General Linear Model Approach Research Articles

Genetic Diversity and Association Analysis of Traits Related to Water-Use Efficiency and Nitrogen-Use Efficiency of Populus deltoides Based on SSR Markers.

Populus deltoides is one of the primary tree species for bioenergy production in temperate regions. In arid/semi-arid northern China, the scarcity of water and nitrogen significantly limits the productivity of poplar plantations. The identification of relevant molecular markers can promote the breeding of resource-efficient varieties. In this study, 188 genotypes of P. deltoides from six provenances served as experimental material. Genetic differentiation analysis, analysis of molecular variance (AMOVA), principal coordinate analysis (PCoA), unweighted pair group method with arithmetic mean (UPGMA) clustering, and genetic structure analysis were performed using selected simple sequence repeat (SSR) markers. Based on these analyses, the association analysis of water-use efficiency (WUE) and nitrogen-use efficiency (NUE) were conducted using general linear model (GLM) and mixed linear model (MLM) approaches. The results showed that 15 pairs of SSR primers successfully amplified across all 188 individuals, with an average of 7.33 alleles (Na) observed per primer pair. The polymorphism information content (PIC) ranged from 0.060 to 0.897, with an average of 0.544, indicating high genetic diversity in the selected markers. The average inbreeding coefficient intra-population (Fis), inbreeding coefficient inter-population (Fit), and inter-population genetic fraction coefficient (Fst) values were 0.005, 0.135, and 0.132, respectively, indicating high heterozygosity, substantial inbreeding within populations, and moderate genetic differentiation, with an average gene flow (Nm) of 1.964, suggesting substantial gene flow between populations. Additionally, molecular variance was primarily within individuals (84.12%). Genetic structure analysis revealed four subgroups, with some degree of genetic admixture among the provenances. In the GLM model, 11 markers were significantly associated with five traits (p < 0.05), with an average contribution rate of 15.82%. Notably, SSR132 and SSR143 were significantly associated with multiple traits (p < 0.05). The MLM model identified two markers (SSR47 and SSR85) significantly associated with ground diameter (p < 0.05) and one marker (SSR80) significantly associated with NUE (p < 0.05). This study identifies loci associated with WUE and NUE, laying a foundation for future genetic improvement and marker-assisted breeding strategies in poplar.

Performance of a negative binomial-GLM in spatial scan statistic: a case study of low-birth weights in Pakistan.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.

Analysis of Claim Frequency and Severity in Auto Insurance Using Generalized Linear Models in Philippines

Abstract Purpose: The aim of the study was to analyze the analysis of claim frequency and severity in auto insurance using generalized linear models in Philippines. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The analysis of claim frequency and severity in auto insurance using Generalized Linear Models (GLMs) in the Philippines revealed that various factors, including driver demographics, vehicle characteristics, and geographic location, significantly impact the likelihood and severity of claims. The GLM approach effectively modeled these relationships, providing insights into risk factors that contribute to higher claim rates and costs. The findings suggest that younger drivers and those in urban areas tend to have higher claim frequencies, while larger vehicles and those with higher engine capacities are associated with more severe claims. Unique Contribution to Theory, Practice and Policy: Utility Theory, Actuarial Fairness Theory &amp; Risk Theory may be used to anchor future studies on analyze the analysis of claim frequency and severity in auto insurance using generalized linear models in Philippines. From a practical perspective, insurers should increasingly adopt telematics and real-time driving data within their GLM frameworks. Policymakers should encourage and potentially mandate the integration of telematics data in auto insurance modeling.

