PostBP: A Python library to analyze outputs from wildfire growth models.

Wildfire is an important natural disturbance agent in Canadian forests, but it has also caused significant economic damage nationwide. Spatial fire growth models have emerged as important tools for representing wildfire dynamics across diverse landscapes, enabling the mapping of key wildfire hazard metrics such as location-specific burn probabilities or likelihoods of fire ignition. While these summary metrics have gained popularity, they often fall short in capturing the directional spread of wildfires and their potential spread distances. The metrics depicting the directional spread of wildfire can be derived from raw outputs generated with fire growth models, such as the perimeters and ignition locations of individual fires, but extracting this information requires complex data processing. To address this data gap, we present PostBP, an open-source Python package designed for post-processing the raw outputs of fire growth models - the ignition locations and perimeters of individual fires simulated over multiple stochastic iterations - into a matrix of fire spread likelihoods between all pairs of forest patches in a landscape. The PostBP also generates several other summary outputs, such as the source-sink ratio and the fire spread rose diagram. We provide an overview of PostBP's capabilities and demonstrate its practical application to a forested landscape.•Wildfire growth models generate large amounts of outputs, which are hard to summarize for practical decision-making.•The PostBP package calculates the summary metrics characterizing the directional spread of wildfires.•The fire risk summaries generated with PostBP can support the assessments of wildfire risk and mitigation measures.

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning.

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning, a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months. The scenarios involve three main meals and one bedtime snack per day, along with different variabilities and uncertainties for insulin sensitivity, mealtime, carbohydrate amount, and glucose measurement time. The results indicate that the proposed approach achieves comparable performance with CGM or SMBG as input signals, without influencing the total daily insulin dose. The results are a promising indication that AI algorithmic approaches can provide personalised adaptive insulin optimization and achieve glucose control-independent of the type of glucose monitoring technology.

Reinforcement learning versus proportional-integral-derivative control of hypnosis in a simulated intraoperative patient.

BACKGROUND: Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable. Model-based and proportional-integral-derivative (PID) controllers outperform manual control. We investigated the application of reinforcement learning (RL), an intelligent systems control method, to closed-loop BIS-guided, propofol-induced hypnosis in simulated intraoperative patients. We also compared the performance of the RL agent against that of a conventional PID controller. METHODS: The RL and PID controllers were evaluated during propofol induction and maintenance of hypnosis. The patient-hypnotic episodes were designed to challenge both controllers with varying degrees of interindividual variation and noxious surgical stimulation. Each controller was tested in 1000 simulated patients, and control performance was assessed by calculating the median performance error (MDPE), median absolute performance error (MDAPE), Wobble, and Divergence for each controller group. A separate analysis was performed for the induction and maintenance phases of hypnosis. RESULTS: During maintenance, RL control demonstrated an MDPE of -1% and an MDAPE of 3.75%, with 80% of the time at BIS(target) ± 5. The PID controller yielded a MDPE of -8.5% and an MDAPE of 8.6%, with 57% of the time at BIS(target) ± 5. In comparison, the MDAPE in the worst-controlled patient of the RL group was observed to be almost half that of the worst-controlled patient in the PID group. CONCLUSIONS: When compared with the PID controller, RL control resulted in slower induction but less overshoot and faster attainment of steady state. No difference in interindividual patient variation and noxious destabilizing challenge on control performance was observed between the 2 patient groups.

Reinforcement learning: a novel method for optimal control of propofol-induced hypnosis.

Reinforcement learning (RL) is an intelligent systems technique with a history of success in difficult robotic control problems. Similar machine learning techniques, such as artificial neural networks and fuzzy logic, have been successfully applied to clinical control problems. Although RL presents a mathematically robust method of achieving optimal control in systems challenged with noise, nonlinearity, time delay, and uncertainty, no application of RL in clinical anesthesia has been reported.

Paternal behavior increases testosterone levels in offspring of the California mouse.

Paternal care during early development influences pup survivorship in the monogamous and biparental California mouse, Peromyscus californicus. Moreover, paternal pup retrievals impact development of adult offspring aggression and the neuropeptide vasopressin, yet little is known about the underlying mechanisms of these developmental changes. Because testosterone can increase arginine vasopressin and aggression, we hypothesized that paternal pup retrievals increase testosterone levels in prepubertal male P. californicus pups. Male pups were assigned to one of three groups: hormonal baseline, nonretrieval control, or retrieval. On postnatal days 18-21, all pups and the mother were removed from the cage, and the focal male pup was placed either outside of the nest to elicit paternal retrievals (retrieval group) or in the nest to discourage paternal retrievals (nonretrieval group). Testosterone was elevated at 45-min, but not 90-min, post-manipulation in retrieved compared to nonretrieved pups. Moreover, there was a significant positive correlation between pup retrievals and testosterone in the 45-min group. This rapid testosterone rise in response to paternal retrievals may facilitate an increase in aggression and vasopressin in adult offspring. Therefore, this period of development previously viewed as hormonally quiescent may be more active in response to paternal behavior than previously thought.

Fuzzy control for closed-loop, patient-specific hypnosis in intraoperative patients: a simulation study.

Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzy controller demonstrated clinically acceptable performance, suggesting that further study is warranted.

Structure and reactivity of TNT and related species: application of spectroscopic approaches and quantum-chemical approximations toward understanding transformation mechanisms.

This paper presents our latest findings regarding the structure and reactivity of the nitroaromatics, TNT and selected derivatives, within their environmental context. We also demonstrate the useful and proactive role of combined computational chemistry and spectroscopy tools in studying competing transformation mechanisms, particularly those with toxic potential. TNT and selected derivatives were reacted via alkaline hydrolysis as well as via free radical initiators through monochromatic irradiation and through Fenton reactions in complex competing transformation mechanisms. Only alkaline hydrolysis produced consistent and effective transformation intermediate and final products in this research. However, irradiation of the product generated by alkaline hydrolysis at 450 nm (wavelength of maximum absorption) caused complete disappearance of the spectra.