Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Visualization Methods for Data-Driven Longitudinal Patient Summary

Digitization of health records has opened avenues for intensive research in the fields of health informatics. Power of machine learning, statistical analysis and visual analytics could be utilized to make optimal use of this information. The proposed project is to develop an interactive visualization tool that summarizes a patient’s medical history, highlighting all his/her important events based on the knowledge of similar patients. Given a set of patients with common conditions, statistical analysis can be used to develop models that prioritize features based on associations between features and condition-specific outcome measures.
The Role of Mouse Wnt9a in MA891 Breast Cancer Cell Proliferation

Wnts are powerful regulators of cell proliferation and differentiation. Activation of Wnt signalling in many tissues has also been associated with cancer. RNAi is a process of posttranscriptional gene silencing, by which dsRNA induces sequence-specific degradation of homologous gene transcripts. In many eukaryotes, expression of nuclear-encoded mRNA can be strongly inhibited by the presence of a small double-stranded RNA corresponding to exon sequences in the mRNA. In this study the pAVU6+27 vector, which has SalI and XbaI clone sites, was used to construct the siRNA expression vectors for mouse wnt9a. Four kinds of small interfering RNA inserts were designed, synthesized and visually tested for efficacy by in situ hybridization, the results demonstrated that dramatically reduced wnt9a signals were observed in the cells transfected with U6+27 cassettes with anti-wnt9a hairpin siRNA inserts compared with the untransfected. The results of flow cytometry analysis showed that the cell proliferation was promoted after lowering expression of the mouse wnt9a in MA891 cells by RNAi. All that suggest the expression level of mouse wnt9a in breast cancer MA891 cells may play a role in adjusting the rate of proliferation.
LRP4 antibodies are frequent in serum and CSF from amyotrophic lateral sclerosis patients (S34.004)

OBJECTIVE: To search for LRP4 antibodies in amyotrophic lateral sclerosis (ALS) patients because of the critical role of LRP4 in motor neuron function. BACKGROUND: ALS and myasthenia gravis (MG) are caused, respectively, by motor neuron degeneration and neuromuscular junction dysfunction. LRP4 is crucial in the development and function of neuromuscular junctions and motor neurons. Recently LRP4 autoantibodies have being detected by us and others in ~8[percnt] of the MG sera. DESIGN/METHODS: We developed a sensitive cell-based assay for LRP4 antibodies by which we studied the sera from 191 ALS patients from 3 countries. RESULTS: LRP4 autoantibodies were detected in sera from 37/191 (19.4[percnt]) ALS patients from Greece (12/51), Italy (12/53) and Israel (13/87), whereas only in 5/138 (3.6[percnt]) sera from patients with other neurological diseases and 0/90 sera from healthy controls. The Greek and Italian ALS cohorts were studied further: LRP4 antibodies were persistent in 5/6 tested patients for >10 months. Cerebrospinal fluid from 6/7 tested LRP4 antibody-seropositive ALS patients was also positive. No autoantibodies to AChR and MuSK were detected in ALS patients. No differences in clinical pattern were seen between ALS patients with or without LRP4 antibodies. Differences were seen in the female/male ratio between LRP4-seropositive ALS and MG patients (~1/1 versus ~3/1, respectively) and in the LRP4-antibody IgG subclass (IgG1 in most ALS and IgG1/IgG2 in most MG samples). CONCLUSIONS: We infer that LRP4 autoantibodies are involved in neurological manifestations affecting LRP4-containing tissues and are found more frequently in ALS than in MG. LRP4 antibodies may have a direct pathogenic activity in ALS by participating in the denervation process. The identification of LRP4 autoantibodies in ALS offers a potentially useful biomarker for diagnostic purposes, but much work is needed to investigate the pathogenic potential of these antibodies. Study Supported by: Greek GSRT (program “Aristeia”) Disclosure: Dr. Tzartos has nothing to disclose. Dr. Zisimopoulou has nothing to disclose. Dr. Tsonis has nothing to disclose. Dr. Evangelakou has nothing to disclose. Dr. Rentzos has nothing to disclose. Dr. Karandreas has nothing to disclose. Dr. Zouvelou has nothing to disclose. Dr. Thomaidis has nothing to disclose. Dr. Lauria has nothing to disclose. Dr. Andreetta has nothing to disclose. Dr. Mantegazza has nothing to disclose. Dr. Brenner has nothing to disclose. Dr. Petrou has nothing to disclose. Dr. Karusis has nothing to disclose. Dr. Tzartos has nothing to disclose.
Unemployment in the 21st Century: The Interaction between Macroeconomic Shocks and Institutions

