Video2Haptics: Converting Video Motion to Dynamic Haptic Feedback with Bio-Inspired Event Processing.

In cinematic VR applications, haptic feedback can significantly enhance the sense of reality and immersion for users. The increasing availability of emerging haptic devices opens up possibilities for future cinematic VR applications that allow users to receive haptic feedback while they are watching videos. However, automatically rendering haptic cues from real-time video content, particularly from video motion, is a technically challenging task. In this paper, we propose a novel framework called "Video2Haptics" that leverages the emerging bio-inspired event camera to capture event signals as a lightweight representation of video motion. We then propose efficient event-based visual processing methods to estimate force or intensity from video motion in the event domain, rather than the pixel domain. To demonstrate the application of Video2Haptics, we convert the estimated force or intensity to dynamic vibrotactile feedback on emerging haptic gloves, synchronized with the corresponding video motion. As a result, Video2Haptics allows users not only to view the video but also to perceive the video motion concurrently. Our experimental results show that the proposed event-based processing methods for force and intensity estimation are one to two orders of magnitude faster than conventional methods. Our user study results confirm that the proposed Video2Haptics framework can considerably enhance the users' video experience.

EV-LFV: Synthesizing Light Field Event Streams from an Event Camera and Multiple RGB Cameras.

Light field videos captured in RGB frames (RGB-LFV) can provide users with a 6 degree-of-freedom immersive video experience by capturing dense multi-subview video. Despite its potential benefits, the processing of dense multi-subview video is extremely resource-intensive, which currently limits the frame rate of RGB-LFV (i.e., lower than 30 fps) and results in blurred frames when capturing fast motion. To address this issue, we propose leveraging event cameras, which provide high temporal resolution for capturing fast motion. However, the cost of current event camera models makes it prohibitive to use multiple event cameras for RGB-LFV platforms. Therefore, we propose EV-LFV, an event synthesis framework that generates full multi-subview event-based RGB-LFV with only one event camera and multiple traditional RGB cameras. EV-LFV utilizes spatial-angular convolution, ConvLSTM, and Transformer to model RGB-LFV's angular features, temporal features, and long-range dependency, respectively, to effectively synthesize event streams for RGB-LFV. To train EV-LFV, we construct the first event-to-LFV dataset consisting of 200 RGB-LFV sequences with ground-truth event streams. Experimental results demonstrate that EV-LFV outperforms state-of-the-art event synthesis methods for generating event-based RGB-LFV, effectively alleviating motion blur in the reconstructed RGB-LFV.

The effect of BMI and age on the outcomes of microsurgical vasoepididymostomy: a retrospective analysis of 181 patients operated by a single surgeon.

To design a treatment plan for patients with epididymal obstruction, we explored the potential impact of factors such as body mass index (BMI) and age on the surgical outcomes of vasoepididymostomy (VE). In this retrospective study, 181 patients diagnosed with obstructive azoospermia (OA) due to epididymal obstruction between September 2014 and September 2017 were reviewed. All patients underwent single-armed microsurgical intussusception VEs with longitudinal two-suture placement performed by a single surgeon (KH) in a single hospital (Peking University Third Hospital, Beijing, China). Six factors that could possibly influence the patency rates were analyzed, including BMI, age, mode of anastomosis, site of anastomosis, and sperm motility and quantity in the intraoperative epididymal fluid. Single-factor outcome analysis was performed via Chi-square test and multivariable analysis was performed using logistic regression. A total of 159 (87.8%, 159/181) patients were followed up. The follow-up time (mean ± standard deviation [s.d.]) was 27.7 ± 9.3 months, ranging from 12 months to 48 months. The overall patency rate was 73.0% (116/159). The multivariable analysis revealed that BMI and age significantly influenced the patency rate (P = 0.008 and 0.028, respectively). Younger age (≤28 years; odds ratio [OR] = 3.531, 95% confidence interval [95% CI]: 1.397-8.924) and lower BMI score (<26.0 kg m-2; OR = 2.352, 95% CI: 1.095-5.054) appeared to be associated with a higher patency rate. BMI and age were independent factors affecting the outcomes of microsurgical VEs depending on surgical expertise and the use of advanced technology.