Analysis and optimization of machining parameters of AZ91 alloy nanocomposite with the Influences of nano ZrO2 through vacuum diecast process

The magnesium alloy composite is a vital material for automotive applications due to its features like high stiffness, superior damping resistance, high strength, and lightweight. Here, the motto of research is to establish the AZ91 alloy nanocomposite with the exposures of 0, 1, 3, and 5 volume percentages (vol%) of nano zirconium dioxide (ZrO2) particles (50nm) through fluid stir metallurgy route associated with 1x105 Pa vacuum die cast process. Exposures on structural morphology, hardness, and impact toughness of composite are analyzed and identified as the nano AZ91 alloy composite enclosed with 5vol% is homogenous particle dispersion, enhanced hardness (97.6HV), and optimum toughness of 21.2J/mm2. However, composite faces machining difficulties due to the hard abrasive particles with higher hardness, resulting in tool wear. This experiment predicts the optimum mill parameters during the end mill operation of magnesium alloy nanocomposite (AZ91/5vol%) by using a tungsten carbide coated end mill cutter to attain the maximum metal removal rate with low surface roughness and tool wear analyzed via the general linear model (GLM) ANOVA approach. The input conditions for end milling operation vary, like feed rate (0.1 -0.4mm/rev), depth of cut (0.05 -0.2mm), and spindle speed (250-1000rpm). During the ANOVA GLM approach, the L16 design experiment is fixed for further interaction analysis. The results predicted by the depth to cut and feed rate were dominant and played a major role in deciding the tool wear, surface roughness, and MRR.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Prothrombin and activated partial thromboplastin times, thromboelastography, hematocrit, and platelet count in a feline hemorrhage/over-resuscitation model using lactated Ringer's solution or 6% tetrastarch 130/0.4.

To describe and compare prothrombin time (PT), activated partial thromboplastin time (aPTT), thromboelastography (TEG), HCT, and platelet count measurements in a hemorrhage/over-resuscitation model. Randomized crossover study. University teaching hospital. Six cats. Anesthetized cats underwent 3 treatments at 2-month intervals. The treatments were as follows: NHR-no controlled hemorrhage and sham resuscitation; LRS-controlled hemorrhage and lactated Ringer's solution (LRS) for resuscitation; and Voluven-controlled hemorrhage and 6% tetrastarch 130/0.4 for resuscitation. The LRS and Voluven were administered at 60 and 20mL/kg/h, respectively, for 120minutes. Blood samples were drawn for PT, aPTT, TEG, HCT, and platelet count measurements at a healthy check (T - 7d), after controlled hemorrhage (T0), at 60 and 120minutes of resuscitation (T60 and T120), and at 24hours after completion of resuscitation (T24h). Data were analyzed using a general linear mixed model approach (significance was P<0.05). Total median blood loss (controlled hemorrhage and blood sampling from T0 to T120) at T120 was 11.4, 31.0, and 30.8mL/kg for NHR, LRS, and Voluven, respectively. PT and aPTT during LRS and Voluven were prolonged at T60 and T120 compared to NHR (P<0.001). On TEG, the reaction time, kinetic time, and alpha-angle were within reference intervals for cats at all time points in all treatments, while maximum amplitude was less than the reference interval (40mm) at T0, T60, and T120 during Voluven and at T60 and T120 during LRS compared to NHR (both P<0.001). The HCT and platelet count were significantly lower at T60 and T120 during LRS and Voluven compared to NHR (P<0.001). Hypocoagulopathy was observed during hemorrhage and liberal fluid resuscitation. Prolongation of PT and aPPT and decreased clot strength may have been caused by hemodilution and platelet loss.

Factors Affecting Postpartum Bone Mineral Density in a Clinical Trial of Vitamin D Supplementation.

