiency expectations. In our computational study, we investigate, for the first time, the phase transition (PT) properties of the WSP, under a model for generation of random instances. We show how PT studies can be extended, in a novel fashion, to support empirical evaluation of scaling of FPT algorithms.
RND3 promotes Snail 1 protein degradation and inhibits glioblastoma cell migration and invasion

Activation of Snail1 signaling promotes the migration and invasion of multiple tumors, including glioblastoma multiforme (GBM). However, the molecular mechanism that augments Snail1 signaling during GBM cell migration and invasion remains largely unknown. Identification of the factors that regulate Snail1 signaling is critical to block tumor cell migration and invasion. By screening human GBM specimens, we found that the expression levels of small GTPase RND3 positively correlated with the expression levels of E-cadherin and claudin, the glioblastoma migration biomarkers negatively regulated by Snail1. Downregulation of E-cadherin and claudin has been associated with the migration and invasion of GBM cells. We demonstrated that RND3 functioned as an endogenous inhibitor of the Snail-directed transcriptional regulation. RND3 physically interacted with Snail1 protein, enhanced Snail1 ubiquitination, and facilitated the protein degradation. Forced expression of RND3 inhibited Snail1 activity, which in turn blocked glioblastoma cell migration and invasion in vitro in cell culture and in vivo in GBM xenograft mice. In contrast, downregulation of RND3 augmented Snail1 activity, and subsequently decreased E-cadherin expression, eventually promoted glioblastoma cell migration and invasion. The pro-migration induced by RND3 downregulation was attenuated by Snail1 knockdown. The findings partially explain why Snail1 activity is augmented in GBM, and defines a new function of RND3 in GBM cell migration and invasion.
A comparison of manual tracing and FreeSurfer for estimating hippocampal volume over the adult lifespan

MRI has become an indispensable tool for brain volumetric studies, with the hippocampus an important region of interest. Automation of the MRI segmentation process has helped advance the field by facilitating the volumetric analysis of larger cohorts and more studies. FreeSurfer has emerged as the de facto standard tool for these analyses, but studies validating its output are all based on older versions. To characterize FreeSurfer's validity, we compare several versions of FreeSurfer software with traditional hand‐tracing. Using MRI images of 262 males and 402 females aged 38 to 84, we directly compare estimates of hippocampal volume from multiple versions of FreeSurfer, its hippocampal subfield routines, and our manual tracing protocol. We then use those estimates to assess asymmetry and atrophy, comparing performance of different estimators with each other and with brain atrophy measures. FreeSurfer consistently reports larger volumes than manual tracing. This difference is smaller in larger hippocampi or older people, with these biases weaker in version 6.0. 0 than prior versions. All methods tested agree qualitatively on rightward asymmetry and increasing atrophy in older people. FreeSurfer saves time and money, and approximates the same atrophy measures as manual tracing, but it introduces biases that could require statistical adjustments in some studies.
Effective Moduli of Microcracked Rocks: Theories and Experiments

This paper compares and contrasts the predicted effective moduli by the self-consistent and noninteracting methods and examines the range of validity for both models; predictions by these two models are compared to experimental observations on natural rocks containing microcracks and artificial rocks containing inserted microcracks. Crack density (e) in real rocks is obtained by counting cracks under microscope and those in replicated rocks is, of course, predetermined; these crack densities are then compared to the crack densities interpreted from the changes in ultrasonic wave speeds, using either self-consistent model or noninteracting crack model. For natural rocks, the noninteracting prediction seems to agree better with experiments than the self-consistent method for E c 0.2, but both theoretical predictions deviate from experimental observation for e > 0.2. For replicated rocks, both theories give prediction comparable to experiments for E c 0.2, but underestimate the actual value for e > 0.2. Therefore, a better damage model is needed for solids with crack density larger than 0.2.
Design of a dual energy storage power converter for a small electric vehicle

Conventional Electric Vehicles (EV) rely on the battery as the sole energy source to power the vehicle. However, the battery being a low power dense type of storage, high power demands (acceleration and braking) will degrade the battery or will not possible at all in some cases. Supercapacitors, on the other hand, have high power density and are more suited to absorb or release high power demands In this paper, the design of a power electronic converter system comprising two bidirectional dc-dc converters to handle power of the two energy sources, namely the battery and the supercapacitor, is presented. The design of voltage regulating controller for the dc-link is also presented. The PSCAD simulation of the controller and the experimental verification of the design followed by an analysis of the results are presented. The experiment has been extended to investigate EV scenario consisting of a brushless dc motor following New European Driving Cycle. (NEDC)
Supervised hashing with jointly learning embedding and quantization

