Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial pattern separation in cognitively normal young and older adults

This study examined the ability of cognitively normal young adults (n = 30) and older adults (n = 30) to perform a delayed match‐to‐sample task involving varying degrees of spatial interference to assess spatial pattern separation. Each trial consisted of a sample phase followed by a choice phase. During the sample phase, a circle appeared briefly on a computer screen. The participant was instructed to remember the location of the circle on the screen. During the choice phase, two circles were displayed simultaneously, and the participant was asked to indicate which circle was in the same location as the sample phase circle. The two circles on choice phase trials were separated by one of four possible spatial separations: 0, 0.5, 1.0, and 1.5 cm. Smaller separations are likely to create increased overlap among memory representations, which may result in heightened interference and a greater need for pattern separation. Consistent with this hypothesis, performance increased as a function of increased spatial separation in both young and older adults. However, young adults outperformed older adults, suggesting that spatial pattern separation may be less efficient in older adults due to potential age‐related changes in the dentate gyrus and CA3 hippocampal subregions. Older adults also were divided into older impaired and older unimpaired groups based on their performance on a standardized test of verbal memory. The older impaired group was significantly impaired relative to both the older unimpaired and young groups, suggesting that pattern separation deficits may be variable in older adults. The present findings may have important implications for designing behavioral interventions for older adults that structure daily living tasks to reduce interference, thus improving memory function. © 2012 Wiley Periodicals, Inc.
Cloud computing is changing how we communicate

Cloud computing refers to a network that distributes processing power, applications, and large systems among many computers. Social media channels and many other applications use cloud computing as their platform. But cloud computing goes beyond that, offering a way for people to expand their local computing power onto the (seemingly) infinite processing power of the Internet. One of the results is a changing method of communication. Cloud computing can be a single-function application, an infrastructure on which these applications (and many others) can run, a set of services that offer the advantages of huge amounts of computing resources, and the ability to store large amounts of data remotely. Many companies and educational institutions are just beginning to realize the benefits of cloud-based applications that have traditionally required site licensing, installation, and maintenance. Cloud computing, SaaS, software as a service, social media.
Predicting Drug–Drug Interactions Involving Multiple Mechanisms Using Physiologically Based Pharmacokinetic Modeling: A Case Study With Ruxolitinib

Physiologically based pharmacokinetic modeling was applied to characterize the potential drug–drug interactions for ruxolitinib. A ruxolitinib physiologically based pharmacokinetic model was constructed using all baseline PK data in healthy subjects, and verified by retrospective predictions of observed drug–drug interactions with rifampin (a potent CYP3A4 inducer), ketoconazole (a potent CYP3A4 reversible inhibitor) and erythromycin (a moderate time‐dependent inhibitor of CYP3A4). The model prospectively predicts that 100–200 mg daily dose of fluconazole, a dual inhibitor of CYP3A4 and 2C9, would increase ruxolitinib plasma concentration area under the curve by ∼two‐fold, and that as a perpetrator, ruxolitinib is highly unlikely to have any discernible effect on digoxin, a sensitive P‐glycoprotein substrate. The analysis described here illustrates the capability of physiologically based pharmacokinetic modeling to predict drug–drug interactions involving several commonly encountered interaction mechanisms and makes the case for routine use of model‐based prediction for clinical drug–drug interactions. A model verification checklist was explored to harmonize the methodology and use of physiologically based pharmacokinetic modeling.
Recent Advances in Electrochemical Immunosensors

Immunosensors have experienced a very significant growth in recent years, driven by the need for fast, sensitive, portable and easy-to-use devices to detect biomarkers for clinical diagnosis or to monitor organic pollutants in natural or industrial environments. Advances in the field of signal amplification using enzymatic reactions, nanomaterials such as carbon nanotubes, graphene and graphene derivatives, metallic nanoparticles (gold, silver, various oxides or metal complexes), or magnetic beads show how it is possible to improve collection, binding or transduction performances and reach the requirements for realistic clinical diagnostic or environmental control. This review presents these most recent advances; it focuses first on classical electrode substrates, then moves to carbon-based nanostructured ones including carbon nanotubes, graphene and other carbon materials, metal or metal-oxide nanoparticles, magnetic nanoparticles, dendrimers and, to finish, explore the use of ionic liquids. Analytical performances are systematically covered and compared, depending on the detection principle, but also from a chronological perspective, from 2012 to 2016 and early 2017.
Reduced order models for HF interconnect over lossy semiconductor substrate

