Miracle-dz/UniFAR-FTUs huggingface dataset:
Left Ventricular Vertical Run‐Length Nonuniformity MRI Adds Prognostic Value to MACE in Patients with End‐Stage Renal Disease

Vertical run‐length nonuniformity (VRLN) is a texture feature representing heterogeneity within native T1 images and reflects the extent of cardiac fibrosis. In uremic cardiomyopathy, interstitial fibrosis was the major histological alteration. The prognostic value of VRLN in patients with end‐stage renal disease (ESRD) remains unclear.
LOAD FORECASTING BASED ON IMPROVED GM(1,1) MODEL

By processing the original data and modifying the initial condition of differential equations, an improved GM(1,1) model based on the modification of boundary condition is established and applied to the load forecasting of regional power network. The calculation results of actual examples show that the improved model can exponentially modify the forecasting values from generating sequences and original sequences, so the forecasting error can be decreased significantly and the forecasting accuracy can be improved.
Reference Values for Blood Pressure of Healthy Schoolchildren in Shiraz (Southern Iran) using Quantile Regression

Background: The Reference values of systolic and diastolic Blood Pressure (BP) levels of school children aged 6–11 years by two different analytic strategies are presented and compared. Methods: From the cross-sectional study a total of 2064 children (52.3% boys and 47.7% girls) aged 6–11 years living in Shiraz (southern Iran) and considering their sex and height were used for this analysis. Polynomial Regression (PR) and Quantile Regression (QR) models based on Restricted Cubic Spline (RCS) were performed to calculate age and height specific reference ranges. To assess comparability of the two techniques, a chi-square goodness-of-fit within sex and age groups was preformed for each method. Results: Both statistical methods generated reference values of systolic and diastolic BP using data from apparently healthy children. Analysis of data by two approaches reflected an increase in BP measurements with age and height in both sexes based on a nonlinear manner up to age 11. We found 50th and 95th percentile differences by two methods in BP level between the tallest and the shortest individuals, ranging from 2-7 mmHg. Conclusion: Using the QR model based on RCS offered the most flexible and better fit than PR model. The advantages of the QR led to a better adaptation of reference limits to the original data. This statistical approach might be preferable for the calculation of reference ranges in particular by non-normal distributed variables. Our results might help clinicians reach a consensus on the definition of hypertension in Iranian children living in Shiraz, south of Iran.
Stability and Power Analysis of Schmitt Trigger Based Low Power SRAM Bit-Cell Using CMOS and CNTFET Technology at 22nm Technology Node

SRAM (Static Random Access Memory) is an essential component of memory devices such as laptops, phones, etc., which act as a semiconductor memory. The “Carbon Nanotube Field Effect Transistor (CNTFET)” is silicon associated high-stability, low-power device with excellent performance. CNTFET has been verified to be very advantageous for Very large-scale integration circuit designs in the nanoscale range because of its remarkable properties of metal oxide semiconductor field effect transistor (MOSFET). The material was brought to light because of its genuinely incredible electrochemical performance. Carbon nanotubes have unique properties such as high charge carrier mobility, high voltage, small footprint, exceptionally short and high control over pulse duration, and large current densities. In traditional MOSFET, bulk silicon is used, which has high leakage current and high field-effect; thus, CNTFET has been used as an alternative in recent years. When compared to the 10T CNTFET SRAM Bit cell is designed using HSPICE Tool in 22nm technology. Long-term stability and significant process variable changes are significant challenges with nanoscale SRAM cells.
Investigation of 4H-SiC UMOSFET Architectures for High Voltage and High Speed Power Switching Applications

A comparative TCAD (Technology Computer Aided Design) simulation study of various 4H-SiC trench gate MOSFET (Metal Oxide Semiconductor Field Effect Transistor) (or U-shaped trench gate MOSFET abbreviated for UMOSFET) architectures for high voltage and high-speed switching applications is reported. The DC (Direct Current) and AC (Alternating Current) characteristics of the different trench gate structures are investigated. Particularly, compared to conventional 4H-SiC UMOSFETs, the breakdown voltage of the UMOSFET having a p-type implanted bottom shield is increased by 44%. However, due to the extra JFET (Junction Field Effect Transistor) region, the specific on resistance also increases by 6%. Furthermore, under 1000 V drain bias, the peak electric field at the bottom oxide of the shielded trench gate is below 0.3 MV/cm. In contrast, the peak electric field of conventional UMOSFETs can be as high as 8 MV/cm, which might cause reliability issues. On the other hand, when the bottom oxide thickness of the trench gate is increased, the UMOSFET exhibits 22% less total gate charge, leading to 76% and 71% shorter switching delay time, compared to conventional UMOSFETs and bottom shield UMOSFETs, respectively. As revealed by the simulation results, the UMOSFETs with the p-type implanted bottom shield or thick bottom oxide are advantageous for high voltage and high-speed power switching applications.
Simultaneous confidence bands for nonparametric, polynomial-trigonometric regression estimators

