Default Parameters Research Articles

Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study.

During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities. The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users. For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application. The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees. This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.

Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation.

Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening.

Binding free energy calculation of a ligand to a protein receptor is a fundamental objective in drug discovery. Molecular mechanics/Generalized-Born (Poisson-Boltzmann) surface area (MM/GB(PB)SA) is one of the most popular methods for binding free energy calculations. It is more accurate than most scoring functions and more computationally efficient than alchemical free energy methods. Several open-source tools for performing MM/GB(PB)SA calculations have been developed, but they have limitations and high entry barriers to users. Here, we introduce Uni-GBSA, a user-friendly automatic workflow to perform MM/GB(PB)SA calculations, which can perform topology preparation, structure optimization, binding free energy calculation and parameter scanning for MM/GB(PB)SA calculations. It also offers a batch mode that evaluates thousands of molecules against one protein target in parallel for efficient application in virtual screening. The default parameters are selected after systematic testing on the PDBBind-2011 refined dataset. In our case studies, Uni-GBSA produced a satisfactory correlation with the experimental binding affinities and outperformed AutoDock Vina in molecular enrichment. Uni-GBSA is available as an open-source package at https://github.com/dptech-corp/Uni-GBSA. It can also be accessed for virtual screening from the Hermite web platform at https://hermite.dp.tech. A free Uni-GBSA web server of a lab version is available at https://labs.dp.tech/projects/uni-gbsa/. This increases user-friendliness because the web server frees users from package installations and provides users with validated workflows for input data and parameter settings, cloud computing resources for efficient job completions, a user-friendly interface and professional support and maintenance.

Geothermal flow in Northern Morocco: A machine learning approach

To model the geothermal flow in northern Morocco, we apply a machine learning (ML) approach by analyzing the geological and geophysical data at boreholes, where direct heat flow data is available. The data includes geothermal flow, geothermal gradient, Bouguer gravity, aero-magnetic field, tectonic fracture density and the proximity level to the major faults, thermal spring data (location, temperature, density), geochronological age of formations, Earthquake data (density, magnitude, location), and the proximity level to the recent volcanism in Morocco. These data represent factors that generally control the spatial variation of geothermal flow. For this purpose, we used four ML techniques: Multi-Layer Perceptron, Support Vector Regression, K-Nearest Neighbors, and Decision trees, and examined the impact of parameter settings for each ML technique on the performance results. In specifically, we examine two parameter tuning methods: Grid Search (GS) and the Python Tool's default parameters. To determine the significance of the performance differences and rank ML techniques according to their performances, the Skott-Knott test, and the Borda Count voting system were investigated. We identified the optimized MLP by means of GS as the best ML technique. The cartographic representation of predicted geothermal flow values, by the optimized MLP (GS + MLP) model over the northern Morocco, shows areas of high geothermal flow values. These areas often correspond to magmatic intrusions at depth connected to the regional geodynamic context. The lowest values of geothermal flow are less than 40 mW m−2 and are mainly predicted in the Anti-Atlas chain of Precambrian age and in which is part of the West African Craton. This method is a new approach that may help to identify areas with high geothermal potential based on geological and geophysical data.

Global spatial distribution of Chromolaena odorata habitat under climate change: random forest modeling of one of the 100 worst invasive alien species

Anthropogenic activities and global climate change increase the risk of Chromolaena odorata invasion and habitat expansion. To predict its global distribution and habitat suitability under climate change, a random forest (RF) model was employed. The RF model, utilizing default parameters, analyzed species presence data and background information. The model revealed that the current spatial distribution of C. odorata covers 7,892,447 km2. Predictions for 2061– 2080 indicate expansion of suitable habitat (42.59 and 46.30%), reduction of suitable habit (12.92 and 12.20%), and preservation of suitable habitat (87.08 and 87.80%) under the SSP (Shared Socio-economic Pathway) 2-4.5 and SSP5-8.5 scenarios, respectively, in comparison to the present distribution. Currently, C. odorata is predominantly found in South America, with limited presence in other continents. However, the data suggest that climate change will elevate the global invasion risk of C. odorata worldwide, particularly in Oceania, Africa, and Australia. Countries such as Gambia, Guinea-Bissau, and Lesotho, which currently have unsuitable habitats, are predicted to have highly suitable habitats with climate change, supporting the idea that global habitat expansion for C. odorata will occur due to climate change. This study indicates that proper management of C. odorata is crucial during the early invasion phase.

