Miracle-dz/UniFAR-FTUs huggingface dataset:
Lateralized anterior mesiotemporal lobe activation: semirandom functional MR imaging encoding paradigm in patients with temporal lobe epilepsy--initial experience.

PURPOSE To prospectively demonstrate anterior mesiotemporal lobe (MTL) activation in healthy volunteers by using a semirandom memory-encoding paradigm and to prospectively compare lateralized functional magnetic resonance (MR) imaging activation with intracarotid amobarbital procedure (IAP) memory test results in patients with temporal lobe epilepsy (TLE) who were scheduled to undergo surgery. MATERIALS AND METHODS The study was approved by a local ethics committee, and written informed consent was obtained from all subjects. Eight healthy volunteers and 18 patients with TLE who were scheduled for surgery were included in the functional MR imaging study involving the use of a memory-encoding paradigm with variable epoch lengths. Subjects were instructed to memorize new pictures that were mixed among pictures that they had seen before. Data analysis entailed computations of the contrast between the MTL activation induced by the new pictures and the MTL activation induced by the old pictures and of the lateralization index, defined as the relative difference in the number of activated voxels between the left and right MTLs. Lateralization indexes were compared between the patients and the volunteers and statistically correlated with the patients' IAP memory test results. To study deviations from perfect correspondence between the functional MR imaging- and IAP-derived lateralization indexes, orthogonal regression analysis was applied. Proportional relations for the patients with left-sided TLE and for those with right-sided TLE were calculated separately. RESULTS The memory paradigm consistently activated the posterior and anterior MTL structures in both the healthy volunteers and the patients. Regression analysis revealed that functional MR imaging activation was stronger than the IAP results when it was lateralized to the contralateral MTL. This analysis also revealed a significant (P < .001) correlation between the functional MR imaging results and the IAP results in the patients with right-sided TLE but not in those with left-sided TLE (P > .1). CONCLUSION The functional MR imaging memory-encoding paradigm consistently yielded MTL activation in the volunteers and the patients with TLE, but lateralized functional MR imaging activation was in concordance with the IAP results in only those patients with right-sided TLE.
Muscle Strength Testing using Wearable Wireless Sensors

Manual muscle testing and its variants have a long history of use for classifying muscle strengths. For the first time, inexpensive wearable wireless sensors combined with machine learning techniques are used to classify different levels of muscle strength, which addresses some limitations of the manual method. A mean accuracy of 93% was obtained across ten subjects using gyroscope and accelerometer data in classifying four distinct levels of strengths of the biceps brachii muscle when performing muscle contraction under appropriate load. This was reduced by 2% for accelerometer-only data, thus offering a potentially inexpensive and viable solution for testing muscle strength.
Optimization of Grazing Management for Watershed Sediment Control

Grazing on rangelands can increase erosion that is a major source of nonpoint source pollution. Grazing management is important in maintaining vegetation cover, which consequently impacts erosion and sediment yield. This paper uses a representative ranch model to define grazing management from an economic perspective. The model maximizes the profit of a representative ranch that can utilize all grazing lands in a watershed with constraints on forage resources, sustainable utilization, and production technology and sediment yield control objectives. A case study for the Walnut Gulch Watershed in Arizona showed a shift of the spatial distribution of optimal stocking rates with increasing sediment control objectives.
IPsec Anti-Replay Algorithm without Bit Shifting

This document presents a new method to do anti-replay check and update, which becomes one alternative to the anti-replay algorithm in RFC 4302 and RFC4303. The new method will deem the bit-shifting unnecessary. It will reduce the number of times to slide the window. In addition, it makes bit-check and bit-update easier as it does not depend on the low index of the sliding window. It is especially beneficial when the window size is much bigger than 64 bits, for example, 1024 bits. IPsec employs one anti-replay sliding window protocol to secure against an adversary that can insert the messages inside the network tunnel. This method still inherits the sliding window protocol, but use one or more redundant bytes to ease the update of sliding window. The bit-shifting is deemed unnecessary with updating the high and low index of the window, which is especially efficient in case of the big window size. Thus the method reduces the number of times to update the window. In addition, the bit location is fixed for one sequence number, thus makes the bit check easier and faster.
Image retargeting based on object inpainting and background seam carving

Image Retargeting is the most important Image resizing techniques in the digitalized world. In this paper, we find the solution for images retargeting technique using harvest festival images. The proposed technology is called Object-CRIST (Content Retargeting Image Seam Carving Technique). The method is a combination of two resizing Methods Region based object inpainting and resizing background image seam carving. The first method identifies the objects in the image and performs Region based retargeting. Second method works on image background regions. We evaluate Object-CRIST method on harvest festival image dataset. The experiment result of proposed system outperforms rest of all retargeting methodologies. The Result accuracy is calculated based on Resolution and Mean Opinion Score (MOS).
Primary renal aspergillosis and renal stones in both kidneys associated with hematopoietic stem cell transplant