Background: Few studies evaluate the effects of vitamin D status and supplementation on maternal bone mineral density (BMD) during lactation and further lack inclusion of diverse racial/ethnic groups, body mass index (BMI), or physical activity. Objective: Determine the effects of vitamin D treatment/status, feeding type, BMI, race/ethnicity, and physical activity on postpartum women's BMD to 7 months. Methods: Women with singleton pregnancies beginning 4-6 weeks' postpartum were randomized into two treatment groups (400 or 6400 IU vitamin D/day). Participant hip, spine, femoral neck, and whole-body BMD using Dual-energy X-ray absorptiometry (DXA Hologic), serum 25-hydroxyvitamin D [25(OH)D] (RIA; Diasorin), BMI, and physical activity were measured at 1, 4, and 7 months postpartum. A general linear mixed modeling approach was undertaken to assess the effects of vitamin D status [both serum 25(OH)D concentrations and treatment groups], feeding type, race/ethnicity, BMI, and physical activity on BMD in postpartum women. Results: During the 6-month study period, lactating women had 1-3% BMD loss in all regions compared with 1-3% gain in nonlactating women. Higher maternal BMI was associated with less bone loss in femoral neck and hip regions. Black American women had less BMD loss than White/Caucasian or Hispanic lactating women in spine and hip regions. Exclusively breastfeeding women in the 6400 IU vitamin D group had less femoral neck BMD loss than the 400 IU group at 4 months sustained to 7 months. Physical activity was associated with higher hip BMD. Conclusion: While there was BMD loss during lactation to 7 months, the loss rate was less than previously reported, with notable racial/ethnic variation. Breastfeeding was associated with loss in BMD compared with formula-feeding women who gained BMD. Higher BMI and physical activity independently appeared to protect hip BMD, whereas higher vitamin D supplementation appeared protective against femoral neck BMD loss.

Association between cigar use, with and without cigarettes, and incident diagnosed COPD: a longitudinal cohort study

BackgroundWhile regular cigar smoking is believed to carry similar health risks as regular cigarette smoking, the impact of cigar use, alone or in combination with cigarettes, on obstructive pulmonary disease (COPD) has not been well characterized. The purpose of this study was to examine the prospective association between exclusive and dual cigar and cigarette use and incident self-reported diagnosed COPD.MethodsThis study used data from Waves 1–5 (2013–2019) of the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative survey of U.S. adults. Longitudinal data from adults aged 40 to 79 at Wave 1, without a pre-existing COPD diagnosis who participated at follow-up interview were analyzed. A time-varying current tobacco exposure, lagged by one wave and categorized as: (a) never/non-current use; (b) exclusive cigar use; (c) exclusive cigarette use; and (d) dual cigar/cigarette use. Multivariable models adjusted for demographics (age, sex, race or ethnicity, education), clinical risk factors (asthma, obesity), and smoking-related confounders (second-hand smoke exposure, other combustible tobacco product use, e-cigarette use, time since quitting, cigarette pack-years). The incidence of self-reported diagnosed COPD was estimated using discrete-time survival models, using a general linear modeling (GLM) approach with a binomial distribution and a complementary log-log link function.ResultsThe analytic sample consisted of 9,556 adults with a mean (SD) age of 56 (10.4), who were predominately female (52.8%) and Non-Hispanic White (70.8%). A total of 906 respondents reported a diagnosis of COPD at follow-up. In the fully adjusted model, exclusive cigar use (adjusted hazard ratio (aHR) = 1.57, 95% CI: 0.77, 3.21) was not associated with increased COPD risk compared to non-use, while exclusive cigarette use (aHR = 1.48, 95% CI: 1.13, 1.93) and dual cigar/cigarette use (aHR = 1.88, 95% CI: 1.24, 2.85) were.ConclusionsExclusive cigarette use and dual cigar/cigarette use were associated with diagnosed incident COPD. These results suggest that cigars, when used in combination with cigarettes, may be associated with poorer COPD health outcomes. Dual use may promote a higher likelihood of inhaling cigar smoke, and future research would benefit from examining whether inhalation of cigar smoke increases COPD risk.

Exploring organizational challenges and psychological wellbeing among remote working employees during the COVID-19 pandemic: A general linear model approach

Exploring organizational challenges and psychological wellbeing among remote working employees during the COVID-19 pandemic: A general linear model approach

Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: A longitudinal fMRI study.

Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold (BH) task is commonly used to understand cerebrovascular reactivity (CVR) in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's BH data set from the Nathan Kline Institute (NKI) Rockland Sample (aged 6-18 years old at enrollment). A general linear model approach was applied to derive CVR from BH data. To model both the longitudinal and cross-sectional effects of age on BH response, we used mixed-effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased BH BOLD signals in multiple networks across age, in which linear and logarithmic mixed-effects models provided the best fit with the lowest Akaike information criterion scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes that occur with age.

