Linear Statistical Models Research Articles

Revisit of Okun's law case of Tunisia, Egypt, Morocco, Lebanon, Jordan and Oman

PurposeIn this paper, the author aims to investigate the relationship between economic growth and unemployment in six Arab countries from Middle East and North Africa (MENA) zone including Tunisia, Egypt, Morocco, Lebanon, Jordan and Oman through the implementation of Okun's law using quarterly dataset covering the time period 2000: 1–2014: 4.Design/methodology/approachIn this paper, static and dynamic linear and nonlinear models are used to test the linkage between cyclical unemployment and cyclical growth rate.FindingsThe empirical results from considered models confirm an inverse linkage between unemployment rate and economic growth, as the Okun's law suggests (except for Oman). In a nonlinear autoregressive dynamic linear (NARDL) framework and gap specification, statistically significant Okun's coefficients indicate that output growth can be translated into employment gains. Absolute effect of an economic contraction is significantly larger than that of an expansion in Tunisia, Egypt, Morocco and Lebanon. The opposite is true for Jordan and Oman.Practical implicationsEmpirical finding provides then an additional proof that Okun's law could exist in a developing countries such as Tunisia, Egypt, Morocco, Lebanon and Jordan. Hence, any attempt to increase gross domestic product (GDP) through some economic fiscal and/or monetary policies in these countries would reduce unemployment rate.Originality/valueBased on asymmetric specification, the author can conclude with precision that an economic upturn of 3.37, 2.98 and 2.5%, respectively, in Tunisia, Morocco and Egypt reduces unemployment by 1%, whilst the downturn of 5.03 and 2.43% (and about 12%), respectively, in Tunisia and Morocco (and Lebanon and Jordan) achieves the opposite.

THE IMPACT OF TRANSACTION COSTS ON MANAGEMENT DECISIONS (ON THE EXAMPLE OF UKRAINIAN COMPANIES)

The purpose of the article is to study the impact of transaction costs on managerial decision-making on the example of Ukrainian enterprises. The article notes that, despite the significant achievements of the institutional theory, there are still questions about the clarity of understanding of its key concepts and definitions, the content of transaction costs and the concept of their minimization, the expediency and efficiency of the existence of certain types of transaction costs. The subject of the study is the essence of transaction costs of the enterprise and their modern classification. The methodological basis of the study was an integrated approach to the essence and classification of transaction costs, as well as general scientific and special research methods: retrospective and systematic analysis, comparison and generalization, grouping and sampling, methods of building linear and nonlinear economic and statistical models with constraints. As noted above, the main issue of the study was the classification of transaction costs. The economic situation in Ukraine was assessed, which allowed to determine that transaction costs are quite high for enterprises due to: insufficient development of markets, unformed structure of institutions, complex and ambiguous legislation, significant tax pressure and existing facts of corruption. The authors noted that the list of components of transaction costs is constantly updated due to the complication of the socio-economic conditions for doing business associated with crisis-forming force majeure factors (in particular, the COVID-19 pandemic), which have become very significant for the world economy and have significantly changed approaches to the allocation of investment resources. As a result of the study, it is proposed to supplement the existing classification of transaction costs with costs associated with adapting to new business conditions ("adaptation cost"), which together characterize the ability of an enterprise to adapt to new conditions of functioning and development. The main conclusions of the study include the fact that, given current trends, reducing transaction costs is becoming a priority issue. At the same time, in the context of the proposed classification, a model for minimizing transaction costs was developed for the first time, which includes a modern classification of transaction costs and provides the possibility of their modeling for a more complete and logical calculation. According to the authors, the presented model will provide an opportunity to more correctly determine the effectiveness of management decisions related to investments in the core business of the enterprise. It is important to note that, given the basic provisions of institutional theory, a significant amount of transaction costs, in principle, can neutralize the investment process. Thus, the study is of both theoretical and practical importance and gives an idea of solving a number of both industrial and social problems and can become the basis for further research.