Invasive aspergillosis (IA) is a leading cause of infectious mortality in patients who have undergone a hematopoietic stem cell transplant (HSCT); the mortality due to IA ranges from 70% to 93% in HSCT patients. Early diagnosis and treatment are the cornerstones for the good prognosis of IA. Primary renal aspergillosis is an extremely rare presentation in patients who have undergone HSCT, and the risk factor for this uncommon presentation is not well known. We report a patient who developed primary renal aspergillosis and renal stones in both the kidneys after HSCT. Invasive renal aspergillosis was diagnosed after a nephrectomy, which was performed to treat massive renal hematoma.
Nonlinear finite element modeling of THUNDER piezoelectric actuators

A NASTRAN non-linear finite element model has been developed for predicting the dome heights of THUNDER (Thin Layer Unimorph Ferroelectric Driver) piezoelectric actuators. To analytically validate the finite element model, a comparison was made with a non-linear plate solution using Von Karmen's approximation. A 500 volt input was used to examine the actuator deformation. The NASTRAN finite element model was also compared with experimental results. Four groups of specimens were fabricated and tested. Four different input voltages, which included 120, 160, 200, and 240 Vp-p with a 0 volts offset, were used for this comparison.
A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects.

BACKGROUND Weight loss has been shown to reduce risk factors associated with cardiovascular disease and diabetes; however, successful maintenance of weight loss continues to pose a challenge. OBJECTIVE The present study was designed to assess whether changes in subcutaneous adipose tissue (scAT) gene expression during a low-calorie diet (LCD) could be used to differentiate and predict subjects who experience successful short-term weight maintenance from subjects who experience weight regain. DESIGN Forty white women followed a dietary protocol consisting of an 8-wk LCD phase followed by a 6-mo weight-maintenance phase. Participants were classified as weight maintainers (WMs; 0-10% weight regain) and weight regainers (WRs; 50-100% weight regain) by considering changes in body weight during the 2 phases. Anthropometric measurements, bioclinical variables, and scAT gene expression were studied in all individuals before and after the LCD. Energy intake was estimated by using 3-d dietary records. RESULTS No differences in body weight and fasting insulin were observed between WMs and WRs at baseline or after the LCD period. The LCD resulted in significant decreases in body weight and in several plasma variables in both groups. WMs experienced a significant reduction in insulin secretion in response to an oral-glucose-tolerance test after the LCD; in contrast, no changes in insulin secretion were observed in WRs after the LCD. An ANOVA of scAT gene expression showed that genes regulating fatty acid metabolism, citric acid cycle, oxidative phosphorylation, and apoptosis were regulated differently by the LCD in WM and WR subjects. CONCLUSION This study suggests that LCD-induced changes in insulin secretion and scAT gene expression may have the potential to predict successful short-term weight maintenance. This trial was registered at clinicaltrials.gov as NCT00390637.
Alterations of Murine Subbasal Corneal Nerves After Environmental Dry Eye Stress.

Purpose To investigate the morphologic changes in the corneal subbasal nerve (CSN) plexus in wild-type mice after exposure to environmental dry eye stress (EDES) using in vivo confocal microscopy (IVCM). Methods We examined 22 eyes of 8-week-old wild-type male mice (Balb/c, n = 11). The mice were exposed to an air fan inside a small compartment 5 hours/day for 3 days (EDES). Aqueous tear secretion and corneal epithelial damage were assessed. The CSNs were investigated by laser-scanning IVCM. Density; tortuosity; and reflectivity of CSNs; and dendritic cell (DC) densities were evaluated using semi-automated NeuronJ software. Results EDES significantly decreased the aqueous tear secretion quantity (P = 0.0019) and significantly increased the corneal fluorescein (P = 0.005) and lissamine green staining scores (P = 0003). The CSN density showed a significant decrease after EDES exposure (before, 2813 ± 762 pixels/frame; after, 1906 ± 896 pixels/frame, P = 0.0071). The tortuosity and the reflectivity grades did not show statistically significant differences after EDES exposure (tortuosity, P = 0.307; reflectivity, P = 0.758). However, the mean DC density showed a significant increase after EDES exposure (before, 12.62 ± 5.94 cells/mm2; after, 15.93 ± 5.30 cells/mm2, P = 0.026). Conclusions Even short-term exposure to EDES induced alterations in the CSN plexus morphology including decreased subbasal corneal nerve density and increased amount of DCs in mice. The EDES mouse model is a promising model to study the ocular surface and corneal nerve changes associated with dry eye disease.
A cerebrospinal fluid microRNA signature as biomarker for glioblastoma