Feasibility of using fNIRS to explore motor-related regional haemodynamic signal changes in patients with sensorimotor impairment and healthy controls: A pilot study.

While functional near-infrared spectroscopy (fNIRS) can provide insight into cortical brain activity during motor tasks in healthy and diseased populations, the feasibility of using fNIRS to assess haemoglobin-evoked responses to reanimated upper limb motor function in patients with tetraplegia remains unknown. The primary objective of this pilot study is to determine the feasibility of using fNIRS to assess cortical signal intensity changes during upper limb motor tasks in individuals with surgically restored grip functions. The secondary objectives are: 1) to collect pilot data on individuals with tetraplegia to determine any trends in the cortical signal intensity changes as measured by fNIRS and 2) to compare cortical signal intensity changes in affected individuals versus age-appropriate healthy volunteers. Specifically, patients presented with tetraplegia, a type of paralysis resulting from a cervical spinal cord injury causing loss of movement and sensation in both lower and upper limbs. All patients have their grip functions restored by surgical tendon transfer, a procedure which constitutes a unique, focused stimulus for brain plasticity. fNIRS is used to assess changes in cortical signal intensity during the performance of two motor tasks (isometric elbow and thumb flexion). Six individuals with tetraplegia and six healthy controls participate in the study. A block paradigm is utilized to assess contralateral and ipsilateral haemodynamic responses in the premotor cortex (PMC) and primary motor cortex (M1). We assess the amplitude of the optical signal and spatial features during the paradigms. The accuracy of channel locations is maximized through 3D digitizations of channel locations and co-registering these locations to template atlas brains. A general linear model approach, with short-separation regression, is used to extract haemodynamic response functions at the individual and group levels. Peak oxyhaemoglobin (oxy-Hb) changes in PMC appear to be particularly bilateral in nature in the tetraplegia group during both pinch and elbow trials whereas for controls, a bilateral PMC response is not especially evident. In M1 / primary sensory cortex (S1), the oxy-Hb responses to the pinch task are mainly contralateral in both groups, while for the elbow flexion task, lateralization is not particularly clear. This pilot study shows that the experimental setup is feasible for assessing brain activation using fNIRS during volitional upper limb motor tasks in individuals with surgically restored grip functions. Cortical signal changes in brain regions associated with upper extremity sensorimotor processing appear to be larger and more bilateral in nature in the tetraplegia group than in the control group. The bilateral hemispheric response in the tetraplegia group may reflect a signature of adaptive brain plasticity mechanisms. Larger studies than this one are needed to confirm these findings and draw reliable conclusions.

Association analysis of production traits of Japanese quail (Coturnix japonica) using restriction-site associated DNA sequencing

This study was designed to perform an association analysis and identify SNP markers associated with production traits of Japanese quail using restriction-site-associated DNA sequencing. Weekly body weight data from 805 quail were collected from hatching to 16 weeks of age. A total number of 3990 eggs obtained from 399 female quail were used to assess egg quality traits. Egg-related traits were measured at the beginning of egg production (first stage) and at 12 weeks of age (second stage). Five eggs were analyzed at each stage. Traits, such as egg weight, egg length and short axes, eggshell strength and weight, egg equator thickness, yolk weight, diameter, and colour, albumen weight, age of first egg, total number of laid eggs, and egg production rate, were assessed. A total of 383 SNPs and 1151 associations as well as 734 SNPs and 1442 associations were identified in relation to quail production traits using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. The GLM-identified SNPs were located on chromosomes 1–13, 15, 17–20, 24, 26–28, and Z, underlying phenotypic traits, except for egg and albumen weight at the first stage and yolk yellowness at the second stage. The MLM-identified SNPs were positioned on defined chromosomes associated with phenotypic traits except for the egg long axis at the second stage of egg production. Finally, 35 speculated genes were identified as candidate genes for the targeted traits based on their nearest positions. Our findings provide a deeper understanding and allow a more precise genetic improvement of production traits of Galliformes, particularly in Japanese quail.

