MLR Analysis Research Articles

A series of regression models to predict the weathering index of tropical granite rock mass

In the recent past, several weathering indicators have been developed to describe its state of weathering. The state of rock weathering is a useful indicator to estimate the integrity of tropically weathered rock material and mass which weatherability plays an important role in a tropical region. Through a ground assessment tool, the strength and durability of the rock mass could be estimated and complex or adopted to simplify the early prediction of the complex engineering parameter. This paper presents several models of the Weathering Index (WI) using selected significant parameters using statistical analysis. For this purpose, several sites have been chosen to represent granitic rock mass. Forty (40) numbers of samples were collected and tested comprising from four (4) sites in Malaysia. Several laboratory tests have been conducted such as Point Load Index (Is(50)), dry density, Slake Durability 1 (SD1), Slake Durability 2 (SD2) and moisture content. The field and laboratory data sets are used to determine the WI by using simple regression and MLR analysis Significant parameters found to be useful in determining the WI are selected namely SD1, dry density, Is(50), and block volume. These parameters were selected based on stepwise analysis using Statistical Package for the Social Sciences (SPSS). Following the models’ implementation, the models were evaluated and the best prediction model was selected after considering statistical coefficients, such as coefficient of determination (R2), variance account for (VAF), and root mean squared error (RMSE), as well as utilizing a straightforward ranking approach. The findings of this study could contribute to the more accurate prediction of WI using a more simplistic field and laboratory parameters. Therefore, the WI is useful during the initial stages and planning of rock excavation work and provides a good description of weathering grade and rock mass properties, which will affect excavatability in granitic areas.

USERS’ SATISFACTION WITH INTERCITY BUS TERMINAL QUALITY IN LAGOS STATE, NIGERIA

Globally, efficient intercity public transport relies on well-designed and high-quality terminal facilities and services to enhance accessibility, mobility, and safety for passengers and freight. Despite governmental efforts, bus terminals in Nigerian urban areas are becoming hotspots for unregulated activities and traffic chaos. In response, this study investigates users’ satisfaction with Lagos State's intercity bus terminal quality, analysing socio-economic profiles, satisfaction levels with quality of terminal facilities (QoTF) and services (QoTS), and challenges impacting terminal operations. Using a cross-sectional survey, 200 questionnaires were distributed to terminal users, employing a multistage sampling technique. Descriptive (weighted mean analysis) and inferential (multiple linear regression [MLR] analysis) statistics were employed for data analysis. Results indicate that the majority of respondents are male, aged below 40, and permanent terminal users. Satisfaction is noted with 13 out of 24 parameters of QoTF, with top-ranking parameters including parking space, restaurant facilities, and waste disposal facilities. However, dissatisfaction is expressed with 8 out of 14 parameters of QoTS. Least-satisfied QoTS parameters include staff attitudes, passenger safety, and bus service reliability. Top-rated challenges affecting terminal operations are security concerns, sanitation facility deficiencies and insufficient passenger amenities. MLR analysis indicates a significant influence of terminal facilities on overall service satisfaction (F= 2.899, p= 0.000 <0.05). In conclusion, enhancing both terminal facilities and services is crucial for efficient operations in Lagos State's bus terminals. Recommendations include improving security measures, sanitary infrastructure and staff training to address user concerns and enhance satisfaction.

Characteristics, trends, and management of Penile cancer in the United States: A population-based study