Compared with unsupervised hashing, supervised hashing commonly illustrates better accuracy in many real applications by leveraging semantic (label) information. However, it is tough to solve the supervised hashing problem directly because it is essentially a discrete optimization problem. Some other works try to solve the discrete optimization problem directly using binary quadratic programming, but they are typically too complicated and time-consuming while some supervised hashing methods have to solve a relaxed continuous optimization problem by dropping the discrete constraints. However, these methods typically suffer from poor performance due to the errors caused by the relaxation manner. In this paper based on the general two-step framework: learning binary embedded codes and learning hash functions, we propose a new method to solve the problem introduced by relaxing the cost function. Inspired by the property of rotation invariance of learning embedding features, our method tries to jointly learn similarity-preserving representation and rotation transformation for better quantization alternatively. In experiments, our method shows significant improvement. Compared with the methods based on discrete optimization our methods obtains the competitive performance and even achieves the state-of-the-art performance in some image retrieval applications.
Apple Extracts Present Catabolic and Hipocolesterolemic Effect in Mice

Dyslipidemia is highly prevalent in the Chilean population: about 40% have total cholesterol levels over 200 mg/dl and 25% are obese (BMI > 30) which produces an increased risk of cardiovascular disease and deaths. Over consumption of foods rich in lipids and carbohydrates unchains these events, due to the high amount of lipid accumulation in adipocytes. These cells are capable of producing a large number of mediators of inflammation and adipokines which in large quantities can compromise the overall metabolism. Apple has been shown to stop these events. We used CF-1 mice that were fed on a high-fat diet which leads to a metabolic status similar to dyslipidemia. Different types of apple waste were direct from orchard and fruit industry. Extracts obtained were characterized and administered in drinking water. At the end of the 40-day experimental period, biochemical parameters in animals were measured and the weight of white adipose tissue (WAT) was quantified. The results were compared with the normal diet and fat diet controls. All apple extracts decrease total and LDL cholesterol to levels similar to normal control and decrease WAT. Apple extracts may be an effective protector against development of risk factors in cardiovascular disease in the Chilean population.
Obesity, Metabolic Syndrome, and Musculoskeletal Disease: Common Inflammatory Pathways Suggest a Central Role for Loss of Muscle Integrity

Inflammation can arise in response to a variety of stimuli, including infectious agents, tissue injury, autoimmune diseases, and obesity. Some of these responses are acute and resolve, while others become chronic and exert a sustained impact on the host, systemically, or locally. Obesity is now recognized as a chronic low-grade, systemic inflammatory state that predisposes to other chronic conditions including metabolic syndrome (MetS). Although obesity has received considerable attention regarding its pathophysiological link to chronic cardiovascular conditions and type 2 diabetes, the musculoskeletal (MSK) complications (i.e., muscle, bone, tendon, and joints) that result from obesity-associated metabolic disturbances are less frequently interrogated. As musculoskeletal diseases can lead to the worsening of MetS, this underscores the imminent need to understand the cause and effect relations between the two, and the convergence between inflammatory pathways that contribute to MSK damage. Muscle mass is a key predictor of longevity in older adults, and obesity-induced sarcopenia is a significant risk factor for adverse health outcomes. Muscle is highly plastic, undergoes regular remodeling, and is responsible for the majority of total body glucose utilization, which when impaired leads to insulin resistance. Furthermore, impaired muscle integrity, defined as persistent muscle loss, intramuscular lipid accumulation, or connective tissue deposition, is a hallmark of metabolic dysfunction. In fact, many common inflammatory pathways have been implicated in the pathogenesis of the interrelated tissues of the musculoskeletal system (e.g., tendinopathy, osteoporosis, and osteoarthritis). Despite these similarities, these diseases are rarely evaluated in a comprehensive manner. The aim of this review is to summarize the common pathways that lead to musculoskeletal damage and disease that result from and contribute to MetS. We propose the overarching hypothesis that there is a central role for muscle damage with chronic exposure to an obesity-inducing diet. The inflammatory consequence of diet and muscle dysregulation can result in dysregulated tissue repair and an imbalance toward negative adaptation, resulting in regulatory failure and other musculoskeletal tissue damage. The commonalities support the conclusion that musculoskeletal pathology with MetS should be evaluated in a comprehensive and integrated manner to understand risk for other MSK-related conditions. Implications for conservative management strategies to regulate MetS are discussed, as are future research opportunities.
Information Mining of Concurrent News and Time Series