Economic assessment of the parameters of reproduction of land resources in the regional agricultural sector

Subject. This article discusses the importance of land resources for the development of the region. Objectives. The article aims to conduct an economic assessment of the reproduction of land resources in the Russian agriculture. Methods. For the study, we used statistical research methods and linear predictive models with a loss functional in the form of quantile functions. Results. The article confirms the hypothesis about the influence of the nature of economic activity on the parameters of reproduction of land resources within certain regional economic systems. The article proves that the increase in the intensity of the use of land resources, accompanied by an increase in the yield of agricultural crops, leads to a deterioration in the parameters of reproduction of land resources. At the same time, there is a statistically significant relationship between the parameters of reproduction and the level of application of organic fertilizers. Conclusions. The allowed losses of elements of land fertility should be compensated by the appropriate application of organic and mineral fertilizers, as well as the use of various soil protection measures.

EPID-18. IATROGENIC IMMUNODEFICIENCY ASSOCIATED LYMPHOPROLIFERATIVE DISORDERS OF THE CENTRAL NERVOUS SYSTEM: A TREATMENT PARADOX

Abstract BACKGROUND Primary central nervous system lymphomas (PCNSLs) have historically had dismal survival rates until the advent of high-dose methotrexate (HD-MTX) based chemotherapy regimens. With increasing prevalence of autoimmune disease and development of new immunosuppressants, a genetically distinct entity known as iatrogenic immunodeficiency associated lymphoproliferative disorder (LPD) has emerged. Many of these cases arise following methotrexate use, challenging feasibility of standard HD-MTX regimens. The aim of this study was to further characterize this disorder and determine the optimal management strategy. METHODS We describe a case of a 76-year-old female with iatrogenic immunodeficiency associated PCNSL successfully treated with surgical resection followed by an antiviral and rituximab based regimen. We then performed a systematic literature review and identified 58 cases of non-transplant iatrogenic immunodeficiency associated LPD involving the CNS. We used a linear probability statistical model to determine correlations with outcome. RESULTS Natalizumab was associated with EBV negative tumors (p = 0.023), and EBV positive tumors were associated with improved outcomes (p = 0.016). Surgical resection was associated with improved outcomes (p = 0.032), although limited by potential confounding effect. Antiviral treatment (p = 0.095), rituximab (p = 0.111), and SCT (p = 0.198) showed a trend toward improved outcomes. The remaining treatments including methotrexate showed no improvement. CONCLUSION We propose that surgical resection, rituximab, and antiviral treatment may be considered as an alternative to standard HD-MTX based regimens when managing iatrogenic immunodeficiency associated LPD of the CNS. Further study through prospective cohort studies or randomized clinical trials is warranted.

Abstract 12528: Variants in Solute Cation Transporters 3 Associate with High Lp(a) Levels Independent of Apo(a) Isoform Size

Introduction: Lipoprotein (a) [Lp(a)] consists of a low-density lipoprotein (LDL) molecule attached to apolipoprotein(a) [apo(a)]. Apo(a) comprises two kringle domains (KIV and KV) derived from the plasminogen gene and an inactive protease domain. The KIV domain is subdivided into 10 subtypes (KIV1-10), with KIV2 occurring as a copy number variation (CNV) that varies among individuals. Elevated Lp(a) levels are a major risk factor for CVDs and are inversely correlated to the KIV2 CNV determining apo(a) isoform size. Objectives: This study aimed to identify variants associated with a high Lp(a) phenotype that were not linked to apo(a) isoform size. Methods: Illumina sequencing of 13 candidate genes was performed on a discovery set comprising 54 high Lp(a) European samples (>50mg/dL). The sequenced genes included LPA, adjacent genes, and those coding for receptors involved in Lp(a) uptake. Variants commonly found in high samples were genotyped in the Otago LPA European cohort (n=589) and their association with Lp(a) levels was checked via Kruskal-Wallis and linear regression statistical models. The associations were then replicated in the much bigger German Chronic Kidney Disease (GCKD) cohort (n=4930). Results: Nine variants were significantly associated with high Lp(a) levels. Two variants were in the LPA gene (rs3124784 and rs3798220) and seven were in neighbouring genes (rs2282143 in SLC22A1, rs3127592, rs3127593, rs3103353 and rs3127594 in SLC22A2 and rs1810126 and rs3088442 in SLC22A3). Interestingly, the minor allele of all nine variants was significantly associated with increased LPA expression in liver tissues in the GTEx database. Seven of the variants were significantly associated with small apo(a) isoform size (P=1x10-4 to 2.9x10-18), while rs1810126 and rs3088442 in SLC22A3 were not associated with the small isoform size (P=0.43 and 0.42). Conclusion: Nine variants in the LPA and nearby genes were significantly associated with elevated Lp(a) levels and increased LPA expression, with the majority being linked to a short apo(a) isoform. The association of two SLC22A3 gene variants with Lp(a) levels independent of apo(a) isoform size warrants further investigation to establish any functional connection between LPA and this gene.