BackgroundPenile cancer (PeCa) is a rare disease. HPV infection, smoking, phimosis, and lichen sclerosus represent well-known associated risk factors. ObjectivesPrimary aim of our study is to evaluate the incidence and risk factors of PeCa and to outline the adopted diagnostic and therapeutic approaches. Secondary aim is to investigate risk factors associated with aggressive disease and to identify the complications arising from its surgical treatment. Materials and MethodsWe conducted a retrospective analysis using the PearlDiver™ Mariner database, from January 1, 2011, to December 31, 2021, identifying all patients diagnosed with PeCa and PeIN, evaluating comorbidities, risk factors, and social and economic conditions. We evaluated the imaging modalities employed for staging as well as the treatment strategies. Finally, we evaluated the most frequent complications associated with inguinal lymphadenectomy (ILND). ResultsDuring the study period, 17,494 patients were diagnosed with PeCa and 5,965 with penile intraepithelial neoplasia (PeIN). US was the most frequently utilized imaging modality, followed by PET and PET/CT. Use of CT and MRI was around 5%. Surgical treatment was the predominant strategy, utilized in 31.3% of PeCa and 22.9% of PeIN. Wide Local Excision/Glansectomy emerged as the most common surgical procedures. MLR analysis identified smoking as a risk factor for metastatic PeCa (OR; 95% CI = 1.49; 1.379–1.609), HPV infections were associated with a 35% decrease in risk (OR; 95% CI = 0.65; 0.562–0.744) (all P < 0.001). Lichen sclerosus and phimosis were associated with a doubled risk of demolitive surgery. Approximately 40% of patients experienced complications associated with ILND. ConclusionDespite advances in PeCa management, there's no significant move toward more conservative treatments. Surgical treatments are still marked by high rates of complications, potentially affect the sexual and psychosocial health of patients. These issues may foster a tendency toward avoidance behaviors, contributing to a delayed clinical presentation and treatment.

Non-carcinogenic risk assessment and sources apportionment of heavy metals in pomegranate in Anhui, China

This study examined the concentrations, sources, and health risks associated with 7 heavy metals in Chinese pomegranates. The results showed that the heavy metal in pomegranates was within standard limits, and the accumulation of heavy metals was low. PCA showed that Cd and Hg in pomegranates originate from irrigation water, Zn, As, and Cu from soil, and Cr and Pb from fertilizers. MLR and Monte Carlo sensitivity analysis showed that soil and irrigation water were the primary sources of heavy metals in pomegranates, with Zn, As, Cu, and Pb primarily originating from soil, and Cd, Cr, and Pb predominantly coming from irrigation water. The presence of organic matter and Ca in soil influenced heavy metal accumulation in pomegranates, other properties (Mg, Fe, and pH value in soil) have minor effects. The TTHQ for both adults and children remained below 1, while the TTRisk exceeded 1×10−4, mainly due to the hazardous impact of As in water. The upper 95% uncertainty limit indicated that residents were exposed to both non-carcinogenic and carcinogenic risks, primarily due to the toxicity of Cr in soil and As in water. Strengthening heavy metal screening in soil and water and enhancing pollution management policies are imperative.

Theoretical studies on the regioselectivity of polar vinyl monomer insertion catalyzed by an α-diimine Pd(ii) complex

This study uses DFT to investigate the regioselectivity of PVM insertion with the DBB–Ipty α-diimine Pd(ii) complex. It combines DFT with MLR analysis to identify key factors influencing regioselectivity in the reaction.

Novel nomogram for predicting pulmonary complications in patients with blunt chest trauma with rib fractures: a retrospective cohort study

The direct consequences of chest trauma may cause adverse outcomes. Therefore, the early detection of high-risk patients and appropriate interventions can improve patient outcomes. This study aimed to investigate the risk factor for overall pulmonary complications in patients with blunt traumatic rib fractures. Prospectively recorded data of patients with blunt chest trauma in a level 1 trauma center between January 2019 and October 2022 were retrospectively analyzed. The primary outcomes were one or more pulmonary complications. To minimize the overfitting of the prediction model, we used the least absolute shrinkage and selection operator (LASSO) logistic regression. We input selected features using LASSO regression into the multivariable logistic regression model (MLR). We also constructed a nomogram to calculate approximate individual probability. Altogether, 542 patients were included. The LASSO regression model identified age, injury severity score (ISS), and flail motion of the chest wall as significant risk factors. In the MLR analysis, age (adjusted OR [aOR] 1.06; 95% confidence interval [CI] 1.03–1.08; p < 0.001), ISS (aOR 1.10; 95% CI 1.05–1.16; p < 0.001), and flail motion (aOR 8.82; 95% CI 4.13–18.83; p < 0.001) were significant. An MLR-based nomogram predicted the individual risk, and the area under the receiver operating characteristic curve was 0.826. We suggest a novel nomogram with good performance for predicting adverse pulmonary outcomes. The flail motion of the chest wall may be the most significant risk factor for pulmonary complications.