Understanding biochemistry: basic aspects of statistics for life sciences.

If the biological world is one thing it is variable. As scientists we seek to measure, quantify and explain the causes of this variation. The approach we take to this is remarkably similar whether our research is exploring global temperature, blood pressure, cancer incidence or enzyme kinetics. This approach involves defining clear research questions and applying statistical methods to answer them robustly. This article will introduce a practical example that will be used throughout, specifically whether genetic variation can explain variation in coffee consumption. We assume little experience with statistics and walk through the statistical approach that biologists can use, firstly by describing our data with summary statistics and then by using statistical tests to help arrive at answers to our research question. A General Linear Model (GLM) approach will be used as this is what many common statistical tests are. We explore how to visualise and report results, while checking the assumptions of our analysis. The better we can understand and apply statistics to biological problems, the better we can communicate results and future research to others. The popular statistical programming language R will be used throughout.

Generalized Additive Modelling of Dependent Frequency and Severity Distributions for Aggregate Claims

Abstract This paper examines the problem of accurately estimating the expected value and variance of aggregate claims for each policyholder. Through an appropriate statistical model to estimate the pure premium, an insurer can find niche markets to operate competitively and profitably. To this end, the framework of generalized linear models (GLMs) for aggregate claims is extended to encompass a species of frequentist generalized additive models (GAMs) based on cubic penalized regression splines. The new structure could allow for the incorporation of more flexible nonlinear and/or nonparametric trend terms for the marginal claim frequency, conditional claim severity, and finally for Tweedie modelling as well. This nonparametric approach is illustrated through simulation and applied to an automobile insurance dataset. A juxtaposition of hypothesis test results, AIC values, and attendant graphical diagnostics effectively demonstrate that the GAMs under both the independent and dependent settings give a better fit than the GLM approach. JEL classification numbers: C14, G22. Keywords: Premium, Generalized Additive Models, Dependence, Splines, Frequency, Severity.

Spatial Analysis of Dense LiDAR Point Clouds for Tree Species Group Classification Using Individual Tree Metrics

This study presents a method of tree species classification using individual tree metrics derived from a three-dimensional point cloud from unmanned aerial vehicle laser scanning (ULS). In this novel approach, we evaluated the metrics of 1045 trees using generalized linear model (GLM) and random forest (RF) techniques to automatically assign individual trees into either a coniferous or broadleaf group. We evaluated several statistical descriptors, including a novel approach using the Clark–Evans spatial aggregation index (CE), which indicates the level of clustering in point clouds. A comparison of classifiers that included and excluded the CE indicator values demonstrated their importance for improved classification of the individual tree point clouds. The overall accuracy when including the CE index was 94.8% using a GLM approach and 95.1% using an RF approach. With the RF approach, the inclusion of CE yielded a significant improvement in overall classification accuracy, and for the GLM approach, the CE index was always selected as a significant variable for correct tree class prediction. Compared to other studies, the above-mentioned accuracies prove the benefits of CE for tree species classification, as do the worse results of excluding the CE, where the derived GLM achieved an accuracy of 92.6% and RF an accuracy of 93.8%.