The goal of this paper is to present a method for construction of simultaneous Scheff´e confidence bands for nonparametric prediction functions. The family of nonparametric functions studied here are of the polynomial-trigonometric series type, with estimation of the model parameters undertaken in the standard least-squares framework. After the method is set forth the performance of the procedure is studied via Monte-Carlo simulations.
Comparison study of sparse data-driven soft tissue registration: preliminary results from the image-to-physical liver registration sparse data challenge

The ability to accurately account for intraoperative soft tissue deformations has been a longstanding barrier to efficacious translation of image-guided frameworks into abdominal interventions. In surgical applications, few data acquisition systems are amenable to stringent operative workflow constraints, and many are too costly for widespread adoption. Consequently, computational methods for surgical guidance based on sparse data obtained over the organ surface have become prevalent within approaches for image-to-patient alignment of soft tissue. However, the sparse data environment presents an especially challenging algorithmic landscape for accurately inferring deformable anatomical alignments between preoperative and intraoperative organ states from incomplete information sources. This work, presented as a preliminary conclusion to the image-to-physical liver registration sparse data challenge introduced at SPIE Medical Imaging 2019, seeks to characterize the potential for sparse data registration algorithms to achieve high fidelity predictions of intraoperative organ deformations from sparse descriptors of organ surface shape. A total of seven rigid and nonrigid biomechanical and deep learning registration techniques are compared, and the findings suggest that the family of biomechanically simulated boundary condition reconstruction techniques offers a promising opportunity for accurately estimating intraoperative organ deformation states from sparse intraoperative point clouds. Over a common dataset of 112 registration scenarios, this family of deformable registration techniques was found to outperform globally optimal rigid registrations, was robust to varying degrees of surface data coverage, and maintained good performance under added sources of measurement noise. Further analysis investigates error correlations among methods to illuminate sparse data performance within state-of-the-art image-to-physical registration algorithms.
The impact of wearing a KN95 face mask on human brain function: evidence from resting state functional magnetic resonance imaging

Background Face masks are widely used in daily life because of the COVID-19 pandemic. The objective of this study was to explore the impact of wearing face masks on brain functions by using resting-state functional MRI (RS-fMRI). Methods Scanning data from 15 healthy subjects (46.20 ± 6.67 years) were collected in this study. Each subject underwent RS-fMRI scans under two comparative conditions, wearing a KN95 mask and natural breathing (no mask). The amplitude of low frequency fluctuation (ALFF) and functional connectivity under the two conditions were analyzed and then compared using the paired t-test. Results Compared with those of the no-mask condition, the ALFF activities when wearing masks were increased significantly in the right middle frontal gyrus, bilateral precuneus, right superior marginal gyrus, left inferior parietal gyrus, and left supplementary motor area and decreased significantly in the anterior cingulate gyrus, right fusiform gyrus, left superior temporal gyrus, bilateral lingual gyrus, and bilateral calcarine cortex (p < 0.05). Taking the posterior cingulate cortex area as a seed point, the correlations with the occipital cortex, prefrontal lobe, and motor sensory cortex were sensitive to wearing masks compared with not wearing masks (p < 0.05). Taking the medial prefrontal cortex region as a seed point, the functional connectivity with the bilateral temporal lobe, bilateral motor sensory cortex, and occipital lobe was influenced by wearing a KN95 mask (p < 0.05). Conclusion This study demonstrated that wearing a KN95 face mask can cause short-term changes in human resting brain function. Both local neural activities and functional connectivity in brain regions were sensitive to mask wearing. However, the neural mechanism causing these changes and its impact on cognitive function still need further investigation.
Research made simple: an introduction to feminist research

10.1136/ebnurs-2023-103749 Writing an article for ‘Research Made Simple’ on feminist research may at first appear slightly oxymoronic, given that there is no agreed definition of feminist research, let alone a single definition of feminism. The literature that examines the historical and philosophical roots of feminism(s) and feminist research is vast, extends over several decades and reaches across an expanse of varying disciplines. Trying to navigate the literature can be daunting and may, at first, appear impenetrable to those new to feminist research. There is no ‘How To’ in feminist research. Although feminists tend to share the same common goals, their interests, values and perspectives can be quite disparate. Depending on the philosophical position they hold, feminist researchers will draw on differing epistemologies (ways of knowing), ask different questions, be guided by different methodologies and employ different methods. Within the confines of space, this article will briefly outline some of the principles of feminist research. It will then turn to discuss three established epistemologies that can guide feminist research (although there are many others): feminist empiricism, feminist standpoint and feminist postmodernism.
Numerical simulation and optimization of turbulent nanofluids in a three-dimensional wavy channel