Purpose To develop a cerebrospinal fluid (CSF) miRNA diagnostic biomarker for glioblastoma. Experimental Design Glioblastoma tissue and matched CSF from the same patient (obtained prior to tumor manipulation) were profiled by TaqMan OpenArray® Human MicroRNA Panel. CSF miRNA profiles from glioblastoma patients and controls were created from three discovery cohorts and confirmed in two validation cohorts. Results miRNA profiles from clinical CSF correlated with those found in glioblastoma tissues. Comparison of CSF miRNA profiles between glioblastoma patients and non-brain tumor patients yielded a tumor “signature” consisting of nine miRNAs. The “signature” correlated with glioblastoma tumor volume (p=0.008). When prospectively applied to cisternal CSF, the sensitivity and specificity of the ‘signature’ for glioblastoma detection were 67% and 80%, respectively. For lumbar CSF, the sensitivity and specificity of the signature were 28% and 95%, respectively. Comparable results were obtained from analyses of CSF extracellular vesicles (EVs) and crude CSF. Conclusion We report a CSF miRNA signature as a “liquid biopsy” diagnostic platform for glioblastoma.
Contribution Association of Antepartum and Postpartum Depression in Ghanaian and Ivorian WomenWith Febrile Illness in Their Offspring : A Prospective Birth Cohort Study

In low-income countries, perinatal depression is common, but longitudinal data on its influence on child health are rare. We examined the association between maternal depression and febrile illness in children. There were 654 mother/child dyads in Ghana and Côte d’Ivoire that were enrolled in a prospective birth cohort in 2010–2011 and underwent 2-years of follow up. Mothers were examined for depression using the Patient Health Questionnaire depression module antepartum and 3 and 12 months postpartum. The hazard of febrile illness in children of depressed and nondepressed mothers was estimated using a recurrent event Cox proportional hazards model. The prevalences of antepartum depression in mothers from Côte d’Ivoire and Ghana were 28.3% and 26.3%, respectively. The prevalences of depression at 3 and 12 months postpartum were 11.8% and 16.1% (Côte d’Ivoire) and 8.9% and 7.2% (Ghana). The crude and adjusted (for country and socioeconomic status) hazard ratios of febrile illness in children of depressed mothers compared with those in children of nondepressed mothers were 1.57 (95% confidence interval: 1.20, 2.07) and 1.32 (95% confidence interval: 1.01, 1.74) respectively. Perinatal depression was frequent and associated with febrile illness in the offspring. Our results showed that a high prevalence of depression in sub-Saharan Africa may pose a serious public health threat to women and their offspring.
Physiological and environmental regulation of interannual variability in CO2 exchange on rangelands in the western United States

For most ecosystems, net ecosystem exchange of CO2 (NEE) varies within and among years in response to environmental change. We analyzed measurements of CO2 exchange from eight native rangeland ecosystems in the western United States (58 site‐years of data) in order to determine the contributions of photosynthetic and respiratory (physiological) components of CO2 exchange to environmentally caused variation in NEE. Rangelands included Great Plains grasslands, desert shrubland, desert grasslands, and sagebrush steppe. We predicted that (1) week‐to‐week change in NEE and among‐year variation in the response of NEE to temperature, net radiation, and other environmental drivers would be better explained by change in maximum rates of ecosystem photosynthesis (Amax) than by change in apparent light‐use efficiency (α) or ecosystem respiration at 10 °C (R10) and (2) among‐year variation in the responses of NEE, Amax, and α to environmental drivers would be explained by changes in leaf area index (LAI). As predicted, NEE was better correlated with Amax than α or R10 for six of the eight rangelands. Week‐to‐week variation in NEE and physiological parameters correlated mainly with time‐lagged indices of precipitation and water‐related environmental variables, like potential evapotranspiration, for desert sites and with net radiation and temperature for Great Plains grasslands. For most rangelands, the response of NEE to a given change in temperature, net radiation, or evaporative demand differed among years because the response of photosynthetic parameters (Amax, α) to environmental drivers differed among years. Differences in photosynthetic responses were not explained by variation in LAI alone. A better understanding of controls on canopy photosynthesis will be required to predict variation in NEE of rangeland ecosystems.
MCI : Modeling-based Causality Inference in Audit Logging for Attack Investigation