Smoothing Module for Optimization Cranium Segmentation Using 3D SlicerSmoothing Module for Optimization Cranium Segmentation Using 3D Slicer

Anatomy is the most essential course in health and medical education to study parts of human body and also the function of it. Cadaver is a media used by medical student to study anatomical subject. Because of limited access to cadaver and also due to high prices, this situation makes it necessary to develope an alternative anatomical education media, one of them is the use 3D printing to produce anatomical models. Before 3D Print the cranium, it is necessary to do the segmentation process and often the segmentation result is not good enough and appear a lot of noises. The purpose of this research is to optimize a 3D cranium based on DICOM (digital imaging and communications in medicine) data processing using the smoothing modules on 3D Slicer. The method of this research is to process the Cranium DICOM data using 3D Slicer software by varying the 5 types of smoothing modules. The results with default parameter fill holes and median have better results compared to others. Kernel size variations are performed for smoothing module fill holes and medians. The result is fill holes get optimal segmentation results using a kernel size of 3 mm and the median is 5 mm.

NORNE, a process-based grass growth model accounting for within-field soil variation using remote sensing for in-season corrections

A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.

Accelerating AI-Based Battery Management System’s SOC and SOH on FPGA

Lithium battery-based electric vehicles (EVs) are gaining global popularity as an alternative to combat the adverse environmental impacts caused by the utilization of fossil fuels. State of charge (SOC) and state of health (SOH) are vital parameters that assess the battery’s remaining charge and overall health. Precise monitoring of SOC and SOH is critical for effectively operating the battery management system (BMS) in a lithium battery. This article presents an experimental study for the artificial intelligence (AI)-based data-driven prediction of lithium battery parameters SOC and SOH with the help of deep learning algorithms such as Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM). We utilized various gradient descent optimization algorithms with adaptive and constant learning rates with other default parameters. Compared between various gradient descent algorithms, the selection of the optimal one depends on mean absolute error (MAE) and root mean squared error (RMSE) accuracy. We developed an LSTM and BiLSTM model with four hidden layers with 128 LSTM or BiLSTM units per hidden layer that use Panasonic 18650PF Li-ion dataset released by NASA to predict SOC and SOH. Our experimental results advise that the selection of the optimal gradient descent algorithm impacts the model’s accuracy. The article also addresses the problem of overfitting in the LSTM/BiLSTM model. BiLSTM is the best choice to improve the model’s performance but increase the cost. We trained the model with various combinations of parameters and tabulated the accuracies in terms of MAE and RMSE. This optimal LSTM model can predict the SOC of the lithium battery with MAE more minor than 0.0179%, RMSE 0.0227% in the training phase, MAE smaller than 0.695%, and RMSE 0.947% in the testing phase over a 25°C dataset. The BiLSTM can predict the SOC of the 18650PF lithium battery cell with MAE smaller than 0.012% for training and 0.016% for testing. Similarly, using the Adam optimization algorithm, RMSE for training and testing is 0.326% and 0.454% over a 25°C dataset, respectively. BiLSTM with an adaptive learning rate can improve performance. To provide an alternative solution to high power consuming processors such as central processing unit (CPU) and graphics processing unit (GPU), we implemented the model on field programmable gate Aarray (FPGA) PYNQ Z2 hardware device. The LSTM model using FPGA performs better.

First report of the plum marbling disease associated agent, plum viroid I, in apricots (Prunus armeniaca) in South Africa.

