Hydraulic conductivity of peat in Western Siberia

The paper aims at estimating hydraulic conductivity of peat. It is a complicated process since peat has some unique properties that are not typical for mineral soils. Other difficulties involve the lack of elaborated procedure for laboratory testing and data processing, high sensitivity of peat soils to external and internal factors; which leads to considerable fluctuation in peat hydraulic conductivity values. The specific properties of hydraulic conductivity in peat soils are proved by numerous experimental data. The article also provides the permeability values for the most typical peat species in Western Siberia. The obtained data allow forecasting peat soil response to the development of peatlands

Synthesis, Structural Elucidation, and Biological Evaluation of NSC12, an Orally Available Fibroblast Growth Factor (FGF) Ligand Trap for the Treatment of FGF-Dependent Lung Tumors

NSC12 is an orally available pan-FGF trap able to inhibit FGF2/FGFR interaction and endowed with promising antitumor activity. It was identified by virtual screening from a NCI small molecule library, but no data were available about its synthesis, stereochemistry, and physicochemical properties. We report here a synthetic route that allowed us to characterize and unambiguously identify the structure of the active compound by a combination of NMR spectroscopy and in silico conformational analysis. The synthetic protocol allowed us to sustain experiments aimed at assessing its therapeutic potential for the treatment of FGF-dependent lung cancers. A crucial step in the synthesis generated a couple of diastereoisomers, with only one able to act as a FGF trap molecule and to inhibit FGF-dependent receptor activation, cell proliferation, and tumor growth when tested in vitro and in vivo on murine and human lung cancer cells

Potential Use of Tea Tree Oil as a Disinfectant Agent against Coronaviruses: A Combined Experimental and Simulation Study

: The COVID-19 pandemic has highlighted the relevance of proper disinfection procedures and renewed interest in developing novel disinfectant materials as a preventive strategy to limit SARS-CoV-2 contamination. Given its widely known antibacterial, antifungal, and antiviral properties, Melaleuca alternifolia essential oil, also named Tea tree oil (TTO), is recognized as a potential effective and safe natural disinfectant agent. In particular, the proposed antiviral activity of TTO involves the inhibition of viral entry and fusion, interfering with the structural dynamics of the membrane and with the protein envelope components. In this study, for the first time, we demonstrated the virucidal effects of TTO against the feline coronavirus (FCoVII) and the human coronavirus OC43 (HCoV-OC43), both used as surrogate models for SARS-CoV-2. Then, to atomistically uncover the possible effects exerted by TTO compounds on the outer surface of the SARS-CoV-2 virion, we performed Gaussian accelerated Molecular Dynamics simulations of a SARS-CoV-2 envelope portion, including a complete model of the Spike glycoprotein in the absence or presence of the three main TTO compounds (terpinen-4-ol, γ-terpinene, and 1,8-cineole). The obtained results allowed us to hypothesize the mechanism of action of TTO and its possible use as an anti-coronavirus disinfectant agent

Benzisothiazolinone Derivatives as Potent Allosteric Monoacylglycerol Lipase Inhibitors That Functionally Mimic Sulfenylation of Regulatory Cysteines

We describe a set of benzisothiazolinone (BTZ) derivatives that are potent inhibitors of monoacylglycerol lipase (MGL), the primary degrading enzyme for the endocannabinoid 2-arachidonoyl-sn-glycerol (2-AG). Structure-activity relationship studies evaluated various substitutions on the nitrogen atom and the benzene ring of the BTZ nucleus. Optimized derivatives with nanomolar potency allowed us to investigate the mechanism of MGL inhibition. Site-directed mutagenesis and mass spectrometry experiments showed that BTZs interact in a covalent reversible manner with regulatory cysteines, Cys201 and Cys208, causing a reversible sulfenylation known to modulate MGL activity. Metadynamics simulations revealed that BTZ adducts favor a closed conformation of MGL that occludes substrate recruitment. The BTZ derivative 13 protected neuronal cells from oxidative stimuli and increased 2-AG levels in the mouse brain. The results identify Cys201 and Cys208 as key regulators of MGL function and point to the BTZ scaffold as a useful starting point for the discovery of allosteric MGL inhibitors

Contribution of Ultrasound in Current Practice for Managing Juvenile Idiopathic Arthritis

