Miracle-dz/UniFAR-FTUs huggingface dataset:
An Inverse Optimization Approach Toward Testing Different Hypotheses of Vascular Homeostasis Using Image-based Models

Vascular mechanical homeostasis is a fundamental assumption in modeling vascular growth and remodeling. Meanwhile, it is a matter of debate, which mechanical quantity is responsible for governing the vascular growth and remodeling process. Recently, an optimization method has been proposed to estimate the optimal distributions of arterial wall thickness and anisotropy, such that a homeostatic condition is satisfied. In this study, the same optimization technique is utilized to investigate variations in the distribution of wall thickness and anisotropy due to different homeostatic assumptions, while two geometric models, one from a healthy aorta and one from a healthy internal iliac artery, are independently used. Prior to optimization, material constitutive parameters are estimated by fitting biaxial mechanical test data from human aorta and prescribed into the optimization process. Objective functions are set to restore both the original arterial geometry and the homeostatic state based on either intramural stress or cyclic circumferential stretch. Different homeostatic assumptions lead to distinct results for the optimal distributions of wall thickness and anisotropy. Namely, the cyclic stretch homeostatic assumption yields lower levels of the wall thickness as well as a less longitudinal variation of anisotropy. However, the arterial wall is consistently found to be thicker on the concave regions rather than on the convex regions. With further improvements in the application of the boundary conditions, the presented computational method seems promising to enhance our understanding of the vascular mechanical homeostasis and shall serve as a basis for conducting validation experiments.
Longitudinal course of disaster-related PTSD among a prospective sample of adult Chilean natural disaster survivors.

Background With an increasing number of individuals surviving natural disasters, it is crucial to understand who is most at risk for developing post-traumatic stress disorder (PTSD). The objective of this study was to prospectively examine the role that pre-existing psychopathology plays in developing PTSD after a disaster. Methods This study uses data from a prospective 5-wave longitudinal cohort (years 2003-11) of Chilean adults from 10 health centres ( N = 1708). At baseline, participants completed the Composite International Diagnostic Interview (CIDI), a comprehensive psychiatric diagnostic instrument. In 2010, the sixth most powerful earthquake on record struck Chile. One year later, a modified version of the PTSD module of the CIDI was administered. Marginal structural logistic regressions with inverse probability censoring weights were constructed to identify pre-disaster psychiatric predictors of post-disaster PTSD. Results The majority of participants were female (75.9%) and had a high-school/college education (66.9%). After controlling for pre-disaster PTSD, pre-existing dysthymia [odds ratio (OR) = 2.21; 95% confidence interval (CI) = 1.39-3.52], brief psychotic disorder (OR = 2.67; 95% CI = 1.21-5.90), anxiety disorders (not including PTSD; OR = 1.49; 95% CI = 1.27-1.76), panic disorder (OR = 2.46; 95% CI = 1.37-4.42), agoraphobia (OR = 2.23; 95% CI = 1.22-4.10), social phobia (OR = 1.86; 95% CI = 1.06-3.29), specific phobia (OR = 2.07; 95% CI = 1.50-2.86) and hypochondriasis (OR = 2.10; 95% CI = 1.05-4.18) were predictors of post-disaster PTSD. After controlling for pre-disaster anxiety disorders, dysthymia, and non-affective psychotic disorders, individuals with pre-disaster PTSD (vs those without pre-disaster PTSD) had higher odds of developing post-disaster PTSD (OR = 2.53; 95% CI = 1.37-4.65). Conclusions This is the first Chilean study to demonstrate prospectively that pre-disaster psychiatric disorders, independent of a prior history of other psychiatric disorders, increase the vulnerability to develop PTSD following a major natural disaster.
Survey of integrated‐circuit‐oscillator phase‐noise analysis

This tutorial distills the salient phase‐noise analysis concepts and key equations developed over the last 75 years relevant to integrated circuit oscillators. Oscillator phase and amplitude fluctuations have been studied since at least 1938 when Berstein solved the Fokker–Planck equations for the phase/amplitude distributions of a resonant oscillator.
NEUTRAMS: Neural network transformation and co-design under neuromorphic hardware constraints