Plum viroid I (PlVd-I) was recently identified as a new viroid in 2020 present in Japanese plum (Prunus salicina) displaying marbling and corky flesh symptoms (Bester et al. 2020). This viroid is a member of the species Apscaviroid plvd-I (genus Apscaviroid, family Pospiviroidae) (Walker et al. 2022). The first observation of apricot fruits with an uneven, indented surface and irregular shape was in 2003 on Prunus armeniaca cv. Charisma in the Western Cape, South Africa. The symptomatic apricot cv 'Charisma' scions showed symptoms only on the fruits, resembling the marbling disease deformities reported previously on fruits from PlVd-I-infected plum trees (Supplementary material 1). In the summer of 2019, representative leaf samples were collected from 13 'Charisma' apricot trees (seven symptomatic and six healthy trees) from two different apricot orchards on two geographical separate farms in the Western Cape. Total RNA was extracted from 1 g leaf petioles using a modified CTAB extraction protocol (Ruiz-García et al. 2019). Ribo-depleted RNA (RiboMinus™ Plant Kit for RNA-Seq, ThermoFisher Scientific) was prepared, and a sequencing library (Ion Total RNA-Seq Kit v2.0, ThermoFisher Scientific) was constructed from a symptomatic sample (La4) (Central Analytical Facility, Stellenbosch University, CAF-SU). High-throughput sequencing was performed on an Ion Torrent™ Proton™ instrument (CAF-SU). De novo assembly using SPAdes 3.13.0 (default parameters) (Nurk et al. 2013) were performed using 93,760,198 reads (average read length: 143 nt). The 174679 scaffolds obtained were annotated using BLAST+ standalone against a local NCBI nucleotide database. One scaffold (443 nt, read coverage: 23.88) had the highest sequence identity (99.59%) to multiple PlVd-I isolates and two scaffolds of 1440 nucleotides (nt) and 2143 nt had high sequence identity to RNA1 and RNA2 of solanum nigrum ilarvirus 1 (SnIV1) (MN216370: 98%; MN216373: 98%) (Ma et al. 2020). These were the only viral sequences identified in the sample. Consensus sequences for SnIV1 were generated by read mapping using CLC Genomics Workbench 11.0.1 (Qiagen) (default parameters) to SnIV1 (MN216370; MN216373; MN216376) and deposited in GenBank (MT900926-MT900928). To confirm the presence of both PlVd-I and the apricot variant of SnIV1, reverse transcription polymerase chain reactions (RT-PCRs) were performed on the RNA of the 13 samples collected. The samples were tested for PlVd-I using primer set 22F/21R (Bester et al. 2020). Only the symptomatic samples tested positive for PlVd-I providing the first evidence of PlVd-I related symptoms in apricots. Three PlVd-I amplicons were bidirectionally Sanger sequenced (CAF-SU) and submitted to GenBank (MT385845-MT385847). The HTS PlVd-I sequence from sample La4 was 100% identical to MT385845, and 99.37% identical to MT385846 and MT385847. An RT-PCR assay was designed, targeting SnIV1 RNA2 (Ilar_RNA2_402F: CTATCTGCCCGAAGGTCAAC, Ilar_RNA2_1161R: CCTATCAAGAGCGAGCAATGG). All samples tested positive for SnIV1 irrespective of symptom status and therefor SnIV1 appears not be associated with specific symptoms in 'Charisma' apricots. This study is the first to report the presence of PlVd-I in symptomatic apricots presenting with uneven, indented surface morphology in South Africa. This study adds towards the investigation into possible alternative hosts for PlVd-I and will assist the South African certification scheme to assess the incidence and severity in apricots.