This paper analyses the driving factors of youth- and adult unemployment in the OECD-countries during the 21st century. Focus is on both macroeconomic shock variables, such as the GDP-gap, the real interest rate and total factor productivity-growth, and institutional variables, such as union density, active labour market policies, the tax wedge, the replacement rate and the share of temporary employment contracts. The interactions between shocks and institutions are also evaluated. This is done empirically in with a panel data regression analysis on 25 OECD-countries annually during 1999-2017. The result of the paper is that the replacement rate, the tax wedge and the real interest rate seem to increase the unemployment rates. Spending on active labour market policies, the share of temporary employment contracts and the GDP-gap are found to reduce the unemployment rates. Adding interactions to the model does not improve the fit very much. A variable on the size of the public sector is added to the analysis but found insignificant in explaining unemployment. (Less)
ASP-SIFT: Using Analog Signal Processing Architecture to Accelerate Keypoint Detection of SIFT Algorithm

The scale-invariant feature transform (SIFT) algorithm is still one of the most reliable image feature extraction methods. Despite its excellent robustness on various image transformations, SIFT’s intensive computational burden has been severely preventing it from being used in real-time and energy-efficient embedded machine vision systems. To reduce processing time and energy cost while executing SIFT, an analog signal processing architecture, analog signal processing (ASP)-SIFT, is proposed in this article. In ASP-SIFT, the Gaussian pyramid construction, difference-of-Gaussian (DoG) pyramid construction and keypoint locating, which are the primary steps of the keypoint detection part of the SIFT algorithm, are done directly with analog circuit networks. Thus, by completing keypoint detection in the analog domain, the total processing time is approximately equal to the settling time of the circuit network. Besides, by adopting a current-mode circuit network operating in the subthreshold region, the power dissipation would be very low. Simulation results show that the total processing speed for a typical video graphics array (VGA)-format (<inline-formula> <tex-math notation="LaTeX">$640\times480$ </tex-math></inline-formula>) image is up to 2.3 kframes per second, which is at least <inline-formula> <tex-math notation="LaTeX">$3.26\times $ </tex-math></inline-formula> faster than the state-of-the-art digital hardware accelerators, while the system power is 94.5 mW and the energy consumption is only <inline-formula> <tex-math notation="LaTeX">$40~\mu \text{J}$ </tex-math></inline-formula> per frame.
Honeycomb Structure Inspired Triboelectric Nanogenerator for Highly Effective Vibration Energy Harvesting and Self‐Powered Engine Condition Monitoring

Vibration in mechanical equipment can serve as a sustainable energy source to power sensors and devices if it can be effectively collected. In this work, a honeycomb structure inspired triboelectric nanogenerator (HSI‐TENG) consisting of two copper electrode layers with sponge bases and one honeycomb frame filled with polytetrafluoroethylene (PTFE) balls is proposed to harvest vibration energy. The application of a compact honeycomb structure increases the maximum power density of HSI‐TENG by 43.2% compared to the square grid structure and provides superior advantages in large‐scale manufacturing. More importantly, the nonspring‐assisted HSI‐TENG can generate electricity once the PTFE balls obtain sufficient kinetic energy to separate from the bottom electrode layer regardless of the vibration frequency and direction. This is fundamentally different from the spring‐assisted harvesters that can only work around their natural frequencies. The vibration model and working criteria of the HSI‐TENG are established. Furthermore, the HSI‐TENG is successfully used to serve as a self‐powered sensor to monitor engine conditions by analyzing the electrical output of the HSI‐TENG installed on a diesel engine. Therefore, the nonspring‐assisted HSI‐TENG provides a novel strategy for highly effective vibration energy harvesting and self‐powered machinery monitoring.
Robust Optimal Control of the Cross-Resonance Gate in Superconducting Qubits