An uncertainty hybrid model for risk assessment and prediction of blast-induced rock mass fragmentation

Blasting is an important mining operation that usually produce several damaging consequences. Adverse rock fragmentation due to bench blasting is one of them. Hence, analysis of risk level and accurate estimation of particle size distribution of fragment size are of interest. This research developed a new model to simultaneously predict and risk assessment of rock fragmentation using 64 collected data from blasting operated in the Zarshouran gold mine in Iran. In this regard, a newly rock engineering system (RES) is developed based on the reliability information of Z-number theory and causal-effect relationship of the fuzzy cognitive map (FCM). This approach is named the reliability rock engineering causality system (RRECS). To do this, 15 principal effective parameters on rock fragment size were considered in the RRECS modeling process. The uncertainty of the interaction matrix was reduced using Z-number concept. Besides, the weight of effective parameters updated based on the combination of the nonlinear Hebbian algorithm (NLH) and differential evolution algorithm (DE) in FCM. The RRECS performance was validated by statistical linear and non-linear models. The results show R2, RSME, BIAS, and Accuracy using the proposed RRECS model calculated to be 0.957, 1.956, 0.001, and 96.741 for training and 0.931, 0.996, 0.016, and 99.053 for testing parts, respectively. Therefore, RRECS has performed better than the exponential, power, logarithmic, polynomial, and linear models. Furthermore, the sensitivity analysis results revealed that the hole diameter and powder factor parameters have the highest and lowest sensitivity on fragmentation, respectively.

Determining the static characteristic of a measuring current transformer at a reduced load of the metering unit

The object of the research is a measuring current transformer of the electromagnetic type, which is used as part of the electricity metering unit. The current transformers functioning in the mode of reduced primary current is accompanied by significant errors. The existence of such a regime for a long time due to downtime of production equipment leads to a significant underestimation of electricity. This leads to unjustified financial losses for energy supply companies as electricity in many countries has become more expensive. The static characteristic of the measuring current transformer at a reduced load of the metering unit is described by a linear statistical model. The parameters of the model are estimated on the basis of empirical data using methods of covariance analysis. The adequacy of the model is confirmed by analysis of regression residuals. The obtained statistical model of the static characteristic, unlike the known ones, is characterized by universality, as it describes current transformers with an arbitrary transformation ratio within the studied accuracy class of 0.5S. The uncertainty of the current error is estimated using the prediction intervals for the transformers secondary currents as a function of the primary currents, calculated on the basis of the obtained model. The prediction intervals for sample values of the current error were obtained through the application of the proposed approach for uncertainty estimation of such an error at a reduced load of the metering unit. It was found that the current error can reach −20 %. Taking into account the indicated intervals for measuring current transformers at a reduced load of the metering unit during billing will allow energy supply companies to increase the accuracy of commercial electricity metering.

Impact of MGMT Promoter Methylation Status on Tumor Dynamics during Weekly Adaptive Radiotherapy for Glioblastoma