We present a unique approach to identifying text segments that forecast the behavior of a time series. Speci cally, we describe the design and implementation of nalyst, a system that correlates news stories with trends in time series. We identify trends in time series using piecewise linear tting and then assign labels to the trends according to an automated clustering algorithm. We use language models to nd and represent text that is highly associated with particular labeled trends. nalyst can then identify news stories that are highly indicative of future trends, and thereby forecast the behavior of the time series. In this paper we evaluate our system by correlating nancial news stories and stock prices. We show that language models successfully capture relationships between new stories and trends. We also evaluate the system in terms of its ability to predict the behavior of the stock market. Combining the two models gives us the ability to mine relationships between concurrent text and time series data.
Case Study of a highly automated Layout Analysis and OCR of an incunabulum: 'Der Heiligen Leben' (1488)

This paper provides the first thorough documentation of a high quality digitization process applied to an early printed book from the incunabulum period (1450-1500). The entire OCR related workflow including preprocessing, layout analysis and text recognition is illustrated in detail using the example of 'Der Heiligen Leben', printed in Nuremberg in 1488. For each step the required time expenditure was recorded. The recognition rate was excellent both on character (97.97%) and word (91.58%) level. Furthermore, a comparison of a highly automated (LAREX) and a manual (Aletheia) method for layout analysis was performed. By substantially automating the segmentation the required human effort was reduced considerably from 39 hours to around eight hours, without any significant drop in OCR accuracy. Realistic estimates for the human effort necessary for full text extraction from incunabula can be derived from this study. The printed pages of the complete work together with the OCR result are available online1 ready to be inspected and downloaded.
Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning

Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in real-time that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of computation offloading depends on the resources afforded onboard and remotely, and the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading.
Toxicological Mechanisms of Carbon-Based Nanomaterials

Abstact:With the development of materials science and combination with medicine, more and more nano-materials are used in clinical medicine.There are much advantage in nano-material . The reason that the nano-materials are utilized widely is its quantum effect and huge superficalarea.But with further research ,more and more disadvantages are found. Many research on the environmental and human health risks of engineered nano-materials focus on their acute toxicity. However,the long-term chronic effects of nanomaterials on living systems is neglected.The potential of nanomaterials to promote the formation of reactive oxygen species is one of the primary reason in their genotoxic. DNA would be damaged by the reactive oxygen species. If the DNA cannot be renew ,it could lead to gene mutation , canceration. This review focuses on the impact of carbon-based nano-materials on DNA .
The Effect of Cement-Sodium Silicate Grout Compounds on Void Ratio and the Coefficient of Secondary Compression of Treated Fibrous Peat

Peats have low shear strength and high deformation characteristics. Peat is a naturally occurring material that is extremely soft with a high moisture content and exists in an unconsolidated state. The conventional binders used are cementitious materials, and the introduction of a new binder, sodium silicate, with other additives gives a better output than the conventional peat treatment. This article describes a laboratory study on the effect of different compounds of cement–sodium silicate grout on void ratio and the coefficient of secondary compression of fibrous peat. It is shown that by increasing the amount of sodium silicate (within 2.5 %), cement, and kaolinite in treated peat, we were able to decrease the mentioned properties of treated peat.
An Improved Hybrid Turbo Equalizer for Single Carrier Transmission with Impulsive Noise and ISI

In this paper, we propose an improved hybrid turbo equalizer (TEQ) for single-carrier transmission over multipath channels with symmetric alpha-stable noise. First, a nonlinear myriad filter is adopted for combating the effect of impulsive noise. The noise output of this nonlinear filter is analyzed by an asymptotic statistic method and shown to be asymptotically Gaussian. Then, a matched minimum mean square error based linear TEQ is proposed for further mitigating the effects of the residual impulsive noise and the intersymbol interference caused by multipath fading. In our proposed TEQ design procedure, the instantaneous estimation of the noise variance is exploited to obtain robust TEQ coefficients. Simulation results show that the proposed hybrid TEQ provides geometric signal-to-noise ratio improvements (of up to 4.5 dB) compared to conventional algorithms designed for Gaussian channels.