Genomic Modeling as an Approach to Identify Surrogates for Use in Experimental Validation of SARS-CoV-2 and HuNoV Inactivation by UV-C Treatment

Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) is responsible for the COVID-19 pandemic that continues to pose significant public health concerns. While research to deliver vaccines and antivirals are being pursued, various effective technologies to control its environmental spread are also being targeted. Ultraviolet light (UV-C) technologies are effective against a broad spectrum of microorganisms when used even on large surface areas. In this study, we developed a pyrimidine dinucleotide frequency based genomic model to predict the sensitivity of select enveloped and non-enveloped viruses to UV-C treatments in order to identify potential SARS-CoV-2 and human norovirus surrogates. The results revealed that this model was best fitted using linear regression with r2 = 0.90. The predicted UV-C sensitivity (D90 – dose for 90% inactivation) for SARS-CoV-2 and MERS-CoV was found to be 21.5 and 28 J/m2, respectively (with an estimated 18 J/m2 obtained from published experimental data for SARS-CoV-1), suggesting that coronaviruses are highly sensitive to UV-C light compared to other ssRNA viruses used in this modeling study. Murine hepatitis virus (MHV) A59 strain with a D90 of 21 J/m2 close to that of SARS-CoV-2 was identified as a suitable surrogate to validate SARS-CoV-2 inactivation by UV-C treatment. Furthermore, the non-enveloped human noroviruses (HuNoVs), had predicted D90 values of 69.1, 89, and 77.6 J/m2 for genogroups GI, GII, and GIV, respectively. Murine norovirus (MNV-1) of GV with a D90 = 100 J/m2 was identified as a potential conservative surrogate for UV-C inactivation of these HuNoVs. This study provides useful insights for the identification of potential non-pathogenic (to humans) surrogates to understand inactivation kinetics and their use in experimental validation of UV-C disinfection systems. This approach can be used to narrow the number of surrogates used in testing UV-C inactivation of other human and animal ssRNA viral pathogens for experimental validation that can save cost, labor and time

Forests and people’s livelihood: Benefiting the poor from community forestry

Abstract This paper provides a brief overview of the main achievements and challenges of the community forestry approach to improve people's livelihood and forest condition. The paper demonstrates that Forest User Groups have been able to manage thousands of hectares of community forests, and as a result have contributed to the improvement of forest condition and people's livelihoods in a number of ways such as capital formation; governance reform, community empowerment and social change. Yet, the livelihoods of the poor and disadvantaged, have not improved as expected. The poorest suffer the most since they cannot afford to participate, are unable to speak out, and are rarely heard when they do. Nevertheless, the community forestry approach is a source of inspiration for the establishment of good forest governance, sustainable forest management and is one of the means to improve people's livelihoods. To make community forestry pro-poor, further innovation, reflection and improvements are required. The paper suggests a number of pro-poor strategies in order to address the livelihood needs of poor people

Effect of Winter Canola Cultivar on Seed Yield, Oil, and Protein Content

Canola (Brassica napus L.) is an oilseed crop that can produce healthy cooking oil and animal feed byproducts. Although it is a relatively new crop, approved for human consumption less than 40 yr ago, advances in breeding have allowed for its production as a winter crop in the southeastern United States. There is little published research, however, related to its performance and quality in this region. Therefore, a study was conducted during the 2014–2015 (Year 1) and 2015–2016 (Year 2) seasons in Tennessee. Twenty-three varieties were planted in a randomized complete block design with four replications across both years to determine seed yield, seed oil, and seed protein content. Differences in fertilizer application rates, planting, and harvest management and differences in weather conditions probably led to significant interactions between years. Cultivar yields ranged from 1269 to 2647 and 1494 to 4199 kg ha−1, seed oil content ranged from 44 to 48% and from 43 to 46%, and seed protein content ranged from 20 to 24% and from 19 to 23% for Years 1 and 2, respectively. In each year, open-pollinated cultivars had significantly lower yield and oil content but significantly greater protein content than hybrid cultivars. There was also a strong negative correlation between seed oil and seed protein and the linear models were significant (r = 0.88, p \u3c 0.0001 for Year 1; r = 0.85, p \u3c 0.0001 for Year 2). Recommended winter canola cultivars include Exp1302 and Hekip

Prevalence of Multidrug-Resistant Foodborne Pathogens and Indicator Bacteria from Edible Offal and Muscle Meats in Nashville, Tennessee