The interest and application of musculoskeletal ultrasound (MSUS) in juvenile idiopathic arthritis (JIA) are increasing. Numerous studies have shown that MSUS is more sensitive than clinical examination for detecting subclinical synovitis. MSUS is a well-accepted tool, easily accessible and non-irradiating. Therefore, it is a useful technique throughout JIA management. In the diagnostic work-up, MSUS allows for better characterizing the inflammatory involvement. It helps to define the disease extension, improving the classification of patients into JIA subtypes. Moreover, it is an essential tool for guiding intra-articular and peritendinous procedures. Finally, during the follow-up, in detecting subclinical disease activity, MSUS can be helpful in therapeutic decision-making. Because of several peculiarities related to the growing skeleton, the MSUS standards defined for adults do not apply to children. During the last decade, many teams have made large efforts to define normal and pathological US features in children in different age groups, which should be considered during the US examination. This review describes the specificities of MSUS in children, its applications in clinical practice, and its integration into the new JIA treat-to-target therapeutic approach

A Stochastic Approach for Scheduling AI Training Jobs in GPU-based Systems

International audienceIn this work, we optimize the scheduling of Deep Learning (DL) training jobs from the perspective of a Cloud Service Provider running a data center, which efficiently selects resources for the execution of each job to minimize the average energy consumption while satisfying time constraints. To model the problem, we first develop a Mixed-Integer Non-Linear Programming formulation. Unfortunately, the computation of an optimal solution is prohibitively expensive, and to overcome this difficulty, we design a heuristic STochastic Scheduler (STS). Exploiting the probability distribution of early termination, STS determines how to adapt the resource assignment during the execution of the jobs to minimize the expected energy cost while meeting the job due dates. The results of an extensive experimental evaluation show that STS guarantees significantly better results than other methods in the literature, effectively avoiding due date violations and yielding a percentage total cost reduction between 32% and 80% on average. We also prove the applicability of our method in real-world scenarios, as obtaining optimal schedules for systems of up to 100 nodes and 400 concurrent jobs requires less than 5 seconds. Finally, we evaluated the effectiveness of GPU sharing, i.e., running multiple jobs in a single GPU. The obtained results demonstrate that depending on the workload and GPU memory, this further reduces the energy cost by 17-29% on average

A Stochastic Approach for Scheduling AI Training Jobs in GPU-based Systems

Deep Learning (DL) methods currently address a variety of complex tasks. GPUs significantly accelerate the training process, expanding the range of problems that can be solved in a reasonable computing time. As a consequence, the demand for high-performance GPU-based cloud servers increased dramatically, dictating the necessity for Cloud Service Providers (CSPs) to exploit effective resource management strategies. In this work, we optimize the scheduling of DL training jobs from the perspective of a CSP running a data center, efficiently selecting resources for the execution of each job in order to minimize the average energy consumption. We develop a Mixed-Integer Non-Linear Programming (MINLP) formulation to model the problem, and a heuristic STochastic Scheduler (STS) that, exploiting the probability distribution of early termination, determines how to vary the resource assignment during the execution of the jobs to minimize the expected energy cost while meeting the jobs due dates. The results of an extensive experimental campaign show that STS guarantees significantly better results than other methods in the literature, effectively avoiding due dates violations and yielding an average total cost reduction between 38% and 80%. We also prove the applicability of our method in real-world scenarios, as obtaining optimal schedules for systems of up to 100 nodes and 400 concurrent jobs requires less than 5 seconds. Finally, we evaluated the effectiveness of GPU sharing, that is, running multiple jobs in a single GPU. The results demonstrate that, depending on the workload and GPU memory, this further reduces the energy cost by 17-29% on average

Combinations of ATR, Chk1 and Wee1 Inhibitors with Olaparib Are Active in Olaparib Resistant <i>Brca1</i> Proficient and Deficient Murine Ovarian Cells

Background. Poly(ADP-ribose) polymerases inhibitor (PARPi) have shown clinical efficacy in ovarian carcinoma, especially in those harboring defects in homologous recombination (HR) repair, including BRCA1 and BRCA2 mutated tumors. There is increasing evidence however that PARPi resistance is common and develops through multiple mechanisms. Methods. ID8 F3 (HR proficient) and ID8 Brca1-/- (HR deficient) murine ovarian cells resistant to olaparib, a PARPi, were generated through stepwise drug concentrations in vitro. Both sensitive and resistant cells lines were pharmacologically characterized and the molecular mechanisms underlying olaparib resistance. Results. In ID8, cells with a HR proficient background, olaparib resistance was mainly caused by overexpression of multidrug resistance 1 gene (MDR1), while multiple heterogeneous co-existing mechanisms were found in ID8 Brca1-/- HR-deficient cells resistant to olaparib, including overexpression of MDR1, a decrease in PARP1 protein level and partial reactivation of HR repair. Importantly, combinations of ATR, Chk1 and Wee1 inhibitors with olaparib were synergistic in sensitive and resistant sublines, regardless of the HR cell status. Conclusion. Olaparib-resistant cell lines were generated and displayed multiple mechanisms of resistance, which will be instrumental in selecting new possible therapeutic options for PARPi-resistant ovarian tumors