ABSTRACT In this study, numerical calculations by single- and two-phase models of nanofluid turbulent forced convection in a three-dimensional wavy channel with uniform wall temperature are investigated. The numerical results for the Nusselt number ratio (Nu/Nu0) show that the heat transfer performance of a symmetric wavy channel performs better than that of an in-line wavy channel. The multi-parameter constrained optimization procedure integrating the design of experiments (DOEs), response surface methodology (RSM), genetic algorithm (GA), and computational fluid dynamics (CFD) is proposed to design the nanofluid turbulent convection of the three-dimensional wavy channel.
From Data to Theory: Understanding the Role of Naive Learners in Cultural Change

A change to a population’s social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover— or the repeated replacement of individuals. Experimental data has led to the hypothesis that naive learners can drive cultural evolution by being better samplers, although this hypothesis has only been expressed verbally. We conduct a formal exploration of this hypothesis using a generative model that concurrently simulates its two key ingredients: social transmission and reinforcement learning. We explore how variation in turnover influences changes in the distributions of cultural behaviors over long and short time-scales. We simulate competition between a high and low reward behavior, while varying turnover magnitude and tempo. We find optimal turnover regimes that amplify the production of higher reward behaviors. We also find that these optimal regimes result in a new population composition, where fewer agents which know both behaviors, and more agents know only the high reward behavior. These two effects depend on network size, density, behavioral transmissibility, and characteristics of the learners. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability, identified by previous experimental studies. By moving from experimental data to theory, we illuminate an under-discussed generative process arising from an interaction between social dynamics and learning that can lead to changes in cultural behavior.
The phototrophic bacteria Rhodomicrobium spp. are novel chassis for bioplastic production

Polyhydroxybutyrate (PHB) is a bio-based, biodegradable alternative to petroleum-based plastics. PHB production at industrial scales remains infeasible, in part due to insufficient yields and high costs. Addressing these challenges requires identifying novel biological chassis for PHB production and modifying known biological chassis to enhance production using sustainable, renewable inputs. Here, we take the former approach and present the first description of PHB production by two prosthecate photosynthetic purple non-sulfur bacteria (PNSB), Rhodomicrobium vannielii and Rhodomicrobium udaipurense. We show that both species produce PHB across photoheterotrophic, photoautotrophic, photoferrotrophic, and photoelectrotrophic growth conditions. Both species show the greatest PHB titers during photoheterotrophic growth on butyrate with dinitrogen gas as a nitrogen source (up to 44.08 mg/L), while photoelectrotrophic growth demonstrated the lowest titers (up to 0.13 mg/L). These titers are both greater (photoheterotrophy) and less (photoelectrotrophy) than those observed previously in a related PNSB, Rhodopseudomonas palustris TIE-1. On the other hand, we observe the highest electron yields during photoautotrophic growth with hydrogen gas or ferrous iron electron donors, and these electron yields were generally greater than those observed previously in TIE-1. These data suggest that non model organisms like Rhodomicrobium should be explored for sustainable PHB production and highlights utility in exploring novel biological chassis.
The geometry of normal tissue and cancer manifolds

Recently, it was shown that both the normal states and their respective tumors span disjoint manifolds in gene expression space. By processing gene expression data we show that, in general, for the vectors defining the centers of cloud samples: 1. The closest tumor to a given normal state is its respective tumor, 2. Two normal states define quasi-orthogonal directions, 3. A tumor may have a projection onto its corresponding normal state, but it is quasi-orthogonal to all others and 4. The cancer manifold is roughly obtained by translating the normal tissue manifold along an orthogonal direction defined by the cancer projection axis.
Cutin-Derived Oligomers Act as Damage-Associated Molecular Patterns in Arabidopsis thaliana

The cuticle constitutes the outermost defensive barrier of most land plants. It comprises a polymeric matrix – cutin, surrounded by soluble waxes. Moreover, the cuticle constitutes the first line of defense against pathogen invasion, while also protecting the plant from many abiotic stresses. Aliphatic monomers in cutin have been suggested to act as immune elicitors in plants. This study analyses the potential of tomato cutin oligomers to act as damage-associated molecular patterns (DAMPs) able to induce a rapid immune response in the model plant Arabidopsis. Cutin oligomeric mixtures led to Ca2+ influx and MAPK activation in Arabidopsis. Comparable responses were measured for cutin, which was also able to induce a reactive oxygen species (ROS) burst. Furthermore, treatment of Arabidopsis with cutin oligomers resulted in a unique transcriptional reprogramming profile, having many archetypal features of pattern-triggered immunity (PTI). Targeted spectroscopic and spectrometric analyses of the cutin oligomers suggest that the elicitors compounds consist mostly of two up to three 10,16-dihydroxyhexadecanoic acid monomers linked together through ester bonds. This study demonstrates that cutin breakdown products can act as DAMPs; a novel class of elicitors deserving further characterization.
GbyE: A New Genome Wide Association and Prediction Model based on Genetic by Environmental Interaction

