Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrition assessment and risk prediction in dialysis patients-a new integrative score.

OBJECTIVE We developed a quantitative nutritional score, based on biochemical measures, taken as part of monthly routine care. The score can be accomplished within a short time after routine laboratory results completion and identify a monthly change in nutritional status. DESIGN A longitudinal observational cohort study SETTING The Institute of Nephrology, Wolfson Medical Center, Holon, Israel. SUBJECTS A total of 179 hemodialysis patients were followed up for up to 2.5 years after study baseline. INTERVENTION The Integrative Clinical Nutrition Dialysis Score (ICNDS) is based on the biochemical measures of albumin, creatinine, urea, cholesterol, C-reactive protein, dialysis adequacy, and weight change. Each parameter is ranked between 1 and 5, with the higher rank derived from recommended National Kidney Foundation Kidney Disease/Dialysis Outcomes and Quality Initiative values and the lower rank indicating deviation from those values. The final ICNDS is the sum of ranks over 7 parameters. MAIN OUTCOME MEASURE The Pearson correlation coefficient was calculated for association between subjective global assessment and ICNDS in 63 randomly selected patients. In 179 dialysis patients, the baseline ICNDS, the slope of 3 subsequent monthly ICNDS values, were tested for their correlation with odds of all-cause mortality, hospitalization frequency, length of stay, after 31 months. Spline Cox regression was used to select the best cutoff point, associated with severe mortality risk. RESULTS Score results were significantly correlated with nutrition evaluation by subjective global assessment (r = 0.842, P < .01). For a unit increase in baseline score, death odds were significantly decreased (hazard ratio [HR] = 0.929, 95% confidence interval [CI] 0.88-0.974, P < .002). Each unit increase in slope significantly reduced mortality risk (HR = 0.485, 95% CI 0.278-0.847, P < .011). Hospitalization frequency was significantly increased across worsening baseline score (HR = 0.935, 95% CI 0.906-0.964, P < .0001). A 1-unit increase in slope significantly decreased hospitalization (HR = 0.799, 95% CI 0.726-0.881, P < .0001). CONCLUSIONS Results confirm that ICNDS is a useful prognostic tool that serves to detect nutrition deterioration at its very beginning.
Neuroscience and Biobehavioral Reviews Different Involvement of Subregions within Dorsal Premotor and Medial Frontal Cortex for Pro-and Antisaccades

Keywords: Eye movements fMRI Medial FEF Lateral FEF SEF aMCC Meta-analysis Functional connectivity (FC) Meta-analytic connectivity modeling (MACM) Resting state a b s t r a c t The antisaccade task has been widely used to investigate cognitive action control. While the general network for saccadic eye movements is well defined, the exact location of eye fields within the frontal cortex strongly varies between studies. It is unknown whether this inconsistency reflects spatial uncertainty or is the result of different involvement of subregions for specific aspects of eye movement control. The aim of the present study was to examine functional differentiations within the frontal cortex by integrating results from neuroimaging studies analyzing pro-and antisaccade behavior using meta-analyses. The results provide evidence for a differential functional specialization of neighboring oculomotor frontal regions, with lateral frontal eye fields (FEF) and supplementary eye field (SEF) more often involved in prosaccades while medial FEF and anterior midcingulate cortex (aMCC) revealed consistent stronger involvement for antisaccades. This dissociation was furthermore mirrored by functional connectivity analyses showing that the lateral FEF and SEF are embedded in a motor output network, while medial FEF and aMCC are integrated in a multiple demand network.
Fully Depleted Ge CMOS Devices and Logic Circuits on Si

We systematically studied Ge CMOS devices and logic circuits fabricated on a GeOI substrate, with the novel recessed channel and source/drain structures. Various channel lengths (Lch) from 500 to 30 nm and channel thicknesses (Tch) from 90 to 10 nm are implemented into the Ge CMOS devices and comprehensive geometry dependence analysis is carried out in terms of both Lch and Tch. With shrinking Tch, the gate electrostatics are enhanced significantly but the on-state performance is found to be deteriorated simultaneously. It is also confirmed that Ge nMOSFETs are more sensitive to Tch than pMOSFETs due to the phosphorus diffusion inside Ge. CMOS circuits, such as inverters, NANDs, NORs, and ring oscillators, are further investigated.
Transaction chopping: algorithms and performance studies

