Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-dimensional modeling and analysis of human gait with application to the gait of transtibial prosthesis users

This dissertation uses a robotics-inspired approach to dev elop a low-dimensional forward dynamic model of normal human walking. The analytical m odel captures the dynamics of walking over a complete gait cycle in the sagittal p l ne. The model for normal walking is extended to model asymmetric gait. The asymmetri c model is applied to study the gait dynamics of a transtibial prosthesis user. Modeling human walking is complex because walking involves (i) the body’s many degrees of freedom (DOF), (ii) constraints that change, and (i ii) intermittent contact with the environment that may be impulsive. Complex forward dynamic m odels that attempt to capture details such as joints with multiple DOF, musculature, etc., are analytically intractable; it is impossible to describe the model’s behavior in mathema tically manageable terms because of the enormous number of variables and redundancies i volved. Observation of human walking from a systems point of view reveals that the hu man body coordinates its many DOF in a parsimonious manner to accomplish the task of mo ving the body’s center of mass from one point to another. This dissertation’s appro ach exploits this parsimony to derive an analytically tractable model that has the minimum DOF necessary to describe the task of walking in the sagittal plane. The low-dimensional hybrid model is derived as an exact subdynamic of a higherdimensional anthropomorphic hybrid model. The hybrid natu re is the result of continuous sub-models of single support (SS) and double support (DS), a nd discrete maps that model ii the transitions from SS to DS and DS to SS. The modeling is vali dated using existing gait data. To extend the clinical usefulness of the modeling approach, the model for normal walking is extended to model asymmetric gait. The asymmetric mod el can accommodate asymmetries in the parameters and joint motions of the left and ri ght legs. The asymmetric model is applied to analyze the gait dynamics of a transtibial pros thesis user. Cost functions are used to evaluate the effect of varying prosthetic alignment , prosthesis mass distribution, and prosthetic foot stiffness. The results agree well with clin i al observations and the results of related gait studies reported in the literature.
Study on a Voltage Estimation of Tap‐Changing Transformer in Consideration of SVC Introduction

Recent years, installation of photovoltaic (PV) system is active by the environmental problem and feed in tariff system. Due to PV's output fluctuation, the static var compensator (SVC) made by power electronics technology is planning to install in distribution system. The SVC is expected for maintain the voltage. But SVC differs in a theory of operation of the existing voltage controller load ratio control transformer (LRT) and step voltage regulator (SVR). Because the SVC operation makes the system impedance changes, the existing voltage controller may not operate effectively. In this study, the intelligent‐control method which used the radial basis function (RBF) network is proposed. This method can reduce the effects of the SVC operation by including the performance characteristics of SVC in the conventional method. An advantage of unnecessary arrangement attention of the SVC setup node and easy cooperation with the existing equipment is obtained by applying this method.
Streamlined Development of Fully Human Antibody Fragments for ImmunoPET Imaging Using Phage Display Technology

Exploiting antibodies' highly specific binding affinities to various biomarkers, immuno-positron emission tomography (immunoPET) provides a non-invasive way to obtain whole body information on in vivo molecular events that may be crucial for diagnosis and prognosis, which is increasingly important for diagnostics and prognostics. Small, fully human or humanized antibody fragments are preferred for immunoPET imaging probe development due to their favorable pharmacokinetic properties and low risk of immunogenicity. Phage display technology provides a convenient approach to rapidly generate fully human antibody candidates, showing the potential to accelerate and streamline the development process of antibody fragment based immunoPET imaging probes, which is strongly needed as increasing numbers of targeted therapies are being developed every year. Chapter 2 and Chapter 3 describe the use of a traditional fully human scFv phage display library for developing anti-MET antibody fragments for both immunoPET imaging and potential therapeutic application. Multiple fully human anti-MET scFv clones were isolated and reformatted. Three cys-diabody clones showed high affinities (0.7 to 5.1 nM) and inhibitory effect on MET over-expressing cells. The H2 cys-diabody and minibody were able to distinguish a MET over-expressing gefitinib resistant tumor from the parental tumor at as early as 4 hours post injection, indicating the potential of same day imaging for patient stratification. To further exploit the benefit of the phage display technology and streamline the antibody fragment devlopment, as described in Chapter 4 and Chapter 5, two novel fully human scFv phage display libraries with customized linkers and restriction sites were constructed to simplify the antibody fragment reformatting process. The libraries were used for selections against several different targets and successfully generated multiple positive clones. ScFvs from these libraries were rapidly reformatted into diabodies using the restriction sites in the customized linkers. Size exclusion chromatography analysis of these antibody fragments proved the shortened linkers can successfully induce dimerization for the majority of the diabody clones. Together, the studies presented in this dissertation highlight a successful example of the use of phage display technology for developing fully human antibody fragments for immunoPET imaging and how phage display libraries can be improved to further streamline this process.
Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.
A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient’s health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.
Genetic loci associated with freezing tolerance in a European rapeseed ( Brassica napus L.) diversity panel identified by genome‐wide association mapping