Prediction of Non-Home Discharge Following Total Hip Arthroplasty in Geriatric Patients.

The majority of total hip arthroplasty (THA) patients are discharged home postoperatively, however, many still require continued medical care. We aimed to identify important characteristics that predict nonhome discharge in geriatric patients undergoing THA using machine learning. We hypothesize that our analyses will identify variables associated with decreased functional status and overall health to be predictive of non-home discharge. Elective, unilateral, THA patients above 65years of age were isolated in the NSQIP database from 2018-2020. Demographic, pre-operative, and intraoperative variables were analyzed. After splitting the data into training (75%) and validation (25%) data sets, various machine learning models were used to predict non-home discharge. The model with the best area under the curve (AUC) was further assessed to identify the most important variables. In total, 19,840 geriatric patients undergoing THA were included in the final analyses, of which 5194 (26.2%) were discharged to a non-home setting. The RF model performed the best and identified age above 78 years (OR: 1.08 [1.07, 1.09], P < .0001), as the most important variable when predicting non-home discharge in geriatric patients with THA, followed by severe American Society of Anesthesiologists grade (OR: 1.94 [1.80, 2.10], P < .0001), operation time (OR: 1.01 [1.00, 1.02], P < .0001), anemia (OR: 2.20 [1.87, 2.58], P < .0001), and general anesthesia (OR: 1.64 [1.52, 1.79], P < .0001). Each of these variables was also significant in MLR analysis. The RF model displayed good discrimination with AUC = .831. The RF model revealed clinically important variables for assessing discharge disposition in geriatric patients undergoing THA, with the five most important factors being older age, severe ASA grade, longer operation time, anemia, and general anesthesia. With the rising emphasis on patient-centered care, incorporating models such as these may allow for preoperative risk factor mitigation and reductions in healthcare expenditure.

2D QSAR ANALYSIS OF CARBONITRILE BASED INHIBITORS OF CATHEPSIN S AS POTENTIAL ANTIRHEUMATIC AGENTS

2D QSAR has been performed on a series of pyridine carbonitrile and trifluoromethyl phenyl derivatives. 53 compounds were divided into training and test sets out of which 37 compounds generated a final QSAR model. The most significant model with n = 37, r = 0.916, r2 = 0.762, r2 cv = 0.759, s value = 0.388, f value = 41.76 was developed using MLR analysis. For PLS, the fraction of variance explained = 0.806 was observed. A comparable PLS model with r2 = 0.806 and Neural model with r2 = 0.853 indicated good internal predictability of the model. External test set validation provided r2 values of 0.744 and 0.768 for MLR and PLS analysis, respectively. Dipole moment Z Component, Log P, Shape flexibility index, and Vamp LUMO descriptors proved to be significant for inhibition of Cathepsin S. These findings will be effective in designing more potent and effective Cathepsin S inhibitors

Genetic Analysis Of Advanced Wheat (Triticum Aestivum L.) Lines Grown Under Different Sowing Conditions

An investigation was made with 50 genotypes to study genetic variability and character association for yield and yield attributing traits under timely and late sown condition and screening of genotypes for spot blotch resistance. All the traits showed high heritability. Of the 12 yield components, only harvest index was correlated positively and significantly with grain yield/plant in both sowing conditions. In timely sown condition highest positive direct effect on Yield/plant was imposed by Days to 50% flowering (3.011) at genotypic level with residual effect R=0.02. In phenotypic path highest positive direct effect on Yield/plant was imposed by Days to 50% heading (0.426) with residual effect R=0.06. In late sown condition highest positive direct effect on Yield/plant was imposed by Days to 50% flowering (0.318) at genotypic level with residual effect R=0.02. In phenotypic path highest positive direct effect on Yield/plant was imposed by Harvest index (0.232) with residual effect R=0.02. MLR analysis revealed Days to 50% flowering in timely sown condition and Days to 50% heading in late sown condition to be most sensitive towards AUDPC in a negative direction. NEST 20-8 performed very well in both the conditions and was moderately resistant against spot blotch.

