Thallium content in vegetables and derivation of threshold for safe food production in soil: A meta-analysis

Soil thallium (Tl) pollution is a serious environmental problem, and vegetables are the primary pathway for human exposure to Tl. Therefore, it is important to investigate the characteristics of soil Tl uptake by vegetables. In this study, the meta-analysis approach was first applied to explore the relationship between Tl content in vegetables and soil environment, as well as key factors influencing soil physical-chemical properties, and to derive soil thresholds for Tl. The results indicated that various types of vegetables have different capabilities for Tl accumulation. Vegetables from contaminated areas showed high Tl accumulation, and the geomean Tl content in different types of vegetables was in the following order: leafy > root-stalk > solanaceous vegetables. Taro and kale had significantly higher capability to accumulate soil Tl among the 35 species studied, with Tl bioconcentration factor values of 0.060 and 0.133, respectively. Pearson correlation analysis and meta-analysis revealed that the Tl content in vegetables was significantly correlated with soil pH and Tl content in soil. The linear predictive model for Tl accumulation in vegetables based on soil Tl content described the data well, and the fitting coefficient R2 increased with soil pH value. According to potential dietary toxicity, the derived soil Tl thresholds for all, leafy and root-stalk vegetables increased with an increase in soil pH, and were in the range of 1.46-6.72, 1.74-5.26 and 0.92-6.06 mg/kg, respectively. The soil Tl thresholds for kale, lettuce and carrot were in the range of 0.24-4.89, 2.94-3.32 and 3.77-14.43 mg/kg, respectively. Ingestion of kale, beet, sweet potato, potato, taro, pepper, turnip, Chinese cabbage, eggplant and carrot poses potential health risks. The study provides scientific guidance for vegetable production in Tl-contaminated areas and can help with the selection of vegetable species suitable for avoiding the absorption of Tl from contaminated soil.

Segregation of formulated powders in direct compression process and evaluations by small bench-scale testers

Powder segregation can cause severe issues in processes of pharmaceutical drugs for control of content uniformity if the powder is likely to be free or easy flowing. Assessing segregation intensity of formulated powders in a process is challenging at the formulation stage because of the limited availability of samples. An advanced segregation evaluation using small bench-scale testers can be useful for formulation decisions and suggestions of operation conditions in the process, which has not been practically investigated before. In this study, eight formulations (two co-processed excipients blended with one active pharmaceutical ingredient at different ratios) were used for the segregation study on two types of bench-scale testers (air-induced and surface rolling segregation tester), and a pilot simulation process rig as a comparative study. The results show that segregation measured on the bench-scale testers can give a good indication of the segregation intensity of a blend if the segregation intensity is not more than 20%. The comparison also shows that both the bench-scale testers have a good correlation to the process rig, respectively, which means either segregation tester can be used independently for the evaluation. A linear regression model was explored for prediction of segregation in the process.

Diagnostic models to predict nuclear DNA and mitochondrial DNA recovery from incinerated teeth

Teeth are frequently used for human identification from burnt remains, as the structure of a tooth is resilient against heat exposure. The intricate composition of hydroxyapatite (HA) mineral and collagen in teeth favours DNA preservation compared to soft tissues. Regardless of the durability, the integrity of the DNA structure in teeth can still be disrupted when exposed to heat. Poor DNA quality can negatively affect the success of DNA analysis towards human identification. The process of isolating DNA from biological samples is arduous and costly. Thus, an informative pre-screening method that could aid in selecting samples that can potentially yield amplifiable DNA would be of excellent value. A multiple linear regression model to predict the DNA content in incinerated pig teeth was developed based on the colourimetry, HA crystallite size and quantified nuclear and mitochondrial DNA. The chromaticity a* was found to be a significant predictor of the regression model. This study outlines a method to predict the viability of extracting nuclear and mitochondrial DNA from pig teeth that were exposed to a wide range of temperatures (27 to 1000 °C) with high accuracy (99.5-99.7%).

A Simple, Reproducible Procedure for Chemiluminescent Western Blot Quantification

Western blotting is a universally used technique to identify specific proteins from a heterogeneous and complex mixture. However, there is no clear and common procedure to quantify the results obtained, resulting in variations due to the different software and protocols used in each laboratory. Here, we have developed a procedure based on the increase in chemiluminescent signal to obtain a representative value for each band to be quantified. Images were processed with ImageJ and subsequently compared using R software. The result is a linear regression model in which we use the slope of the signal increase within the combined linear range of detection to compare between samples. This approach allows to quantify and compare protein levels from different conditions in a simple and reproducible way. Graphical overview.

Modelling count, bounded and skewed continuous outcomes in physical activity research: beyond linear regression models

BACKGROUND

Inference using standard linear regression models (LMs) relies on assumptions that are rarely satisfied in practice. Substantial departures, if not addressed, have serious impacts on any inference and conclusions; potentially rendering them invalid and misleading. Count, bounded and skewed outcomes, common in physical activity research, can substantially violate LM assumptions. A common approach to handle these is to transform the outcome and apply a LM. However, a transformation may not suffice.