Chopping transactions into pieces is good for performance but may lead to nonserializable executions. Many researchers have reacted to this fact by either inventing new concurrency-control mechanisms, weakening serializability, or both. We adopt a different approach. We assume a user who —has access only to user-level tools such as (1) choosing isolation degrees 1ndash;4, (2) the ability to execute a portion of a transaction using multiversion read consistency, and (3) the ability to reorder the instructions in transaction programs; and —knows the set of transactions that may run during a certain interval (users are likely to have such knowledge for on-line or real-time transactional applications). Given this information, our algorithm finds the finest chopping of a set of transactions TranSet with the following property: If the pieces of the chopping execute serializably, then TranSet executes serializably. This permits users to obtain more concurrency while preserving correctness. Besides obtaining more intertransaction concurrency, chopping transactions in this way can enhance intratransaction parallelism. The algorithm is inexpensive, running in O(n×(e+m)) time, once conflicts are identified, using a naive implementation, where n is the number of concurrent transactions in the interval, e is the number of edges in the conflict graph among the transactions, and m is the maximum number of accesses of any transaction. This makes it feasible to add as a tuning knob to real systems.
Interaction of Feature Selection Methods and Linear Classification Models

In this paper we explore effects of various feature selection algorithms on document classification performance. We propose to use two, possibly distinct linear classifiers: one used exclusively for feature selection in order to obtain the feature space for training the second classifier, using possibly a different training set. The resulting classifier is used to classify new documents. Experiments show that feature selection based on the linear SVM algorithm combines well with different types of classifiers. Based on the experimental results we make a conjecture that it is the level of sophis tication at which the scoring method takes into account information about features, rather than its compatibility with the classifier in terms of its design, that makes the feature selection method more or less successful.
A Low-Error Energy-Efficient Fixed-Width Booth Multiplier With Sign-Digit-Based Conditional Probability Estimation

Fixed-width multipliers are intensively used in many DSP applications whose accuracy and energy efficiency affect the whole digital system to a large extent. To improve the computation accuracy, a Booth-encoded sign-digit-based conditional probability estimation approach is proposed. A symmetric error distribution is obtained by taking the sign bit of the Booth-encoded multiplier into consideration when applying the conditional probability. In addition, a more generalized mux-based estimation method is formulated for the circuit implementation, which reduces the delay time and power dissipation. Simulation results show that the proposed multiplier exhibits the best computation accuracy with the least energy per operation. It performs even better for those operand lengths that are not multiples of 4. The maximum reduction on energy-delay-error product can reach 14.8% compared with all its contenders among various operand lengths.
Finding effectors in social networks

Assume a network (V,E) where a subset of the nodes in V are active. We consider the problem of selecting a set of k active nodes that best explain the observed activation state, under a given information-propagation model. We call these nodes effectors. We formally define the k-Effectors problem and study its complexity for different types of graphs. We show that for arbitrary graphs the problem is not only NP-hard to solve optimally, but also NP-hard to approximate. We also show that, for some special cases, the problem can be solved optimally in polynomial time using a dynamic-programming algorithm. To the best of our knowledge, this is the first work to consider the k-Effectors problem in networks. We experimentally evaluate our algorithms using the DBLP co-authorship graph, where we search for effectors of topics that appear in research papers.
Virulence Factors for Hemolytic Uremic Syndrome, Denmark

We present an analysis of strain and patient factors associated with the development of bloody diarrhea and hemolytic uremic syndrome (HUS) among Shiga toxin-producing Escherichia coli (STEC) patients registered in Denmark in a 6-year period. Of 343 STEC patients, bloody diarrhea developed in 36.4% and HUS in 6.1%. In a multivariate logistic regression model, risk factors for bloody diarrhea were the eae and stx2 genes, O groups O157 and O103, and increasing age. Risk factors for HUS were presence of the stx2 (odds ratio [OR] 18.9) and eae (OR undefined) genes, being a child, and having bloody diarrhea. O group O157, although associated with HUS in a univariate analysis (OR 4.0), was not associated in the multivariate analysis (OR 1.1). This finding indicates that, rather than O group, the combined presence of the eae and stx2 genes is an important predictor of HUS.
The western diet and lifestyle and diseases of civilization