Profiles of citrus orchard nutrition and fruit quality in Hunan Province, China

ABSTRACT Understanding of orchard nutrition status can facilitate the proper management of orchards for higher fruit yield and quality. From 2011 to 2019, the nutrition status of orchards in Hunan Province, a major citrus-producing area in china, were assessed. A total of 361 soil samples, 378 leaf samples and 285 fruit samples of sweet oranges including navel orange (citrus sinensis l. Osbeck ‘newhall’) and common sweet orange (citrus sinensis l. Osbeck ‘bingtangcheng’), and mandarins including satsuma mandarin (citrus unshiu marc. ‘miyagawa’) and ponkan (citrus reticulata blanco ‘xinnu’) were collected. These samples were from112 sweet orange orchards and 140 mandarin orchards. Parameters of soil, leaf and fruit nutrition and fruit quality were analyzed based on these samples. The results revealed that soil acidification occurred in 79.0% of sweet orange orchards (ph 3.56 to 8.12) and 74.7% of mandarin orchards (ph 2.75 to 8.15). Soil organic matter (om) was abundant in both sweet orange orchards (21.3 g/kg on average) and mandarin orchards (19.77 g/kg on average). Nutrient analysis demonstrated deficiencies of nitrogen (n), potassium (k) and boron (b) in soils and leaves of sweet orange and mandarin orchards. Zinc (zn) was abundant in soils but deficient in leaves, suggesting a low zn utilization efficiency in these orchards. Besides, excessive iron (fe), magnesium (mn) and copper (cu) were found in sweet orange and mandarin leaves, which may be associated with low soil ph and fungicides application in this area. The average contents of n, phosphorus (p) and k in the fruit were about 0.80%, 0.11% and 0.51%, respectively, with a ratio close to 1:0.14:0.6. Multiple linear regression (mlr) analysis revealed that leaf npk and molybdenum (mo) significantly affect fruit fresh fruit weight (ffw) and total soluble solids (tss), and leaf p is closely correlated with fruit titratable acid (ta).

Analysis on the Path of Digital Villages Affecting Rural Residents' Consumption Upgrade: Based on the Investigation and Research of 164 Administrative Villages in the Pilot Area of Digital Villages in Zhejiang Province.

The weak consumption of rural residents has become an important factor restricting the healthy development of the Chinese economy. With the continuous development of digital construction in rural areas, its role in the upgrading of rural residents' consumption is becoming more and more important. Based on the mechanism of 164 administrative villages in the pilot area of Zhejiang Province, and using staggered cluster analysis, MLR analysis, mediation effect model, etc., this paper discusses the impact and transmission mechanism of digital villages on rural residents' consumption upgrade. The results show that farmers' wage income, property income, and total income all play a significant positive mediating role in the process of promoting consumption upgrading in digital villages; information literacy is a latent variable in the process of promoting consumption upgrading in digital villages. It also played a significant positive mediating role. The research provides the micromechanism of the digital village affecting the consumption upgrade of rural residents and puts forward to promote the process of infrastructure construction, improve the construction of e-commerce system, pay attention to the differences among residents and other suggestions.

Examining the groundwater level in a semi-arid district of eastern India: spatiotemporal trends, determinants, and future prospects