METHODS

In this paper, we introduce the generalized linear model (GLM), a generalization of the LM, as an approach for the appropriate modelling of count and non-normally distributed (i.e., bounded and skewed) outcomes. Using data from a study of physical activity among older adults, we demonstrate appropriate methods to analyse count, bounded and skewed outcomes.

RESULTS

We show how fitting an LM when inappropriate, especially for the type of outcomes commonly encountered in physical activity research, substantially impacts the analysis, inference, and conclusions compared to a GLM.

CONCLUSIONS

GLMs which more appropriately model non-normally distributed response variables should be considered as more suitable approaches for managing count, bounded and skewed outcomes rather than simply relying on transformations. We recommend that physical activity researchers add the GLM to their statistical toolboxes and become aware of situations when GLMs are a better method than traditional approaches for modeling count, bounded and skewed outcomes.

A cross-cultural study of high-altitude botanical resources among diverse ethnic groups in Kashmir Himalaya, India

BACKGROUND

In the Himalayas, traditional knowledge and biodiversity are strongly linked due to the symbiotic interaction between plant and cultural diversity, as well as the support provided by cultural memories, ecological awareness, and social norms. Our study was focused on documenting the vanishing knowledge in the Kashmir Himalaya with the following main objectives: 1) to document the ethnomedical and cultural knowledge of the local flora, 2) to evaluate the cross-cultural use of the flora in the region, and, finally, 3) to identify the key indicator species utilized by each ethnic group using multivariate statistical analysis.

METHODS

We used semi-structured questionnaires to conduct interviews with people of different ethnicity, gender, age, and occupational categories. The intercultural relationships of species utilization among ethnic groups were examined using a Venn diagram. The overall trends between the indicator values and the plant species used by diverse ethnic groups were illustrated using the linear regression model.

RESULTS

We recorded 46 species belonging to 25 different families used by the local people of the Kashmir Valley belonging to four ethnic groups (Gujjar, Bakarwal, Pahari, and Kashmiri). The dominant families recorded were Asteraceae and Ranunculaceae followed by Caprifoliaceae. Rhizomes were the most utilized plant part, followed by leaves. A total of 33 ailments were treated with plants, and gastrointestinal disorders were treated with most species followed by musculoskeletal diseases and dermatological problems. Across cultural relationships, the Gujjar and Pahari showed greater similarity (17%). This may be due to the fact that both ethnic groups share a common geographical landscape and are exogamous to each other. We identified key indicator species used by different ethnic groups with significant (p ≤ 0.05) values. For instance, in the Gujjar ethnic group, Aconitum heterophyllum and Phytolacca acinosa had significant indicator value, which was due to the fact that these plants were easily accessible and also had a wide range of uses. In contrast, the Bakarwal ethnic group showed different indicator species, with Rheum spiciforme and Rhododendron campanulatum being highly significant (p ≤ 0.05), because this ethnic group spends the majority of their time in high-altitude pastures, using a particularly wide variety of plant species for medicine, food, and fuelwood. While indicator values and plant usage were positively correlated for the Gujjar, Kashmiri, and Pahari ethnic groups, they were negatively correlated for the Bakarwal. The positive correlation indicates cultural preferences for certain plant use and underlines the cultural significance of each species. The current study reported new uses for the following species: raw roots of Jurinea dolomiaea used for tooth cleaning, seeds of Verbascum thapsus applied for respiratory diseases, and flowers of Saussurea simpsoniana given to anyone as a good luck wish.

CONCLUSION

The current study highlights historical ethnic group stratifications and cultural standing while comparing reported taxa across cultures. Each ethnic group made extensive ethnomedical use of plants, and knowledge, originally transmitted verbally, is now available in writing for reference. This could pave the way for providing incentives to local communities to showcase their talents, celebrate them, and gain from potential development initiatives.

The level of serum albumin is associated with renal prognosis and renal function decline in patients with chronic kidney disease

OBJECTIVE

The study's purpose is to explore the link of serum albumin on renal progression in patients with chronic kidney disease (CKD).

METHODS

This study was a secondary analysis of a prospective cohort study in which a total of 954 participants were non-selectively and consecutively collected from the research of CKD-ROUTE in Japan between November 2010 and December 2011. We evaluated the association between baseline ALB and renal prognosis (initiation of dialysis or 50% decline in eGFR from baseline) and renal function decline (annual eGFR decline) using the Cox proportional-hazards and linear regression models, respectively. We performed a number of sensitivity analyses to ensure the validity of the results. In addition, we performed subgroup analyses.

RESULTS

The included patients had a mean age of (66.86 ± 13.41) years, and 522 (69.23%) were male. The mean baseline ALB and eGFR were (3.89 ± 0.59) g/dL and (33.43 ± 17.97) ml/min/1.73 m². The annual decline in eGFR was 2.65 mL/min/1.73 m²/year. 218 (28.9%) individuals experienced renal prognosis during a median follow-up period of 36.0 months. The baseline ALB was inversely linked with renal prognosis (HR = 0.61, 95%CI: 0.45, 0.81) and renal function decline (β = -1.41, 95%CI: -2.11, -0.72) after controlling for covariates. The renal prognosis and ALB had a non-linear connection, with ALB's inflection point occurring at 4.3 g/dL. Effect sizes (HR) were 0.42 (0.32, 0.56) and 6.11 (0.98, 38.22) on the left and right sides of the inflection point, respectively. There was also a non-linear relationship between ALB and renal function decline, and the inflection point of ALB was 4.1 g/dL. The effect sizes(β) on the left and right sides of the inflection point were -2.79(-3.62, -1.96) and 0.02 (-1.97, 1.84), respectively.