Bidirectional Mendelian randomization analyses reveal APOE status mediates the effects of statin medications on Alzheimer’s disease

AbstractBackgroundThere is currently no unanimous conclusion on the statin medications and Alzheimer’s disease (AD) risk, which has long been one of the most concerns in lipid‐lowering and anti‐atherosclerosis treatment. We performed a bidirectional Mendelian Randomization (MR) to evaluate whether there were causal links between statin medications and AD.MethodSingle nucleotide polymorphisms (SNPs) identified from genome‐wide association studies (GWAS) were used as instrumental variables. One GWAS of statin medications (N = 462,933) and AD (N = 63,926) with summary statistics available was included in the MR analysis. All statistical analyses were conducted using the TwoSampleMR package with default parameters. Inverse variance weighted (IVW) was used as the primary method to calculate the causal estimates (β), accompanied by two complement methods, MR‐Egger and weighted median. Pleiotropic effects and heterogeneity driving causal association were also assessed.ResultIVW results showed that genetically predicted use of statins was causally associated with increased risk of AD (atorvastatin: β = 33.67, P = 0.048; simvastatin: β = 4.88, P = 0.044). However, after removing SNPs inside the apolipoprotein E (APOE) gene region (CHR19:45,111,942‐45,711,941 based on GRCh37/hg19), the causal links of statin medications on AD disappeared (Figure 1A). Two SNPs, rs429358 (C;C) and rs7412 (C;C) known as APOE4 mainly contributed to the controversial data involved in use of statins and AD risk. The results of reverse causal analysis showed that patients with AD tended to have a higher exposure to statins (atorvastatin: β = 0.005, P = 0.001; simvastatin: β = 0.012, P = 0.004). Consistently, when discarding the SNPs within the APOE gene region, no relationship was found between diagnosis of AD and statins exposure (Figure 1B). The directional horizontal pleiotropy and heterogeneities found in MR‐PRESSO global and MR‐Egger regression test were largely disappeared after excluding SNPs inside APOE cluster region.ConclusionOur results suggested that the inconsistent association between statins use and AD risk observed in previous observational investigations might be mainly attributed to various APOE status (APOE2, APOE3, and APOE4). Thus, it reminds clinicians that statins can be used with confidence in lipid‐lowering and anti‐atherosclerosis and are unlikely to affect AD risk.

Predicting the immunotherapy response based on molecular subtypes of pan-immune signatures in gastrointestinal stromal tumor.

e23512 Background: Gastrointestinal stromal tumor (GIST) is the most common sarcoma, often originate in the stomach or the small intestine. Although the application of imatinib has brought a breakthrough in the clinical treatment of GIST, not all GIST patients are effective, and most GIST patients who receive imatinib sensitivity will develop drug resistance and eventually malignant progression. The tumor microenvironment (TME) is a key mediator of cancer progression and treatment outcome, and plays an important role in predicting cancer prognosis and treatment response. This study aims to investigate the role of TME in the treatment of GIST and the response to immunotherapy. Methods: Raw microarray data of 199 GIST patients were downloaded from GEO, including 159 KIT-mutant, 21 PDGFRA-mutant and 19 WT. All affymetrix datasets were re-normalized using the gcRMA package with default parameters. We integrated 275 TME and malignant signaling pathways related signatures from different database and previous researches, including CIBERSORT, MCP-counter, TIMER, and others. Fisher exact test's was used to identify signatures that differential between KIT-mutant, PDGFRA-mutant, and WT (p < 0.05), and 85 signatures were finally obtained. To classify the molecular profiles, the expression patterns were assessed in all 199 GIST patients using unsupervised clustering based on their ssGSEA scores, and can be clustered into three distinct microenvironments. Results: Using GSEA with all TME signatures genes ranked by the log2(fold change) between the threee clustes, we found that gene sets were considered immune favorable in cluster A, including gene sets related to activated CD8+ T cells (p = 0.0003)and activated CD4 T cell signaling pathway (p = 0.009). Cluster B enriched to the epithelial mesenchymal transformation pathway (p = 0.04) and TGFb signaling pathway (p = 0.003), cluster C enriched into DNA-repair pathway (p = 0.02). In an independent skin cutaneous melanoma cohorts (n = 86) treated with anti-PD-1 therapy, patients were classified into three molecular clusters, which significantly corrleated with response to ipilimumab (p = 0.007). The percentage of responders to anti-PD-1 therapy in cluster A was 75% in contrast to only 10% in cluster B. The three molecular clusters discern a potential responder versus non-responder (AUC = 0.87; p = 0.0015), with essentially a 80% chance of correctly predicting the response, which supporting the utilization of the classification method. Conclusions: In this study, the immuno-infiltrating landscape was characterized based on pan-immune signatures using transcriptomic profile in GIST. The molecular subtypes derived from pan-immune signatures have the potential to predict the efficacy of immunotherapy.