<h3>Purpose/Objective(s)</h3> Adaptive MRI-guided radiotherapy (RT) on a 1.5T MR-Linac using a reduced clinical target volume (CTV) of 5mm instead of the 15mm standard for glioblastoma (GBM) is currently being evaluated on the UNITED clinical trial (NCT04726397). We present a preliminary comparison of morphological changes during a course of adaptive RT with concurrent temozolomide between tumors with MGMT promotor methylation (MGMT-m) and those that are MGMT promoter unmethylated (MGMT-um). <h3>Materials/Methods</h3> The first 30 patients with GBM (all IDH wildtype) enrolled on the UNITED trial were analyzed. RT consisted of 60 Gy in 30 fractions (n=12) or 40 Gy in 15 fractions (n=18) (Fx). Expansions on the gross tumor volume (GTV) consisted of a 5 mm CTV with the provision to include FLAIR hyperintense areas at-risk and a 3 mm planning target volume (PTV). A pre-treatment reference plan was developed from a standard planning MRI (FxRef) followed by weekly on-line fully adaptive re-planning at Fx1, Fx6, Fx11, etc., based on a gadolinium contrast-enhanced MRI acquired on the MR-Linac. Remaining fractions were image-guided by pre-beam-on onboard non-contrast MRI, to ensure stability of the treatment volumes. The GTV and CTV were quantified by their absolute volumes, volumes relative to the FxRef and the maximum linear distance from the edges of the reference contour at FxRef to the weekly adapted contours (migration distance, d_mig). MGMT promoter methylation status was explored as a fixed effect in a linear mixed statistical model. <h3>Results</h3> The median changes in GTV relative to FxRef at Fx1, Fx6, Fx11, Fx16, Fx21, and Fx25 in MGMT-um tumors (n=12) were 10.3%, 9.2%, 10.6%, 14.5%, 18.0% and 17.3%, respectively, while for MGMT-m (n=18) were 3.4%, 0.0%, -8.6%, -11.3%, -11.3% and -5.6% (p=0.021). A similar significant trend was observed with the CTV. With a median time interval of 6 days (range, 1-18 days) between FxRef and Fx1, the GTV increased by over 10% in 58% of MGMT-um tumors compared to only 33% of MGMT-m tumors. MGMT-um tumors had significantly larger maximum d_mig compared to tumors with MGMT-m, with a median d_mig of 9.6 mm vs. 5.8 mm, respectively (p=0.018). The maximum GTV migration distance was greater than 5, 10 and 15 mm in 83%, 50% and 17% of MGMT-um tumors but only 56%, 11% and 0% for MGMT-m tumors, respectively. <h3>Conclusion</h3> MGMT-um GBM exhibited significant changes in morphology and migration distance between the time of treatment planning to the first treatment fraction, as well as throughout a course of RT. In this population, our results support a greater frequency of imaging and plan adaptation when applying personalized reduced CTV margins.

Synchronized refixation saccades in enhanced VVOR test. A new application for PR score.

Main objectives for this study were to develop a quantification method to obtain a Perez-Rey (PR) score adapted to the VVOR test and to evaluate the correlation of the PR score obtained with quantified VVOR with the PR score of the vHIT test. A new PR score calculation method for quantified VVOR test was developed using the MATLAB computational software based on saccadic response time latency variability between each head oscillation cycle of the VVOR test. Retrospective correlation between PR scores in VVOR and vHIT tests, performed in the same vHIT testing session for patients with vestibular neuritis and vestibular neurectomy, was performed to correlate new PR (VVOR) score with the classic PR (vHIT) score. Thirty patients were included: 11 post-neurectomy and 19 subacute vestibular neuritis. Pearson's correlation coefficient (R2) for the overall sample was 0.92 (p < 0.001) and 95% confidence interval was 0.85 -0.96. In the linear mixed-effects statistical model developed, only PRVHIT and PRVVOR scores showed statistical association in Wald X2 test (p = 0.008). The new developed PR score for synchronization measurement of saccadic responses in VVOR testing is a valid method that outputs synchronization values and highly correlates with PR score in vHIT test.

The Influence of Density on the Mechanical Response of Reinforced High-Density Polyurethane Foams: A Statistical Approach

In this work, rigid polyurethane foams (RPUF) reinforced by micro fibrillated cellulose (MFC) were manufactured using the free rising method and also under confinement inside a closed mould, aiming to increase apparent density and improve mechanical response. Neat RPUF were also manufactured for comparison. The mechanical response, evaluated by compression (following ASTM D1621 standard) tests were correlated with the final composite apparent density (evaluated following ASTM D1622 standard). Simple linear regression statistical models, based on F-test, were developed using stat graphics software, aiming to understand and correlate the increment in density and its influence on the improvement in mechanical response. Different models were developed to describe the foam behavior. The main results show a more significant influence of the density on strength than stiffness for the neat RPUF, unlike the MFC-reinforced RPUF, which presented an opposite response. These effects could be caused by the lower content of voids when the foams were produced under confinement, and by the greater crosslink density, when MFC was added.