CONCLUSION

This study shows a negative and non-linear association between ALB and renal function decline as well as renal prognosis in Japanese CKD patients. When ALB is lower than 4.1 g/dL, ALB decline was closely related to poor renal prognosis and renal function decline. From a therapeutic point of view, reducing the decline in ALB makes sense for delaying CKD progression.

Variability and time series trend analysis of rainfall in the mid-hill sub humid zone: a case study of Nauni

The spatiotemporal variability of rainfall, particularly in the context of climate change, has been imperative for examining the cropping patterns, farming sustainable crop production, and food security in rainfed areas. To that end, trend analysis was done to study the change in rainfall patterns in the mid-hills of Himachal Pradesh. The study investigated the historical rainfall data from 1971 to 2020 on a monthly, annual, seasonal, and decadal basis by using the variability analysis methods, viz., standard deviation (SD), coefficient of variance (CV), and transformed annual precipitation departure (Z). The trend analysis was also done by Mann-Kendall (MK) and Sen's slope estimator (SSE) test and linear regression model. The annual rainfall in the region was 1115.1 mm, which showed a decreasing trend (Z =  - 0.79 mm/year). Based on the linear regression model, the decrease in annual rainfall was about - 2.28 mm/year. The monthly and seasonal variability of rainfall exhibited a sensitivity to change. The months of January, April, July, and September showed an increasing trend, whereas the rest of the other months showed a decreasing trend. The seasonal rainfall (summer, monsoon, and post-monsoon) showed a decreasing trend, whereas the winter season depicted an increasing trend. During the entire study period, 1988 recorded as the wettest year, with highest annual rainfall of about 2205.0 mm and monsoon rainfall of about 1653.0 mm. The highest annual (2205.0 mm) and monsoon (1653.0 mm) rainfall was recorded in the year 1988. The decadal analysis of the rainfall on an annual basis revealed a decrease in rainfall during the periods 1971-1980, 2001-2010, and 2011-2020 as compared to 1981-1990 and 1991-2000. The rainfall over the study region confirms the strength of the change in trend. Thus, the erratic rainfall pattern makes the cropping calendar shorter and affects the agricultural productivity.

Assessment of urban thermal field variance index and thermal comfort level of Addis Ababa metropolitan city, Ethiopia

Land use land cover (LULC) conversion around urban areas is the root cause for the increasing trend of land surface temperature (LST) in many cities. The increase in LST is driven by the replacement of vegetation cover and other LULC by impervious surface. This study is aimed to assess the extent of urban thermal field variance index (UTFVI) and thermal comfort level of Addis Ababa city using geospatial techniques and linear regression model. Landsat image of 1990 TM, 2000 of ETM+ and 2020 of OLI/TIRS are used to analyze LST and Urban Heat Islands (UHI) for assessing UTFVI and urban thermal comfort level. The results showed that the UHI over Addis Ababa city is substantial increased over the past decades. The results reveled that LST has increased by 7.9 °C due to decline of vegetation cover and expansion of built-up area. Results show that about 225 km² (42.7%) is excellent comfort for urban resident while about 241.4 km² (45.8%) is categorized as worst ecological evaluation index, which results discomfort to the city dwellers. The key findings of from this study are crucial for informing city administrators and urban planners to reduce urban heat islands by investing on urban green areas and open spaces.

Netizens' risk perception in new coronary pneumonia public health events: an analysis of spatiotemporal distribution and influencing factors

Background

Internet search volume reflects the level of Internet users’ risk perception during public health events. The Internet search volume index model, an algorithm of concentration of Internet users, and statistical analysis of popular topics on Weibo are used to analyze the effects of time, space, and space-time interaction. We conducted in-depth research on the characteristics of the spatial and temporal distribution of Internet users’ risk perceptions of public health events and the associated influential factors.

Methods

We analyzed the spatiotemporal distribution characteristics of Internet users’ risk perception after the Wuhan “city closing” order during the coronavirus disease 2019 (COVID-19) pandemic. We established five linear regression models according to different time periods and analyzed factors influencing Internet users’ risk perception by employing a Poisson and spatial distribution and topic modeling analysis.

Results

Economy, education, health, and the degree of information disclosure affect Internet users’ risk perception significantly. Internet users’ risk perception conforms to the exponential distribution law in time and has periodic characteristics and stability trends. Additionally, Internet users’ average arrival rate dropped from week 1 to week 8 after the “city closing.” Internet users’ risk perception has a uniform distribution in space, economic and social development level distribution consistency, spatial agglomeration, and other characteristics. The results of the time-space interaction show that after 8 weeks of COVID-19, Internet search hot topics have become more stable, and Internet users’ information demand structure has become more rational.

