Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder.

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain a real-time pseudo point cloud from image sensors (cameras) instead of laser-based sensors (LiDARs). Previous studies (such as PSMNet-based point cloud generation) built the algorithm based on accuracy but failed to operate in real time as LiDAR. We propose an approach to use different depth estimators to obtain pseudo point clouds similar to LiDAR to achieve better performance. Moreover, the depth estimator has used stereo imagery data to achieve more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms other existing approaches on KITTI depth prediction while yielding point clouds significantly faster than other approaches as well. Additionally, the proposed approach is evaluated on the KITTI stereo benchmark, where it shows effectiveness in runtime.

A Quality Improvement Project to Assess if Preoperative Trauma Patients Are Receiving Their Prescribed Medications Preoperatively.

Introduction All preoperative trauma patients should receive their preoperative medications regardless of being nil per os (NPO). Anticoagulants, angiotensin-converting enzyme inhibitors (ACE-I), and angiotensin II receptor blockers (ARBs) should be omitted. This is according to both local and national guidelines. We noticed that some preoperative patients have not been receiving their morning medications prior to having their operations. This has led to pre and perioperative complications. The aim of this study, therefore, was to conduct a quality improvement project to assess if preoperative trauma patients are receiving their prescribed medications preoperatively when placed NPO. We then aimed to determine the adverse outcome associated with omission and to furthermore devise a robust system to prevent recurrence. Methods Data were collected from the relevant patients' drug charts on the day of the operation. These cases were available on the daily trauma list. We evaluated if there was any clear reason documented for not providing the medications. Following the first round, we implemented our action plan for posters to be taped to drug trolleys on the orthopedic wards and informed the nursing staff of the need to give preoperative medications. We deemed the following to be important medication classes: calcium channel blockers, neuromodulators, beta-blockers, anti-epileptics, digoxin, bronchodilators, anti-anginals, anti-epileptics, and benzodiazepines. This was re-audited after one month to assess compliance and monitor for improvement. Results Forty patients were included in the first round and 41 in the re-audit. In the first round, 16/41 (39%) patients received their medications correctly. In patients who did not receive their medications (n=25), 22 had important medication classes omitted. Post implementation of the posters, 25/41 (61%) patients received their medications correctly. In patients who did not receive their medications (n=16), 10 had important medication classes omitted. The main reason why medications were incorrectly not given was that patients were NPO. Conclusion This quality improvement audit shows that our interventions between audit cycles have made a significant improvement in patients receiving their medications and therefore this has a direct positive impact on patient safety and outcomes. We should continue to have a close rapport with the nursing staff to maintain standards of correct practice, and these audit findings should be escalated to the emergency theatre thereafter.

Perspective of a Teaching Fellow: Innovation in Medical Education: The Changing Face of Clinical Placements During COVID-19.

The Mid Essex teaching team have adapted and innovated to ensure delivery of the curriculum to over 150 students from two medical schools during COVID-19. A fully immersive education experience was created through protected teaching time, the introduction of a simulation package (virtual reality simulation, ward simulation and bleep simulation), the proactive recruitment of fourteen dedicated teaching fellows, and the procurement of technology-based platforms. The blended learning approach we designed supplemented student-patient interaction on the ward with virtual and face-to-face teaching sessions where appropriate. Preliminary data from student feedback suggests that the pandemic induced adaptations, technological innovations and simulation-based teaching have been successful. Students have rapidly gained confidence, knowledge and skills that they need to progress. It may be a consideration for teaching hospitals elsewhere to consider such methods to ensure students are prepared for life as a doctor both in the midst of and after the pandemic.

Reinventing Undergraduate Clinical Placements with a Switch to Delivery by Clinical Teaching Fellows.

PURPOSE: Undergraduate clinical placements have the potential for significant improvement. Previous research has shown the growing value of clinical teaching fellows (CTFs) within medical education. Changing traditional placements to a model whereby CTFs have defined roles and lead the majority of teaching can positively reinvent undergraduate clinical teaching. We wanted to see how a structured teaching programme delivered by CTFs could affect student experience and personal development within a large associate teaching hospital. We consider how such a model could be implemented and explore the opportunities for CTFs to develop in personal and professional capacities. METHODS: A mixed methods study was organised to assess student experience of a CTF-led placement. A novel structured teaching programme was delivered by 14 CTFs, who provided or were involved with the majority of teaching for all medical students. Thematic analysis was conducted on focus groups with 48 final year medical students from Queen Mary University of London following completion of their clinical placements. The same students were asked to complete an anonymous survey from which results were analysed using modified 5-point Likert scales. RESULTS: Eight themes were identified from the focus groups. Students appreciated the increased individualisation, relevance and variety of teaching and the ability to record progress. Other perceived effects were higher teacher to student ratios, more learning opportunities and increased familiarity and reliability with CTFs. Of the students surveyed, 96% felt their overall placement experience was very good in comparison to previous placements elsewhere. Survey results supported focus group themes and demonstrated perceived growth in students' personal development. CONCLUSION: Placement models where CTFs lead most teaching can improve medical undergraduate experience and training. A move towards CTF-delivered teaching can be of financial benefit to hospital trusts whilst allowing time for junior doctors to explore different clinical specialities and hone their teaching skills.

Fast acting hemostatic agent based on self-assembled hybrid nanofibers from chitosan and casein.

Hemorrhage is a leading cause of preventable death in both military combat and civilian accidents. To overcome these challenges, an affordable and effective bandage is must required substance. A novel strategy is reported for developing chitosan-casein (CC) based self-assembled nanofibrous polyelectrolyte complex (PEC) for rapid blood clotting. The amide group (1630 cm-1) and phosphate group (910 cm-1) of chitosan-casein can form PEC at pH 8.2 ± 0.2. The PECs contain intertwined nanofibers (≤100 nm diameter) with a high surface area. Increasing chitosan percentage from 30% (CC30) to 50% (CC50) or 70% (CC70) results, increase in zeta potential of PEC from -9.14 ± 3.3 to 7.46 ± 3.7 and 14.8 ± 3.3 mV, respectively. Under in vitro conditions, the CC30, CC50, and CC70 PECs allow platelet adhesion and rapidly absorbs blood fluid to form mechanically stable blood clots within 9 ± 3, 16 ± 3, and 30 ± 4 s, respectively, which are better than Celox™ (90 ± 3 s). In vivo application of PEC (CC50) causes clotting within 37 ± 6 s of large (1 cm) arterial incision in rabbit models. The PEC is biocompatible with promising hemostatic efficiency. This is the first report of nanofibrous PEC from chitosan and casein for rapid clotting, to the best of our knowledge.

Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

Prevalence of virulence genes and cytolethal distending toxin production in Campylobacter jejuni isolates from diarrheal patients in Bangladesh.

From 300 stool samples, 58 Campylobacter strains were isolated by standard microbiological and biochemical methods. Of these, 40 strains were identified as Campylobacter jejuni and 5 as Campylobacter coli. The presence of flaA (100%), cadF (100%), racR (100%), dnaJ (100%), pldA (100%), ciaB (95%), virB11 (0%), ceuE (82.5%), cdtA (97.5%), cdtB (97.5%), cdtC (97.5%), and wlaN (7.5%) genes was detected in C. jejuni by PCR. All C. jejuni strains but one produced cytolethal distending toxin in a HeLa cell assay.