Identifying a Leading Predictor of Arctic Stratospheric Ozone for April Precipitation in Eastern North America

An analysis of the relationship between changes in Arctic stratospheric ozone (ASO) and precipitation in eastern North America (38°–54°N, 65°–87°W; PENA) was performed using observational and reanalysis data coupled with the Whole Atmosphere Community Climate Model version 4 (WACCM4). We found that March ASO exhibits a strong correlation with PENA in April, indicating that the one-month leading ASO exerts a potentially strong impact on April PENA. Changes in tropospheric circulation over the North Pacific and North America can be influenced by ASO anomalies via stratosphere–troposphere interactions. Increased ASO typically results in the transport of drier, colder air from northwest to eastern North America and suppresses local convective activity by enhancing regional downwelling. These conditions lead to a decrease in regional atmospheric water vapor content (1000–600 hPa). Abnormally high ASO may therefore suppress precipitation, whereas abnormally low ASO serves to enhance precipitation, and the finding is supported by WACCM4 simulations incorporating these ASO anomaly signals. We also present an ASO-based statistical linear model for predicting April PENA. Results confirm that the linear model reproduces April PENA for both training and testing periods, based on March ASO, demonstrating the reliability and stability of this linear model. This study verifies that ASO is a viable predictor for projecting April PENA and thus improving forecasts of regional seasonal precipitation.

Prediction of stream water quality in Godavari River Basin, India using statistical and artificial neural network models

Abstract The successful prediction of the stream or river water quality is gaining the attention of various governmental agencies, and pollution control boards worldwide due to its useful applications in determining watershed health, biodiversity, ecology, and suitability of potable water needs of the river basin. The physically based computational water quality models would require large spatial and temporal information databases of climatic, hydrologic, and environmental variables and solutions of nonlinear, partial differential equations at each grid point in a river basin. These models suffer from estimability, convergence, stability, approximation, dispersion, and consistency issues. In such a problematic modeling scenario, an artificial neural network (ANN) modeling of 22 stream water quality parameters (SWQPs) is performed from easily measurable data of precipitation, temperature, and novel land use parameters obtained from Geographic Information System (GIS) analysis for the Godavari River Basin, India. The ANN models are compared with the more traditional, statistical linear, and nonlinear regression models for accuracy and performance statistics. This study obtains regression coefficients of 0.93, 0.78, 0.83, and 0.74 for electrical conductivity, dissolved oxygen, biochemical oxygen demand, and nitrate in testing using feedforward ANNs compared with a maximum of 0.45 using linear and nonlinear regressions. Principal component analysis (PCA) is performed to reduce the input data dimension. The subsequent modeling using radial basis function and ANNs is found to improve the overall regression coefficients slightly for the chosen four water quality parameters (WQPs). A closed form equation for electrical conductivity has been derived from MATLAB simulations. The successful modeling results indicate the effectiveness and potential of ANNs over the statistical regression approaches for estimating the highly nonlinear problem of stream water quality distributions.

Exogenous regulation of the potatoes’ adaptive potential when using bio stimulants

Potato from the Solanaceae family is one of the most important crops in the world and its cultivation is common in many places. The average yield of this crop is 20 Mg·ha –1 and it is compatible with climatic conditions in many parts of the world. The experiment studied the possibility of exogenous regulation of the adaptive potential available for four potato cultivars through the use of growth stimulants with different action mechanisms: 24-epibrassinolide (EBL) and chitosan biopolymer (CHT). The results allowed us to establish significant differences in growth parameters, plant height, leaf index, vegetation index, chlorophyll content, and yield structure. Monitoring growth and predicting yields well before harvest are essential to effectively managing potato productivity. Studies have confirmed the empirical relationship between the normalised difference vegetation index ( NDVI) and N-tester vegetation index data at various stages of potato growth with yield data. Statistical linear regression models were used to develop an empirical relationship between the NDVI and N-tester data and yield at different stages of crop growth. The equations have a maximum determination coefficient (R 2) of 0.63 for the N-tester and 0.74 for the NDVI during the flowering phase (BBCH 1 65). NDVI and N-tester vegetation index positively correlated with yield data at all growth stages.