Superconducting circuits are one of the leading architectures in quantum computing. To undertake quantum computing one must be able to perform quantum gates; however, two-qubit gates are still limited in fidelity and gate time. The cross-resonance gate is a two-qubit gate that uses direct microwave drives and has seen much success in its implementation; but, there are theoretical indications that it has not yet reached the coherence limited fidelity value and its gate time is still relatively long compared with other quantum gate methods. Quantum optimal control theory is a powerful tool in the design of controls for quantum operations and has shown the capability to improve gate fidelities and reduce gate times. Robust quantum optimal control methodologies have further built on this to develop high fidelity quantum gates that are robust to uncertainties and noise in the system. In this thesis we use robust quantum optimal control theory to achieve these goals for the cross-resonance gate in a variety of superconducting qubit architectures. First, we investigate two superconducting qubits embedded in a common 3D microwave cavity in which the control drive is implemented via the common cavity mode of the cavity. We determine pulse shapes that implement the cross-resonance gate that are robust to uncertainty in the qubit transition frequencies for both a strictly two-level superconducting qubit and a three-level qubit. Second, we look at the cross-resonance gate with direct drives on each qubit, finding the minimal time to perform the cross-resonance gate with pulses that are robust to uncertainty in a measured system parameter for three cases: two three-level qubits with no drive crosstalk, two three-level qubits with some drive crosstalk, and two two-level qubits. Lastly, we report on simulations undertaken towards implementing a robust, high fidelity cross-resonance gate in a novel superconducting quantum device known as the coaxmon.
National Cultural Dimensions and Electronic Clinical Records Acceptance: an Exploratory Study

The purpose of the present paper is to describe the development of a measurement scale, to assess the impact of the national cultural factors on the electronic clinical records acceptance in the Ibn Sina Hospital Center(CHUIS)in Morocco. The methodology assumed is based on the Churchill paradigm (1979). Thus, our contribution focuses on the exploratory phase, where the items have been analysed using principal components analysis (PCA) and internal consistency with Cronbach’s Alpha (α). The results show a satisfactory factorial structure and excellent reliability of all the items.
A New Two-Stage Approach for a Bi-Objective Facility Layout Problem Considering Input/ Output Points Under Fuzzy Environment

Facility layout problem is one of the most important problems in a huge range of industries and services organizations. Simultaneous study of some qualitative and quantitative parameters like closeness relationship between facilities, physical constraints such as input/output points and how to arrange facilities can play a key role to determine the facility layout. Considering these parameters can lead to reduce production costs, increase production capacity, and remove additional displacements. A two-stage approach is proposed to achieve these goals. In the first stage, a goal programming model is proposed to determine weights of the attributes based on the experts’ opinions with different preference representation structures. Afterwards, the closeness ratings of the facilities are calculated using weights of attributes. In the second stage, an efficient layout is designed by determining facilities placement sequence, location of the next facility adjacent to the previous one, location of input/output points, and rectilinear feasible shortest path between facilities. Two meta-heuristic algorithms including particle swarm optimization and genetic algorithm are designed due to computational complexity. The objective function is to minimize the sum of products distance between facilities and closeness rating and also to minimize the dead space. A case study of an Auto Body Parts company is demonstrated to verify the efficiency of the proposed two-stage approach. Furthermore, in order to assess the performance of the proposed approach, a comparison is drawn between the proposed approach and five existing approaches in the literature to solve different problems by using the two meta-heuristic algorithms.
Nanobubble Liposome Complexes for Diagnostic Imaging and Ultrasound-Triggered Drug Delivery in Cancers: A Theranostic Approach