The present study aims to examine the spatio-temporal and seasonal fluctuation of groundwater levels in an eastern Indian semi-arid region. Also, it attempted to investigate the involvement of different significant groundwater usage factors in causing such variations and trends. Furthermore, by evaluating temporal patterns and the influence of the crucial drivers, generate meaningful predictions for raising community awareness about the future. A combination of statistical, spatial analysis, and machine learning techniques were used to fulfill the objectives. Overall, the findings revealed that the south-eastern corner (consisting of Bundwan and Barabazar Blocks), the far western region (especially Jhalda I and II Blocks), and some of the eastern Blocks are most vulnerable to groundwater scarcity. The monitoring stations in this region are typically suffering a significant declining trend in groundwater level, with a substantial amount of Sen's Slope. In contrast, the centrally positioned Blocks have lesser susceptibility. The MLR analysis and spatial thematic maps demonstrate a strong association between this spatial pattern of declining groundwater table and the groundwater drivers in consideration. Thus, it is acceptable to suggest that drivers from the human world must be included in the groundwater management planning of the Purulia district. Finally, the predictions from the time series confirm that if no interventions are implemented, the average groundwater level will decline by 2.5 m by 2030. Therefore, Purulia urgently requires a realistic plan to prevent future generations from becoming refugees in their own land.

The application of the WSES classification system for open pelvic fractures—validation and supplement from a nationwide data bank

BackgroundOpen pelvic fractures are rare but complex injuries. Concomitant external and internal hemorrhage and wound infection-related sepsis result in a high mortality rate and treatment challenges. Here, we validated the World Society Emergency Society (WSES) classification system for pelvic injuries in open pelvic fractures, which are quite different from closed fractures, using the National Trauma Data Bank (NTDB).MethodsOpen pelvic fracture patients in the NTDB 2015 dataset were retrospectively queried. The mortality rates associated with WSES minor, moderate and severe injuries were compared. A multivariate logistic regression model (MLR) was used to evaluate independent factors of mortality. Patients with and without sepsis were compared. The performance of the WSES classification in the prediction of mortality was evaluated by determining the discrimination and calibration.ResultsA total of 830 open pelvic fracture patients were studied. The mortality rates of the mild, moderate and severe WSES classes were 3.5%, 11.2% and 23.8%, respectively (p < 0.001). The MLR analysis showed that the presence of sepsis was an independent factor of mortality (odds of mortality 9.740, p < 0.001). Compared with patients without sepsis, those with sepsis had significantly higher mortality rates in all WSES classes (minor: 40.0% vs. 3.1%, p < 0.001; moderate: 50.0% vs. 9.1%, p < 0.001; severe: 66.7% vs. 22.2%, p < 0.001). The receiver operating characteristic (ROC) curve showed an acceptable discrimination of the WSES classification alone for evaluating the mortality of open pelvic fracture patients [area under curve (AUC) = 0.717]. Improved discrimination with an increased AUC was observed using the WSES classification plus sepsis (AUC = 0.767).ConclusionsThe WSES guidelines can be applied to evaluate patients with open pelvic fracture with accurate evaluation of outcomes. The presence of sepsis is recommended as a supplement to the WSES classification for open pelvic fractures.

The E23K Polymorphism of KCNJ11 and Diabetic Retinopathy in Northern Iran

Background: Diabetic Retinopathy (DR) is one of the most severe micro-vascular complications of diabetes mellitus (DM), involving interactions between environmental and genetic risk factors. KCNJ11 gene has a key role in insulin secretion and is of substantial interest in various populations. Methods: A population-based association of 524 T2DM patients was performed to delineate the genetic influence of KCNJ11 polymorphisms (rs5219, c.67A>G or E23K) on the risk of DR in an Iranian population. Genotyping was performed using TaqMan assay. Univariate and MLR analysis controlling for confounders was conducted to evaluate the association between rs5219 and DR. Results: No significant difference was observed in either genotypes distribution (p = 0.83) or allele frequency (p = 0.66) between T2DM individuals with and without DR in any models of inheritance. Genotype-phenotype association showed that DR group carrying GA genotypes, a significantly higher mean age was observed compared with two other genotypes (p = 0.04). MLR analysis indicated that HbAlc with adjusted OR of 1.84 (95% CI, 1.46–2.33, p = 0.00) and first-degree relatives of family history with adjusted OR of 2.85 (95% CI, 1.45–5.58, p = 0.002) were significantly associated with DR, but the c.67A>G genotype is not an independent predictor of retinopathy. Conclusion: Collectively, rs5219 was not associated with DR among Iranians with T2DM.

