Personalized Three-Dimensional Printed Models in Congenital Heart Disease

Patient-specific three-dimensional (3D) printed models have been increasingly used in cardiology and cardiac surgery, in particular, showing great value in the domain of congenital heart disease (CHD). CHD is characterized by complex cardiac anomalies with disease variations between individuals; thus, it is difficult to obtain comprehensive spatial conceptualization of the cardiac structures based on the current imaging visualizations. 3D printed models derived from patient’s cardiac imaging data overcome this limitation by creating personalized 3D heart models, which not only improve spatial visualization, but also assist preoperative planning and simulation of cardiac procedures, serve as a useful tool in medical education and training, and improve doctor–patient communication. This review article provides an overall view of the clinical applications and usefulness of 3D printed models in CHD. Current limitations and future research directions of 3D printed heart models are highlighted

Association between SGLT2 Inhibitors vs DPP-4 Inhibitors and Risk of Pneumonia Among Patients with Type 2 Diabetes

Context: Patients with diabetes are at a higher risk of pneumonia and pneumonia mortality. Sodium glucose co-transporter 2 inhibitors (SGLT2is), the latest class of glucose-lowering agents, were shown to reduce the risk of pneumonia in clinical trials. However, the real-world effectiveness of SGLT2is on the risk of pneumonia is largely unknown. Objective: To investigate the associations between SGLT2is use and the risk of pneumonia and pneumonia mortality compared with dipeptidyl peptidase-4 inhibitors (DPP4is) using an electronic medical database in Hong Kong. Design A retrospective cohort study. The “prevalent new-user” design was adopted to account for the previous exposure to the study drugs being compared. Propensity score (PS) matching (1:4) was used to balance the baseline characteristics of the 2 groups. Setting and participants Electronic health data of type 2 diabetes patients using SGLT2is and DPP4is between 2015 and 2018 was collected from the Clinical Data Analysis and Reporting System. Main Outcome Measures: Pneumonia incidence and mortality. Results: The PS-matched cohort consisted of 6664 users of SGLT2is and 26 656 users of DPP4is, with a mean follow-up of 3.8 years. Poisson regression showed that SGLT2is use was associated with lower risk of pneumonia compared with DPP4is with an absolute rate difference of 4.05 per 1000 person-years (95% CI, 2.61-5.51). The corresponding incidence rate ratio was 0.71 (95% CI, 0.62-0.81). Similar reduction in risk of pneumonia death was observed (hazard ratio 0.57; 95% CI, 0.42-0.77). Conclusion: Compared with DPP4is, SGLT2is use was associated with a reduced risk of pneumonia and pneumonia mortality in a real-world setting

Spin-Electromagnetic Hydrodynamics and Magnetization Induced by Spin-Magnetic Interaction

The hydrodynamic model including the spin degree of freedom and the electromagnetic field was discussed. In this derivation, we applied electromagnetism for macroscopic medium proposed by Minkowski. For the equation of motion of spin, we assumed that the hydrodynamic representation of the Pauli equation is reproduced when the many-body effect is neglected. Then the spin-magnetic interaction in the Pauli equation was converted to a part of the magnetization. The fluid and spin stress tensors induced by the many-body effect were obtained by employing the algebraic positivity of the entropy production in the framework of the linear irreversible thermodynamics, including the mixing effect of the irreversible currents. We further constructed the constitutive equation of the polarization and the magnetization. Our polarization equation is more reasonable compared to another result obtained using electromagnetism for macroscopic medium proposed by de Groot-Mazur.Comment: 24 pages, no figure, the discussion for the modifed thermodynamic relation is added, several errors are corrected, accepted for publication in PR

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Relativistic Modification of the Gamow Factor

In processes involving Coulomb-type initial- and final-state interactions, the Gamow factor has been traditionally used to take into account these additional interactions. The Gamow factor needs to be modified when the magnitude of the effective coupling constant increases or when the velocity increases. For the production of a pair of particles under their mutual Coulomb-type interaction, we obtain the modification of the Gamow factor in terms of the overlap of the Feynman amplitude with the relativistic wave function of the two particles. As a first example, we study the modification of the Gamow factor for the production of two bosons. The modification is substantial when the coupling constant is large.Comment: 13 pages, in LaTe