Correspondence: Pedro Carrera-Bastos R. Gorgel do Amaral, No. 5, 1 e; Lisbon 1250-119, Portugal Tel +351 967 088 799 email pedro. carrera_bastos@med.lu. se Abstract: It is increasingly recognized that certain fundamental changes in diet and lifestyle that occurred after the Neolithic Revolution, and especially after the Industrial Revolution and the Modern Age, are too recent, on an evolutionary time scale, for the human genome to have completely adapted. This mismatch between our ancient physiology and the western diet and lifestyle underlies many so-called diseases of civilization, including coronary heart disease, obesity, hypertension, type 2 diabetes, epithelial cell cancers, autoimmune disease, and osteoporosis, which are rare or virtually absent in hunter–gatherers and other non-westernized populations. It is therefore proposed that the adoption of diet and lifestyle that mimic the beneficial characteristics of the preagricultural environment is an effective strategy to reduce the risk of chronic degenerative diseases.
Individual Diversity of Functional Brain Network Economy

On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.
Morphotropic Phase Boundary in Ferroelectric Materials

Certain solid solutions of perovskite-type ferroelectrics show excellent properties such as giant dielectric response and high electromechanical coupling constant in the vicinity of the morphotropic phase boundary (MPB). These materials are of importance to applications such as electrostrictive actuators and sensors, because of the large dielectric and piezoelectric constants (Jaffe et al., 1971; Sawaguchi, 1953; Kuwata et al., 1982; Newnham, 1997). The term “morphotropic” was originally used to refer to refer to phase transitions due to changes in composition (Ahart et al., 2008). Nowadays, the term ‘morphotropic phase boundaries’ (MPB) is used to refer to the phase transition between the tetragonal and the rhombohedral ferroelectric phases as a result of varying the composition or as a result of mechanical pressure (Jaffe et al., 1954; Yamashita, 1994; Yamamoto & Ohashi, 1994; Cao & Cross, 1993; Amin et al., 1986; Ahart et al., 2008). In the vicinity of the MPB, the crystal structure changes abruptly and the dielectric properties in ferroelectric (FE) materials and the electromechanical properties in piezoelectric materials become maximum. The common ferroelectric materials used for MPB applications is usually complexstructured solid solutions such as lead zirconate titanate PbZr1−xTixO3 (PZT) and Lead Magnesium niobate-lead titanate (1-x)PbMg1/3Nb2/3O3-xPbTiO3), shortly known as PMNPT. For example, PZT is a perovskite ferroelectrics which has a MPB between the tetragonal and rhombohedral FE phases in the temperature-composition phase diagram. However, these materials are complex-structured and require a complicated and costly process to prepare its solid solutions. Furthermore, the study of the microscopic origin of its properties is very complicated. Recently, scientists started to pay attention to the MPB in simple-structured pure compound ferroelectric materials such as ferroelectric oxides. For example, a recent experimental study on lead titanate proved that PbTiO3 can display a large MPB under pressure (Ahart et al., 2008). These experimental results even showed richer phase diagrams than those predicted by first-principle calculations. Therefore, it is of particular importance to study the
Rapid Direct Action of Estradiol in GnRH Neurons: Findings and Implications

Estradiol plays a pivotal role in the control of gonadotropin-releasing hormone (GnRH) neuronal function and female reproduction. While positive and negative feedback actions of estradiol that enhance and suppress release of GnRH and LH are primarily mediated through estrogen receptor alpha located in interneurons, a series of recent studies in our laboratory indicate that rapid excitatory actions of estradiol also directly modify GnRH neuronal activity. We observed this phenomenon in cultured primate GnRH neurons, but similar rapid direct actions of estradiol are also described in cultured GnRH neurons and green fluorescent protein-labeled GnRH neurons of mice. Importantly, rapid direct action of estradiol in GnRH neurons is mediated through membrane or membrane associated receptors, such as GPR30, STX-sensitive receptors, and ERβ. In this review, possible implications of this rapid estradiol action in GnRH neurons are discussed.
Molecular Profiling of Multiple Human Cancers Defines an Inflammatory Cancer-Associated Molecular Pattern and Uncovers KPNA2 as a Uniform Poor Prognostic Cancer Marker

