Use of a subcutaneous ureteral bypass device for treatment of bilateral proximal ureteral injury in a 9-month-old cat

Case summary A 9-month-old male domestic longhair cat presented following iatrogenic ureteral trauma after an attempted laparoscopic ovariectomy. Prior to identifying that the cat was male, both ureters were transected approximately 4 mm from the renal pelves. Initial management involved a left-sided Boari flap neoureterocystostomy, cystonephropexy and right ureteronephrectomy. Thirty-six hours later, the cat developed uroabdomen due to leakage from the neoureterocystostomy site. At a tertiary referral institution, the ureter was reconstructed via end-to-end anastomosis and a left-sided subcutaneous ureteral bypass (SUB) device was placed in the event the anastomosis failed. Five weeks after SUB placement, the cat was dysuric and stranguric. A urine culture was negative and clinical signs were attributed to sterile cystitis secondary to device placement. Blood urea nitrogen (BUN) was 22 mg/dl and creatinine was 1.2 mg/dl. Contrast pyelography confirmed device patency, but no contrast was identified through the ureteral anastomosis. At 12 months, BUN and creatinine were 1.5 mg/dl and 25 mg/dl, respectively, and a subclinical urinary tract infection was identified ( Enterococcus faecalis ). Antibiotic therapy was not prescribed in order to prevent multidrug resistance. At 42 months, BUN was 38 mg/dl and creatinine was 2.0 mg/dl. The cat had occasional and intermittent signs of pollakiuria and stranguria but was otherwise doing well. Relevance and novel information To our knowledge, this is the first case report to describe the use of a SUB device for management of traumatic proximal ureteral injury in a cat with one kidney. The case outcome provides valuable information about the direct effect of the SUB device and the presence of chronic Enterococcus species infection on long-term renal function

JAX-LOB: a GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading

Financial exchanges across the world use limit order books (LOBs) to process orders and match trades. For research purposes it is important to have large scale efficient simulators of LOB dynamics. LOB simulators have previously been implemented in the context of agent-based models (ABMs), reinforcement learning (RL) environments, and generative models, processing order flows from historical data sets and hand-crafted agents alike. For many applications, there is a requirement for processing multiple books, either for the calibration of ABMs or for the training of RL agents. We showcase the first GPU-enabled LOB simulator designed to process thousands of books in parallel, whether for identical or different securities, with an up to 75x faster per-message processing time. The implementation of our simulator - JAX-LOB - is based on design choices that aim to best exploit the powers of JAX without compromising on the realism of LOB-related mechanisms. We integrate JAX-LOB with other JAX packages, to provide an example of how one may address an optimal execution problem with reinforcement learning, and to share some preliminary results from end-to-end RL training on GPUs. The project code is available on GitHub