Conclusions

The Internet search cycle of the COVID-19 event is synchronized with the evolution cycle of the epidemic. The physical risk of Internet users is at the top of the risk structure, focusing on the strong concern about the government’s ability to control COVID-19 and its future trend. The government should strengthen network management; seize the risk control focus of key time nodes, regional locations, and information content of online communication; actively adjust the information content supply; effectively control the rebound of Internet users’ risk perception; establish a data-driven, risk-aware intelligence system for internet users; and guide people to actively face and overcome the potential risks and threats of COVID-19.

An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique

Air pollution is the existence of atmospheric chemicals damaging the health of human beings and other living organisms or damaging the environment or resources. Rarely any common contaminants are smog, nicotine, mold, yeast, biogas, or carbon dioxide. The paper will primarily observe, visualize and anticipate pollution levels. In particular, three algorithms of Artificial Intelligence were used to create good forecasting models and a predictive AQI model for 4 distinct gases: carbon dioxide, sulphur dioxide, nitrogen dioxide, and atmospheric particulate matter. Thus, in this paper, the Air Qualification Index is developed utilizing Linear Regression, Support Vector Regression, and the Gradient Boosted Decision Tree GBDT Ensembles model over the next 5 h and analyzes air qualities using various sensors. The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. It may be used to detect air quality from distance in large cities and can assist lower the degree of environmental pollution.

Peptidomic analysis characterising proteolysis in thaw-aging of beef short plate

Recent studies have suggested that thaw-aging can improve sensory attributes of freeze-thawed meat. Acceleration of proteolysis is expected to promote tenderisation and improve taste; however, the details of protein degradation, including substrate proteins and cleavage sites, remain unclear. Here, we report a time course overview of the peptidome of beef short plates during thaw-aging. The accelerated degradation of key proteins for meat tenderisation, such as troponin T and desmin, was confirmed. Additionally, 11 cleavage sites in troponin T related to taste-active peptide generation were identified. Terminome analysis showed that the contribution of each protease varies depending on the substrate proteins and the thaw-aging period. Based on our results; proteases, not only calpains, but also others contributed to the degradation of myofibrillar proteins. The techniques employed indicate that meat proteolysis during thaw-aging is not constant but dynamic.

A national survey of ambient air pollution health literacy among adult residents of Taiwan

OBJECTIVE

To investigate the level of and covariates associated with ambient air pollution health literacy (AAPHL) among adult residents of Taiwan.

METHODS

With a cross-sectional study design, we conducted telephone interviews using a Chinese version AAPHL scale, which consisted of 24 items assessing 12 subdomains of AAPHL formed by 4 information processing competence matrices (i.e., access, understand, appraise, and apply) and 3 health contexts (i.e., healthcare, disease prevention, and health promotion). The AAPHL was with the lowest and highest score at 1 to 4, respectively. Between September and November 2020, a sample of 1017 and 280 adults was successfully interviewed via home phones and mobile phones, respectively. We employed multiple linear regression models to identify covariates significantly associated with overall and 4 matric-specific AAPHL scores.

RESULTS

The mean and standard deviation (±SD) of overall AAPHL score was considered as moderate at 2.90 (±0.56), with the highest and lowest metric-specific score for "apply" (3.07 ± 0.59) and "appraise" (2.75 ± 0.66). Lower education was significantly associated with a lower overall score; and living with children < 12 years and single were both significantly associated with higher overall scores. We also noted a significant geographic variation in overall score in which people living in the east/remote islands had highest scores.

CONCLUSIONS

People in Taiwan had only moderate level of AAPHL; and covariates including education, living arrangement, marital status, and area of living were significantly associated with AAPHL. These covariates should be considered in future educational interventions aiming to improve the AAPHL in the community.

Predictive model estimating the decrease of postoperative gastrointestinal quality of life index (GIQLI) in patients after elective laparoscopic sigmoid resection for diverticular disease

Background

Growing consideration in quality of life (QoL) has changed the therapeutic strategy in patients suffering from diverticular disease. Patients’ well-being plays a crucial role in the decision-making process. However, there is a paucity of studies investigating patients’ or surgery-related factors influencing the postoperative gastrointestinal function. The aim of this study was to investigate in a predictive model patients or surgical variables that allow better estimation of the postoperative gastrointestinal QoL.

Methods

This observational study retrospectively analyzed patients undergoing elective laparoscopic sigmoidectomy for diverticulitis between 2004 and 2017. The one-time postoperative QoL was assessed with the gastrointestinal quality of life index (GIQLI) in 2019. A linear regression model with stepwise selection has been applied to all patients and surgery-related variables.

Results

Two hundred seventy-two patients with a mean age of 62.30 ± 9.74 years showed a mean GIQLI of 116.39±18.25 at a mean follow-up time of 90.4±33.65 months. Women (n=168) reported a lower GIQLI compared to male (n=104; 112.85±18.79 vs 122.11±15.81, p<0.001). Patients with pre-operative cardiovascular disease (n=17) had a worse GIQLI (106.65 ±22.58 vs 117.08±17.66, p=0.010). Finally, patients operated less than 5 years ago (n=63) showed a worse GIQLI compared to patients operated more than 5 years ago (n=209; 111.98±19.65 vs 117.71±17.63, p=0.014).

