Relationships between vegetation types and soil properties along a topographical gradient on the northern coast of the Brgger Peninsula, Svalbard

Vegetation patterns in the high Arctic vary not only with proglacial chronosequence but also with a topographical gradient on older deglaciated areas which are covered by mature vegetation. A preliminary survey of local scale vegetation patterns along a topographical gradient with special reference to soil properties was conducted on the northern coast of the Brgger Peninsula, Svalbard in the high Arctic. Three community types of vascular plants were distinguished by common dominants: Cardamine type, Salix type and Oxyria/Luzula type. Each community type was comprised of different habitat types. The Cardamine type was restricted to near the bottom of depressions, where there was high soil water content. The Oxyria/Luzula type was characterized by habitats belonging to upper slopes to ridges, having high gravel content soils. On the other hand, the habitat preference of the Salix type, which had higher vegetation cover and biomass of vascular plants compared to the other two types, was not clear. The core habitat of fertile/undisturbed area, as described by the centrifugal community organization model, might be dominated by Salix polaris in this area. The other species had a refugium from interspecific competition in their preferred peripheral habitats such as exposed ridge or wetland

Evaluation of extensional and torsional stiffness of single actin filaments by molecular dynamics analysis.

It is essential to investigate the mechanical behaviour of cytoskeletal actin filaments in order to understand their critical role as mechanical components in various cellular functional activities. These actin filaments consisting of monomeric molecules function in the thermal fluctuations. Hence, it is important to understand their mechanical behaviour on the microscopic scale by comparing the stiffness based on thermal fluctuations with the one experimentally measured on the macroscopic scale. In this study, we perform a large-scale molecular dynamics (MD) simulation for a half-turn structure of an actin filament. We analyse its longitudinal and twisting Brownian motions in equilibrium and evaluated its apparent extensional and torsional stiffness on the nanosecond scale. Upon increasing the sampling-window durations for analysis, the apparent stiffness gradually decreases and exhibits a trend to converge to a value that is close to the experimental value. This suggests that by extrapolating the data obtained in the MD analysis, we can estimate the experimentally determined stiffness on the microsecond to millisecond scales. For shorter temporal scales, the apparent stiffness is larger than experimental values, indicating that fast, local motions of the molecular structure are dominant. To quantify the local structural changes within the filament on the nanosecond scale and investigate the molecular mechanisms, such as the binding of the actin-regulatory proteins to the filaments, it is preferable to analyse the mechanical behaviour on the nanometre and nanosecond scales using MD simulation

Investigation of the Coefficient of Earth Pressure for Improved Ground by Compaction Grouting in the Full-Scale Field Liquefaction Experiment

This paper describes the effect of countermeasures for liquefaction by compaction grouting, which was investigated by the experiment of full-scale field liquefaction by controlled blast technique. The experiment was conducted to assess the performance of airport facilities subjected to liquefaction, to investigate damage mechanism, and to estimate the effect of countermeasures for liquefaction by compaction grouting applied to liquefiable sand layer under runway pavement. In this study, before and after grouting and after artificial liquefaction caused by in-situ blasting, self boring pressure-meter tests at the center and the edge of a grouted area were carried out to investigate the coefficient of earth pressure, K, for evaluation of the improved ground because it is generally known that compaction grouting makes K-value increase in and around the grouted area. Additionally, to estimate the continuation of improving effect after liquefaction, K-values after blast were also investigated at same points. As the results of investigation, it was found that post-liquefaction K-value was higher than that of untreated ground before improvement and compaction grouting with cost-reduction design examined in this study, that is, the cost-reduction design is effective

Rescue with an anti-inflammatory peptide of chickens infected H5N1 avian flu

Chickens suffering from avian flu caused by H5N1 influenza virus are destined to die within 2 days due to a systemic inflammatory response. Since HVJ infection (1,2) and influenza virus infection (3,4) cause infected cells to activate homologous serum complement, the systemic inflammatory response elicited could be attributed to the unlimited generation of C5a anaphylatoxin of the complement system, which is a causative peptide of serious inflammation. In monkeys inoculated with a lethal dose of LPS (4 mg/kg body weight), inhibition of C5a by an inhibitory peptide termed AcPepA (5) rescued these animals from serious septic shock which would have resulted in death within a day (6). Therefore, we tested whether AcPepA could also have a beneficial effect on chickens with bird flu. On another front, enhanced production of endothelin-1 (ET-1) and the activation of mast cells (MCs) have been implicated in granulocyte sequestration (7). An endothelin receptor derived antisense homology box peptide (8) designated ETR-P1/fl was shown to antagonize endothelin A receptor (ET-A receptor) (9) and reduce such inflammatory responses as endotoxin-shock (10) and hemorrhagic shock (11), thereby suppressing histamine release in the circulation (12). Thus, we also administered ETR-P1/fl to bird flu chickens expecting suppression of a systemic inflammatory response

Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature

Real-world optimisation problems often feature complex combinations of (1) diverse constraints, (2) discrete and mixed spaces, and are (3) highly parallelisable. (4) There are also cases where the objective function cannot be queried if unknown constraints are not satisfied, e.g. in drug discovery, safety on animal experiments (unknown constraints) must be established before human clinical trials (querying objective function) may proceed. However, most existing works target each of the above three problems in isolation and do not consider (4) unknown constraints with query rejection. For problems with diverse constraints and/or unconventional input spaces, it is difficult to apply these techniques as they are often mutually incompatible. We propose cSOBER, a domain-agnostic prudent parallel active sampler for Bayesian optimisation, based on SOBER of Adachi et al. (2023). We consider infeasibility under unknown constraints as a type of integration error that we can estimate. We propose a theoretically-driven approach that propagates such error as a tolerance in the quadrature precision that automatically balances exploitation and exploration with the expected rejection rate. Moreover, our method flexibly accommodates diverse constraints and/or discrete and mixed spaces via adaptive tolerance, including conventional zero-risk cases. We show that cSOBER outperforms competitive baselines on diverse real-world blackbox-constrained problems, including safety-constrained drug discovery, and human-relationship-aware team optimisation over graph-structured space.Comment: 24 pages, 5 figure

Causes and consequences of stress generation : Longitudinal associations of negative events, aggressive behaviors, rumination, and depressive symptoms

The present study examined the causes and consequences of stress generation in university students in Japan. A two-wave longitudinal study with an 8- or 9-week interval was conducted in the fall of 2020. Undergraduate and graduate students at four universities in Japan (N = 201) completed self-report measures assessing experiences of negative interpersonal dependent events, negative non-interpersonal events, and negative independent events at two times. At the same time, they also responded to measures of aggressive behaviors, trait rumination, and depressive symptoms. Path analyses revealed that baseline aggressive behaviors were positively associated with an increase in subsequent negative interpersonal dependent events, even after controlling for the influences of negative interpersonal dependent events, rumination, and depressive symptoms at baseline. However, aggressive behaviors were not significantly associated with subsequent negative non-interpersonal dependent events or negative independent events. These findings suggest that aggressive behaviors may have been a factor leading to interpersonal stress generation. Furthermore, all categories of negative event experiences predicted an increase in subsequent depressive symptoms, but not subsequent rumination, and rumination was not significantly associated with subsequent depressive symptoms. This research extends previous studies on the causes and consequences of stress generation conducted in the US by using specific measures of aggressive behaviors and including a non-restricted sample of university students in Japan

SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces

Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel. However, current methods do not scale to large batch sizes -- a frequent desideratum in practice (e.g. drug discovery or simulation-based inference). We present a novel algorithm, SOBER, which permits scalable and diversified batch global optimisation and quadrature with arbitrary acquisition functions and kernels over discrete and mixed spaces. The key to our approach is to reformulate batch selection for global optimisation as a quadrature problem, which relaxes acquisition function maximisation (non-convex) to kernel recombination (convex). Bridging global optimisation and quadrature can efficiently solve both tasks by balancing the merits of exploitative Bayesian optimisation and explorative Bayesian quadrature. We show that SOBER outperforms 11 competitive baselines on 12 synthetic and diverse real-world tasks.Comment: 34 pages, 12 figure

Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using Bayesian quadrature. The model evidence is adopted as the selection metric, choosing the simplest model that describes the data, in the spirit of Occam's razor. However, estimating this requires integral computations over parameter space, which is usually prohibitively expensive. Bayesian quadrature offers sample-efficient integration via model-based inference that minimises the number of battery model evaluations. The posterior distribution of model parameters can also be inferred as a byproduct without further computation. Here, the simplest lithium-ion battery models, equivalent circuit models, were used to analyse the sensitivity of the selection criterion to given different datasets and model configurations. We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior. The model evidence can spot the optimal model in such cases, simultaneously providing the variance of the evidence inference itself as an indication of confidence. We also show that Bayesian quadrature can compute the evidence faster than popular Monte Carlo based solvers.Comment: 11 pages, 2 figures, accepted at IFAC202