A Q-learning-based routing scheme for smart air quality monitoring system using flying ad hoc networks.

Air pollution has changed ecosystem and atmosphere. It is dangerous for environment, human health, and other living creatures. This contamination is due to various industrial and chemical pollutants, which reduce air, water, and soil quality. Therefore, air quality monitoring is essential. Flying ad hoc networks (FANETs) are an effective solution for intelligent air quality monitoring and evaluation. A FANET-based air quality monitoring system uses unmanned aerial vehicles (UAVs) to measure air pollutants. Therefore, these systems have particular features, such as the movement of UAVs in three-dimensional area, high dynamism, quick topological changes, constrained resources, and low density of UAVs in the network. Therefore, the routing issue is a fundamental challenge in these systems. In this paper, we introduce a Q-learning-based routing method called QFAN for intelligent air quality monitoring systems. The proposed method consists of two parts: route discovery and route maintenance. In the part one, a Q-learning-based route discovery mechanism is designed. Also, we propose a filtering parameter to filter some UAVs in the network and restrict the search space. In the route maintenance phase, QFAN seeks to detect and correct the paths near to breakdown. Moreover, QFAN can quickly identify and replace the failed paths. Finally, QFAN is simulated using NS2 to assess its performance. The simulation results show that QFAN surpasses other routing approaches with regard to end-to-end delay, packet delivery ratio, energy consumption, and network lifetime. However, communication overhead has been increased slightly in QFAN.

CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR.

To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence (AI) applications, in particular Convolutional Neural Network (CNN), can speed the COVID-19 diagnostic process. In this paper, we optimize the data augmentation and the CNN hyperparameters for detecting COVID-19 from CXRs in terms of validation accuracy. This optimization increases the accuracy of the popular CNN architectures such as the Visual Geometry Group network (VGG-19) and the Residual Neural Network (ResNet-50), by 11.93% and 4.97%, respectively. We then proposed CovidXrayNet model that is based on EfficientNet-B0 and our optimization results. We evaluated CovidXrayNet on two datasets, including our generated balanced COVIDcxr dataset (960 CXRs) and the benchmark COVIDx dataset (15,496 CXRs). With only 30 epochs of training, CovidXrayNet achieves state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task (COVID-19, normal or pneumonia). The CovidXRayNet model, the COVIDcxr dataset, and several optimization experiments are publicly available at https://github.com/MaramMonshi/CovidXrayNet.

Stochastic Dual Simplex Algorithm: A Novel Heuristic Optimization Algorithm.

A new heuristic optimization algorithm is presented to solve the nonlinear optimization problems. The proposed algorithm utilizes a stochastic method to achieve the optimal point based on simplex techniques. A dual simplex is distributed stochastically in the search space to find the best optimal point. Simplexes share the best and worst vertices of one another to move better through search space. The proposed algorithm is applied to 25 well-known benchmarks, and its performance is compared with grey wolf optimizer (GWO), particle swarm optimization (PSO), Nelder-Mead simplex algorithm, hybrid GWO combined with pattern search (hGWO-PS), and hybrid GWO algorithm combined with random exploratory search algorithm (hGWO-RES). The numerical results show that the proposed algorithm, called stochastic dual simplex algorithm (SDSA), has a competitive performance in terms of accuracy and complexity.

Deep learning in generating radiology reports: A survey.

Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets. Generating radiology coherent paragraphs that do more than traditional medical image annotation, or single sentence-based description, has been the subject of recent academic attention. This presents a more practical and challenging application and moves towards bridging visual medical features and radiologist text. So far, the most common approach has been to utilize publicly available datasets and develop DL models that integrate convolutional neural networks (CNN) for image analysis alongside recurrent neural networks (RNN) for natural language processing (NLP) and natural language generation (NLG). This is an area of research that we anticipate will grow in the near future. We focus our investigation on the following critical challenges: understanding radiology text/image structures and datasets, applying DL algorithms (mainly CNN and RNN), generating radiology text, and improving existing DL based models and evaluation metrics. Lastly, we include a critical discussion and future research recommendations. This survey will be useful for researchers interested in DL, particularly those interested in applying DL to radiology reporting.