The ability of ultrasound contrast agents to enhance the cell membrane permeability in response to an ultrasound pulse has unveiled avenues to facilitate the delivery of a higher intracellular payload at target sites. In light of the above, we report the development of submicron-sized (528.7 ± 31.7 nm) nanobubble-paclitaxel liposome (NB-PTXLp) complexes for ultrasound imaging and ultrasound responsive drug delivery in cancer cells. With a paclitaxel entrapment efficiency of 85.4 ± 4.39%, the 200 nm-sized liposomes tethered efficiently (conjugation efficiency ∼98.7 ± 0.14%) with the nanobubbles to form conjugates. Sonoporation of MiaPaCa-2 cells upon treatment with nanobubbles and ultrasound enhanced cellular permeability, resulting in 2.5-fold higher uptake of liposomes in comparison to only liposome treatment. This manifested into more than 300-fold higher anticancer activity of NB-PTXLps in the presence of ultrasound in MiaPaCa-2, Panc-1, MDA-MB-231, and AW-8507 cell lines, compared to commercial formulation ABRAXANE. Also, the NB-PTXLp conjugates were found to exhibit echogenicity comparable to the commercial ultrasound contrast agent SonoVue. In addition, the developed nanobubbles were found to exhibit more than 1 week echogenic stability as opposed to 6 h stability of the commercially available ultrasound contrast agent SonoVue. Hence, the NB-PTXLps developed herein could prove to be a promising and minimally invasive theranostic platform for cancer treatments in the future.
The time-varying characteristics of the Chinese financial cycle and impact from the United States

ABSTRACT Although there have been many empirical studies about the financial cycle since the financial crisis of 2008, few have analysed the structural changes in the Chinese financial cycle over time. The Chinese financial development process is short, and it is difficult to obtain accurate results on the measurement of the financial cycle. Based on wavelet analysis, this paper analyzes the time-varying characteristics of the Chinese financial cycle and the relationship between the financial and business cycles. In addition, we measure the impact from the United States. This paper draws three conclusions. Firstly, in terms of the characteristics of cycles, the existence of Chinese business cycle and financial cycle is proved, while the credit cycle, leverage cycle, stock market cycle and property cycle are quite different. Specifically, China has a 5.8-year credit cycle, an 8-year stock market cycle, 3.4-year and 12-year business cycles and a 15-year leverage cycle. Secondly, the financial cycles can serve as leading indicators of the business cycle, though the relationships between them are change overtime. Finally, the United States has a significant impact on the Chinese financial cycle with a ‘decoupling-recoupling’ effect, which is mainly reflected in the leverage cycle and the stock market cycle.
Unsupervised-Learning Assisted Artificial Neural Network for Optimization

UNSUPERVISED-LEARNING ASSISTED ARTIFICIAL NEURAL NETWORK FOR OPTIMIZATION Varun Kote Old Dominion University, 2019 Director: Dr. Oktay Baysal Innovations in computer technology made way for Computational Fluid Dynamics (CFD) into engineering, which supported the development of new designs by reducing the cost and time by lowering the dependency on experimentation. There is a further need to make the process of development more efficient. One such technology is Artificial Intelligence. In this thesis, we explore the application of Artificial Intelligence (AI) in CFD and how it can improve the process of development. AI is used as a buzz word for the mechanism which can learn by itself and make the decision accordingly. Machine learning (ML) is a subset of AI which learns any method without the need for any explicit algorithm. Deep Learning is another subset of ML, which is different in its composition. Deep Learning, or Neural Networks (NN), is made up of nodes like the neurons and works on the principle of the human brain. NN can be exploited for any problem without the need for any explicit algorithm for the task. It can be achieved by analyzing and inferring from the observations. Artificial Neural Network (ANN) is used for data analysis and Convolutional Neural Networks (CNN) for image analysis. Our area of interest herein is ANN and its application for a medical equipment called Convective Polymerase Chain Reaction (cPCR) device. Many have relied on engineering experimentation to develop an optimized PCR device, which requires high cost and time. That makes the use of PCR devices less cost-effective as a commonplace for healthcare. We optimize a convective PCR reactor using a high-fidelity CFDbased surrogate model to find an economical and performance-effective one. We plan numerous possible design combinations, evaluating DNA doubling time. Based on these results, an accurate surrogate model is developed for optimization using Deep Learning. We produce two kinds of surrogate models using ANN; one by directly employing ANN and another by using unsupervised learning called, k-Means-Clustering-Assisted ANN, and then compare the results from these two methods. For developing a suitable model of ANN to fit our data, we carry out the analysis of model accuracy and obtain the best design by using a differential evolution method. The best designs obtained by the two methods are verified with the corresponding result obtained from CFD. This shows an effective way of designing an optimized device by reducing the number of CFD simulations required for the development. Consequently, the computational results demonstrate that the convective PCR device can be efficiently developed using our proposed methodology, making it viable for point-of-care applications.
Reproducibility of concentric and eccentric isokinetic strength of the shoulder rotators in normal subjects 40 to 55 years old

