Biodiversity of commercially important finfish species caught by mid-water and bottom trawls from two different coasts of Arabian Sea: Threats and conservation strategies

This study was conducted to estimate the diversity and the occurrence of commercially important finfish species collected by twenty fish sampling site of Sindh and Baluchistan coasts of the Arabian Sea in Pakistan from January to December 2019. Additionally, physicochemical characteristics of seawater were analyzed from these selected sites and found to be within suitable ranges required for fish growth and survive. A total of 81287 fish individuals were collected and identified as 49 species belonging to 26 families in our study. The most diversified family was Sparidae (13 species) followed by Carangidae and Lutjanidae (4 species), Mullidae, Serranidae, Ariidae (3 species), and Sciaenidae (2 species). The remaining 20 families were represented by only one species. The values of Shannon diversity index calculated for the four selected habitats revealed that high fish diversity was reported at Sonmiani Coast (H'=1.81), while less at Ormara Coast (H'=0.23). Likewise, Evenness index (E) was high at Sonmiani Coast (E=0.50) and less fish diversity was reported at Ormara Coast (E=0.06). Reducing risks to threatened marine species in coastal habitats also requires conservation actions at multiple scales. Thus, it was concluded that our study could be valuable in providing the more information’s regarding to the diversity of finfish species and their occurrence along the Pakistan Coast. Further, to better understand the effects, regular monitoring and conservation measures should be taken to mitigate the influence of anthropogenic activities and protect finfish diversity from further decline

Multifractal and joint multifractal analysis of soil invertebrate fauna, altitude, and organic carbon

ABSTRACT The objectives of this study were to evaluate the degree of multifractality of the spatial distribution of altitude, organic carbon concentration, and invertebrate fauna diversity, and to characterize the degree of joint multifractal association among these variables. Soil sampling was performed every 20 m across a 2,540 m transect, with a total of 128 sampling points in a sugarcane area in Goiana municipality, Pernambuco State. For each sampling point, the altitude, organic carbon concentration, and macrofauna diversity (diversity indices and functional groups) were evaluated. Spatial distributions of altitude, organic carbon concentration, and macrofauna diversity were characterized by the generalized dimension spectrum (Dq) and singularity spectrums [f(α) versus α], which presented multifractal behavior with different degrees of heterogeneity in scales. Joint multifractal analysis was useful for revealing the relationships at multiple scales between the studied variables, as demonstrated by the non-detected associations using traditional statistical methods. To quantify the spatial variability of edaphic fauna based on the multiple scales and association sets in the joint dimension, the impact of agricultural production systems on biological diversity can be described. All of the studied variables displayed a multifractal behavior with greater or lower heterogeneity degree depending on the variable, with altitude and organic carbon being the most homogeneous attributes.

Digital In Situ Data Collection in Earth Observation, Monitoring and Agriculture—Progress towards Digital Agriculture

Digital solutions in agricultural management promote food security and support the sustainable use of resources. As a result, remote sensing (RS) can be seen as an innovation for the fast generation of reliable information for agricultural management. Near real-time processed RS data can be used as a tool for decision making on multiple scales, from subplot to the global level. This high potential is not yet fully applied, due to often limited access to ground truth information, which is crucial for the development of transferable applications and acceptance. In this study we present a digital workflow for the acquisition, processing and dissemination of agroecological information based on proprietary and open-source software tools with state-of-the-art web-mapping technologies. Data is processed in near real-time and thus can be used as ground truth information to enhance quality and performance of RS-based products. Data is disseminated by easy-to-understand visualizations and download functionalities for specific application levels to serve specific user needs. It thus can increase expert knowledge and can be used for decision support at the same time. The fully digital workflow underpins the great potential to facilitate quality enhancement of future RS products in the context of precision agriculture by safeguarding data quality. The generated FAIR (findable, accessible, interoperable, reusable) datasets can be used to strengthen the relationship between scientists, initiatives and stakeholders.