Conclusions

Female gender and the presence of pre-operative cardiovascular disease are predictive for a decreased postoperative gastrointestinal QoL. Furthermore, patients’ estimation of gastrointestinal functioning seems to improve up to 5 years after surgery.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00423-021-02186-w.

Effect of CGM in the HbA1c and Coefficient of Variation of glucose in a pediatric sample

AIM OF THE STUDY

Previous studies have found no significant improvements in glycated hemoglobin (HbA1c), while using Continuous Glucose Monitoring (CGM), with children and adolescents. The aim of this paper is to measure the change in HbA1c, and the Coefficient of Variation in glucose levels, when using CGM, once the effect of other relevant variables, such as gender, actual age, the years the patient has had diabetes, use of an insulin pump, the presence of autoimmune disease, other associated pathologies, and weekly hours of exercise, are controlled for.

METHODS

This is a retrospective study that uses a linear regression model. Data was collected from Type 1 Diabetes Mellitus (T1DM), children diagnosed between 2003 and 2017 in the Pediatric Unit for Diabetes in Zaragoza, Spain. We used a linear regression and the method of estimation is Ordinary Least Squares.

RESULTS

Results show that the use of CGM decreased the HbA1c value by 3.5% and the Coefficient of Variation by 14%.

CONCLUSIONS

The implication of these results is that this device helped in the management of diabetes, although more research is needed to distinguish between different devices in terms of their efficacy.

Modeling the behavior of Listeria innocua in Italian salami during the production and high-pressure validation of processes for exportation to the U.S

A model describing Listeria innocua evolution according to process parameters of 51 Italian salami processes and HPP in 31 companies was developed. A total of 51 challenge tests were performed. During processing a L. innocua reduction of 0.34-4.32 Log10 CFU/g was observed and HPP further reduced the count of 0.48-3.47 Log10 CFU/g; an overall reduction of 1.04-5.68 is reached. PH after acidification/drying process, a_w after seasoning, duration of the seasoning and caliber resulted associated (p < 0.05) with L. innocua decrease. HPP efficacy was associated (p < 0.05) with a_w and pH of the product: higher the pH and a_w after the acidification/drying and seasoning phases, higher resulted the L. innocua reduction after HPP. No significant association was observed between L.innocua and salt, nitrate and starter content and other characteristics of process. The model meets companies and Authorities needs and represents a useful tool to predict L. monocytogenes lethality, giving recommendations to food business operators interested in exportation to the U.S.

Factors associated with self-rated health in a Norwegian population of older people participating in a preventive home visit program: a cross-sectional study

Background

Assessing self-rated health by preventive home visits of older people can provide information about the person’s well-being, quality of life and risk of developing illness. The aim of this study was to examine associations between self-rated health and factors related to demographics, lifestyle, health conditions and medical diagnoses by older people participating in a preventive home visit program.

Methods

A cross-sectional study including 233 participants (age 75–79) from three municipalities of Western Norway was conducted. Data were collected through preventive home visits performed by six nurses, using a questionnaire including self-rated health assessment and questions and tests related to demographics (e.g. education and housing), lifestyle (e.g. social activities, alcohol and smoking), health conditions (e.g. sensory impairment, pain and limited by disease) and medical diagnoses. Descriptive and inferential statistics including linear block-wise regression model were applied.

Results

The block-wise regression model showed that the variables Limited by disease and Pain were negatively associated with self-rated health and Use internet was positively associated. The model had a R² 0.432. The variable that contributed to largest change in the model was Limited by disease (R² Change; 0.297, p-value< 0.001).

Conclusions

In the present study, being limited by disease and pain were strongly associated with poor self-rated health, indicating that these are important factors to assess during a preventive home visit. Also, digital competence (Use internet) was associated with a better self-rated health, suggesting that it could be useful to ask, inform and motivate for the use of digital tools that may compensate for or improve social support, social contact and access to health -related information.

Predictive modelling of COVID-19 confirmed cases in Nigeria

The coronavirus outbreak is the most notable world crisis since the Second World War. The pandemic that originated from Wuhan, China in late 2019 has affected all the nations of the world and triggered a global economic crisis whose impact will be felt for years to come. This necessitates the need to monitor and predict COVID-19 prevalence for adequate control. The linear regression models are prominent tools in predicting the impact of certain factors on COVID-19 outbreak and taking the necessary measures to respond to this crisis. The data was extracted from the NCDC website and spanned from March 31, 2020 to May 29, 2020. In this study, we adopted the ordinary least squares estimator to measure the impact of travelling history and contacts on the spread of COVID-19 in Nigeria and made a prediction. The model was conducted before and after travel restriction was enforced by the Federal government of Nigeria. The fitted model fitted well to the dataset and was free of any violation based on the diagnostic checks conducted. The results show that the government made a right decision in enforcing travelling restriction because we observed that travelling history and contacts made increases the chances of people being infected with COVID-19 by 85% and 88% respectively. This prediction of COVID-19 shows that the government should ensure that most travelling agency should have better precautions and preparations in place before re-opening.