In Vitro and In Silico Determination of Some N-ferrocenylmethylaniline Derivatives as Anti-Proliferative Agents Against MCF-7 Human Breast Cancer Cell Lines.

Since the binding of estradiol to its receptor promotes breast cancer cell proliferation (in the ER+ tumours), many molecules targeting this protein have been synthesized to counteract the estradiol action. Ferrocene derivatives have proved their efficiency against hormone-dependent breast cancer cells (MCF-7). In this study, we aimed to find new ferrocene derivatives having pharmacochemistry properties as potential drugs against human breast cancer cells. A series of 29 N-ferrocenylmethylaniline derivatives A0-A28 were synthesised, and their anti-proliferative activity against both hormone-dependent (MCF-7) and independent (MDA-MB 231) human breast cancer cell lines were performed using the MTT test. Molecular docking and drug-likeness prediction were also performed for the five most active derivatives towards MCF-7. A QSAR model was also developed for the perdition of the anti-proliferative activity against MCF-7 cell lines using molecular descriptors and MLR analysis. All studied derivatives demonstrated better cytotoxicity against MCF-7 compared to the MDA-MB-231 cell lines, and compounds A2, A9, A14, A17 and A27 were the most potent ones but still less active than the standard anticancer drug, crizotinib. The QSAR study revealed good predictive ability, as shown by R2 cv = 0.848. In vitro and in silico results indicated that derivatives A2, A9, A14, A17, and A27 possess the highest anti-proliferative activity; these results can be used to design more potent N-ferrocenylmethylaniline derivatives as anti-proliferative agents.

Modelling of Greek Lakes Water Quality Using Earth Observation in the Framework of the Water Framework Directive (WFD)

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchigeneral; R = 0.78; RMSE = 0.24 m), natural (Secchinatural; R = 0.95; RMSE = 0.14 m) and artificial (Secchiartificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TPgeneral (R = 0.71; RMSE = 1.41 mg/L) and TPnatural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth &lt; 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited.

FORECASTING AND ON THE INFLUENCE OF CLIMATIC FACTORS ON RISING DENGUE INCIDENCE IN BAGUIO CITY, PHILIPPINES

Dengue fever or dengue has been a concern for individuals living in Baguio City, Philippines. Every year, incidence counts rise during rainy seasons experienced from June to October. Several researches suggest that meteorological factors have great influence on the life, growth, and reproduction of dengue-carrying mosquitoes, resulting in higher dengue incidence in the area. With the continuing rise of dengue incidence in Baguio City, we aim to forecast dengue incidence in the area for the year 2019, starting from January until the end of the outbreak period in the area. Here, we use the projections package of R as it involves the serial interval distribution and 𝑅𝑡 value of dengue incidence. We also aim to use multiple linear regression analysis to determine if meteorological factors have significant effects in the rise of dengue incidence in the city. With the inclusion of time-varying reproduction number and serial interval distribution of dengue, we projected that dengue incidence may reach up to 101 cases by June 16, 2021, and without further actions, cases may rise up to 529 cases by August 29, 2021. Based on the average two-year period, such increase is attributed to relative humidity and average temperature as these are the most significant factors associated with dengue incidence based on the MLR analysis. The highest and mean maximum temperatures remain as key meteorological variables that influence dengue incidence in the city. As early as possible, local officials are recommended to uphold proper safety and health procedures in preventing the spread of dengue in Baguio City.

Molecular modeling and docking analysis of bis-indolymethanes derivatives as human β-glucuronidase enzyme inhibitors

