Post-Fire Management Impact on Natural Forest Regeneration through Altered Microsite Conditions

High severity stand-replacing wildfires can deeply affect forest ecosystems whose composition includes plant species lacking fire-related traits and specific adaptations. Land managers and policymakers need to be aware of the importance of properly managing these ecosystems, adopting post-disturbance interventions designed to reach management goals, and restoring the required ecosystem services. Recent research frequently found that post-fire salvage logging negatively affects natural regeneration dynamics, thereby altering successional pathways due to a detrimental interaction with the preceding disturbance. In this study, we compared the effects of salvage logging and other post-disturbance interventions (adopting different deadwood management strategies) to test their impact on microclimatic conditions, which potentially affect tree regeneration establishment and survival. After one of the largest and most severe wildfires in the Western Alps that affected stand-replacing behavior (100% tree mortality), a mountain forest dominated by Pinus sylvestris L., three post-fire interventions were adopted (SL-Salvage Logging, logging of all snags; CR-Cut and Release, cutting snags and releasing all deadwood on the ground; NI-No Intervention, all snags left standing). The differences among interventions concerning microclimatic conditions (albedo, surface roughness, solar radiation, soil moisture, soil temperature) were analyzed at different spatial scales (site, microsite). The management interventions influenced the presence and density of safe sites for regeneration. Salvage logging contributed to the harsh post-fire microsite environment by increasing soil temperature and reducing soil moisture. The presence of deadwood, instead, played a facilitative role in ameliorating microclimatic conditions for seedlings. The CR intervention had the highest soil moisture and the lowest soil temperature, which could be crucial for seedling survival in the first post-fire years. Due to its negative impact on microclimatic conditions affecting the availability of preferential microsites for regeneration recruitment, salvage logging should not be considered as the only intervention to be applied in post-fire environments. In the absence of threats or hazards requiring specific management actions (e.g., public safety, physical hazards for facilities), in the investigated ecosystems, no intervention, leaving all deadwood on site, could result in better microclimatic conditions for seedling establishment. A preferred strategy to speed-up natural processes and further increase safe sites for regeneration could be felling standing dead trees whilst releasing deadwood (at least partially) on the ground

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Out of kernel tuning and optimizations for portable large-scale docking experiments on GPUs

Virtual screening is an early stage in the drug discovery process that selects the most promising candidates. In the urgent computing scenario, finding a solution in the shortest time frame is critical. Any improvement in the performance of a virtual screening application translates into an increase in the number of candidates evaluated, thereby raising the probability of finding a drug. In this paper, we show how we can improve application throughput using Out-of-kernel optimizations. They use input features, kernel requirements, and architectural features to rearrange the kernel inputs, executing them out of order, to improve the computation efficiency. These optimizations’ implementations are designed on an extreme-scale virtual screening application, named LiGen, that can hinge on CUDA and SYCL kernels to carry out the computation on modern supercomputer nodes. Even if they are tailored to a single application, they might also be of interest for applications that share a similar design pattern. The experimental results show how these optimizations can increase kernel performance by 2 X, respectively, up to 2.2X in CUDA and up to 1.9X, in SYCL. Moreover, the reported speedup can be achieved with the best-proposed parameterization, as shown by the data we collected and reported in this manuscript

GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns

An extreme-scale virtual screening platform for drug discovery

Virtual screening is one of the early stages that aims to select a set of promising ligands from a vast chemical library. Molecular Docking is a crucial task in the process of drug discovery and it consists of the estimation of the position of a molecule inside the docking site. In the contest of urgent computing, we designed from scratch the EXSCALATE molecular docking platform to benefit from heterogeneous computation nodes and to avoid scaling issues

Plasma concentration of presepsin and its relationship to the diagnosis of infections in multiple trauma patients admitted to intensive care

Background and aims: Septic complications represent the pre- dominant cause of late death in poly-trauma patients. The necessi- ty to differentiate septic from non septic patients is more relevant at the early stage of the illness in order to improve the clinical out- come and to reduce the mortality. The identification of a sensitive and specific, clinically reliable, biomarker capable to early recog- nize incoming septic complications in trauma patients whose expression is not influenced by concomitant traumatic injuries, is still a challenge for the researchers in the field. patients (9 females and 39 males, mean age 47.6\ub119 years) with mul- tiple trauma was performed. The inclusion criterion was to suffer from acute trauma since no more than 24 hours and the exclusion cri- teria were the following: antibiotic treatment on admission and main- tained for more than 48 hours; on-going infection on admission not associated with trauma; treatment with immunosuppressors/ immunomodulants; age <18 years old. Presepsin was measured using an automated chemiluminescence analyser at 1, 3, 5 and 8 days post of hospitalization. The diagnosis of systemic inflammatory response syndrome (SIRS)/infection was established according to the criteria of the Surviving Sepsis Campaign. Materials and methods: A retrospective analysis on 48 adult Results and conclusions: In patients with SIRS, the mean pre- sepsin concentration was 917,08 (\ub169.042) ng/L vs 980,258 (\ub11951.32) ng/L in patients without SIRS (P=0.769). In the infected patients, the mean presepsin concentration was 1513.25 (\ub12296.54) ng/L vs 654.21 (\ub1511,068) ng/L (P<0.05) calculated among the non infected upon admission. The plasma presepsin concentration increased progressively during the first 8 days of hospitalization. Presepsin concentration in the infected patients was significantly higher than in non-infected patients. On the other hands no signifi- cant differences were found in the plasma level of presepsin among patients with and without SIRS. Any other clinical condition related to the trauma did not affect presepsin. Our data clearly suggest that presepsin may be considered an helpful diagnostic tool to early diagnose sepsis in trauma patients

Life during COVID-19 lockdown in Italy: the influence of cognitive state on psychosocial, behavioral and lifestyle profiles of older adults.

Few studies have examined lockdown effects on the way of living and well-being of older adults stratified by cognitive state. Since cognitive deficits are common in this population, we investigated how cognition influenced their understanding of the pandemic, socio-behavioral responses and lifestyle adaptations during lockdown, and how these factors affected their mood or memory.Telephone-based survey involving 204 older adults ≥65 y/o (median: 82) with previous assessments of cognitive state: 164 normal-old (NOLD), 24 mild-neurocognitive disorder (mild-NCD), 18 mild-moderate dementia. A structured questionnaire was developed to assess psychological and socio-behavioral variables. Logistic regression was used to ascertain their effects on mood and memory.With increasing cognitive deficits, understanding of the pandemic and the ability to follow lockdown policies, adapt to lifestyle changes, and maintain remote interactions decreased. Participants with dementia were more depressed; NOLDs remained physically and mentally active but were more bored and anxious. Sleeping and health problems independently increased the likelihood of depression (OR: 2.29; CI: 1.06-4.93;NOLD and mild-NCD groups showed similar mood-behavioral profiles suggesting better tolerance of lockdown. Those with dementia were unable to adapt and suffered from depression and cognitive complaints. To counteract lockdown effects, physical and mental activities and digital literacy should be encouraged