Quantifying the primary biotic resource use by fisheries: A global assessment

In this paper, the specific primary production required (SPPR expressed as kg-NPP/kg-fish in wet weight) of more than 1700 marine species were calculated directly from 96 published food web models using the newly developed SPPR calculation framework. The relationship between SPPR and other ecological factors were then statistically analyzed. Among- and within-species variability of SPPR were found to be both explained by trophic level (TL), suggesting similar mechanisms underpinning both sources of variability. Among species, we found that harvesting species at higher mean trophic levels (MTL) increases the mean SPPR by a factor of 19 per 1 unit increase in MTL. Based on our empirical relationship, the mean SPPR of more than 9000 marine species were predicted and subsequently used to assess the primary production required (PPR) to support fisheries in five major fishing countries in Europe. The results indicated that conventional approach to estimating PPR, which neglects food web ecology, can underestimate PPR by up to a factor of 5. Within species, we found that harvesting populations occupying a higher TL leads to a higher SPPR. For example, the SPPR of Atlantic cod in the Celtic Sea (TL = 4.75) was 5 times higher than in the Gilbert Bay (TL = 3.3). Our results, which are based on large amounts of field data, highlight the importance of properly accounting for ecological factors during the impact assessment of fisheries.

COVID-19 pandemic, socioeconomic crisis and human stress in resource-limited settings: A case from Bangladesh

Considering the population density, healthcare capacity, limited resources and existing poverty, environmental factors, social structure, cultural norms, and already more than 18,863 people infected, the community transmission of COVID-19 is happening fast. These exacerbated a complex fear among the public. The aim of this article is, therefore, to understand the public perception of socioeconomic crisis and human stress in resource-limited settings of Bangladesh during the COVID-19 outbreak. The sample comprised of 1066 Bangladeshi participants. Principal component analysis (PCA) was considered to design a standardized scale to measure the mental stress and socioeconomic crisis, one-way ANOVA and t-test were conducted to perceive different demographic risk groups; multiple linear regression was applied to estimate the statistically significant association between each component, and classical test theory (CTT) analysis was applied to examine the reliability of each item according to the components to develop a composite score. Without safeguarding the fundamental needs for the vulnerable ultra-poor group can undeniably cause the socioeconomic crisis and mental stress due to the COVID-19 lockdown. It has further created unemployment, deprivation, hunger, and social conflicts. The weak governance in the fragile healthcare system exacerbates the general public's anxiety as the COVID-19 testing facilities are centered around in the urban areas, a long serial to be tested, minimum or no treatment facilities in the dedicated hospital units for COVID-19 patients are the chief observations hampered along with the disruption of other critical healthcare services. One-way ANOVA and t-test confirmed food and nutritional deficiency among the vulnerable poorest section due to loss of livelihood. Also, different emergency service provider professions such as doctors, healthcare staff, police forces, volunteer organizations at the frontline, and bankers are at higher risk of infection and subsequently mentally stressed. Proper risk assessment of the pandemic and dependable risk communications to risk groups, multi-sectoral management taskforce development, transparency, and good governance with inter-ministerial coordination is required along with strengthening healthcare capacity was suggested to reduce mental and social stress causing a socioeconomic crisis of COVID-19 outbreak. Moreover, relief for the low-income population, proper biomedical waste management through incineration, and preparation for the possible natural disasters such as flood, cyclones, and another infectious disease such as dengue was suggested. Finally, this assessment process could help the government and policymakers to judge the public perceptions to deal with COVID-19 pandemic in densely populated lower-middle-income and limited-resource countries like Bangladesh.

Reduction of Salmonella spp. populations in Italian salami during production process and high pressure processing treatment: Validation of processes to export to the U.S

This study involved ten enterprises producing Italian salami, 20 different samples of fermented sausages underwent challenge tests to assess and record the following parameters: time, temperature, pH, a_w, and Salmonella counts. A linear regression model was used to describe the Salmonella spp. decay: at the end of the process the result of total Salmonella reduction was 0.97-5.84 Log₁₀ CFU/g and it was significantly associated with pH at the end of acidification/drying process, a_w at the end of seasoning period, the duration of seasoning, and the caliber of salami respectively. High Pressure Processing (HPP) further reduced the Salmonella level by 2.41-5.84 Log₁₀ CFU/g with an efficacy that resulted inversely associated with a_w of salami at the end of seasoning; the objective of 5-Log reduction was always reached in all the cases tested by the production process plus HPP. This model could be a useful tool for enterprises and Authorities to evaluate the efficacy of the processes to reduce Salmonella load for exportation to the U.S.

Coronal deformity angular ratio may serve as a valuable parameter to predict in-brace correction in patients with adolescent idiopathic scoliosis

BACKGROUND CONTEXT

In-brace correction (IBC) plays an important role in curve progression of patients with adolescent idiopathic scoliosis (AIS) under brace treatment. We evaluated the coronal deformity angular ratio (C-DAR) as a potential predictor of IBC. Based on our experience, we postulated that a high C-DAR may result in low IBC. This relationship had not been previously studied.