Association of Cerebral Blood Flow With Longitudinal Changes in Cerebral Microstructural Integrity in the Coronary Artery Risk Development in Young Adults (CARDIA) Study

Decreased cerebral tissue integrity and cerebral blood flow (CBF) are features of neurodegenerative diseases. Brain tissue maintenance is an energy-demanding process, making it particularly sensitive to hypoperfusion. However, little is known about the association between blood flow and brain microstructural integrity, including in normative aging. To assess associations between CBF and changes in cerebral tissue integrity in white matter and gray matter brain regions. In this longitudinal cohort study, magnetic resonance imaging was performed on 732 healthy adults from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a prospective longitudinal study (baseline age of 18-30 years) that examined participants up to 8 times during 30 years (1985-1986 to 2015-2016). Cerebral blood flow was measured at baseline (year 25 of the CARDIA study), and changes in diffusion tensor indices of fractional anisotropy (FA) and mean diffusivity (MD), measures of microstructural tissue integrity, were measured at both baseline and after approximately 5 years of follow-up (year 30). Analyses were conducted from November 5, 2020, to January 29, 2022. Automated algorithms and linear mixed-effects statistical models were used to evaluate the associations between CBF at baseline and changes in FA or MD. After exclusion of participants with missing or low-quality data, 654 at baseline (342 women; mean [SD] age, 50.3 [3.5] years) and 433 at follow-up (230 women; mean [SD] age, 55.1 [3.5] years) were scanned for CBF or FA and MD imaging. In the baseline cohort, 247 participants were Black (37.8%) and 394 were White (60.2%); in the follow-up cohort, 156 were Black (36.0%) and 277 were White (64.0%). Cross-sectionally, FA and MD were associated with CBF in most regions evaluated, with lower CBF values associated with lower FA or higher MD values, including the frontal white matter lobes (for CBF and MD: mean [SE] β = -1.4 [0.5] × 10-6; for CBF and FA: mean [SE] β = 2.9 [1.0] × 10-4) and the parietal white matter lobes (for CBF and MD: mean [SE] β = -2.4 [0.6] × 10-6; for CBF and FA: mean [SE] β = 4.4 [1.1] × 10-4). Lower CBF values at baseline were also significantly associated with steeper regional decreases in FA or increases in MD in most brain regions investigated, including the frontal (for CBF and MD: mean [SE] β = -1.1 [0.6] × 10-6; for CBF and FA: mean [SE] β = 2.9 [1.0] × 10-4) and parietal lobes (for CBF and MD: mean [SE] β = -1.5 [0.7] × 10-6; for CBF and FA: mean [SE] β = 4.4 [1.1] × 10-4). Results of this longitudinal cohort study of the association between CBF and diffusion tensor imaging metrics suggest that blood flow may be significantly associated with brain tissue microstructure. This work may lay the foundation for investigations to clarify the nature of early brain damage in neurodegeneration. Such studies may lead to new neuroimaging biomarkers of brain microstructure and function for disease progression.

Gut and oral microbiota associations with viral mitigation behaviors during the COVID-19 pandemic.

Imposition of social and health behavior mitigations are important control measures in response to the coronavirus disease 2019 (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Although postulated that these measures may impact the human microbiota including losses in diversity from heightened hygiene and social distancing measures, this hypothesis remains to be tested. Other impacts on the microbiota and host mental and physical health status associations from these measures are also not well-studied. Here we examine changes in stool and oral microbiota by analyzing 16S rRNA gene sequence taxonomic profiles from the same individuals during pre-pandemic (before March 2020) and early pandemic (May-November 2020) phases. During the early pandemic phase, individuals were also surveyed using questionnaires to report health histories, anxiety, depression, sleep and other lifestyle behaviors in a cohort of predominantly Caucasian adults (mean age = 61.5 years) with the majority reporting at least one underlying co-morbidity. We identified changes in microbiota (stool n = 288; oral n = 89) between pre-pandemic and early pandemic time points from the same subject and associated these differences with questionnaire responses using linear statistical models and hierarchical clustering of microbiota composition coupled to logistic regression. While a trend in loss of diversity was identified between pre-pandemic and early pandemic time points it was not statistically significant. Paired difference analyses between individuals identified fewer significant changes between pre-pandemic and early pandemic microbiota in those who reported fewer comorbidities. Cluster transition analyses of stool and saliva microbiota determined most individuals remained in the same cluster assignments from the pre-pandemic to early pandemic period. Individuals with microbiota that shifted in composition, causing them to depart a pre-pandemic cluster, reported more health issues and pandemic-associated worries. Collectively, our study identified that stool and saliva microbiota from the pre-pandemic to early pandemic periods largely exhibited ecological stability (especially stool microbiota) with most associations in loss of diversity or changes in composition related to more reported health issues and pandemic-associated worries. Longitudinal observational cohorts are necessary to monitor the microbiome in response to pandemics and changes in public health measures.