Artificial intelligence analysis of CCNG1 expression in tumors: A novel biomarker in development for anti-CCNG1 targeted therapy.

e15083 Background: Metastatic cancer is associated with an invariably fatal outcome. Therefore, innovative therapies are urgently needed. Although expanded access for DeltaRex-G, a tumor targeted retrovector encoding a CCNG1 inhibitor gene, is on-going for an intermediate-size (n = up to 40) population of advanced sarcoma and pancreatic cancer, more data is needed to identify patients who are likely to benefit from DeltaRex-G gene therapy. In this study, we retrospectively analyzed CCNG1 expression in archived tumors of patients who were previously treated with DeltaRex-G and who are active candidates for DeltaRex-G therapy. Methods: Archived formalin-fixed paraffin embedded (FFPE) tumor specimens (n = 58) from patients with solid malignancies who are actively followed at the Cancer Center of Southern California were collected, processed, and subjected to RNA sequencing. Briefly, RNA-seq libraries were sequenced to generate 50 million reads that were aligned using Kallisto v0.42.4 to GENCODE v23 transcripts with default parameters. Only protein-coding, IGH/K/L- and TCR-related transcripts were retained for downstream processing, resulting in 20,062 protein coding genes. Gene expression was quantified as transcripts per million (TPM) and log2-transformed (Goldman et al., 2020). A gene expression level is presented as low/medium/high depending on the expression level of a such gene in patients of the reference cohort. Low = < 17%; Medium = 17%-83%; High = > 83%. Results: Thirty-two male and 26 female subjects, ages ranging from 16 to 86 years were studied. Forty-nine (84.4%) patients had sarcoma, 3 (5.2%) had urothelial carcinoma, 2 (3.5%) had breast carcinoma, 2 (3.5%) had pancreatic cancer, 1 (1.7%) had Sertoli cell tumor, 1 (1.7%) had adenocarcinoma of appendix. Eleven (19%) tumors showed high CCNG1 expression, 44 (76%) tumors had medium expression, and 3 (5.2%) tumors had low CCNG1 expression. Of note, the tumor of one 14-year survivor with metastatic pancreatic adenocarcinoma in sustained remission had 24 % CCNG1 expression, one 3-year survivor with metastatic chondrosarcoma metastatic to lung with stable disease had 74% CCNG1 expression, one 2-year survivor with early stage HR+ HER2+ breast cancer in remission had 23% CCNG1 expression, and one 2-year survivor with early stage triple negative breast cancer in remission had 74% CCNG1 expression. Conclusions: Taken together, these data indicate that (1) Medium to high CCNG1 expression was found in 95% of tumors studied, and (2) Patients with medium CCNG1 expression who received DeltaRex-G had clinical benefit and are alive in sustained remission or with stable disease 2-14 years from DeltaRex-G treatment initiation, and (3) Prospective studies are warranted to correlate CCNG1 expression level and response to DeltaRex-G therapy.

Multidimensional climatic niche conservatism and invasion risk of Phenacoccus solenopsis.