This study investigated the prevalence of antimicrobial-resistant bacteria in retail edible offal and muscle meats in Nashville, Tennessee. A total of 348 retail meats (160 edible offal and 188 muscle) were analyzed for Salmonella enterica serovar, Campylobacter, Escherichia coli, E. coli O157:H7, and enterococci. Bacteria was identified using biochemical and PCR methods. Salmonella enterica serovar (4.4% and 4.3%), Campylobacter (1.9% and 1.1%), E. coli (79.4% and 89.4%), and enterococci (88.1% and 95.7%) was detected in offal and muscle meats, respectively. Chicken liver (9.7%) was most frequently contaminated with Salmonella enterica serovar, followed by ground chicken (6.9%) and chicken wings (4.2%). No Salmonella enterica serovar was detected in beef liver, beef tripe, and ground beef. The prevalence of Campylobacter was 6.9%, 2.3%, and 1.4% in beef liver, ground beef, and ground chicken, respectively. None of the meats were positive for E. coli O157:H7. Resistance of isolates was significantly (p \u3c 0.05) highest in erythromycin (98.3%; 99.1%), followed by tetracycline (94%; 98.3%), vancomycin (88.8%; 92.2%) as compared to chloramphenicol (43.1%; 53.9%), amoxicillin/clavulanic (43.5%; 45.7%), and ciprofloxacin (45.7%; 55.7%) in offal and muscle meats, respectively. Imipenem showed the lowest resistance (0%; 0.9%). A total of 41 multidrug-resistant patterns were displayed. Edible offal could be a source of antibiotic-resistant bacteria

Efficacy of ultraviolet (UV-C) light in reducing foodborne pathogens and model viruses in skim milk

The efficacy of low wavelength ultraviolet light (UV-C) as a disinfection process for a scattering fluid such as skim milk was investigated in this study. UV-C inactivation kinetics of two surrogate viruses (bacteriophages MS2 and T1UV) and three bacteria Escherichia coli ATCC 25922, S. Typhimurium ATCC 13311, Listeria monocytogenes ATCC 19115 in buffer and skim milk were investigated. UV-C irradiation was applied to stirred samples, using a collimated beam operating at 253.7 nm wavelength. A series of known UV-C doses (0–40 mJ·cm−2) were delivered to the samples except MS2 where higher doses (0–150 mJ·cm−2) were delivered. Biodosimetry, utilizing D values of viruses inactivated in buffer, was carried out to verify and calculate reduction equivalent dose. At the highest dose of 40 mJ·cm−2, the three pathogenic organisms were inactivated by more than 5 log10 (p \u3c .05). Results provide evidence that UV-C irradiation effectively inactivated bacteriophage and pathogenic microbes in skim milk. The inactivation kinetics of microorganisms was well described by log linear and exponential models with a low root mean squared error and high coefficient of determination (r2 \u3e 0.96). Models were validated and parameterized for predicting log reduction as a function of UV-C irradiation dose (p \u3c .05). This study clearly demonstrated that high levels of inactivation of pathogens can be achieved in skim milk, and suggests significant potential for UV-C treatment of treating fluids that exhibit significant scattering. Practical application This research paper provides scientific evidence of the potential use of UV technology in inactivating pathogenic bacteria and model viruses in skim milk. UV-C doses were validated and verified using biodosimetry. UV-C irradiation is an attractive food preservation technology and offers opportunities for dairy and food processing industries to meet the growing demand from consumers for safer food products. This study clearly shows the potential for using UV-C treatment for treating highly scattering fluid such as skim milk. Results from this work will be used to further develop continuous flow-through UV-C systems based on dean or turbulent flow patterns

Microbial inactivation and cytotoxicity evaluation of UV irradiated coconut water in a novel continuous flow spiral reactor

A continuous-flow UV reactor operating at 254 nm wave-length was used to investigate inactivation of microorganisms including bacteriophage in coconut water, a highly opaque liquid food. UV-C inactivation kinetics of two surrogate viruses (MS2, T1UV) and three bacteria (E. coli ATCC 25922, Salmonella Typhimurium ATCC 13311, Listeria monocytogenes ATCC 19115) in buffer and coconut water were investigated (D10 values ranging from 2.82 to 4.54 mJ·cm− 2). A series of known UV-C doses were delivered to the samples. Inactivation levels of all organisms were linearly proportional to UV-C dose (r2 \u3e 0.97). At the highest dose of 30 mJ·cm− 2, the three pathogenic organisms were inactivated by \u3e 5 log10 (p \u3c 0.05). Results clearly demonstrated that UV-C irradiation effectively inactivated bacteriophage and pathogenic microbes in coconut water. The inactivation kinetics of microorganisms were best described by log linear model with a low root mean square error (RMSE) and high coefficient of determination (r2 \u3e 0.97). Models for predicting log reduction as a function of UV-C irradiation dose were found to be significant (p \u3c 0.05) with low RMSE and high r2. The irradiated coconut water showed no cytotoxic effects on normal human intestinal cells and normal mouse liver cells. Overall, these results indicated that UV-C treatment did not generate cytotoxic compounds in the coconut water. This study clearly demonstrated that high levels of inactivation of pathogens can be achieved in coconut water, and suggested potential method for UV-C treatment of other liquid foods. Industrial relevance This research paper provides scientific evidence of the potential benefits of UV-C irradiation in inactivating bacterial and viral surrogates at commercially relevant doses of 0–120 mJ·cm− 2. The irradiated coconut water showed no cytotoxic effects on normal intestinal and healthy mice liver cells. UV-C irradiation is an attractive food preservation technology and offers opportunities for horticultural and food processing industries to meet the growing demand from consumers for healthier and safe food products. This study would provide technical support for commercialization of UV-C treatment of beverages