Background Immune evasion is one of the recognized hallmarks of cancer. Inflammatory responses to cancer can also contribute directly to oncogenesis. Since the immune system is hardwired to protect the host, there is a possibility that cancers, regardless of their histological origins, endow themselves with a common and shared inflammatory cancer-associated molecular pattern (iCAMP) to promote oncoinflammation. However, the definition of iCAMP has not been conceptually and experimentally investigated. Methods and Findings Genome-wide cDNA expression data was analyzed for 221 normal and 324 cancer specimens from 7 cancer types: breast, prostate, lung, colon, gastric, oral and pancreatic. A total of 96 inflammatory genes with consistent dysregulation were identified, including 44 up-regulated and 52 down-regulated genes. Protein expression was confirmed by immunohistochemistry for some of these genes. The iCAMP contains proteins whose roles in cancer have been implicated and others which are yet to be appreciated. The clinical significance of many iCAMP genes was confirmed in multiple independent cohorts of colon and ovarian cancer patients. In both cases, better prognosis correlated strongly with high CXCL13 and low level of GREM1, LOX, TNFAIP6, CD36, and EDNRA. An “Inflammatory Gene Integrated Score” was further developed from the combination of 18 iCAMP genes in ovarian cancer, which predicted overall survival. Noticeably, as a selective nuclear import protein whose immuno-regulatory function just begins to emerge, karyopherin alpha 2 (KPNA2) is uniformly up-regulated across cancer types. For the first time, the cancer-specific up-regulation of KPNA2 and its clinical significance were verified by tissue microarray analysis in colon and head-neck cancers. Conclusion This work defines an inflammatory signature shared by seven epithelial cancer types and KPNA2 as a consistently up-regulated protein in cancer. Identification of iCAMP may not only serve as a novel biomarker for prognostication and individualized treatment of cancer, but also have significant biological implications.
Risk for depression and neural responses to fearful facial expressions of emotion.

BACKGROUND Depression is associated with neural abnormalities in emotional processing. AIMS This study explored whether these abnormalities underlie risk for depression. METHOD We compared the neural responses of volunteers who were at high and low-risk for the development of depression (by virtue of high and low neuroticism scores; high-N group and low-N group respectively) during the presentation of fearful and happy faces using functional magnetic resonance imaging (fMRI). RESULTS The high-N group demonstrated linear increases in response in the right fusiform gyrus and left middle temporal gyrus to expressions of increasing fear, whereas the low-N group demonstrated the opposite effect. The high-N group also displayed greater responses in the right amygdala, cerebellum, left middle frontal and bilateral parietal gyri to medium levels of fearful v. happy expressions. CONCLUSIONS Risk for depression is associated with enhanced neural responses to fearful facial expressions similar to those observed in acute depression.
Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, “dynamic global signal regression” (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional “static” global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.
Does Heel Height Cause Imbalance during Sit-to-Stand Task: Surface EMG Perspective

The purpose of this study was to determine whether electromyography (EMG) muscle activities around the knee differ during sit-to-stand (STS) and returning task for females wearing shoes with different heel heights. Sixteen healthy young women (age = 25.2 ± 3.9 years, body mass index = 20.8 ± 2.7 kg/m2) participated in this study. Electromyography signals were recorded from the two muscles, vastus medialis (VM) and vastus lateralis (VL) that involve in the extension of knee. The participants wore shoes with five different heights, including 4, 6, 8, 10, and 12 cm. Surface electromyography (sEMG) data were acquired during STS and stand-to-sit-returning (STSR) tasks. The data was filtered using a fourth order Butterworth (band pass) filter of 20–450 Hz frequency range. For each heel height, we extracted median frequency (MDF) and root mean square (RMS) features to measure sEMG activities between VM and VL muscles. The experimental results (based on MDF and RMS-values) indicated that there is imbalance between vasti muscles for more elevated heels. The results are also quantified with statistical measures. The study findings suggest that there would be an increased likelihood of knee imbalance and fatigue with regular usage of high heel shoes (HHS) in women.