The cotton mealybug Phenacoccus solenopsis, a globally invasive insect, is listed as a national quarantine pest in agriculture and forestry, which seriously threatens biological safety of China. Niche conservatism is a key assumption of species distribution model. An evaluation of the applicability of using ecological niche models to assess the invasion risk of cotton mealybug, and further optimizing model complexity, are of both theoretical and practical significance. Based on 706 occurrence records and key bioclimatic variables, we used n-dimensional hypervolume niche analysis method to quantify the climatic niche hypervolumes of this pest in both native and invasive sites, and further tested the niche conservatism hypothesis. MaxEnt model parameters were optimized to predict the invasion risk of the mealybug under current and future climate scenarios in China. The results showed that four climatic variables (annual mean temperature, mean temperature of wettest quarter, mean temperature of warmest quarter, and precipitation of driest quarter) were the key climate factors affecting the distribution of cotton mealybug. Compared with native climatic niche (hypervolume volume, HV=40.43), the niche hypervolume of cotton mealybug in the invasive areas was significantly reduced (HV=6.04). Niche contraction (the net differences between the amount of space enclosed by each hypervolume was 0.84) explained 98.8% of niche differentiation, whereas niche shift (the replacement of space between hypervolumes was 0.01) contributed less than 2%. The direction of climatic niche contraction of the pest in different invasive areas was not exactly consistent. The default parameters of MaxEnt model were unreliable (ΔAICc=14.27), and the optimal parameter combination was obtained as follows: feature combination was linear-quadratic-hinge-product and regularization multiplier was 0.5. The most suitable habitats of cotton mealybug were concentrated in the south of Huaihe River-Qinling Mountains line, and the north-central provinces contained a large area of low suitable habitat. The increase of suitable habitat was not significant at the end of 21 century (SSP1-2.6: 1.7%, SSP5-8.5: 0.7%). The multidimensional climatic niche of P. solenopsis was highly conservative. The species distribution model was suitable for analyzing its invasion risk. The northward spread was obvious, and climate change had less impact on the pest.

Species with larger vessel area have higher bias for the original Granier equation in calculating sap flux density

Accurate estimate of transpiration is crucial to understanding global water and carbon cycles. The thermal dissipation (TD) method with Granier’s equation, which is the most popular approach to measure whole-tree water use, requires species-specific calibration parameters to calculate transpiration. Calibrations require direct measurement of water uptake, often through gravimetric or potometric methods; therefore, it is often disregarded because of limited time and resources. It remains unclear which factor influences the variation of calibration parameters. In this study, we compiled published calibration experiments to elucidate the factors that could influence the variation in coefficients and attempted to predict the correction parameter. The results showed that: (1) only 14 % of the experiments provided accurate sap flux density estimates using the default parameter values in the Granier’s equation, and sap flux was underestimated to varying degrees in 72% of the experiments; (2) the regression slope of TD-measured and the actual sap flow with the y-axis intercept set to zero (hereinafter referred to as “Slope”) was significantly correlated to the mean vessel area (VA), i.e., species with larger VA consistently had higher Slope and greater bias. Stem diameter also had a positive influence on the Slope after accounting for covariation with the VA. Finally, a multiple linear regression model based on these two characteristics (VA and stem diameter) explained 89% of the variation of Slope. This study provides a simple calibration procedure for the users of TD-sensors with a linear correction model based on easily measurable vessel area and stem diameter. It may also provide a new development avenue for sensor manufacturers or others to improve the TD method to reduce its sensitivity to wood characteristics – or provide some “out-of-the-box” correction factors across a range of wood characteristics.

Machine learning techniques to predict the fundamental period of infilled reinforced concrete frame buildings

Predicting the structure’s fundamental period is a challenging task since its value changes when the features of buildings change. However, it is more cumbersome for reinforced concrete buildings with infill walls due to their nonlinear behavior. The objective of this study is to highlight the importance of hyperparameter optimization in machine learning models to achieve the best performance in predicting the fundamental period of infilled RC frame buildings. To this end, three types of boosting algorithms have been chosen, namely: gradient boosting decision trees, lightGBM, and catboost. Compared to the default parameters for each model, the fine-tuning hyperparameter models with the Optuna framework yielded the best results, with a higher coefficient of determination and lower error values. Furthermore, the lightGBM model set by its optimized parameters outperformed the other two boosting methods. The feature selection technique emphasizes the five parameters’ contributions in estimating the building’s fundamental period by demonstrating the effect of removing one or more parameters each time on determining this dynamic characteristic. Moreover, the multivariate adaptive regression splines is used to develop a mathematical expression for the fundamental period that outperformed building code formulas and some authors’ proposed equations. Finally, a set of smooth curves derived from the optimum lightGBM and MARS models were introduced, revealing that the models were not overfitting.

