Use of the cross-leg distally based sural artery flap for the reconstruction of complex lower extremity defects

Cross-leg flaps are a useful reconstructive option for complex lower limb defects when free flaps cannot be performed owing to vessel damage. We describe the use of the extended distally based sural artery flap in a cross-leg fashion for lower extremity coverage in three patients. To maximise the viability of these extended flaps, a delay was performed by raising them in a bipedicled fashion before gradual division of the tip over 5 to 7 days for cross-leg transfer. Rigid coupling of the lower limbs with external fixators was critical in preventing flap avulsion and to promote neovascular takeover. The pedicle was gradually divided over the ensuing 7 to 14 days before full flap inset and removal of the external fixators. In all three patients, the flaps survived with no complications and successful coverage of the critical defect was achieved. One patient developed a grade 2 pressure injury on his heel that resolved with conservative dressings. The donor sites and external fixator pin wounds healed well, with no functional morbidity. The cross-leg extended distally based sural artery flap is a reliable reconstructive option in challenging scenarios. Adequate flap delay, manoeuvres to reduce congestion, and postoperative rigid immobilization are key to a successful outcome

A fast tunable driver of light source for the TRIDENT Pathfinder experiment

TRIDENT (The tRopIcal DEep-sea Neutrino Telescope) is a proposed next-generation neutrino telescope to be constructed in the South China Sea. In September 2021, the TRIDENT Pathfinder experiment (TRIDENT EXplorer, T-REX for short) was conducted to evaluate the in-situ optical properties of seawater. The T-REX experiment deployed three digital optical modules at a depth of 3420 meters, including a light emitter module (LEM) and two light receiver modules (LRMs) equipped with photomultiplier tubes (PMTs) and cameras to detect light signals. The LEM emits light in pulsing and steady modes. It features a fast tunable driver to activate light-emitting diodes (LEDs) that emit nanosecond-width light pulses with tunable intensity. The PMTs in the LRM receive single photo-electron (SPE) signals with an average photon number of approximately 0.3 per 1-microsecond time window, which is used to measure the arrival time distribution of the SPE signals. The fast tunable driver can be remotely controlled in real-time by the data acquisition system onboard the research vessel, allowing for convenient adjustments to the driver's parameters and facilitating the acquisition of high-quality experimental data. This paper describes the requirements, design scheme, and test results of the fast tunable driver, highlighting its successful implementation in the T-REX experiment and its potential for future deep-sea experiments

Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection

Striking a balance between precision and efficiency presents a prominent challenge in the bird's-eye-view (BEV) 3D object detection. Although previous camera-based BEV methods achieved remarkable performance by incorporating long-term temporal information, most of them still face the problem of low efficiency. One potential solution is knowledge distillation. Existing distillation methods only focus on reconstructing spatial features, while overlooking temporal knowledge. To this end, we propose TempDistiller, a Temporal knowledge Distiller, to acquire long-term memory from a teacher detector when provided with a limited number of frames. Specifically, a reconstruction target is formulated by integrating long-term temporal knowledge through self-attention operation applied to feature teachers. Subsequently, novel features are generated for masked student features via a generator. Ultimately, we utilize this reconstruction target to reconstruct the student features. In addition, we also explore temporal relational knowledge when inputting full frames for the student model. We verify the effectiveness of the proposed method on the nuScenes benchmark. The experimental results show our method obtain an enhancement of +1.6 mAP and +1.1 NDS compared to the baseline, a speed improvement of approximately 6 FPS after compressing temporal knowledge, and the most accurate velocity estimation