The purpose of this study was to define the test-retest reproducibility of concentric and eccentric isokinetic measurements of shoulder rotators. It was carried out at a 26 day ±4 interval. Fourteen healthy volunteer subjects (7 men and 7 women), aged between 40 and 55 years (47.5 ± 5.6 years), were included. Evaluation was carried out with a Cybex Norm dynamometer and the reliability was calculated according to the intra-class correlation coefficient (ICC 2. 1). The protocol was randomised for the shoulder and for sets of concentric (con) and eccentric (ecc) tests. Concentric sets consisted of 5 movements of internal (IR) and external rotation (ER) at the speed of 60°/sec. then 120°/sec. Eccentric performance was recorded only for the external rotators, to determine the ratio ER ecc /IR con . Results indicated excellent reproducibility for the peak torque (0.91-0.98), total work (0.88-0.96) and average power (0.83-0.94) in concentric muscle action for internal and external rotators and in eccentric muscle action for external rotators at 60 and 120°/sec. On the other hand the strength ratio: ER con /IR con and ER ecc /IR con should be interpreted with caution because of a lower reproducibility (ICC: 0.53-0.83). In conclusion, isokinetic findings relating to concentric and eccentric performance of the internal and external rotators of the shoulder are reproducible under the present protocol. These findings may serve for assessing rotator cuff function at the age where the incidence of rotator cuff overuse becomes a serious problem.
Property valuation methods in practice: evidence from Australia

Purpose Improving valuation accuracy, especially for sale and acquisition purposes, remains one of the key targets of the global real estate research agenda. Among other recommendations, it has been argued that the use of technology-based advanced valuation methods can help to narrow the gap between asset valuations and actual sale prices. The purpose of this paper is to investigate the property valuation methods being adopted by Australian valuers and the factors influencing their level of awareness and adoption of the methods. Design/methodology/approach An online questionnaire survey was conducted to elicit information from valuers practising in Australia. They were asked to indicate their level of awareness and adoption of the different property valuation methods. Their response was analysed using frequency distribution, χ2 test and mean score ranking. Findings The results show that the traditional methods of valuation, namely, comparative, investment and residual, are the most adopted methods by the Australian valuers, while advanced valuation methods are seldom applied in practice. The results confirm that professional bodies, sector of practice and educational institutions are the three most important drivers of awareness and adoption of the advanced valuation methods. Practical implications There is a need for all the property valuation stakeholders to synergise and transform the property valuation practice in a bid to promote the awareness and adoption of advanced valuation methods, (e.g. hedonic pricing model, artificial neural network, expert system, fuzzy logic system, etc.) among valuers. These are all technology-based methods to improve the efficiency in the prediction process, and the valuer still needs to input reliable transaction data into the systems. Originality/value This study provides a fresh and most recent insight into the current property valuation methods adopted in practice by valuers practising in Australia. It identifies that the advanced valuation methods could supplement the traditional valuation methods to achieve good practice standard for improving the professional valuation practice in Australia so that the valuation profession can meet the industry’s expectations.