Large-scale statistical analysis and modelling of real and regulatory total energy use in existing single-family houses in Flanders

ABSTRACT Large-scale statistical studies on the gap between the real and regulatory energy use in residential buildings in Europe have shown that the regulatory calculation overestimated the real energy use, inflated true energy savings and undermined national energy policy making. Using data from 122,680 Flemish existing single-family houses, this research builds further on existing studies by contributing results for Flanders. The study also examines to what extent available aggregated variables explain the real annual total building energy use using statistical linear models and addresses the problem of multicollinearity and the importance of bootstrapped confidence intervals for model quality control. The overestimation of the real total energy use (and potential energy savings) by the Flemish regulatory method is exceedingly large compared to studies from other EU countries. The Flemish labels prove very poor indicators of the real energy use. Statistical linear models explain up to 46.6% of all variability and indicate that a significant extent of multicollinearity had to be corrected. Half of the variability has been left unexplained and has to be attributed to variables that were not available and the fact that the data were insufficiently accurate. Future analysis will explore whether more complex models identify more evidence.

A Statistical Analysis and Exploration on Atlantic Hurricanes Database (HURDAT2); Do We Expect the Worst?

Hurricane occurrence exploration is a heavy task that requires sophisticated methods to accurately determine the occurrence and the impact of damage on the tropical environs. To control massive damage, several techniques have been developed to measure the accuracy of track forecasting. The accurate analysis and exploration of the occurrence of hurricanes are very crucial for the affected environs evaluation within a short time to minimize the loss of human life and property. Nevertheless, the exact analysis and exploration of these hurricanes is challenging and time-consuming. Therefore, this study proposed a statistical model whereby the analysis of variance (ANOVA) a linear regression scientific data statistical analysis model is applied on the dataset of Atlantic hurricane database (HURDAT2) for the exploration of hurricane occurrence and damage in the tropical environs. The best tracks of a 6 hour interval, location (latitude, and longitude), wind speed, central pressure of all identified hurricanes and subtropical typhoons from the year 2008 to 2017 (10 year period) are used to determine the R2 coefficient, which measures the goodness and fitness of the model to reveal the variability of the real data. Statistical significance and reliability of the data are tested on significance P-value &lt;0.05 where four different parameters are considered for the analysis in order to determine the destructive of these hurricanes in the tropical environs; latitude (x1), longitude (x2), the wind speed (x3), and central pressure. The results of our model proved significant with an accuracy of 99.3%, and a mean standard error (MSE) of 1.4952 for all the hurricanes that were analyzed, and the year 2012 was established as a year that had much damage.

245.10: Very Low Dose Anti-thymocyte Globulins Versus Basiliximab in Non-immunized Kidney Transplant Recipients

Background: The choice between Basiliximab (BSX) or Anti-Thymocyte Globulins (ATG) as induction therapy in non-immunized kidney transplant recipients remains uncertain. Whilst ATG can permit steroid withdrawal and CNI decrease, it also increases the risk of viral reactivations due to a prolonged dose-dependent lymphopenia. We compared transplant outcomes in non-immunized patients receiving a very low dosage of ATG (75 mg daily twice) versus BSX as induction therapy. Methods: We included non-immunized patients receiving a first kidney transplant between 2015 and 2020. Studies outcomes were patient and graft survival, cumulative probabilities of acute rejection, infectious episode, CMV infection and Post-Transplant Diabetes (PTD). Cox, logistic or linear statistical models were used depending on the studied outcome and models were weighted on propensity scores. Results: 183 patients were included, 100 receiving ATG and 83 receiving BSX. Maintenance therapy was comparable between groups. Patient and graft survival did not differ between groups, as infectious and CMV episodes. There was a trend to a lower occurrence of BPAR in the ATG group (HR at 1.92; 95% CI: 0.77; 4.78, p-value = 0.1598). Occurrence of PTD was significatively higher in the BSX group (HR at 2.44; 95% CI: 1.09; 5.46; p-value = 0.0304), due to a high number of treated rejection episodes (14.5% vs 7%). Conclusion: Induction with a very low dose of ATG in non-immunized recipients is safe, decreasing the occurrence of treated rejections episodes compared to BSX, thus leading to a lower rate of PTD.