PURPOSE

To evaluate the relationship of C-DAR and IBC in patients with AIS.

STUDY DESIGN/SETTING

A retrospective study.

PATIENT SAMPLE

A total of 119 patients with AIS treated with a Gensingen brace in our scoliosis center from July 2015 to October 2017 were included.

OUTCOME MEASURES

In-brace correction.

METHODS

Data were collected before and upon brace placement. Correlation analyses between study variables and IBC were performed. A linear regression model was established on the basis of C-DAR.

RESULTS

At brace fitting, the average age was 12.62±1.16 (range, 10-15) years and mean major curve Cobb angle was 32.14±4.66° (range, 25-40°). Mean IBC was 59.62%±22.03% (range, 16.2-100%). IBC had significant correlation with C-DAR (r=-0.69; 95% confidence interval, -0.77 to -0.61; p<.001). IBC was not significantly correlated with age, sex, height, weight, BMI, menstrual status, or Risser sign. A simple linear regression model established that in-brace correction=115.4-10.7×C-DAR.

CONCLUSIONS

C-DAR has strong negative correlation with IBC and may estimate the expected IBC. The usage of C-DAR may obviate the need for flexibility radiographs, such as supine or supine lateral bending radiographs.

Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS

In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.

Evaluation of four modelling approaches to estimate nitrous oxide emissions in China's cropland

Process-based models are useful tools to integrate the effects of detailed agricultural practices, soil characteristics, mass balance, and climate change on soil N₂O emissions from soil - plant ecosystems, whereas static, seasonal or annual models often exist to estimate cumulative N₂O emissions under data-limited conditions. A study was carried out to compare the capability of four models to estimate seasonal cumulative N₂O fluxes from 419 field measurements representing 65 studies across China's croplands. The models were 1) the DAYCENT model, 2) the DNDC model, 3) the linear regression model (YLRM) of Yue et al. (2018), and 4) IPCC Tier 1 emission factors. The DAYCENT and DNDC models estimated crop yields with R² values of 0.60 and 0.66 respectively, but both models showed significant underestimation for all measurements. The estimated seasonal N₂O emissions with R² of 0.31, 0.30, 0.21 and 0.17 for DAYCENT, DNDC, YLRM, and IPCC, respectively. Based on RMSE, modelling efficiency and bias analysis, YLRM performed well on N₂O emission prediction under no fertilization though bias still existed, while IPCC performed well for cotton and rapeseed and DNDC for soybean. The DAYCENT model accurately predicted the emissions with no bias across other crop and fertilization types whereas the DNDC model underestimated seasonal N₂O emissions by 0.42 kg N₂O-N ha^-1 for all observed values. Model evaluation indicated that the DAYCENT and DNDC models simulated temporal patterns of daily N₂O emissions effectively, but both models had difficulty in simulating the timing of the N₂O fluxes following some events such as fertilization and water regime. According to this evaluation, algorithms for crop production and N₂O emission should be improved to increase the accuracy in the prediction of unfertilized fields both for DAYCENT and DNDC. The effects of crop types and management modes such as fertilizations should also be further refined for YLRM.

Design Method Using Statistical Models for Miniature Left Ventricular Assist Device Hydraulics

Left ventricular assist devices (LVADs) are increasingly used to treat heart failure patients. These devices' impeller blades and diffuser vanes must be designed for hydraulic performance and hemocompatibility. The traditional design method, applying mean-line theory, is not applicable to the design of small-scale pumps such as miniature LVADs. Furthermore, iterative experimental testing to determine how each geometric variable affects hydraulic performance is time and labor intensive. In this study, we tested a design method wherein empirical hydraulic results are used to establish a statistical model to predict pump hydraulic performance. This method was used to design an intra-atrial blood pump. Five geometric variables were chosen, and each was assigned two values to define the variable space. The experimental results were then analyzed with both correlation analysis and linear regression modeling. To validate the linear regression models, 2 test pumps were designed: mean value of each geometric variable within the boundaries, and random value of each geometric variable within the boundaries. The statistical model accurately predicted the hydraulic performance of both pump designs within the boundary space. This method could be expanded to include more geometric variables and broader boundary conditions, thus accelerating the design process for miniature LVADs.

Empirical Likelihood for Censored Linear Regression and Variable Selection

The linear regression model for right censored data, also known as the accelerated failure time model using the logarithm of survival time as the response variable, is a useful alternative to the Cox proportional hazards model. Empirical likelihood as a nonparametric approach has been demonstrated to have many desirable merits thanks to its robustness against model misspecification. However, the linear regression model with right censored data cannot directly benefit from the empirical likelihood for inferences mainly due to dependent elements in estimating equations of the conventional approach. In this paper, we propose an empirical likelihood approach with a new estimating equation for linear regression with right censored data. A nested coordinate algorithm with majorization is used for solving the optimization problems with nondifferentiable objective function. We show that the Wilks theorem holds for the new empirical likelihood. We also consider the variable selection problem with empirical likelihood when the number of predictors can be large. Since the new estimating equation is nondifferentiable, a quadratic approximation is applied to study the asymptotic properties of penalized empirical likelihood. We prove the oracle properties and evaluate the properties with simulated data. We apply our method to a SEER small intestine cancer dataset.

