Carbon Footprint: A New Farm Management Consideration in the Southern High Plains

As concerns continue to mount regarding man induced impacts to the global climate, the SHPT region could be faced with a unique scenario in which the net carbon balance should be considered in the producer’s enterprise selection and production systems. Currently, the SHPT produces nearly one third of the U.S. cotton crop. Under a potential cap and trade system the challenge for the agricultural industry in the SHPT may be how to sustain the region’s economic base and production capabilities. Thus, the objective of this study was to measure the net carbon relationships between irrigated cotton and irrigated corn production systems on the SHPT using data from the Texas Alliance for Water Conservation (TAWC, 2009). Due to the unique management and production challenges in the SHPT, additional comparisons were made regarding economic viability and irrigation efficiency. Within the parameters of this study, it is apparent that irrigated corn has an advantage over cotton in both its ability to return carbon to the soil, maintain profitability, and use water resources efficiently. If the agricultural industry is included in CO2 regulation, it would appear that irrigated agricultural producers in the SHPT who have the ability to move between irrigated cotton and corn should be aware of the advantages corn possesses. However, even under changing commodity prices and profitability scenarios, corn still presents a significant advantage over cotton in its ability to reduce atmospheric CO2 by depositing larger amounts of biomass carbon into the soil.Cape and Trade, carbon, farm management, Environmental Economics and Policy, Farm Management, Q18, Q28, Q54, Q56,

RECENT MODALITIES IN DRUG DELIVERY VIA INHALATION THERAPY – AN ADVANCED TREATMENT STRATEGY FOR PULMONARY CARCINOMA

The potential benefit of nanoparticles (NPs) as a colloidal delivery system for pharmaceutical relevance has earned substantial concern in the past decades. Fatality rate due to cancer sustained to hike; advances in nanotechnology have quite become a trenchant approach for enhancing effective drug targeting to cancer tissues by circumventing all the imperfections of traditional chemotherapy. Inhalation drug delivery directly targeting the lungs through respiratory tract is a rapidly maturing field of research and most recently explored approaches for both local and systemic therapy. With the recent advances in synthesis and manipulation of nanoparticles, inhalation drug delivery has shown great impact on pulmonary practice. Inhalation drug delivery has diverse actions over traditional chemotherapy including a) non-invasive b) avoiding first pass metabolism and systemic toxicity c) minimized frequent dosing and d) target delivery of drug to the lung epithelium thereby enhancing local drug concentrations. Dry powder inhalers, meter dose inhalers and nebulizers are some few efficient methods to deliver therapeutic agents directly targeting to the lungs. The ultimatum of inhalation therapy is to generate particles with an ample range of particle sizes. With the recent interest in the development of pulmonary targeted therapy, this review presents how the inhalation drug delivery overcomes conventional chemotherapy and focuses the recent treatment modalities that have been established for pulmonary carcinoma by the route of inhalation as well as discusses the advantages of inhalation drug delivery.Â

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum

Seasonal variability of evapotranspiration and carbon exchanges over a biomass sorghum field in the Southern U.S. Great Plains

The eddy covariance method was used to investigate carbon fluxes and evapotranspiration (ET) from a high biomass forage sorghum (Sorghum bicolor L.) field in the Southern U.S. Great Plains for three growing seasons (2013-2015). Above normal precipitation and narrow row spacing (50 cm) led to higher biomass production (25 Mg ha-1) and leaf area index (LAI = 7.2) development in 2014. This also resulted in higher carbon uptake or net ecosystem production (NEP) and ET during that year. Early and late season precipitation enhanced ecosystem respiration (Reco) resulting in lower NEP in 2015. Shorter growing season (119 days) also contributed to lower cumulative NEP in 2015. Estimated gross primary production (GPP) in 2014 (1780 g m-2) was 10% higher than the GPP in 2013 (1591 g m-2) and 24% higher than the GPP in 2015 (1353 g m-2). During all growing seasons, the site was a source of carbon (negative NEP) at the beginning and transitioned to a sink (positive NEP) later in the season. Biomass-GPP relationship indicated that approximately 65% of total GPP was allocated to above ground biomass (AGB). Average monthly ecosystem WUE (expressed as gross carbon gain per unit of ET) ranged from 1.7 g mm-1 to 4.2 g mm-1. Results from our study indicate that weather conditions, growing season length and crop management are important factors in determining the magnitude of carbon uptake and release, and ET of this cellulosic biofuel feedstock crop in the Southern U.S. Great Plains.Peer reviewedPlant and Soil Science

Carbon and evapotranspiration dynamics of a non-native perennial grass with biofuel potential in the southern U.S. Great Plains

Old world bluestem cultivar WW-B Dahl [Bothriochloa bladhii (Retz.), S. T. Blake] is a non-native perennial C4 bunch grass with biofuel production potential grown predominantly in the Southern U.S. Great Plains. Although this is a popular introduced grass cultivar, data on carbon fluxes and evapotranspiration (ET) from this warm-season grass is rare. In this study, the eddy covariance method was used to measure CO 2 and ET from an established stand of bluestem for three years (2013-2015). Year 2015 had the highest gross primary production (GPP; 1358 +/- 143 g C m -2 ) followed by 2014 (1250 +/- 31 g C m -2 ) and 2013 (1024 +/- 91 g C m -2 ). The average loss of GPP as ecosystem respiration (R eco ) was 76%. Annual NEE sums were -302 +/- 15 g C m -2 in 2013, -265 +/- 41 g C m -2 in 2014, and -287 +/- 32 g C m -2 in 2015. Results from this study show that the NEE in grasslands in years with normal precipitation that is well distributed may not vary from years with above-normal precipitation. This is because precipitation enhances R eco along with carbon uptake, which may result in lower net carbon uptake in perennial grasslands in higher precipitation years than normal precipitation years. Gross primary production showed a linear relationship with ET (R 2 = 0.90) and above ground biomass (R 2 = 0.74). Only 26% of the GPP was allocated to above ground biomass indicating a higher allocation of carbon to below ground biomass. The water use efficiency of bluestem (2.9 g C kg -1 of water) matched well with that of native prairies and other dedicated biomass crops grown in the Southern Great Plains. As the demand for cellulosic biofuels is increasing, results from field experiments quantifying seasonal changes in carbon fluxes and ET could be important in understanding the contributions of large-scale production of novel biomass crops to regional carbon and hydrologic cycles.Peer reviewedPlant and Soil Science

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Normalizing and Converting Image DC Data Using Scatter Plot Matching

Remote sensing image data from sources such as Landsat or airborne multispectral digital cameras are typically in the form of digital count (DC) values. To compare images acquired by the same sensor system on different dates, or images acquired by different sensor systems, it is necessary to correct for differences in the DC values due to sensor characteristics (gain and offset), illumination of the surface (a function of sun angle), and atmospheric clarity. A method is described for normalizing one image to another, or converting image DC values to surface reflectance. This method is based on the identification of pseudo-invariant features (bare soil line and full canopy point) in the scatter plot of red and near-infrared image pixel values. The method, called “scatter plot matching” (SPM), is demonstrated by normalizing a Landsat-7 ETM+ image to a Landsat-5 TM image, and by converting the pixel DC values in a Landsat-5 TM image to values of surface reflectance. While SPM has some limitations, it represents a simple, straight-forward method for calibrating remote sensing image data