The paper describes an effective procedure to extract the reduced order models of multi-wire on-chip interconnects. These models take into consideration effects of the high frequency electromagnetic field, such as propagation, skin effect, substrate losses, etc. The frequency dependent p.u. l. line parameters are extracted by solving with finite integral technique (FIT) two field problems with appropriate boundary conditions: 2D-EQS field for Y1(omega) and EMQS-TM field for Z1(omega). After the line extension, the reduced model is extracted by vector fitting (VFIT), and then the equivalent compact spice sub-circuit is synthesized by differential-equation method (DEM).
PLGA nanoparticles for the oral delivery of nuciferine: preparation, physicochemical characterization and in vitro/in vivo studies

Abstract This article reports a promising approach to enhance the oral delivery of nuciferine (NUC), improve its aqueous solubility and bioavailability, and allow its controlled release as well as inhibiting lipid accumulation. NUC-loaded poly lactic-co-glycolic acid nanoparticles (NUC-PLGA-NPs) were prepared according to a solid/oil/water (s/o/w) emulsion technique due to the water-insolubility of NUC. PLGA exhibited excellent loading capacity for NUC with adjustable dosing ratios. The drug loading and encapsulation efficiency of optimized formulation were 8.89 ± 0.71 and 88.54 ± 7.08%, respectively. NUC-PLGA-NPs exhibited a spherical morphology with average size of 150.83 ± 5.72 nm and negative charge of −22.73 ± 1.63 mV, which are suitable for oral administration. A sustained NUC released from NUC-PLGA-NPs with an initial exponential release owing to the surface associated drug followed by a slower release of NUC, which was entrapped in the core. In addition, ∼77 ± 6.67% was released in simulating intestinal juice, while only about 45.95 ± 5.2% in simulating gastric juice. NUC-PLGA-NPs are more efficient against oleic acid (OA)-induced hepatic steatosis in HepG2 cells when compared to naked NUC (n-NUC, *p < 0.05). The oral bioavailability of NUC-PLGA-NPs group was significantly higher (**p < 0.01) and a significantly decreased serum levels of total cholesterol (TC), triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C), as well as a higher concentration of high-density lipoprotein cholesterol (HDL-C) was observed, compared with that of n-NUC treated group. These findings suggest that NUC-PLGA-NPs hold great promise for sustained and controlled drug delivery with improved bioavailability to alleviating lipogenesis.
Effects of S 15511, a therapeutic metabolite of the insulin‐sensitizing agent S 15261, in the Zucker Diabetic Fatty rat

Aim: S 15261 is a novel oral antihyperglycaemic drug with both insulin secretagogue and insulin‐sensitizing effects. The study was designed to determine the biological activity of its two major metabolites, S 15511 and Y 415, and whether or not they have an additive effect.
The Interpretation of Random-Effects Meta-Analysis in Decision Models

This article shows that the interpretation of the random-effects models used in meta-analysis to summarize heterogeneous treatment effects can have a marked effect on the results from decision models. Sources of variation in meta-analysis include the following: random variation in outcome definition (amounting to a form of measurement error), variation between the patient groups in different trials, variation between protocols, and variation in the way a given protocol is implemented. Each of these alternatives leads to a different model for how the heterogeneity in the effect sizes previously observed might relate to the effect size(s) in a future implementation. Furthermore, these alternative models require different computations and, when the net benefits are nonlinear in the efficacy parameters, result in different expected net benefits. The authors’ analysis suggests that the mean treatment effect from a random-effects meta-analysis will only seldom be an appropriate representation of the efficacy expected in a future implementation. Instead, modelers should consider either the predictive distribution of a future treatment effect, or they should assume that the future implementation will result in a distribution of treatment effects. A worked example, in a probabilistic, Bayesian posterior framework, is used to illustrate the alternative computations and to show how parameter uncertainty can be combined with variation between individuals and heterogeneity in meta-analysis.
Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment. However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper, we propose a novel temporallyconstrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term thatrequires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term thatrequires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers.
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection

Anomaly detection is playing an increasingly important role in hyperspectral image (HSI) processing. The traditional anomaly detection methods mainly extract knowledge from the background and use the difference between the anomalies and the background to distinguish them. Anomaly contamination and the inverse covariance matrix problem are the main difficulties with these methods. The low-rank and sparse matrix decomposition (LRaSMD) technique may have the potential to solve the aforementioned hyperspectral anomaly detection problem since it can extract knowledge from both the background and the anomalies. This paper proposes an LRaSMD-based Mahalanobis distance method for hyperspectral anomaly detection (LSMAD). This approach has the following capabilities: 1) takes full advantage of the LRaSMD technique to set the background apart from the anomalies; 2) explores the low-rank prior knowledge of the background to compute the background statistics; and 3) applies the Mahalanobis distance differences to detect the probable anomalies. Extensive experiments were carried out on four HSIs, and it was found that LSMAD shows a better detection performance than the current state-of-the-art hyperspectral anomaly detection methods.
Market Price of Risk Analysis from Three Major Industrial Countries on the Stability of the Brennan-Schwartz Model