In this paper, we develop a model based causality inference technique for audit logging that does not require any application instrumentation or kernel modification. It leverages a recent dynamic analysis, dual execution (LDX), that can infer precise causality between system calls but unfortunately requires doubling the resource consumption such as CPU time and memory consumption. For each application, we use LDX to acquire precise causal models for a set of primitive operations. Each model is a sequence of system calls that have inter-dependences, some of them caused by memory operations and hence implicit at the system call level. These models are described by a language that supports various complexity such as regular, context-free, and even context-sensitive. In production run, a novel parser is deployed to parse audit logs (without any enhancement) to model instances and hence derive causality. Our evaluation on a set of real-world programs shows that the technique is highly effective. The generated models can recover causality with 0% false-positives (FP) and false-negatives (FN) for most programs and only 8.3% FP and 5.2% FN in the worst cases. The models also feature excellent composibility, meaning that the models derived from primitive operations can be composed together to describe causality for large and complex real world missions. Applying our technique to attack investigation shows that the system-wide attack causal graphs are highly precise and concise, having better quality than the state-of-the-art.
Update on Photorespiration

Photorespiration has been a target for crop improvement ever since the energy losses associated with this pathway were identified in the 1970s. However, recent research highlights the importance of photorespiration as a recycling pathway for the products of ribulose-1,5bisphosphate (RuBP) oxygenation and its intimate interconnection with primary metabolism. Nevertheless, reducing photorespiratory losses by installation of alternative salvage pathways in Arabidopsis (Arabidopsis thaliana) resulted in enhanced growth and biomass. Such approaches will probably also prove useful under field conditions, and in a future atmosphere containing higher CO2 concentrations combined with high temperature or limiting water.
High-precision surrogate data based tests for Gaussianity and linearity of discrete time random processes

We have put Hinich's asymptotic tests for Gaussianity and linearity under scrutiny, and we show that these tests suffer from severe statistical problems. We propose the use of carefully designed surrogate data to ensure correct false alarm rate. Using theoretical considerations about estimation of higher order spectra, we propose new high-precision detections statistics for Gaussian and linear signals. Results from synthetic and experimental data demonstrate the applicability of the proposed tests.
Uncertain Random Approach to Multiobjective Programming Problem Based on Chance Theory

The traditional solution method for the uncertain random multiobjecitve programming (URMOP) problem usually considers the transformation of converting the URMOP problem into a deterministic multiobjective programming (MOP) problem directly, and then solves the deterministic MOP problem, which neglects the nature of the uncertainty and randomness. To avoid this defect, this paper mainly focuses on a novel solution method for the URMOP problem based on chance theory. To present the solution method, we first define relationship between uncertain random variables under some principles, and give the concepts of Pareto efficient solutions of the URMOP problem. Then, on the basis of PE principle, the novel solution method by transforming the URMOP problem into uncertain random single objective programming (URSOP) problem is put forward to obtain the Pareto efficient solutions, in which the uncertainty and randomness are taken into account. Finally, the linear weighted method is employed to obtain the URSOP problem, and some theoretical results are also acquired, which can provide the theoretical foundation for solving the optimization problem. c ©2014 World Academic Press, UK. All rights reserved.
Extracting Key Segments of Videos for Event Detection by Learning From Web Sources

In this paper, we present a novel approach of extracting the key segments for event detection in unconstrained videos. The key segments are automatically extracted by transferring the knowledge learned from Web images and Web videos to consumer videos. We propose an adaptive latent structural support vector machine model, where the locations of key segments in videos are regarded as latent variables due to the unavailability of the ground truth of key-segment locations in training data. In order to alleviate the time-consuming and labor-expensive manual annotation of huge amounts of training videos, a large number of loosely labeled Web images as well as videos are collected from the Web sources. Additionally, a limited number of labeled consumer videos are utilized to guarantee the precision of the model. Considering the semantic diversity of key segments, we learn a set of concepts as the semantic description of key segments and explore the temporal information of concepts to capture the sequential relations between the segments. The concepts are automatically discovered by using Web images and videos with their associated tags and description sentences. Comprehensive experiments on the Columbia's consumer video and the TRECVID 2014 Multimedia Event Detection datasets demonstrate that our method outperforms the state-of-the-art methods.
Interacting Particle Markov Chain Monte Carlo

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both noninteracting PMCMC samplers and a single PM-CMC sampler with an equivalent memory and computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.
Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage

This paper proposes a stochastic dynamic programming framework for the optimal energy management of a smart home with plug-in electric vehicle (PEV) energy storage. This paper is motivated by the challenges associated with intermittent renewable energy supplies and the local energy storage opportunity presented by vehicle electrification. This paper seeks to minimize electricity ratepayer cost, while satisfying home power demand and PEV charging requirements. First, various operating modes are defined, including vehicle-to-grid, vehicle-to-home, and grid-to-vehicle. Second, we use equivalent circuit PEV battery models and probabilistic models of trip time and trip length to formulate the PEV to smart home energy management stochastic optimization problem. Finally, based on time-varying electricity price and time-varying home power demand, we examine the performance of the three operating modes for typical weekdays.