With the recent reincarnations of neuromorphic computing comes the promise of a new computing paradigm, with a focus on the design and fabrication of neuromorphic chips. A key challenge in design, however, is that programming such chips is difficult. This paper proposes a systematic methodology with a set of tools to address this challenge. The proposed toolset is called NEUTRAMS (Neural network Transformation, Mapping and Simulation), and includes three key components: a neural network (NN) transformation algorithm, a configurable clock-driven simulator of neuromorphic chips and an optimized runtime tool that maps NNs onto the target hardware for better resource utilization. To address the challenges of hardware constraints on implementing NN models (such as the maximum fan-in/fan-out of a single neuron, limited precision, and various neuron models), the transformation algorithm divides an existing NN into a set of simple network units and retrains each unit iteratively, to transform the original one into its counterpart under such constraints. It can support both spiking neural networks (SNNs) and traditional artificial neural networks (ANNs), including convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). With the combination of these tools, we have explored the hardware/software co-design space of the correlation between network error-rates and hardware constraints and consumptions. Doing so provides insights which can support the design of future neuromorphic architectures. The usefulness of such a toolset has been demonstrated with two different designs: a real Complementary Metal-Oxide-Semiconductor (CMOS) neuromorphic chip for both SNNs and ANNs and a processing-in-memory architecture design for ANNs.
Nanoflower‐like Yttrium‐doped ZnO Photocatalyst for the Degradation of Methylene Blue Dye

Pure ZnO and Yttrium‐doped (Y‐doped) ZnO at various mol% with flower‐like nanostructures are synthesized by a microwave‐assisted sol–gel method, followed by investigating the morphologies, crystal structures, optical properties and photocatalytic performances. While the phase formations are detected by X‐ray diffraction technique, both scanning and transmission electron microscopy images clearly depict the flower‐like morphology of ZnO and Y‐doped ZnO samples. Formation of flower petals is from the nanoparticles that grew and connected by orientation attachment process. The flower‐like architecture is addressed in terms of an Ostwald ripening mechanism. The UV‐Vis absorption studies show enhanced absorption for the Y‐doped ZnO, whereas the photoluminescence spectra confirm the significance of sample defects in the photocatalytic degradation of organic pollutants. Effects of various experimental parameters such as the amount of photocatalysts, dye concentration and dopant concentration on the dye degradation are also optimized.
Patterns of Alcohol Use in Italian Emerging Adults: A Latent Class Analysis Study

Aims The goals of the current study were to identify latent classes of alcohol users among Italian emerging adults (18-33 years), examine differences in the class solution based on socio-demographic characteristics, and examine whether differences exist across classes in experiencing different types of alcohol-related negative consequences. Methods Participants (N = 5955; 62.72% female; mean age 27.19 years) were drawn from a pre-recruited Web panel designed to be representative of the Italian young adults (18-33 years) population. Latent class analysis (LCA) was used to identify common patterns of alcohol use. Results Four classes of drinking patterns were identified: (a) Current Nondrinkers (15%); (b) Weekend Non-Risky Drinkers (51%); (c) Weekend Risky Drinkers (20%); and (d) Daily Drinkers (13%). The number and type of classes did not differ across several demographic variables, although proportions within classes varied by sex, age, occupation status and geographic area. Weekend Risky Drinkers experienced the greatest number of alcohol-related negative consequences. Conclusions The great majority of the sample displayed drinking patterns with relatively infrequent involvement in risky drinking. Preventive interventions should, instead, target those who drink alcohol on weekends only, but show the greatest negative consequences. Short Summary Four classes of drinking patterns were identified in a large representative sample of Italian young adults. The great majority of the sample displayed drinking patterns with relatively little involvement in risky drinking, though those misusing alcohol at weekends were at greatest risk to experience alcohol-related negative consequences.
Tackling Spuriousness with Similarity

The goal of Semantic Parsing is to translate natural language utterances into logical forms that are executed against a target knowledge base. Ideally one would train the parser on the utterances and their annotations in formal language. However, annotating logical forms is a costly operation so work in recent years has suggested training the parser on examples labeled only with their correct output, and not with the correct formula that produced it (Clarke et al., 2010; Liang et al., 2011). This weak supervision facilitates the training process but comes at the price of learning from spurious formulas, logical forms which execute to the correct output but do not capture the meaning of the original utterance. Consider Table 1 and the utterance ”When did China host the Olympics? ”, its correct logical form should be ”value of Year where Country is China”, although the logical form ”first value of Year after 2004” will also yield the correct output, 2008. Both logical forms are consistent (execute to the correct output) but the latter is clearly spurious.
Stochastic Modeling of Expression Kinetics Identifies Messenger Half-Lives and Reveals Sequential Waves of Co-ordinated Transcription and Decay