At any given time, market price of risk must be the same for all derivatives and it is linked in par-ticular to interest rate. The Brennan-Schwartz model is one of the stochastic differential equations for the interest rate under the risk neutral probability measure. To estimate parameters of this model, it is required that the real data which are collected in the real world in which the distribution of interest rate process is under the actual probability measure. Therefore, parameter estimators are obtained by changing the measure which is determined by the market price of risk. Hence, market price of risk must make the Brennan-Schwartz model becomes stable, which is important to describe resistance of the model to the perturbation in the initial state or parameters of the model. This paper aims to analyze the market price of risk from three major industrial countries: USA, Japan, and Canada. This analysis can be used as a guideline to decide that the interest rate of these three major industrial countries can be modeled as Brennan-Schwartz model. ");} // --> activate javascript
Managing Extracavitary Prosthetic Vascular Graft Infections: A Pathway to Success

Prosthetic vascular graft infections portend grave consequences if not treated expediently. Despite the low incidence of infection, the potential for limb loss or death greatly magnifies this complication. The surgical management of prosthetic graft infections has evolved over the last 2 decades. With the myriad therapeutic options now available, an algorithm is necessary to provide the optimal surgical treatment of Samson groups 1 through 5 extracavitary infected vascular prostheses. An extensive review of the literature was undertaken to evaluate the most effective management schemes. The authors found that 3 factors—Samson classification, bacteriology, and patient vascular anatomy—are vital to the surgical strategy. These 3 criteria were examined, and an algorithm was developed based on successful clinical and experimental results. This review provides a step-by-step rationale for the surgical management of extracavitary prosthetic graft infections according to the most successful reported outcomes.
The impact of risk management practices on wheat productivity in France and Hungary

Wheat is a key staple crop for global food security, but its production is strongly concentrated in a few regions, among which the EU is the first producer. EU farmers are struggling to keep high productivity levels due to global market and climate challenges. Risk management practices (RMP) are often advocated as viable tools to cope against these uncertainties, but their adoption can also subtract resources to the production activity, resulting in a controversial impact on the overall farm productivity. This paper analysis whether and how much four RMP contribute to wheat farming efficiency in France and Hungary using i) a stochastic frontier model to obtain a measure of farms efficiency; ii) an endogenous switching regression model to quantify the RMP impact. Results show that RMP can benefit farm efficiency, but not all the RMP have the same effect. While insurance, diversification and contract farming can positively affect farm efficiency, cultivating different varieties can reduce farm efficiency of about 10% depending on the production conditions.
On-Chip Liquid Cooling With Integrated Pump Technology

In this paper, the capability of a novel cooling system for microchannels based on the principle of electrowetting is examined. To start with, the elcctrowetting effect in microchannels is experimentally investigated. The used electrowetting system consists of a liquid droplet deposited on a conductive Si substrate and electrically insulated from this substrate by a dielectric, layer. Microchannels of 100 mum times 100 mum are etched in the substrate. By applying an ac voltage signal between the droplet and the substrate, the microchannels can be periodically tilled and emptied with the liquid of the droplet. This oscillating liquid flow will be used to cool the chip. For the 100 mum times 100 mum microchannels a voltage of 51 V is required for the actuation. Further, based upon the results of the filling of the channels the cooling capacity of the proposed system is theoretically investigated. The theoretically achievable cooling rate of this enhanced system is compared to the heat transfer by conduction through a silicon substrate. A critical filing period is found; for shorter filling periods, the heat transfer will be improved by inserting microchannels, for higher filling periods the electrowetting deteriorates the cooling. It can be concluded that the proposed system is promising, especially when frequencies in the range of a few Hz can be achieved.
A Role for Protein Kinase Casein Kinase2 α-Subunits in the Arabidopsis Circadian Clock1[W][OA]

