A Clinical and Epidemiological Investigation of the First Reported Human Infection With the Zoonotic Parasite Trypanosoma evansi in Southeast Asia

Background. Trypanosoma is a genus of unicellular parasitic flagellate protozoa. Trypanosoma brucei species and Trypanosoma cruzi are the major agents of human trypanosomiasis; other Trypanosoma species can cause human disease, but are rare. In March 2015, a 38-year-old woman presented to a healthcare facility in southern Vietnam with fever, headache, and arthralgia. Microscopic examination of blood revealed infection with Trypanosoma. Methods. Microscopic observation, polymerase chain reaction (PCR) amplification of blood samples, and serological testing were performed to identify the infecting species. The patient's blood was screened for the trypanocidal protein apolipoprotein L1 (APOL1), and a field investigation was performed to identify the zoonotic source. Results. PCR amplification and serological testing identified the infecting species as Trypanosoma evansi. Despite relapsing 6 weeks after completing amphotericin B therapy, the patient made a complete recovery after 5 weeks of suramin. The patient was found to have 2 wild-type APOL1 alleles and a normal serum APOL1 concentration. After responsive animal sampling in the presumed location of exposure, cattle and/or buffalo were determined to be the most likely source of the infection, with 14 of 30 (47%) animal blood samples testing PCR positive for T. evansi. Conclusions. We report the first laboratory-confirmed case of T. evansi in a previously healthy individual without APOL1 deficiency, potentially contracted via a wound while butchering raw beef, and successfully treated with suramin. A linked epidemiological investigation revealed widespread and previously unidentified burden of T. evansi in local cattle, highlighting the need for surveillance of this infection in animals and the possibility of further human cases

A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using sentinel-1 SAR imagery and geospatial data

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility

G-TRACE: rapid Gal4-based cell lineage analysis in Drosophila.

We combined Gal4-UAS and the FLP recombinase-FRT and fluorescent reporters to generate cell clones that provide spatial, temporal and genetic information about the origins of individual cells in Drosophila melanogaster. We named this combination the Gal4 technique for real-time and clonal expression (G-TRACE). The approach should allow for screening and the identification of real-time and lineage-traced expression patterns on a genomic scale

Concrete Analysis of Quantum Lattice Enumeration

Lattice reduction algorithms such as BKZ (Block-Korkine-Zolotarev) play a central role in estimating the security of lattice-based cryptography. The subroutine in BKZ which finds the shortest vector in a projected sublattice can be instantiated with enumeration algorithms. The enumeration procedure can be seen as a depth-first search on some ``enumeration tree\u27\u27 whose nodes denote a partial assignment of the coefficients, corresponding to lattice points as a linear combination of the lattice basis with the coefficients. This work provides a concrete analysis for the cost of quantum lattice enumeration based on Montanaro\u27s quantum tree backtracking algorithm. More precisely, we give a concrete implementation in the quantum circuit model. We also show how to optimize the circuit depth by parallelizing the components. Based on the circuit designed, we discuss the concrete quantum resource estimates required for lattice enumeration

Saponin Inhibits Hepatitis C Virus Propagation by Up-regulating Suppressor of Cytokine Signaling 2

Saponins are a group of naturally occurring plant glycosides which possess a wide range of pharmacological properties, including anti-tumorigenic and antiviral activities. To investigate whether saponin has anti-hepatitis C virus (HCV) activity, we examined the effect of saponin on HCV replication. HCV replication was efficiently inhibited at a concentration of 10 µg/ml of saponin in cell culture grown HCV (HCVcc)-infected cells. Inhibitory effect of saponin on HCV replication was verified by quantitative real-time PCR, reporter assay, and immunoblot analysis. In addition, saponin potentiated IFN-α-induced anti-HCV activity. Moreover, saponin exerted antiviral activity even in IFN-α resistant mutant HCVcc-infected cells. To investigate how cellular genes were regulated by saponin, we performed microarray analysis using HCVcc-infected cells. We demonstrated that suppressor of cytokine signaling 2 (SOCS2) protein level was distinctively increased by saponin, which in turn resulted in inhibition of HCV replication. We further showed that silencing of SOCS2 resurrected HCV replication and overexpression of SOCS2 suppressed HCV replication. These data imply that saponin inhibits HCV replication via SOCS2 signaling pathway. These findings suggest that saponin may be a potent therapeutic agent for HCV patients

Effects of hydraulic retention time and bioflocculant addition on membrane fouling in a sponge-submerged membrane bioreactor

© 2016 Elsevier Ltd. The characteristics of activated sludge and membrane fouling were evaluated in a sponge-submerged membrane bioreactor (SSMBR) at different hydraulic retention times (HRTs) (6.67, 5.33 and 4.00 h). At shorter HRT, more obvious membrane fouling was caused by exacerbated cake layer formation and aggravated pore blocking. Activated sludge possessed more extracellular polymeric substances (EPS) due to excessive growth of biomass and lower protein to polysaccharide ratio in soluble microbial products (SMP). The cake layer resistance was aggravated by increased sludge viscosity together with the accumulated EPS and biopolymer clusters (BPC) on membrane surface. However, SMP showed marginal effect on membrane fouling when SSMBRs were operated at all HRTs. The SSMBR with Gemfloc® addition at the optimum HRT of 6.67 h demonstrated superior sludge characteristics such as larger floc size, less SMP in mixed liquor with higher protein/polysaccharide ratio, less SMP and BPC in cake layer, thereby further preventing membrane fouling

Current application of algae derivatives for bioplastic production: A review.

Improper use of conventional plastics poses challenges for sustainable energy and environmental protection. Algal derivatives have been considered as a potential renewable biomass source for bioplastic production. Algae derivatives include a multitude of valuable substances, especially starch from microalgae, short-chain length polyhydroxyalkanoates (PHAs) from cyanobacteria, polysaccharides from marine and freshwater macroalgae. The algae derivatives have the potential to be used as key ingredients for bioplastic production, such as starch and PHAs or only as an additive such as sulfated polysaccharides. The presence of distinctive functional groups in algae, such as carboxyl, hydroxyl, and sulfate, can be manipulated or tailored to provide desirable bioplastic quality, especially for food, pharmaceutical, and medical packaging. Standardizing strains, growing conditions, harvesting and extracting algae in an environmentally friendly manner would be a promising strategy for pollution control and bioplastic production

Simple clinical and laboratory predictors to improve empirical treatment strategies in areas of high scrub typhus and dengue endemicity, central Vietnam

BACKGROUND: Dengue fever is highly endemic in Vietnam, but scrub typhus-although recognized as an endemic disease-remains underappreciated. These diseases together are likely to account for more than half of the acute undifferentiated fever burden in Vietnam. Scrub typhus (ST) is a bacterial disease requiring antimicrobial treatment, while dengue fever (DF) is of viral etiology and does not. The access to adequate diagnostics and the current understanding of empirical treatment strategies for both illnesses remain limited. In this study we aimed to contribute to the clinical decision process in the management of these two important etiologies of febrile illness in Vietnam. METHODS: Using retrospective data from 221 PCR-confirmed scrub typhus cases and 387 NS1 protein positive dengue fever patients admitted to five hospitals in Khanh Hoa province (central Vietnam), we defined predictive characteristics for both diseases that support simple clinical decision making with potential to inform decision algorithms in future. We developed models to discriminate scrub typhus from dengue fever using multivariable logistic regression (M-LR) and classification and regression trees (CART). Regression trees were developed for the entire data set initially and pruned, based on cross-validation. Regression models were developed in a training data set involving 60% of the total sample and validated in the complementary subsample. Probability cut points for the distinction between scrub typhus and dengue fever were chosen to maximise the sum of sensitivity and specificity. RESULTS: Using M-LR, following seven predictors were identified, that reliably differentiate ST from DF; eschar, regional lymphadenopathy, an occupation in nature, increased days of fever on admission, increased neutrophil count, decreased ratio of neutrophils/lymphocytes, and age over 40. Sensitivity and specificity of predictions based on these seven factors reached 93.7% and 99.5%, respectively. When excluding the "eschar" variable, the values dropped to 76.3% and 92.3%, respectively. The CART model generated one further variable; increased days of fever on admission, when eschar was included, the sensitivity and specificity was 95% and 96.9%, respectively. The model without eschar involved the following six variables; regional lymphadenopathy, increased days of fever on admission, increased neutrophil count, increased lymphocyte count, platelet count >/= 47 G/L and age over 28 years as predictors of ST and provided a sensitivity of 77.4% and a specificity of 90.7%. CONCLUSIONS: The generated algorithms contribute to differentiating scrub typhus from dengue fever using basic clinical and laboratory parameters, supporting clinical decision making in areas where dengue and scrub typhus are co-endemic in Vietnam

Risk Factors of Streptococcus suis Infection in Vietnam. A Case-Control Study

Background: Streptococcus suis infection, an emerging zoonosis, is an increasing public health problem across South East Asia and the most common cause of acute bacterial meningitis in adults in Vietnam. Little is known of the risk factors underlying the disease. Methods and Findings: A case-control study with appropriate hospital and matched community controls for each patient was conducted between May 2006 and June 2009. Potential risk factors were assessed using a standardized questionnaire and investigation of throat and rectal S. suis carriage in cases, controls and their pigs, using real-time PCR and culture of swab samples. We recruited 101 cases of S. suis meningitis, 303 hospital controls and 300 community controls. By multivariate analysis, risk factors identified for S. suis infection as compared to either control group included eating "high risk" dishes, including such dishes as undercooked pig blood and pig intestine (OR1 = 2.22; 95% CI = [1.15-4.28] and OR2 = 4.44; 95% CI = [2.15-9.15]), occupations related to pigs (OR1 = 3.84; 95% CI = [1.32-11.11] and OR2 = 5.52; 95% CI = [1.49-20.39]), and exposures to pigs or pork in the presence of skin injuries (OR1 = 7.48; 95% CI = [1.97-28.44] and OR2 = 15.96; 95% CI = [2.97-85.72]). S. suis specific DNA was detected in rectal and throat swabs of 6 patients and was cultured from 2 rectal samples, but was not detected in such samples of 1522 healthy individuals or patients without S. suis infection. Conclusions: This case control study, the largest prospective epidemiological assessment of this disease, has identified the most important risk factors associated with S. suis bacterial meningitis to be eating 'high risk' dishes popular in parts of Asia, occupational exposure to pigs and pig products, and preparation of pork in the presence of skin lesions. These risk factors can be addressed in public health campaigns aimed at preventing S. suis infectio