The transcriptome in a cell is finely regulated by a large number of molecular mechanisms able to control the balance between mRNA production and degradation. Recent experimental findings have evidenced that fine and specific regulation of degradation is needed for proper orchestration of a global cell response to environmental conditions. We developed a computational technique based on stochastic modeling, to infer condition-specific individual mRNA half-lives directly from gene expression time-courses. Predictions from our method were validated by experimentally measured mRNA decay rates during the intraerythrocytic developmental cycle of Plasmodium falciparum. We then applied our methodology to publicly available data on the reproductive and metabolic cycle of budding yeast. Strikingly, our analysis revealed, in all cases, the presence of periodic changes in decay rates of sequentially induced genes and co-ordination strategies between transcription and degradation, thus suggesting a general principle for the proper coordination of transcription and degradation machinery in response to internal and/or external stimuli.
Symmetric Three-Way Doherty Power Amplifier for High Efficiency and Linearity

This brief presents a three-way Doherty power amplifier (DPA) with a symmetric structure in terms of the output power capacities between the carrier and peaking amplifiers for high efficiency and linearity. Based on the analysis for the efficiency peak at the output power back-off, a symmetric structure was adopted to have higher overall efficiency for the modulated signal. Through the optimized bias condition for the two peaking amplifiers, the proposed three-way DPA can be linearized in the wide output power range. To validate the proposed scheme, a three-way DPA was designed and implemented using a 60 W GaN–HEMT for the carrier amplifier and two 30 W GaN–HEMTs for the peaking amplifiers. Using a 2.14 GHz long-term evolution downlink signal with a peak-to-average power ratio of 6.5 dB, the implemented symmetric three-way DPA exhibited a drain efficiency (DE) of 33.7% and an ACLR of −38 dBc at an average output power of 41.4 dBm. Compared to the condition with the same bias for the peaking amplifiers, the DE and average output power were improved by 8.0% and 2.4 dB for the given ACLR of −38 dBc, respectively.
Dynamics of PFC power converters subject to time‐delayed feedback control

Power factor correction converters are power electronics circuits used as AC‐DC power supplies. These systems are well known to exhibit nonlinear phenomena such as subharmonic oscillations and chaotic regimes. These undesirable behaviors increase the THD and therefore can jeopardize enormously the system performances. In this paper, time delay feedback control is applied to stabilize a two‐stage power factor correction AC‐DC converter when it exhibits these instabilities under traditional controllers. This control technique introduces many advantages to the most and widely used average current mode control through widening the stability domain of the system. By appropriately selecting the time delay feedback gain and the time delay period, the undesirable subharmonic components are eliminated, whereas the desired ones remain unchanged. A harmonic balance approach is used for studying the dynamics of the system under the new control scheme and to obtain the stabilization domain. Copyright © 2010 John Wiley & Sons, Ltd.
The data not collected on community forestry

Conservation and development practitioners increasingly promote community forestry as a way to conserve ecosystem services, consolidate resource rights, and reduce poverty. However, outcomes of community forestry have been mixed; many initiatives failed to achieve intended objectives. There is a rich literature on institutional arrangements of community forestry, but there has been little effort to examine the role of socioeconomic, market, and biophysical factors in shaping both land‐cover change dynamics and individual and collective livelihood outcomes. We systematically reviewed the peer‐reviewed literature on community forestry to examine and quantify existing knowledge gaps in the community‐forestry literature relative to these factors. In examining 697 cases of community forest management (CFM), extracted from 267 peer‐reviewed publications, we found 3 key trends that limit understanding of community forestry. First, we found substantial data gaps linking population dynamics, market forces, and biophysical characteristics to both environmental and livelihood outcomes. Second, most studies focused on environmental outcomes, and the majority of studies that assessed socioeconomic outcomes relied on qualitative data, making comparisons across cases difficult. Finally, there was a heavy bias toward studies on South Asian forests, indicating that the literature on community forestry may not be representative of decentralization policies and CFM globally.
The Role of Ire1alpha in the Degradation of Insulin Mrna in Pancreatic Beta-cells Repository Citation the Role of Ire1a in the Degradation of Insulin Mrna in Pancreatic B-cells

The role of IRE1alpha in the degradation of insulin mRNA in pancreatic beta-cells" (2008). Abstract Background: The endoplasmic reticulum (ER) is a cellular compartment for the biosynthesis and folding of newly synthesized secretory proteins such as insulin. Perturbations to ER homeostasis cause ER stress and subsequently activate cell signaling pathways, collectively known as the Unfolded Protein Response (UPR). IRE1a is a central component of the UPR. In pancreatic b-cells, IRE1a also functions in the regulation of insulin biosynthesis.
High Incidence of Virulence Factors Among Clinical Enterococcus faecalis Isolates in Southwestern Iran