Critical evaluation of diameter increment modelling in the Great Lakes region

Simulations of forest stand dynamics in a modelling framework including Forest Vegetation Simulator (FVS) are diameter driven, thus the diameter or basal area increment model needs a special attention. This dissertation critically evaluates diameter or basal area increment models and modelling approaches in the context of the Great Lakes region of the United States and Canada. A set of related studies are presented that critically evaluate the sub-model for change in individual tree basal diameter used in the Forest Vegetation Simulator (FVS), a dominant forestry model in the Great Lakes region. Various historical implementations of the STEMS (Stand and Tree Evaluation and Modeling System) family of diameter increment models, including the current public release of the Lake States variant of FVS (LS-FVS), were tested for the 30 most common tree species using data from the Michigan Forest Inventory and Analysis (FIA) program. The results showed that current public release of the LS-FVS diameter increment model over-predicts 10-year diameter increment by 17% on average. Also the study affirms that a simple adjustment factor as a function of a single predictor, dbh (diameter at breast height) used in the past versions, provides an inadequate correction of model prediction bias. In order to re-engineer the basal diameter increment model, the historical, conceptual and philosophical differences among the individual tree increment model families and their modelling approaches were analyzed and discussed. Two underlying conceptual approaches toward diameter or basal area increment modelling have been often used: the potential-modifier (POTMOD) and composite (COMP) approaches, which are exemplified by the STEMS/TWIGS and Prognosis models, respectively. It is argued that both approaches essentially use a similar base function and neither is conceptually different from a biological perspective, even though they look different in their model forms. No matter what modelling approach is used, the base function is the foundation of an increment model. Two base functions – gamma and Box-Lucas – were identified as candidate base functions for forestry applications. The results of a comparative analysis of empirical fits showed that quality of fit is essentially similar, and both are sufficiently detailed and flexible for forestry applications. The choice of either base function in order to model diameter or basal area increment is dependent upon personal preference; however, the gamma base function may be preferred over the Box-Lucas, as it fits the periodic increment data in both a linear and nonlinear composite model form. Finally, the utility of site index as a predictor variable has been criticized, as it has been widely used in models for complex, mixed species forest stands though not well suited for this purpose. An alternative to site index in an increment model was explored, using site index and a combination of climate variables and Forest Ecosystem Classification (FEC) ecosites and data from the Province of Ontario, Canada. The results showed that a combination of climate and FEC ecosites variables can replace site index in the diameter increment model

Evaluating alternative implementations of the Lake States FVS diameter increment model

We evaluated the STEMS family of diameter increment models that have been incorporated in the Lake States variant of the Forest Vegetation Simulator. We paired validation using regression-based equivalence tests with evaluation of trends in errors across species and predictor variables, using independent data from the Michigan Forest Inventory and Analysis program. Our evaluation shows that 10-year increment bias is substantial, almost 17% on average, and our tests failed to validate the model for every one of the 30 most common tree species in the region. A comparative analysis among all alternative implementations demonstrated that error arose from structural weaknesses in the underlying model. Furthermore, the way the model is currently implemented in the Forest Vegetation Simulator partly masks poor performance at the tree level, but likely amplifies error at the stand level, a particularly troubling result in many conceivable applications. Our results also affirm that a simple adjustment factor as a function of dbh provides an inadequate correction of prediction bias. We argue that the diameter increment model needs to be re-engineered. © 2007 Elsevier B.V. All rights reserved

Representing site productivity in the basal area increment model for FVS-Ontario

The utility of site index as a predictor variable in models for complex, mixed species stands is limited because the site index concept is not well suited for these stand types. Additionally, there is no standard protocol of estimating site index for uneven-aged mixed species stands, which is evident in permanent sample plot (PSP) and co-operative (COOP) data sets available from the Province of Ontario, Canada. Under such circumstances, an alternative to site index in a basal area increment model was explored, using a combination of climate and Forest Ecosystem Classification (FEC) variables from the Ontario boreal region. Among the four candidate climate variables chosen, mean annual temperature (MAT) explained the most variability in basal area increment for the four selected tree species - trembling aspen (Populus tremuloides Michx.), balsam fir (Abies balsamea (L.) Mill.), jack pine (Pinus banksiana Lamb.), and black spruce (Picea mariana (Mill.) B.S.P.). Our results indicated that a combination of the climate variable, MAT, and FEC explained a substantially higher proportion of variation in the basal area increment than site index alone. Thus, climate and FEC variables are superior substitutes in the basal area increment model even when error-free site index values are possible to obtain. © 2009 Elsevier B.V. All rights reserved