Development and validation of a direct-comparison method for cardiac (123)I-metaiodobenzylguanidine washout rates derived from late 3-hour and 4-hour imaging

PURPOSE

The washout rate (WR) has been used in (123)I-metaiodobenzylguanidine (MIBG) imaging to evaluate cardiac sympathetic innervation. However, WR varies depending on the time between the early and late MIBG scans. Late scans are performed at either 3 or 4 hours after injection of MIBG. The aim of this study was to directly compare the WR at 3 hours (WR3h) with the WR at 4 hours (WR4h).

METHODS

We hypothesized that the cardiac count would reduce linearly between the 3-hour and 4-hour scans. A linear regression model for cardiac counts at two time-points was generated. We enrolled a total of 96 patients who underwent planar (123)I-MIBG scintigraphy early (15 min) and during the late phase at both 3 and 4 hours. Patients were randomly divided into two groups: a model-creation group (group 1) and a clinical validation group (group 2). Cardiac counts at 15 minutes (countearly), 3 hours (count3h) and 4 hours (count4h) were measured. Cardiac count4h was mathematically estimated using the linear regression model from countearly and count3h.

RESULTS

In group 1, the actual cardiac count4h/countearly was highly significantly correlated with count3h/countearly (r = 0.979). In group 2, the average estimated count4h was 92.8 ± 31.9, and there was no significant difference between this value and the actual count4h (91.9 ± 31.9). Bland-Altman analysis revealed a small bias of -0.9 with 95 % limits of agreement of -6.2 and +4.3. WR4h calculated using the estimated cardiac count4h was comparable to the actual WR4h (24.3 ± 9.6 % vs. 25.1 ± 9.7 %, p = ns). Bland-Altman analysis and the intraclass correlation coefficient showed that there was excellent agreement between the estimated and actual WR4h.

CONCLUSION

The linear regression model that we used accurately estimated cardiac count4h using countearly and count3h. Moreover, WR4h that was mathematically calculated using the estimated count4h was comparable to the actual WR4h.

Estimation and residual analysis with R for a linear regression model with an interval-censored covariate

Interval-censored covariates are sometimes encountered in longitudinal studies and considered as possible predictors in a regression model. This paper, motivated by an AIDS study, proposes an implementation in R for the estimation of parameters and the assessment of the assumptions of a linear regression model with an interval-censored covariate. The properties of the parameters estimators and the behavior of three proposed residuals are addressed through two simulation studies. Also, guidelines are provided to check the goodness-of-fit of the fitted model in terms of the length of the censoring interval of the covariate. The methodology is illustrated with real data coming from the AIDS study. R functions and scripts are provided.

Duration perception in crossmodally-defined intervals

How humans perform duration judgments with multisensory stimuli is an ongoing debate. Here, we investigated how sub-second duration judgments are achieved by asking participants to compare the duration of a continuous sound to the duration of an empty interval in which onset and offset were marked by signals of different modalities using all combinations of visual, auditory and tactile stimuli. The pattern of perceived durations across five stimulus durations (ranging from 100 ms to 900 ms) follows the Vierordt Law. Furthermore, intervals with a sound as onset (audio-visual, audio-tactile) are perceived longer than intervals with a sound as offset. No modality ordering effect is found for visualtactile intervals. To infer whether a single modality-independent or multiple modality-dependent time-keeping mechanisms exist we tested whether perceived duration follows a summative or a multiplicative distortion pattern by fitting a model to all modality combinations and durations. The results confirm that perceived duration depends on sensory latency (summative distortion). Instead, we did not find evidence for multiplicative distortions. The results of the model and the behavioural data support the concept of a single time-keeping mechanism that allows for judgments of durations marked by multisensory stimuli.

More discussions for granger causality and new causality measures

Granger causality (GC) has been widely applied in economics and neuroscience to reveal causality influence of time series. In our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011), we proposed new causalities in time and frequency domains and particularly focused on new causality in frequency domain by pointing out the shortcomings/limitations of GC or Granger-alike causality metrics and the advantages of new causality. In this paper we continue our previous discussions and focus on new causality and GC or Granger-alike causality metrics in time domain. Although one strong motivation was introduced in our previous paper (Hu et al., in IEEE Trans on Neural Netw, 22(6), pp. 829-844, 2011) we here present additional motivation for the proposed new causality metric and restate the previous motivation for completeness. We point out one property of conditional GC in time domain and the shortcomings/limitations of conditional GC which cannot reveal the real strength of the directional causality among three time series. We also show the shortcomings/limitations of directed causality (DC) or normalize DC for multivariate time series and demonstrate it cannot reveal real causality at all. By calculating GC and new causality values for an example we demonstrate the influence of one of the time series on the other is linearly increased as the coupling strength is linearly increased. This fact further supports reasonability of new causality metric. We point out that larger instantaneous correlation does not necessarily mean larger true causality (e.g., GC and new causality), or vice versa. Finally we conduct analysis of statistical test for significance and asymptotic distribution property of new causality metric by illustrative examples.