A Drosophila genetic model of nephrolithiasis: transcriptional changes in response to diet induced stone formation.

BACKGROUND: Urolithiasis is a significant healthcare issue but the pathophysiology of stone disease remains poorly understood. Drosophila Malpighian tubules were known to share similar physiological function to human renal tubules. We have used Drosophila as a genetic model to study the transcriptional response to stone formation secondary to dietary manipulation. METHODS: Wild-type male flies were raised on standard medium supplemented with lithogenic agents: control, sodium oxalate (NaOx) and ethylene glycol (EG). At 2 weeks, Malpighian tubules were dissected under polarized microscope to visualize crystals. The parallel group was dissected for RNA extraction and subsequent next-generation RNA sequencing. RESULTS: Crystal formation was visualized in 20%(±2.2) of flies on control diet, 73%(±3.6) on NaOx diet and 84%(±2.2) on EG diet. Differentially expressed genes were identified in flies fed with NaOx and EG diet comparing with the control group. Fifty-eight genes were differentially expressed (FDR <0.05, p < 0.05) in NaOx diet and 20 genes in EG diet. The molecular function of differentially expressed genes were assessed. Among these, Nervana 3, Eaat1 (Excitatory amino acid transporter 1), CG7912, CG5404, CG3036 worked as ion transmembrane transporters, which were possibly involved in stone pathogenesis. CONCLUSIONS: We have shown that by dietary modification, stone formation can be manipulated and visualized in Drosophila Malpighian tubules. This genetic model could be potentially used to identify the candidate genes that influence stone risk hence providing more insight to the pathogenesis of human stone disease.

Ureteroscopy in the diagnosis of upper tract transitional cell cancer: a 10-year experience providing outcome data for informed consent.

PURPOSE: The aim of the study was to assess the effectiveness of ureteroscopy and biopsy in the diagnosis and management of upper tract urothelial carcinoma. METHODS: We retrospectively collated data from pathology, radiology and operating theatre data sets from two large UK hospitals. During the 10-year period examined, 160 patients underwent ureteroscopy prior to nephro-ureterectomy (NU)/distal ureterectomy (DU). RESULTS: Of the 160 patients identified, 140 were ureteroscopically evaluated and biopsied, 104 (74.3%) had positive biopsies, 5 (3.6%) had negative biopsies and 31 (22.1%) had nondiagnostic biopsies.One hundred and forty patients of 160 (88.8%) resulted in positive findings as a result of their ureteroscopy [positive biopsy/positive operative cytology/visible upper tract urothelial carcinoma (UTUC) at ureteroscopy)].A total of 108 patients had cytological samples sent for histological analysis where grade was able to be assessed at DU/NU. Of these samples, 35 had positive cytology, 58 had an abnormal result that was not diagnostic and 15 had negative cytology.Patients with positive biopsies had upstaging (58.4%) and/or upgrading (30.5%) of their initial ureteroscopic histology at NU/DU.The overall success rate of ureteroscopy for cancer diagnosis was 88.8%. CONCLUSIONS: Upper tract endoscopy and biopsy yielded positive biopsy results in 74.3% of cases and confirmed a cancer diagnosis in 88.8%. Five patients had negative histology at biopsy, all of whom eventually underwent a NU/DU confirming cancer.

Proteomic changes in response to crystal formation in Drosophila Malpighian tubules.

Kidney stone disease is a major health burden with a complex and poorly understood pathophysiology. Drosophila Malpighian tubules have been shown to resemble human renal tubules in their physiological function. Herein, we have used Drosophila as a model to study the proteomic response to crystal formation induced by dietary manipulation in Malpighian tubules. Wild-type male flies were reared in parallel groups on standard medium supplemented with lithogenic agents: control, Sodium Oxalate (NaOx) and Ethylene Glycol (EG). Malpighian tubules were dissected after 2 weeks to visualize crystals with polarized light microscopy. The parallel group was dissected for protein extraction. A new method of Gel Assisted Sample Preparation (GASP) was used for protein extraction. Differentially abundant proteins (p<0.05) were identified by label-free quantitative proteomic analysis in flies fed with NaOx and EG diet compared with control. Their molecular functions were further screened for transmembrane ion transporter, calcium or zinc ion binder. Among these, 11 candidate proteins were shortlisted in NaOx diet and 16 proteins in EG diet. We concluded that GASP is a proteomic sample preparation method that can be applied to individual Drosophila Malpighian tubules. Our results may further increase the understanding of the pathophysiology of human kidney stone disease.