Abstract Winter biotypes of rapeseed ( Brassica napus L.) require a vernalization treatment to enter the reproductive phase and generally produce greater yields than spring rapeseed. To find genetic loci associated with freezing tolerance in rapeseed, we first performed genotyping‐by‐sequencing (GBS) on a diversity panel consisting of 222 rapeseed accessions originating primarily from Europe, which identified 69,554 high‐quality single‐nucleotide polymorphisms (SNPs). Model‐based cluster analysis suggested that there were eight subgroups. The diversity panel was then phenotyped for freezing survival (visual damage and Fv/Fo and Fv/Fm) after 2 months of cold acclimation (5°C) and a freezing treatment (−15°C for 4 h). The genotypic and phenotypic data for each accession in the rapeseed diversity panel was then used to conduct a genome‐wide association study (GWAS). GWAS results showed that 14 significant markers were mapped to seven chromosomes for the phenotypes scored. Twenty‐four candidate genes located within the mapped loci were identified as previously associated with lipid, photosynthesis, flowering, ubiquitination, and cytochrome P450 in rapeseed or other plant species.
Diverse Novel Viruses Coinfecting the Tropical Ornamental Plant Polyscias balfouriana in China

The viromic profile of Polyscias balfouriana cv. Marginata, a perennial woody and ornamental plant, was determined using ribosomal RNA-depleted total RNA (rRNA-depleted totRNA) sequencing. Five viruses (i.e., polyscias mosaic virus, PoMV; one potential novel rhabdovirus; and three novel viruses of Betaflexiviridae and Closteroviridae) were detected and prevalence-surveyed in Hainan province, China. The genomes of polyscias capillovirus 1 (PCaV-1) and polyscias citrivirus 1 (PCiV-1) of family Betaflexiviridae were completed, and the genomes of polyscias crinivirus 1 (PCrV-1) of Closteroviridae were nearly completed lacking the 5′ and 3′ termini. PCaV-1 shares 68% genome nucleotide (nt) identity and 66% replicase (Rep) amino acid (aa) identity with homologues in apple stem grooving virus (ASGV). PCiV-1 shares 65% genome nt identity and 64% Rep aa identity with homologs in citrus leaf blotch virus (CLBV). Meeting the species demarcation criteria, PCaV-1 and PCiV-1 were considered to be new species in genera Capillovirus and Citrivirus, respectively. PCrV-1 shares high genome nt identity (62%), heat shock protein 70-like protein (HSP70h) and RNA-dependent RNA polymerase (RdRp) aa identity (78–80%) with homologues in tomato chlorosis virus (ToCV). We tentatively consider PCrV-1 to be an unclassified member of the Crinivirus genus. PoMV, PCaV-1, PCiV-1, and PCrV-1 are the prevalent viruses with >73% occurrence in the Xinglong Tropical Botanical Garden, Hainan, China.
Inhibition of In Vitro Clostridioides difficile Biofilm Formation by the Probiotic Yeast Saccharomyces boulardii CNCM I-745 through Modification of the Extracellular Matrix Composition