Nowadays, studies on genetic by environment interactions (G×E) are receiving increasing attention because of its theoretical and practical importance in explaining individual behavioral traits. Use information from different environments to improve the statistical power of genome-wide association and prediction in the hope of obtaining individuals with better breeding value is the most expedient way. However, there are significant challenges when performing genome-wide association studies (GWAS) and genomic selection (GS) using multiple environments or traits, mainly because most diseases and quantitative traits have numerous associated loci with minimal effects. Therefore, this study constructed a new genotype design model program (GbyE) for genome-wide association and prediction using Kronecker product, which can enhance the statistical power of GWAS and GS by utilizing the interaction effects of multiple environments or traits. The data of 282 maize, 354 yaks and 255 peaches were used to evaluate the power of the model at different levels of heritability and genetic correlation. The results show that GbyE can provide higher statistical power for the traditional GWAS and GS models in any heritability and genetic correlation, and can detect more real loci. In addition, GbyE has increased statistical power to three Bayesian models (BRR, BayesA, and BayesCpi). GbyE can make full use of multiple environment or trait informations to increase the statistical power of the model, which can help us understand the G×E and provide a method for predicting association loci for complex traits.
Comparing mental imagery experiences across visual, auditory, and other sensory modalities

Although mental imagery is often studied as a visual phenomenon, it can occur in any sensory modality. Given that mental images may recruit similar modality-specific neural systems to those which support veridical perception, the properties of mental images may be constrained by the modality in which they are experienced. Yet, little is known about how mental images are experienced at all, let alone how such experiences may vary depending on the modality in which they occur. Here we explored how mental images are experienced in different modalities using an extensive questionnaire. Mainly focusing on visual and auditory mental imagery, we surveyed participants on if and how they experienced their thought content in a sensory way when thinking about the appearance or sound of the letter “O”. Specifically, we investigated temporal properties of imagined content (e.g. onset latency, duration), as well as spatial properties (e.g. apparent location), effort (e.g. ease, spontaneity, control), dependence on body movements (e.g. eye movements), interactions between real and imagined content (e.g. inner speech during reading), the perceived normality of imagery experiences, and how participants labeled their own experiences. Participants also ranked their mental imagery experiences in the five traditional sensory modalities and reported on the involvement of each modality during their thoughts, imagination, and dreams. Confidence ratings were taken for every answer recorded. Overall, visual and auditory experiences tended to dominate mental events relative to other sensory modalities. However, most people reported that auditory mental imagery was superior to visual mental imagery on almost every metric tested, except with respect to spatial properties. Our findings suggest that mental images are restrained in a similar matter to other modality-specific sensory processes in the brain. Broadly, our work also provides a wealth of insights and observations into how mental images are experienced by individuals, acting as a useful resource for future investigations.
Gas-Liquid Two-Phase Flow Monitoring Using Sub-THz Radar Imaging

Two-phase flow monitoring is widely present in various industrial processes. The most popular methods used to estimate flow parameters can be divided into the noncontact and the contact -based modalities. Conductance-based and capacitive sensors are the most popular contact-based techniques used to provide flow parameter estimations. Among the noncontact sensors, optical-based systems are commonly utilized for multiphase flow classification. However, contact-based systems require specific proprieties from the analyzed fluid mixture such as high conductivity or low conductivity and they must be attached to the tube, so accurate measurements can be provided. On the other hand, optical devices lack robustness against ambient light conditions and might require complex computational processing pipelines. In this work, gas-liquid two-phase flow monitoring based on radar technology is proposed. Characteristic flow velocity distributions associated with different two-phase flows are remotely recognized by studying the reflected sub-THz signals that are phase-modulated by the moving gas-liquid interface. Experimental results confirm the feasibility of the proposed scheme for the identification of different gas-liquid two-phase flows using sub-THz radar imaging.
Approaching 6G Use Case Requirements with Multicasting

The shift towards 6G networks is expected to be accompanied by an increased capability to support group-oriented services, such as extended reality and holographic communications, in many different contexts, from high-precision manufacturing to healthcare and remote control. This range of applications will rely heavily on multicast and mixed multicast-broadcast delivery modes. This article focuses on the technological perspectives of 6G multicasting, highlighting requirements, challenges, and enabling solutions. We then run a simulation campaign to test practical solutions and draw conclusive remarks for forthcoming 6G multicast systems.