Background Over the past two decades, enterococci have emerged as an important agent responsible for hospital acquired infection. Several virulence factors contribute to the adherence, colonization, evasion of the host immune response, and pathogenicity and severity of the infection. Enterococcus faecalis is the most common and virulent species causing infections in hospitalized patients. The aim of the present study was to examine the prevalence of genes encoding virulence factors and antimicrobial resistance patterns of E. faecalis strains isolated from hospitalized patients in Shiraz, south west of Iran. Materials and Methods A total of 51 E. faecalis isolates from the urine, blood, pleural fluid, peritoneal fluid, eye discharge, endotracheal tube (ETT) and transjugular intrahepatic portosystemic shunt (TIPS) specimens of patients were identified by phenotypic and genotypic methods. Antimicrobial sensitivity tests and detection of virulence factors were performed using standard methods. Results The efa and asa1 were the most frequently detected gene (100%) among the isolates, followed by esp (94.1%), ace (90.2%), gelE (80.4%), cylA (64.7%), and hyl (51%). More than half of the isolates (52.9%) were high level gentamicin resistant (HLGR). Vancomycin resistance was observed among 23 (45.1%) isolates. The lowest antimicrobial activity was related to erythromycin (3.9%), tetracycline (5.9%) and ciprofloxacin (9.8%). No isolate was found resistant to fosfomycin and linezolid. Conclusion Our data indicated a high incidence of virulence factors among E. faecalis strains isolated from clinical samples. Colonization of drug resistant virulent isolates in hospital environment may lead to life threatening infection in hospitalized patients. Therefore, infection control procedures should be performed.
GeneHancer: genome-wide integration of enhancers and target genes in GeneCards

Abstract A major challenge in understanding gene regulation is the unequivocal identification of enhancer elements and uncovering their connections to genes. We present GeneHancer, a novel database of human enhancers and their inferred target genes, in the framework of GeneCards. First, we integrated a total of 434 000 reported enhancers from four different genome-wide databases: the Encyclopedia of DNA Elements (ENCODE), the Ensembl regulatory build, the functional annotation of the mammalian genome (FANTOM) project and the VISTA Enhancer Browser. Employing an integration algorithm that aims to remove redundancy, GeneHancer portrays 285 000 integrated candidate enhancers (covering 12.4% of the genome), 94 000 of which are derived from more than one source, and each assigned an annotation-derived confidence score. GeneHancer subsequently links enhancers to genes, using: tissue co-expression correlation between genes and enhancer RNAs, as well as enhancer-targeted transcription factor genes; expression quantitative trait loci for variants within enhancers; and capture Hi-C, a promoter-specific genome conformation assay. The individual scores based on each of these four methods, along with gene–enhancer genomic distances, form the basis for GeneHancer’s combinatorial likelihood-based scores for enhancer–gene pairing. Finally, we define ‘elite’ enhancer–gene relations reflecting both a high-likelihood enhancer definition and a strong enhancer–gene association. GeneHancer predictions are fully integrated in the widely used GeneCards Suite, whereby candidate enhancers and their annotations are displayed on every relevant GeneCard. This assists in the mapping of non-coding variants to enhancers, and via the linked genes, forms a basis for variant–phenotype interpretation of whole-genome sequences in health and disease. Database URL: http://www.genecards. org/
Analytical synthesis and comparison of voltage‐mode Nth‐order OTA‐C universal filter structures

Complementary single‐ended‐input operational transconductance amplifier (OTA)‐based filter structures are introduced in this paper. Through two analytical synthesis methods and two transformations, one of which is to convert a differential‐input OTA to two complementary single‐ended‐input OTAs, and the other to convert a single‐ended‐input OTA and grounded capacitor‐based one to a fully differential OTA‐based one, four distinct kinds of voltage‐mode nth‐order OTA‐C universal filter structures are proposed. TSMC H‐Spice simulations with 0. 35µm process validate that the new complementary single‐ended‐input OTA‐based one holds the superiority in output precision, dynamic and linear ranges than other kinds of filter structures. Moreover, the new voltage‐mode band‐pass, band‐reject and all‐pass (except the fully differential one) biquad structures, all enjoy very low sensitivities. Both direct sixth‐order universal filter structures and their equivalent three biquad stage ones are also simulated and validated that the former is not absolutely larger in sensitivity than the latter. Finally, a very sharp increment of the transconductance of an OTA is discovered as the operating frequency is very high and leads to a modified frequency‐dependent transconductance. Copyright © 2010 John Wiley & Sons, Ltd.
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.