Clostridioides difficile is responsible for post-antibiotic diarrhea and most of the pseudomembranous colitis cases. Multiple recurrences, one of the major challenges faced in C. difficile infection (CDI) management, can be considered as chronic infections, and the role of biofilm formation in CDI recurrences is now widely considered. Therefore, we explored if the probiotic yeast Saccharomyces boulardii CNCM I-745 could impact the in vitro formation of C. difficile biofilm. Biomass staining and viable bacterial cell quantification showed that live S. boulardii exerts an antagonistic effect on the biofilm formation for the three C. difficile strains tested. Confocal laser scanning microscopy observation revealed a weakening and an average thickness reduction of the biofilm structure when C. difficile is co-incubated with S. boulardii, compared to the single-species bacterial biofilm structure. These effects, that were not detected with another genetically close yeast, S. cerevisiae, seemed to require direct contact between the probiotic yeast and the bacterium. Quantification of the extrapolymeric matrix components, as well as results obtained after DNase treatment, revealed a significant decrease of eDNA, an essential structural component of the C. difficile biofilm matrix, in the dual-species biofilm. This modification could explain the reduced cohesion and robustness of C. difficile biofilms formed in the presence of S. boulardii CNCM I-745 and be involved in S. boulardii clinical preventive effect against CDI recurrences.
Investigation of Value Trace Problem for C++ Programming Self-study of Novice Students

Presently, C++ programming has been taught in many universities around the world as the first object-oriented programming language for undergraduate students to start studying programming concepts and computer architecture. However, many of them may struggle in studying C++ programming due to the nature in the formal language. Thus, hands-on self-study tools can be effective. In previous, we have developed Java programming learning assistant system (JPLAS) for assisting self-study of Java programming. JPLAS offers the value trace problem (VTP) for novice students to study Java programming through code reading study. In a VTP instance, actual values of important variable or standard output messages in a given source code are questioned, where the correctness of each answer is checked through string matching. In this paper, we investigate the effectiveness of VTP for hands-on self-study of C++ programming by novice students. We generated 37 VTP instances for basic grammar concepts using source codes in textbooks or websites for C++ programming, and asked 46 students in three universities in three countries to solve them using the answer interface for JPLAS. The results suggest that most of the students are satisfactory, but some students need cares at early programming study stage.
Ageing- and AAA-associated differentially expressed proteins identified by proteomic analysis in mice

Background Abdominal aortic aneurysm (AAA) is a disease of high prevalence in old age, and its incidence gradually increases with increasing age. There were few studies about differences in the circulatory system in the incidence of AAA, mainly because younger patients with AAA are fewer and more comorbid nonatherosclerotic factors. Method We induced AAA in ApoE−/− male mice of different ages (10 or 24 weeks) and obtained plasma samples. After the top 14 most abundant proteins were detected, the plasma was analyzed by a proteomic study using the data-dependent acquisition (DDA) technique. The proteomic results were compared between different groups to identify age-related differentially expressed proteins (DEPs) in the circulation that contribute to AAA formation. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein–protein interaction (PPI) network analyses were performed by R software. The top 10 proteins were determined with the MCC method of Cytoscape, and transcription factor (TF) prediction of the DEPs was performed with iRegulon (Cytoscape). Results The aortic diameter fold increase was higher in the aged group than in the youth group (p < 0.01). Overall, 92 DEPs related to age and involved in AAA formation were identified. GO analysis of the DEPs showed enrichment of the terms wounding healing, response to oxidative stress, regulation of body fluid levels, ribose phosphate metabolic process, and blood coagulation. The KEGG pathway analysis showed enrichment of the terms platelet activation, complement and coagulation cascades, glycolysis/gluconeogenesis, carbon metabolism, biosynthesis of amino acids, and ECM-receptor interaction. The top 10 proteins were Tpi1, Eno1, Prdx1, Ppia, Prdx6, Vwf, Prdx2, Fga, Fgg, and Fgb, and the predicted TFs of these proteins were Nfe2, Srf, Epas1, Tbp, and Hoxc8. Conclusion The identified proteins related to age and involved in AAA formation were associated with the response to oxidative stress, coagulation and platelet activation, and complement and inflammation pathways, and the TFs of these proteins might be potential targets for AAA treatments. Further experimental and biological studies are needed to elucidate the role of these age-associated and AAA-related proteins in the progression of AAA.
The Development and Prospect of the New OFDM Radar

Orthogonal Frequency Division Multiplexing (OFDM) radar is a newly developed radar system, which uses the OFDM principle in communication systems, and has some unique advantages over traditional radar systems. This paper formulates the characteristics of OFDM radar, reviews thoroughly the Literatures on OFDM new radar system, summarizes the research achievements in the key research directions of signal properties and waveform design, signal processing, and new radar system, and finally analyzes the future development tendencies
Migrant remittances and consumption expenditure under rain-fed agricultural income: micro-level evidence from Ghana