Circadian rhythms are autoregulatory, endogenous rhythms with a period of approximately 24 h. A wide variety of physiological and molecular processes are regulated by the circadian clock in organisms ranging from bacteria to humans. Phosphorylation of clock proteins plays a critical role in generating proper circadian rhythms. Casein Kinase2 (CK2) is an evolutionarily conserved serine/threonine protein kinase composed of two catalytic α-subunits and two regulatory β-subunits. Although most of the molecular components responsible for circadian function are not conserved between kingdoms, CK2 is a well-conserved clock component modulating the stability and subcellular localization of essential clock proteins. Here, we examined the effects of a cka1a2a3 triple mutant on the Arabidopsis (Arabidopsis thaliana) circadian clock. Loss-of-function mutations in three nuclear-localized CK2α subunits result in period lengthening of various circadian output rhythms and central clock gene expression, demonstrating that the cka1a2a3 triple mutant affects the pace of the circadian clock. Additionally, the cka1a2a3 triple mutant has reduced levels of CK2 kinase activity and CIRCADIAN CLOCK ASSOCIATED1 phosphorylation in vitro. Finally, we found that the photoperiodic flowering response, which is regulated by circadian rhythms, was reduced in the cka1a2a3 triple mutant and that the plants flowered later under long-day conditions. These data demonstrate that CK2α subunits are important components of the Arabidopsis circadian system and their effects on rhythms are in part due to their phosphorylation of CIRCADIAN CLOCK ASSOCIATED1.
Glycosylation of the Severe Acute Respiratory Syndrome Coronavirus Triple-Spanning Membrane Proteins 3a and M

ABSTRACT The severe acute respiratory syndrome coronavirus (SARS-CoV) open reading frame 3a protein has recently been shown to be a structural protein. The protein is encoded by one of the so-called group-specific genes and has no sequence homology with any of the known structural or group-specific proteins of coronaviruses. It does, however, have several similarities to the coronavirus M proteins; (i) they are triple membrane spanning with the same topology, (ii) they have similar intracellular localizations (predominantly Golgi), (iii) both are viral structural proteins, and (iv) they appear to interact with the E and S proteins, as well as with each other. The M protein plays a crucial role in coronavirus assembly and is glycosylated in all coronaviruses, either by N-linked or by O-linked oligosaccharides. The conserved glycosylation of the coronavirus M proteins and the resemblance of the 3a protein to them led us to investigate the glycosylation of these two SARS-CoV membrane proteins. The proteins were expressed separately using the vaccinia virus T7 expression system, followed by metabolic labeling. Pulse-chase analysis showed that both proteins were modified, although in different ways. While the M protein acquired cotranslationally oligosaccharides that could be removed by PNGaseF, the 3a protein acquired its modifications posttranslationally, and they were not sensitive to the N-glycosidase enzyme. The SARS-CoV 3a protein, however, was demonstrated to contain sialic acids, indicating the presence of oligosaccharides. O-glycosylation of the 3a protein was indeed confirmed using an in situ O-glycosylation assay of endoplasmic reticulum-retained mutants. In addition, we showed that substitution of serine and threonine residues in the ectodomain of the 3a protein abolished the addition of the O-linked sugars. Thus, the SARS-CoV 3a protein is an O-glycosylated glycoprotein, like the group 2 coronavirus M proteins but unlike the SARS-CoV M protein, which is N glycosylated.
Cognitive work analysis to evaluate the problem of patient falls in an inpatient setting

OBJECTIVE To identify factors in the nursing work domain that contribute to the problem of inpatient falls, aside from patient risk, using cognitive work analysis. DESIGN A mix of qualitative and quantitative methods were used to identify work constraints imposed on nurses, which may underlie patient falls. MEASUREMENTS Data collection was done on a neurology unit staffed by 27 registered nurses and utilized field observations, focus groups, time-motion studies and written surveys (AHRQ Hospital Survey on Patient Culture, NASA-TLX, and custom Nursing Knowledge of Fall Prevention Subscale). RESULTS Four major constraints were identified that inhibit nurses' ability to prevent patient falls. All constraints relate to work processes and the physical work environment, opposed to safety culture or nursing knowledge, as currently emphasized. The constraints were: cognitive 'head data', temporal workload, inconsistencies in written and verbal transfer of patient data, and limitations in the physical environment. To deal with these constraints, the nurses tend to employ four workarounds: written and mental chunking schemas, bed alarms, informal querying of the previous care nurse, and informal video and audio surveillance. These workarounds reflect systemic design flaws and may only be minimally effective in decreasing risk to patients. CONCLUSION Cognitive engineering techniques helped identify seemingly hidden constraints in the work domain that impact the problem of patient falls. System redesign strategies aimed at improving work processes and environmental limitations hold promise for decreasing the incidence of falls in inpatient nursing units.