Market basket analysis of grasshopper (Orthoptera: Acrididae) assemblages in eastern Wyoming: a 17‐year case study using associative analysis for ecological insights into grasshopper outbreaks

1. This study utilised an associative analysis (AA) technique named market basket analysis (MBA) to investigate whether particular grasshopper (Orthoptera: Acrididae) species associations are common during outbreaks (>9.6 grasshoppers m^?2) that last >3 years. This study is the first of its kind to use MBA on animal communities. 2. A subset of the 17 years of grasshopper density data from the Wyoming Grasshopper Survey Dataset was used to explore associations among grasshopper species. 3. Associations of certain species were found with over 80% confidence. Life‐history traits of those species commonly found together were examined and compared (a posteriori), creating opportunities to hypothesise certain ecological relationships (e.g. interspecific competition, indirect mutualism) for future studies. 4. This case study shows that further MBA analysis of grasshopper assemblages should prove useful in discovering ecological relationships of grasshopper species during outbreaks. Preliminary examples are demonstrated.     

Integrating Linguistics,Social Structure,and Geography to Model Genetic Diversity within India

India represents an intricate tapestry of population substructure shaped by geography, language, culture, and social stratification. Although geography closely correlates with genetic structure in other parts of the world, the strict endogamy imposed by the Indian caste system and the large number of spoken languages add further levels of complexity to understand Indian population structure. To date, no study has attempted to model and evaluate how these factors have interacted to shape the patterns of genetic diversity within India. We merged all publicly available data from the Indian subcontinent into a data set of 891 individuals from 90 well-defined groups. Bringing together geography, genetics, and demographic factors, we developed Correlation Optimization of Genetics and Geodemographics to build a model that explains the observed population genetic substructure. We show that shared language along with social structure have been the most powerful forces in creating paths of gene flow in the subcontinent. Furthermore, we discover the ethnic groups that best capture the diverse genetic substructure using a ridge leverage score statistic. Integrating data from India with a data set of additional 1,323 individuals from 50 Eurasian populations, we find that Indo-European and Dravidian speakers of India show shared genetic drift with Europeans, whereas the Tibeto-Burman speaking tribal groups have maximum shared genetic drift with East Asians.     

Mechanical biomarkers of medial compartment knee osteoarthritis diagnosis and severity grading: Discovery phase

ObjectiveTo investigate, as a discovery phase, if 3D knee kinematics assessment parameters can serve as mechanical biomarkers, more specifically as diagnostic biomarker and burden of disease biomarkers, as defined in the Burden of Disease, Investigative, Prognostic, Efficacy of Intervention and Diagnostic classification scheme for osteoarthritis (OA) (Altman et al., 1986). These biomarkers consist of a set of biomechanical parameters discerned from 3D knee kinematic patterns, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation measurements, during gait recording.Methods100 medial compartment knee OA patients and 40 asymptomatic control subjects participated in this study. OA patients were categorized according to disease severity, by the Kellgren and Lawrence grading system. The proposed biomarkers were identified by incremental parameter selection in a regression tree of cross-sectional data. Biomarker effectiveness was evaluated by receiver operating characteristic curve analysis, namely, the area under the curve (AUC), sensitivity and specificity.ResultsDiagnostic biomarkers were defined by a set of 3 abduction/adduction kinematics parameters. The performance of these biomarkers reached 85% for the AUC, 80% for sensitivity and 90% for specificity; the likelihood ratio was 8%. Burden of disease biomarkers were defined by a 3-decision tree, with sets of kinematics parameters selected from all 3 movement planes.ConclusionThe results demonstrate, as part of a discovery phase, that sets of 3D knee kinematic parameters have the potential to serve as diagnostic and burden of disease biomarkers of medial compartment knee OA.     

PFA: Program for the Quantitative Assessment of Cell Metabolism by Spectral Data Analysis

Assessment of mitochondrial oxidative metabolism has wide-ranging importance, from pharmacokinetic analysis to studies in cell viability and apoptosis. Here we present the Perfusion File Analyzer (PFA) application for the real-time analysis of spectral data to measure cytochrome c reduction, cytochrome a3 reduction, and other parameters important to cellular metabolism, which are collected during tissue perfusion experiments. Our current efforts are focused on quantitating changes in mitochondrial function by normalizing baseline drift of spectral data while addressing two major challenges: (1) a lack of real-time feedback from the system when aiming is compromised, and (2) an inability to adjust calculated data in the event of spectral shift. PFA has been developed to address these issues, and is currently used for quality assessment of human islets prior to clinical transplantation.     

Multiple early factors anticipate post-acute COVID-19 sequelae

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Simulating tick distributions over sub-Saharan Africa: the use of observed and simulated climate surfaces

Aim A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or high‐resolution model‐derived climate data [Division of Atmospheric Research limited‐area model (DARLAM)] for predictive species modelling, using tick distributions from sub‐Saharan Africa. Location The analysis is restricted to sub‐Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 × 60 km. Methods Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. Results The predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels. Main conclusions The use of data derived from high‐resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km² for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.     

Predictability of the terrestrial carbon cycle

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool‐dominant transfers, and the first‐order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.     

Assessment of soil quality indices for salt-affected agricultural land in Kurdistan Province,Iran

Soil quality indices (SQIs) were an important tool for evaluating agro-ecosystems. Salinization and alkalization are major environmental problems that have threatened agricultural productivity since ancient times. The aim of this study is to assess soil quality in salt-affected agricultural land in Kurdistan Province, Iran, using three indices; the Additive Soil Quality Index (SQI_a), the Weighted Additive Soil Quality Index (SQI_w), and the Nemoro Soil Quality Index (SQI_n). Each of the soil quality indices were calculated using a Total Data Set (TDS) and a Minimum Data Set (MDS) approach. The TDS consisted of nine soil quality parameters measured on 150 samples (0–30 cm depth): pH, Electrical Conductivity (EC), Organic Carbon (OC), Cation Exchange Capacity (CEC), Carbonate Calcium Equivalent (CCE), Exchangeable Sodium Percentage (ESP), Sodium Adsorption Ratio (SAR), Mean Weight Diameter (MWD), and Bulk Density (BD). Principal components analysis (PCA) was used to determine which indicators were to be included in the MDS. Indicator Kriging (IK) highlighted areas with a high risk of exceeding critical threshold values of EC, ESP, and SAR and having low soil quality. In non-salt-affected areas soil quality and the risk of exceeding critical threshold values and having low soil quality were lower and higher, respectively, compared to salt-affected regions. The MDS method showed a decrease in the area and proportion of grades with high and very high quality (I and II) and an increase in grades with low and very low quality (IV and V) compared to the TDS. The results of linear correlation, match, and kappa statistic analysis showed that soil quality was better estimated using the SQI_w compared to the SQI_a and the SQI_n. In addition there were higher values of agreement (match and kappa statistic) for the TSD than MSD. However, using the SQI_w index and MDS method can adequately represent the TDS (R² = 0.82) and thus reduce the time and cost involved in evaluating soil quality.