Effect of Dietary Enrichment with Flaxseed, Vitamin E and Selenium, and of Market Class on the Broiler Breast Meat-Part 2: Technological and Sensorial Traits.

The influence of diet enrichment with flaxseed, selenium and vitamin E, and market class on breast meat was investigated in terms of technological and sensorial quality of breast meat. A randomized complete block design with an experimental unit of n = 6000 broilers receiving a standard or enriched diet, and slaughtered at 37 (light class), 47 (medium class), or 57 (heavy class) days of life, was developed. Then, enriched and standard breast muscles from every market class were studied for their technological and sensorial traits—both at 24 h post-mortem and after one month of frozen storage—by a statistical multiple linear model. Redness and yellowness of muscles significantly (p < 0.05) increased and decreased with increasing market age. Moreover, the yellowness significantly (p < 0.05) raised after frozen storage. However, obtained data were always indicative of a normal meat color. The water holding capacity improved following fed enrichment and significantly (p < 0.05) worsened after frozen storage. For the sensory analysis, juiciness and chewing rest of meat resulted significantly (p < 0.05) improved with increasing slaughtering age and diet enrichment, as well as their mutual interaction, while they deteriorated after frozen storage. Overall, fresh and enriched muscles from heavy broilers had the best technological and sensorial traits, thus, confirming that market size and diet should be highly considered to obtain breast meat with greater consumer acceptance.

Artificial intelligence for predicting survival following deceased donor liver transplantation: Retrospective multi-center study

BackgroundPrevious studies have indicated that the model for end-stage liver disease (MELD) score may fail to predict post-transplantation patient survival. Similarly, other scores (donor MELD score, balance of risk score) that have been developed to predict transplant outcomes have not gained widespread use. These scores are typically derived using linear statistical models. This study aimed to compare the performance of traditional statistical models with machine learning approaches for predicting survival following liver transplantation. Materials and methodsData were obtained from 785 deceased donor liver transplant recipients enrolled in the Korean Organ Transplant Registry (2014–2019). Five machine learning methods (random forest, artificial neural networks, decision tree, naïve Bayes, and support vector machine) and four traditional statistical models (Cox regression, MELD score, donor MELD score and balance of risk score) were compared to predict survival. ResultsAmong the machine learning methods, the random forest yielded the highest area under the receiver operating characteristic curve (AUC-ROC) values (1-month = 0.80; 3-month = 0.85; and 12-month = 0.81) for predicting survival. The AUC-ROC values of the Cox regression analysis were 0.75, 0.86, and 0.77 for 1-month, 3-month, and 12-month post-transplant survival, respectively. However, the AUC-ROC values of the MELD, donor MELD, and balance of risk scores were all below 0.70. Based on the variable importance of the random forest analysis in this study, the major predictors associated with survival were cold ischemia time, donor ICU stay, recipient weight, recipient BMI, recipient age, recipient INR, and recipient albumin level. As with the Cox regression analysis, donor ICU stay, donor bilirubin level, BAR score, and recipient albumin levels were also important factors associated with post-transplant survival in the RF model. The coefficients of these variables were also statistically significant in the Cox model (p < 0.05). The SHAP ranges for selected predictors for the 12-month survival were (−0.02,0.10) for recipient albumin, (−0.05,0.07) for donor bilirubin and (−0.02,0.25) for recipient height. Surprisingly, although not statistically significant in the Cox model, recipient weight, recipient BMI, recipient age, or recipient INR were important factors in our random forest model for predicting post-transplantation survival. ConclusionMachine learning algorithms such as the random forest were superior to conventional Cox regression and previously reported survival scores for predicting 1-month, 3-month, and 12-month survival following liver transplantation. Therefore, artificial intelligence may have significant potential in aiding clinical decision-making during liver transplantation, including matching donors and recipients.