Novel Mode Selection Schemes for Buffer-Aided Cooperative NOMA System

This paper investigates a cooperative non-orthogonal multiple access (C-NOMA) system, where the NOMA and buffer-aided cooperative transmission modes between the users are integrated. Two novel mode selection schemes are proposed, which adaptively select the NOMA and cooperative modes according to different buffer states and communication environments. These two proposed schemes are termed single-core state (SCS) and dual-core state (DCS) schemes since they correspond to single and dual-core buffer states. These core states are carefully chosen, which ensure not only a sufficient amount of available transmission modes or links but also a small number of stored packets at each buffer. The closed-form expressions of the outage probabilities and average delays of the proposed schemes are derived and verified by simulation results. Asymptotic performance analysis is also performed, revealing that both proposed schemes achieve the full diversity within the minimum required buffer size of two. Analytical and simulation results show that the proposed SCS and DCS schemes ensure favourable outage performance and the lowest delay, respectively

Risk of self-harm after the diagnosis of psychiatric disorders in Hong Kong, 2000–10: a nested case-control study

Background Psychiatric disorders are established risk factors for self-harm. However, variation in risk of self-harm by specific psychiatric disorder, stratified by gender and age, is rarely examined using population representative samples. This study aims to investigate the risk of self-harm following the diagnosis of different psychiatric disorders based on inpatient records retrieved from the Hong Kong Clinical Data Analysis and Reporting System (CDARS). Method A cohort of 86,353 people with a first-recorded diagnosis of depression, alcohol abuse/dependence, personality disorders, bipolar disorders, anxiety disorders, schizophrenia, or substance abuse/dependence, along with 134,857 matched controls, were followed between 2000 and 2010. For each diagnostic category, a Cox proportional hazard regression model was fitted to estimate the adjusted hazard ratio (aHR) (95% confidence intervals) of associated self-harm, adjusting for gender, age, admission time, district of residence, and comorbidities. Outcomes The personality disorders and substance abuse/dependence groups had the highest self-harm incidences of 3,174 and 3,018 per 100,000 patient-years, respectively. The highest risk of self-harm was found in the substance abuse/dependence group (aHR, 9·6; 95% CI, 8·4-11·0), followed by the groups with personality disorders (3·7; 2·8-4·9) and alcohol abuse/dependence (3·2; 2·9-3·7). When stratified by gender and age, the highest risk was found in substance abuse/dependence group for both genders (female: aHR, 7·7; 95% CI, 6·0-9·8; male: 10·5; 95% CI, 8·9-12·4) and all age groups (adolescent: aHR, 9·6; 95% CI, 7·2-12·7; young: 10·2; 95% CI, 8·4-12·3; middle-aged: 11·2; 95% CI, 8·0-15·6; Elderly: 3·2; 95% CI, 1·7-6·1). Interpretation First-recorded diagnosis of psychiatric disorders were significantly associated with elevated risks of subsequent self-harm. The associations varied considerably by diagnostic categories across gender-age subgroups. This finding highlighted the needs to develop more efficient and targeted preventive measures in psychiatric care management. Specific attention should be paid to demographic characteristics linked to increased risk within the same diagnostic category

Extremal Optimization of Graph Partitioning at the Percolation Threshold

The benefits of a recently proposed method to approximate hard optimization problems are demonstrated on the graph partitioning problem. The performance of this new method, called Extremal Optimization, is compared to Simulated Annealing in extensive numerical simulations. While generally a complex (NP-hard) problem, the optimization of the graph partitions is particularly difficult for sparse graphs with average connectivities near the percolation threshold. At this threshold, the relative error of Simulated Annealing for large graphs is found to diverge relative to Extremal Optimization at equalized runtime. On the other hand, Extremal Optimization, based on the extremal dynamics of self-organized critical systems, reproduces known results about optimal partitions at this critical point quite well.Comment: 7 pages, RevTex, 9 ps-figures included, as to appear in Journal of Physics

Fast Meta Learning for Adaptive Beamforming

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance

Performance evaluation of video streaming on LTE with coexistence of Wi-Fi signal

The continuous growth in mobile data traffic and limited license wireless spectrum have led to dramatically increase the demand of the radio spectrum. It is widespread the concern about the coexistence of long term evolution (LTE) and Wi-Fi in the unlicensed band. There are several techniques have been proposed to enable the coexistence of LTE and Wi-Fi in the unlicensed band, but these works are targeted on the impact of the LTE to the Wi-Fi network performance. An experiment is carried out in this work to evaluate the impact of Wi-Fi signal on the video streaming in the LTE network. The experimental test comprised of the national instrument (NI) universal software radio peripheral (USRP) 2953R that is controlled by the LabVIEW Communication LTE application framework. Extensiveexperiments are carried out under two scenarios, i.e. (1) Coexistence of LTE and Wi-Fi signal, (2) LTE signal only. Performance evaluations are carried out with different Modulation and coding schemes (MCS) values and different mode of operations, i.e. frequency division duplex (FDD) and time division duplex (TDD) mode. The results illustrated that the interference from Wi-Fi signal caused the performance degradation of the LTE network in throughput and the power received by user equipment (UE)