ABSTRACT Using a repeated cross-section data set from Ghana for 1991/1992, 1998/1999, 2005/2006, 2012/2013 and 2016/17, and a Two-Stage Least Squares estimator, this paper investigates the effect of agricultural income on remittances and consumption expenditure. It is found that households in Ghana use remittances to protect themselves from decline in agricultural income due to rainfall failure. The results suggest that a 100 Ghana Cedis decrease in agricultural income leads to a 30 Ghana Cedis increase in remittances. The results further posit that rainfall-induced agricultural income changes affect total consumption and food expenditures of rural households. A 100 Ghana Cedis decrease in agricultural income due to rainfall failure leads to a 60 Ghana Cedis fall in total consumption expenditure, and 36 Ghana Cedis fall in food expenditure of rural households. Very poor households in rural areas are found to be more vulnerable to such rainfall-driven agricultural income changes.
Single Wavelength Simultaneous Optical Intensity-Polarization-Phase Modulation for Multi-Dimensional Optical Transmission

We propose a novel multi-dimensional optical transmission system based on a direct detection system. In the proposed system, quadrature phase shift keying (QPSK), binary polarized intensity rotational frequency shift keying (PIRBFSK), and binary differential phase shift keying (DBPSK) signals modulated by optical intensity, polarization, and phase are simultaneously transmitted for optical multi-dimensional transmission. The signal transmission performance of the proposed modulation technique and the possible interference of each dimension modulation were numerically and experimentally analyzed. The performance of the proposed system was verified using the bit error rate (BER), eye diagram, and spectrum of the received multi-dimensional signal.
Effect of Fabric Pattern and Color on Impression Evaluation of Textile Images Rendered by a Textile Simulator

The expression ability of a commercial textile simulator was investigated by simulating textile images taking into account the effect of yarn count, weave density, yarn types and weave. The performance of the simulator was investigated by evaluating and comparing impressions of real textiles and simulated images. The effect of pattern and textile color on the simulation performance was also studied. It was found that simulated images of textiles with more complicated and multicolored patterns were perceived to be more alike to the real textiles. Simulated images with large patterns were also evaluated and found to compare favorably to real textiles. Color was observed to have no effect on the impression of simulated textile images. Therefore, textile simulation in the textile trade is more effective on complex variations of textile patterns and colors.
Reconstruction and Localization of Tumors in Breast Optical Imaging via Convolution Neural Network based on Batch Normalization Layers

The Near-Infrared (NIR) Diffuse Optical Tomography (DOT) aims to reconstruct optical-property images of tissue and identify and localize breast tumors. This study has developed an efficient and fast DOT reconstruction method based on Deep Learning (DL) algorithm. The DL has already been applied in DOT application with limitations such as a limited dataset and the reconstruction of absorption coefficients only. We solve the problem of a limited dataset by generating a large dataset with multiple phantoms and inclusions at various positions. Moreover, a single Deep Neural Network (DNN) model has been designed to reconstruct absorption coefficients and scattering coefficients. For evaluation of the proposed DNN models, the phantom experimental dataset has been used where the results of the proposed DNN models outperform the results of the Tikhonov Regularization (TR) method and other Artificial Neural Networks (ANN). Moreover, it is shown that the DNN model with batch normalization layer results in improved spatial resolution, based on Contrast-and-Size Detail (CSD) analysis, as compared to DNN models without batch normalization layers.
Where are you really from? Understanding misrecognition from the experiences of French and Dutch Muslim women students

We investigate experiences of misrecognition through comparative focus groups with headscarf-wearing Muslim women students in France (N = 46) and in the Netherlands (N = 32). In both countries, women reported experiencing misrecognition across four interrelated dimensions: (1) totalising misrecognition, having their Muslim identity highlighted at the expense of other group affiliations; (2) membership misrecognition, having their national belonging denied; (3) content misrecognition, having negative characteristics associated with their religious identity, and (4) invisibility, having their voices unheard in society and/or their identities excluded from (public) professions. Participants conceptualised misrecognition as a product of deficient intergroup (Muslims vs. non-Muslims) contact and as being worse in France. French women felt relatively more invisible in the public sphere than their Dutch counterparts and perceived politicians across the political spectrum as an important source of misrecognition. These findings suggest that misrecognition is present in Europe, and potentially worse in France, raising the question about what measures might be taken to counter this form of group-based exclusion.