DISTANCE EDUCATION STUDENTS’ INDULGENCE IN SIX SHARP PRACTICES: GENERAL LINEAR MODELLING OF PREDICTIVE PARAMETERS

This study examined the degree to which students indulge in six prominent misconducts in Distance Education Institutions (DEIs). The study also quantified how class size, instructional delivery and institutional policies predict students’ indulgence in sharp practices using a general linear modelling approach. A sample of 871 participants was drawn from 1,742 final-year students across two DEIs in Nigeria. A structured questionnaire was used for data collection. The questionnaire had acceptable psychometric estimates of dimensionality, content and construct validity, as well as reliability. Sharp practices such as cheating, plagiarism, falsification, impersonation, and arm-twisting were more prevalent in large classes, whereas only inducement was higher in small classes. Class size influenced students’ indulgence in sharp practices in DEIs. Instructional delivery and institutional policies negatively predicted students’ indulgence in the six sharp practices. Almost all the six sharp practices correlated positively and significantly, except for impersonation and inducement. Therefore, distance education students who cheat, plagiarize, arm-twist, falsify records, induce lecturers, and promote impersonation are more likely to indulge in other forms of sharp practices. It was concluded that large class sizes, poor instructional delivery, and poorly implemented institutional policies promote sharp practices among distance education students. Based on this conclusion, key policy and research implications are discussed.

Building energy prediction using artificial neural networks and analysis of covariance in the six thermal zones of Morocco

Energy management is essential in light of the current energy issues, particularly in the building sector, which accounts for a sizable amount of global energy consumption. Incorporating intelligent energy management solutions in this sector is one of the answers that could assist the nation in achieving its goals for environmental changes and overcoming its energy challenges. Energy prediction in buildings is therefore essential for efficient management. The present paper aims to evaluate the performance of two different approaches: Artificial intelligence (AI) and General Linear model (GLM) approaches, in predicting heating energy consumption of an administrative building for which localization concerns the six Moroccan thermal zones. The chosen models for AI and GLM are respectively ANN and ANCOVA. In the TRNSYS environment, building energy simulation was carried out in order to produce a database for the models training and validation. Only two meteorological data were used: External temperature and internal temperature. While the ANN model was trained on mixed data of different thermal zones, ANCOVA model established an equation for each thermal zone separately, with respect of model hypothesis. Results show that ANN model outperforms the ANCOVA model with a correlation coefficient of 0.95 and 0.90 respectively. Thus, the use of Artificial Neural Network for building energy prediction is more accurate than ANCOVA model.

Comparative Analysis of Statistical and Machine Learning Techniques for Rice Yield Forecasting for Chhattisgarh, India

Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur (Rcal2 = 1, Rval2= 1 and RMSEcal = 0.002, RMSEval = 0.003) and Surguja (Rcal2 = 1, Rval2= 0.99 and RMSEcal = 0.004, RMSEval = 0.214) districts as compared to the other models, whereas for Bastar, ELNET (Rcal2 = 90, Rval2= 0.48) and LASSO (Rcal2 = 93, Rval2= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R2 = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.

Deciphering the genomic regions governing major agronomic traits and blast resistance using genome wide association mapping in finger millet

Finger millet (Eleusine coracana L.) is climate resilient minor millet of Asia and Africa with wide adaptation and unparallel nutritional profile. To date, genomic resources available in finger millet are scanty and genetic control of agronomic traits remains elusive. Here, a collection of eco-geographically diverse 186 genotypes was quantified for variation in 13 agronomic traits and reaction to blast to identify marker-trait associations (MTAs) using genotyping-by-sequencing (GBS) and genome-wide association study (GWAS). GBS generated 2977 high quality single nucleotide polymorphism (SNPs) markers and identified three subpopulations with varying admixture levels. General linear and mixed model approaches of GWAS to correct for population structure and genetic relatedness identified 132 common MTAs for agronomic traits across the years. The phenotypic variance explained by the makers varied from 4.8% (TP692389-flag leaf width) to 20% (TP714446-green fodder weight). Of these, 26 MTAs showed homology with candidate genes having role in plant growth, development and photosynthesis in the genomes of foxtail millet, rice, maize, wheat and barley. We also found 4 common MTAs for neck blast resistance, which explained 5.9–15.1% phenotypic variance. Three MTAs for neck blast resistance showed orthologues in related genera having putative functions in pathogen defense in plants. The results of this work lay a foundation for understanding the genetic architecture of agronomic traits and blast resistance in finger millet and provide a framework for genomics assisted breeding.

Generalized models for quantifying laterality using functional transcranial Doppler ultrasound.

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N=154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N=31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM-based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks.