Novel insights into host-fungal pathogen interactions derived from live-cell imaging

Acknowledgments The authors acknowledge funding from the Wellcome Trust (080088, 086827, 075470 and 099215) including a Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377 and FP7-2007–2013 grant agreement HEALTH-F2-2010-260338–ALLFUN to NARG.Peer reviewedPublisher PD

Armed and accurate: engineering cytotoxic T cells for eradication of leukemia

Translational medicine depends on a rapid and efficient exchange of results between the bench and the bedside. A recent example from the field of cancer immunotherapy highlights the essential nature of this exchange. Methods have been developed to convert a patient's cytotoxic T cells into efficient and specific killers of cancer cells in patients with leukemia. By using recombinant DNA techniques, a lentiviral vector was constructed to express chimeric antigen receptors in cytotoxic T cells from patients with advanced chronic lymphocytic leukemia. The purpose of the chimeric receptors was to direct the cytotoxic T cell activity against cells causing the cancer. The effect of infusing the engineered T cells back into the cancer patients was tested in a Phase I trial at the University of Pennsylvania, and the initial results were described in two articles from the research team of Dr. Carl June. The remarkable success of this trial should energize further applications of biotechnology in the development of new cancer immunotherapies

Dry semi-continuous anaerobic digestion of food waste in the mesophilic and thermophilic modes: New aspects of sustainable management and energy recovery in South Korea

© 2016 Elsevier Ltd In this study, parallel, bench-scale, mesophilic and thermophilic, dry, semi-continuous anaerobic digestion (DScAD) of Korea food waste (FW, containing 22% total solids (TS) and 20% volatile solids (VS)) was investigated thoroughly under varying operational conditions, including hydraulic retention times (HRTs) and organic loading rates (OLRs). The aim was to evaluate the start-up, stability, overall removal efficiency, and inhibitory effects of toxic compounds on process performance over a long-term operation lasting 100 days. The results from both digesters indicate that the simultaneous reduction of VS and the production of gas improved as the HRT decreased or the OLR increased. The highest average rates of VS reduction (79.67%) and biogas production (162.14 m3biogas/ton of FW, 61.89% CH4), at an OLR of 8.62 ± 0.34 kg VS/m3day (25 days of HRT), were achieved under thermophilic DScAD. In addition, the average rates of reduction of VS and the production of biogas in thermophilic DScAD were higher by 6.88% and 16.4%, respectively, than were those in mesophilic DScAD. The inhibitory effects of ammonia, H2S, and volatile fatty acids (VFAs) on methane production was not clear from either of the digesters, although, apparently, their concentrations did fluctuate. This fluctuation could be attributed to the self-adaptation of the microbial well. However, digestion that was more stable and faster was observed under thermophilic conditions compared with that under mesophilic conditions. Based on our results, the optimum operational parameters to improve FW treatment and achieve higher energy yields could be determined, expanding the application of DScAD in treating organic wastes

Genome of the Avirulent Human-Infective Trypanosome—Trypanosoma rangeli

Background: Trypanosoma rangeli is a hemoflagellate protozoan parasite infecting humans and other wild and domestic mammals across Central and South America. It does not cause human disease, but it can be mistaken for the etiologic agent of Chagas disease, Trypanosoma cruzi. We have sequenced the T. rangeli genome to provide new tools for elucidating the distinct and intriguing biology of this species and the key pathways related to interaction with its arthropod and mammalian hosts.  Methodology/Principal Findings: The T. rangeli haploid genome is ,24 Mb in length, and is the smallest and least repetitive trypanosomatid genome sequenced thus far. This parasite genome has shorter subtelomeric sequences compared to those of T. cruzi and T. brucei; displays intraspecific karyotype variability and lacks minichromosomes. Of the predicted 7,613 protein coding sequences, functional annotations could be determined for 2,415, while 5,043 are hypothetical proteins, some with evidence of protein expression. 7,101 genes (93%) are shared with other trypanosomatids that infect humans. An ortholog of the dcl2 gene involved in the T. brucei RNAi pathway was found in T. rangeli, but the RNAi machinery is non-functional since the other genes in this pathway are pseudogenized. T. rangeli is highly susceptible to oxidative stress, a phenotype that may be explained by a smaller number of anti-oxidant defense enzymes and heatshock proteins.  Conclusions/Significance: Phylogenetic comparison of nuclear and mitochondrial genes indicates that T. rangeli and T. cruzi are equidistant from T. brucei. In addition to revealing new aspects of trypanosome co-evolution within the vertebrate and invertebrate hosts, comparative genomic analysis with pathogenic trypanosomatids provides valuable new information that can be further explored with the aim of developing better diagnostic tools and/or therapeutic targets

Supporting new product commercialization through managerial social ties and market knowledge development in an emerging economy

University of Tasmania, Australi

A hybrid constructed wetland for organic-material and nutrient removal from sewage: Process performance and multi-kinetic models

© 2018 Elsevier Ltd A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order–PFR (M2) model did not fit the data (P > 0.05, and R2 0.5). The pollutant removal rates in the case of M1 were 0.19 m/d (CODCr) and those for M3 were 25.2 g/m2∙d for CODCr and 2.63 g/m2∙d for NH4-N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD5) and NH4-N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models

Using Verbal Autopsy to Measure Causes of Death: the Comparative Performance of Existing Methods.

Monitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability. We investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution. Three automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause. Physician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices

A quantitative model for estimating risk from multiple interacting natural hazards: an application to northeast Zhejiang, China

Multi-hazard risk assessment is a major concern in risk analysis, but most approaches do not consider all hazard interactions when calculating possible losses. We address this problem by developing an improved quantitative model - Model for multi-hazard Risk assessment with a consideration of Hazard Interaction (MmhRisk-HI). This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between those hazards. There are two main components to the model. In the first, based on the hazard-forming environment, relationships among hazards are classified into four types for calculation of the exceedance probability of multiple hazards occurrence. In the second, a Bayesian network is used to calculate possible loss caused by multiple hazards with different exceedance probabilities. A multi-hazard risk map can then be drawn addressing the probability of multi-hazard occurrence and corresponding loss. This model was applied in northeast Zhejiang, China and validated by comparison against an observed multi-hazard sequence. The validation results show that the model can more effectively represent the real world, and that the modelled outputs, possible loss caused by multiple hazards, are reliable. The outputs can additionally help to identify areas at greatest risk, and allows a determination of the factors that contribute to that risk, and hence the model can provide useful further information for planners and decision-makers concerned with risk mitigation

Understanding fire regime across the Gran Chaco Americano

Fire is a natural element in the Gran Chaco, one of the last remnants of tropical dry forests worldwide and a global deforestation hotspot. However, fire has become a critical hazard in recent years, given its increased dynamic and unprecedented negative impacts. These impacts have reached native indigenous peoples that inhabit the region. The Gran Chaco is an essentially diverse cultural region, and those who inhabit it maintain strong ties with the environment, their main livelihood, and their food source. However, little is known about how fire affects these vulnerable communities and how to tackle wildfires in this ecoregion. Therefore, we developed a spatiotemporal analysis of fire activity based on different fire regime components to understand the fire behavior across this region. To do this, we used remote sensing data from MODIS product collection 6 and the MOSEV database to compute the frequency, area, severity, and time since the last fires during the last two decades. We then selected the K-means cluster method to combine these variables using different configuration groups to build up pyrozones. Afterward, we studied the causes of fire incidence during the same period using three predictive modeling algorithms, namely Random Forest (RF), Generalized Linear Model (GLM), and Generalized Additive Model (GAM), to relate climatic, topographic, and anthropic variables with fire occurrence to identify key drivers of recent fire activities. We found four different clusters that show well-defined spatially-aggregated groups, where the variables describe the gradient of the fire dynamic. Moreover, we found that maximum temperature, livestock, and tree cover percentage were the most critical drivers in defining the fire pattern across this landscape. Our results contributed to a better understanding of the Gran Chaco Americano fire regime. According to current fire dynamics, this information helps us identify critical zones for the most vulnerable communities, like indigenous people that inhabit the Gran Chaco Americano, and contributes significantly to fire management in tropical dryland ecosystems

Safeguarding pollinators and their values to human well-being

Wild and managed pollinators provide a wide range of benefits to society in terms of contributions to food security, farmer and beekeeper livelihoods, social and cultural values, as well as the maintenance of wider biodiversity and ecosystem stability. Pollinators face numerous threats, including changes in land-use and management intensity, climate change, pesticides and genetically modified crops, pollinator management and pathogens, and invasive alien species. There are well-documented declines in some wild and managed pollinators in several regions of the world. However, many effective policy and management responses can be implemented to safeguard pollinators and sustain pollination services