Plant community data from a statewide survey of paired serpentine and non-serpentine soils in California, USA

Soils derived from ultramafic parent materials (hereafter serpentine) provide habitat for unique plant communities containing species with adaptations to the low nutrient levels, high magnesium: calcium ratios, and high metal content (Ni, Zn) that characterize serpentine. Plants on serpentine have long been studied in evolution and ecology, and plants adapted to serpentine contribute disproportionately to plant diversity in many parts of the world. In 2000-2003, serpentine plant communities were sampled at 107 locations representing the full range of occurrence of serpentine in California, USA, spanning large gradients in climate. In 2009-2010, plant communities were similarly sampled at 97 locations on nonserpentine soil, near to and paired with 97 of the serpentine sampling locations. (Some serpentine locations were revisited in 2009-2010 to assess the degree of change since 2000-2003, which was minimal.) At each serpentine or nonserpentine location, a north- and a south-facing 50 m x10 m plot were sampled. This design produced 97 “sites” each consisting of four “plots” (north-south exposure, serpentine-nonserpentine soil). All plots were initially visited >3 times over 2 years to record plant diversity and cover, and a subset were revisited in 2014 to examine community change after a drought. The original question guiding the study was how plant diversity is shaped by the spatially patchy nature of the serpentine habitat. Subsequently, we investigated how climate drives plant diversity at multiple scales (within locations, between locations on the same and different soil types, and across entire regions) and at different levels of organization (taxonomic, functional, and phylogenetic).

Multiscale Variability and the Comparison of Ground and Satellite Radar Based Measures of Peatland Surface Motion for Peatland Monitoring

Peatland surface motion is highly diagnostic of peatland condition. Interferometric Synthetic Aperture Radar (InSAR) can measure this at the landscape scale but requires ground validation. This necessitates upscaling from point to areal measures (80 × 90 m) but is hampered by a lack of data regarding the spatial variability of peat surface motion characteristics. Using a nested precise leveling approach within two areas of upland and low-lying blanket peatland within the Flow Country, Scotland, we examine the multiscale variability of peat surface motion. We then compare this with InSAR timeseries data. We find that peat surface motion varies at multiple scales within blanket peatland with decreasing dynamism with height above the water table e.g., hummocks < lawn < hollows. This trend is dependent upon a number of factors including ecohydrology, pool size/density, peat density, and slope. At the site scale motion can be grouped into central, marginal, and upland peatlands with each showing characteristic amplitude, peak timing, and response to climate events. Ground measurements which incorporate local variability show good comparability with satellite radar derived timeseries. However, current limitations of phase unwrapping in interferometry means that during an extreme drought/event InSAR readings can only qualitatively replicate peat movement in the most dynamic parts of the peatland e.g., pool systems, quaking bog.

A 3D Agent-Based Model of Lung Fibrosis

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).

An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

Electroencephalogram (EEG) based motor imagery (MI) classification is an important aspect in brain-machine interfaces (BMIs) which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, the MI classification task is challenging due to inherent complex properties, inter-subject variability, and low signal-to-noise ratio (SNR) of EEG signals. To overcome the above-mentioned issues, the current work proposes an efficient multi-scale convolutional neural network (MS-CNN) which can extract the distinguishable features of several non-overlapping canonical frequency bands of EEG signals from multiple scales for MI-BCI classification. In the framework, discriminant user-specific features have been extracted and integrated to improve the accuracy and performance of the CNN classifier. Additionally, different data augmentation methods have been implemented to further improve the accuracy and robustness of the model. The model achieves an average classification accuracy of 93.74% and Cohen's kappa-coefficient of 0.92 on the BCI competition IV2b dataset outperforming several baseline and current state-of-the-art EEG-based MI classification models. The proposed algorithm effectively addresses the shortcoming of existing CNN-based EEG-MI classification models and significantly improves the classification accuracy. The current framework can provide a stimulus for designing efficient and robust real-time human-robot interaction.

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

Motivation: Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. Results: PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former PhysiBoSS and expands its functionalities such as using user-defined models and cells' specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. Availability and implementation: PhysiBoSS 2.0 is open-source and publicly available on GitHub (https://github.com/PhysiBoSS/PhysiBoSS) under the BSD 3-clause license. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (https://nanohub.org/tools/pba4tnf/).