β-glucuronidase enzyme is present mostly in mammals’ tissues. β-glucuronidase is present in kidney, bile, serum, urine and spleen. In eukaryotic and prokaryotic organisms, it is important in the process of breaking down of β-glucuronide. It also helps in the neutralization of reactivity of some metabolites that are associated to many diseases. The most stable geometry of the dataset were obtained adopting DFT method at B3LYP/6-31G* level of theory. The model was developed using MLR analysis adopting GFA method. Molecular docking was also performed to portray the binding mode of these bis-indolymethanes derivatives in the binding pocket of their target receptor (human β-glucuronidase). The selected model was assessed and chosen based on its statistical fitness with R2trng=0.907233, R2adj=0.881465, Qcv2=0.833795, and R2test=0.609841. And also, the significance and impart of each physicochemical parameters to the selected model were determine by their ME values. Molecular docking analysis revealed that amino acid such as ALA49, SER52, ASP53, PHE51, VAL96, LEU92, TYR188, TYR199 and PHE200 might be responsible for the most promised binding affinity of the reported docked ligands. The molecular docking results showed that the reported compounds were better than the standard β-glucuronidase inhibitor. The results of this findings paved way for designing novel β-lucuronidase inhibitors.

Pre- and postnatal exposure to multiple ambient air pollutants and child behavioral problems at five years of age

Ambient air pollution is emerging as a risk factor for adverse neurological symptoms and early childhood diseases. This study aimed to evaluate the association between pre- and postnatal exposure to air pollutants and childhood behavior by using MOCEH prospective birth cohort data. In total, 353 mother-child pairs at birth, who completed child behavioral assessments using the Korean version of the Child Behavior Checklist at five years of age, were included in the study.Multivariate linear regression (MLR) for single pollutant and Bayesian kernel machine regression (BKMR) for multiple pollutants were conducted. MLR analysis showed that air pollutant exposures during the first trimester were significantly associated with the internalizing problems score after adjusting for covariates. The estimates were 0.19 (0.05–0.32) per 1 μg/m3 increase in PM2.5, 0.13 (0.04–0.22) per 1 μg/m3 increase in PM10, and 0.20 (0.02–0.37) per 1 ppb increase in NO2. The BKMR model analysis revealed that the overall effects of multiple air pollutants during the first trimester of pregnancy and 0–6 months of the infantile period were significantly associated with behavioral problems. Boys showed a stronger associations than girls.Taken together, these results showed that the first trimester of pregnancy and 0–6 months of the infantile period were important for air pollutant exposure because exposure at these periods was associated with behavioral problems in 5-year-old children. Future efforts are required to control air pollution levels and reduce the health burden of vulnerable populations, including pregnant women and children.

Relationships between Tomato Cluster Growth Indices and Cumulative Environmental Factors during Greenhouse Cultivation

Growth characteristics of plants are mainly defined by genetic information and affected by environmental factors, such as air temperature, solar radiation, and humidity. There is a strong potential and demand to predict plant growth by environmental factors. Therefore, this study established mathematical models via multiple linear regression analysis (MLR) to describe the relationship between tomato cluster's growth indices and environmental factors in greenhouse cultivation. The Japanese tomato variety (Solanum lycopersicum L. Var. Culta. Momotarou Haruka) was cultivated in a hydroponic culture system using a porous solid medium from September 2019 to June 2020 in a glass greenhouse. The significant correlations among growth indices and environmental factors showed that the cumulative solar radiation (CSR) had a stronger effect on the number of flowers (NFl) rather than the cumulative heat unit (CHU) and vapor pressure deficit (VPD). Meanwhile, the number of fruits (NFr) relied much on VPD than CHU and CSR. Also, pollination condition was sensitive to VPD; NFr and enlargement of fruit cells during fruit maturation might be important factors in fruit-cluster weight (CWt). Mathematical models via MLR of cumulative environmental factors, such as CHU, CSR, and VPD could explain tomato cluster's growth indices, such as NFl, NFr, fruit perimeter (PFr), CWt, and duration of tomato clusters with the coefficient of determination (R2) ranging from 0.742 in the fruit maturation duration (FM) model to 0.953 in the PFr model. In MLR models, CHU was the most important factor in the duration of tomato clusters and PFr with the relative importance metrics (RIM) of ranging 29.47%–43.66%; CSR for NFl with RIM was 36.83%; and VPD for NFr and CWt with RIM were 37.37% and 29.37%, respectively. By MLR analysis, results showed the potential of using CHU, CSR, and VPD to describe and predict the growth indices of tomato clusters by mathematical models.