The success of shock wave lithotripsy (SWL) in treating moderate-sized (10-20 mm) renal stones.

Many centres favour endourological management over shock wave lithotripsy (SWL) in the management of moderate-sized (10-20 mm) renal stones. International guidelines support all available modalities for the treatment of these stones. The aim of this study was to evaluate the efficacy of SWL in the treatment of 10- to 20-mm renal stones. From January 2013 to October 2014, all patients with a renal stone measuring between 10 and 20 mm in maximum diameter on CT scan that were eligible for lithotripsy were included. 130 consecutive patients were evaluated. Demographics, location of stone within the kidney, number of SWL sessions and treatment outcomes were analysed. Treatment success was classified into complete stone clearance and the presence of clinically insignificant residual fragments <4 mm (CIRF). 119 patients (92 %) completed treatment and radiological follow-up. Eleven patients were excluded due to incomplete follow-up data. The mean age was 56.8 (23-88). Male to female ratio was 1.9:1 (78:41) and the mean BMI was 28.4 (17.9-58). The mean stone size was 12.8 mm (10-14 mm: n = 87; 15-20 mm: n = 32). The mean number of treatments was 2.14 and 2.82 for stones 10-14 and 15-20 mm, respectively. Overall treatment success was 66.4 % (combined complete stone clearance and CIRFs). Subdivided by stone size <15 mm and ≥15 mm, the success rate was 70.4 and 53.1 %, respectively. The treatment success by stone location was 65, 64 and 70 % for upper, middle and lower pole stones, respectively and 67 % for PUJ stones. For those who failed SWL treatment, the majority 50 % (n = 20) were managed expectantly, 42.5 % (n = 17) required URS, and 7.5 % (n = 3) required PNL. This study suggests that SWL has an efficacy for treating larger renal stones (10-20 mm) that is equivalent to success rates for smaller stones in other series. As a low-risk and non-invasive procedure SWL should be considered a first-line treatment for these stones.

Severe acute pyelonephritis: a review of clinical outcome and risk factors for mortality.

OBJECTIVE. To review demographics of patients with acute pyelonephritis, their outcomes of severe upper urinary tract infection, and to identify risk factors for long hospital stay and mortality. DESIGN. Case series. SETTING. A regional hospital in Hong Kong. PATIENTS. Patients admitted between June 2007 and June 2012 for acute pyelonephritis were identified. Those with the most severe outcomes were analysed of their mortality, need for care in the intensive care unit, or necessitation of urological intervention. RESULTS. Overall, 68 patients fulfilled our criteria for severe acute pyelonephritis. The female-to-male ratio was 7:3. Their mean age was 58 years. Overall, 57% of the patients had impaired renal function and 37% were diabetic; 47% developed shock after admission and 56% required further intensive care unit care; 75% of the patients demonstrated radiological evidence of urinary tract obstruction and required subsequent drainage procedures. Five patients died due to severe acute pyelonephritis. The prevalence of bacteraemia and bacteriuria was 57% and 74%, respectively. Escherichia coli accounted for the majority of causative organisms. Four risk factors-bacteraemia, shock, need for intensive care, and suppurative pyelonephritis-were associated with hospital stay of longer than 14 days. Old age (≥65 years), male sex, deranged renal function, and presence of disseminated intravascular coagulation were associated with mortality. CONCLUSION. There was high prevalence of bacteraemia and septic shock in patients with severe acute pyelonephritis. The factors of old age (≥65 years), male sex, deranged renal function, and presence of disseminated intravascular coagulation were associated with mortality. With the support of intensive care, early recognition of urinary tract obstruction and timely drainage, patients with severe acute pyelonephritis generally carry a good prognosis.