Genome Sequence Resource of Curvularia clavata Causing Leaf Spot Disease on Tobacco by Oxford Nanopore PromethION.

Genome Sequence Resource of Curvularia clavata Causing Leaf Spot Disease on Tobacco by Oxford Nanopore PromethION.

6LoWPAN MAC layer parameters optimization using evolutionary algorithm based ANN topology in wireless body area networks

Since the IPv6 Wireless Personal Area Network (6LoWPAN) can be utilized for information dissemination, this network gains significant attention in recent years. Proxy mobile IPv6 (PMIPv6) is standard for mobility control based on network at entire IP wireless applications. But, group-based body area networks cannot respond effectively. A new improved group flexibility system decrease the number of control messages contain router requests as well as advertising messages when compared to the group-based PMIPv6 protocol, in order to minimize delay and signaling costs. The IEEE 802.15.4 standard for low-power personal area networks (6LoWPAN) complies through IPv6-compliant MAC and physical layers. If the default parameters, excessive collisions, packet loss, and great latency occur arbitrarily in high traffic by default MAC parameters while using a great number of 6LoWPAN nodes. The implemented Whale optimization algorithm is based on artificial neural network optimization, genetic algorithm or particle swarm optimization to choose and authenticate MAC parameters. This manuscript proposes a novel intelligent method for choosing optimally configured MAC 6LoWPAN layer set parameters. Results of simulations based on the metrics such as Average delay time (ADT), Average signaling cost, Delivery ratio, Energy consumption, Latency, Network Life time (Nlt), Packet Overhead (PO), Packet loss. The performance of the proposed method provides 19.08%, 25.87%, 31.98%, 26.98%, 31.98%, 26.98% and 23.89% lower Latency, 12.67%, 25.98%, 31.98%, 26.98%, 27.98%, 31.97% and 27.85% lower Packet Overhead and 19.78%, 27.96%, 37.98%, 18.09%, 28.97%, 27.98% and 56.04% higher Delivery ratio compared with the existing methods such as 6LoWPAN-NUM-OHCA-FFA, 6LoWPAN-GTCCF-PSO, 6LoWPAN- DODAG-ACO, 6LoWPAN- MAC-GA-PSO, 6LoWPAN-NCG-DTC-NGIPSA and 6LoWPAN-TDMA-GTS-SHJA algorithms respectively.

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

AbstractLand surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance.

Error‐Correction Mechanisms in Language Learning: Modeling Individuals

AbstractSince its first adoption as a computational model for language learning, evidence has accumulated that Rescorla–Wagner error‐correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla–Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial‐by‐trial behavioral choices of participants, rather than fitting a one‐for‐all model using a single set of default parameters, we show that the model accurately captures participants’ choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla–Wagner model captures language learning.

Online intervention siamese tracking

Online target update excels in helping a visual tracking algorithm adapt to variations in target appearance during inference, and thus is preferred by recent advances. However, this technique typically operates in a heuristic manner by updating numerous network parameters with large amounts of online-collected data. This poses a distinct challenge: can the online target update be effectively executed without using the aforementioned approach? To this end, we propose a novel target feature update scheme to reduce the need for tedious data collection and computation-intensive parameter updates. This scheme operates on the principle of causal intervention and is just as effective as default parameter updates in visual tracking. Besides, we explore a novel video-specific target label to capture the context of a specific target in video frames for feature discrimination. This makes target features better fit for appearance changes. Such schemes together with the off-shelf pre-trained classification backbone form a novel online intervention siamese tracker (OIS). When equipped with an unsupervised pre-trained backbone, OIS outperforms current state-of-the-art unsupervised trackers on the OTB and VOT. When exploiting a supervised trained backbone, it competes with typical supervised trackers trained on massive offline training and online tracking data.