A tick saliva serpin, IxsS17 inhibits host innate immune system proteases and enhances host colonization by Lyme disease agent.

Lyme disease (LD) caused by Borrelia burgdorferi is among the most important human vector borne diseases for which there is no effective prevention method. Identification of tick saliva transmission factors of the LD agent is needed before the highly advocated tick antigen-based vaccine could be developed. We previously reported the highly conserved Ixodes scapularis (Ixs) tick saliva serpin (S) 17 (IxsS17) was highly secreted by B. burgdorferi infected nymphs. Here, we show that IxsS17 promote tick feeding and enhances B. burgdorferi colonization of the host. We show that IxsS17 is not part of a redundant system, and its functional domain reactive center loop (RCL) is 100% conserved in all tick species. Yeast expressed recombinant (r) IxsS17 inhibits effector proteases of inflammation, blood clotting, and complement innate immune systems. Interestingly, differential precipitation analysis revealed novel functional insights that IxsS17 interacts with both effector proteases and regulatory protease inhibitors. For instance, rIxsS17 interacted with blood clotting proteases, fXII, fX, fXII, plasmin, and plasma kallikrein alongside blood clotting regulatory serpins (antithrombin III and heparin cofactor II). Similarly, rIxsS17 interacted with both complement system serine proteases, C1s, C2, and factor I and the regulatory serpin, plasma protease C1 inhibitor. Consistently, we validated that rIxsS17 dose dependently blocked deposition of the complement membrane attack complex via the lectin complement pathway and protected complement sensitive B. burgdorferi from complement-mediated killing. Likewise, co-inoculating C3H/HeN mice with rIxsS17 and B. burgdorferi significantly enhanced colonization of mouse heart and skin organs in a reverse dose dependent manner. Taken together, our data suggests an important role for IxsS17 in tick feeding and B. burgdorferi colonization of the host.

Efficient model-based bioequivalence testing.

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and $C_{\max}$ are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the $90\%$ confidence interval for these ratios falls between $0.8$ and $1.25 $. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of $\rm AUC$ and $C_{\max}$ using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of $0.05$, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

Chemical Fractionations of Lead and Zinc in the Contaminated Soil Amended with the Blended Biochar/Apatite.

Heavy metal contamination in agricultural land is an alarming issue in Vietnam. It is necessary to develop suitable remediation methods for environmental and farming purposes. The present study investigated the effectiveness of using peanut shell-derived biochar to remediate the two heavy metals Zn and Pb in laboratory soil assays following Tessier's sequential extraction procedure. The concentration of heavy metals was analyzed using Inductively coupled plasma mass spectrometry (ICP-MS). This study also compared the effectiveness of the blend of biochar and apatite applied and the mere biochar amendment on the chemical fractions of Pb and Zn in the contaminated agricultural soil. Results have shown that the investigated soil was extremely polluted by Pb (3047.8 mg kg−1) and Zn (2034.3 mg kg−1). In addition, the pH, organic carbon, and electrical conductivity values of amended soil samples increased with the increase in the amendment's ratios. The distribution of heavy metals in soil samples was in the descending order of carbonate fraction (F2) > residue fraction (F5) > exchangeable fraction (F1) > Fe/Mn oxide fraction (F3) > organic fraction (F4) for Pb and F5 ≈ F2 > F1 > F3 > F4 for Zn. The peanut shell-derived biochar produced at 400 °C and 600 °C amended at a 10% ratio (PB4:10 and PB6:10) could significantly reduce the exchangeable fraction Zn from 424.82 mg kg−1 to 277.69 mg kg−1 and 302.89 mg kg−1, respectively, and Pb from 495.77 mg kg−1 to 234.55 mg kg−1 and 275.15 mg kg−1, respectively, and immobilize them in soil. Amending the biochar and apatite combination increased the soil pH, then produced a highly negative charge on the soil surface and facilitated Pb and Zn adsorption. This study shows that the amendment of biochar and biochar blended with apatite could stabilize Pb and Zn fractions, indicating the potential of these amendments to remediate Pb and Zn in contaminated soil.

Integrated methods and scenarios for assessment of sand dunes ecosystem services.

Anthropogenic and natural ecosystems in coastal dunes provide considerable benefits to human well-being. However, to date, we still lack a good understanding of how ecosystem services (ES) supply varies from young dunes (e.g., embryo and fore dunes) to mature dunes (e.g., brown and red dunes). This study proposed a novel modelling methodology by integrating an expert-based matrix, a Bayesian Belief Network (BBN), a structural equation model, and a scenario development method. It aims at evaluating dune ecosystem services for the sustainable development of coastal areas. The model was tested using data collected from dunes in Vietnam. An expert-based matrix to assess the supply capacity of 18 ES in different types of dunes was generated with the participation of 21 interdisciplinary scientists. It was found that red dune ecosystems could supply the most regulation and cultural ecosystem services, while gray dunes provided the least amount. Results from a scenario analysis recommended that decision-making is able to optimize multiple ES by: (i) keeping embryo/fore dunes in their natural state instead of using them for mineral mining and urbanization; (ii) enlarging certified and protected forests areas in gray and yellow dunes; and (iii) optimizing cultural ES supply in red dunes.

Robust designs in longitudinal studies accounting for parameter and model uncertainties - application to count data.

Nonlinear mixed effect models (NLMEMs) are widely used for the analysis of longitudinal data. To design these studies, optimal designs based on the expected Fisher information matrix (FIM) can be used. A method evaluating the FIM using Monte-Carlo Hamiltonian Monte-Carlo (MC-HMC) has been proposed and implemented in the R package MIXFIM using Stan. This approach, however, requires a priori knowledge of models and parameters, which leads to locally optimal designs. The objective of this work was to extend this MC-HMC-based method to evaluate the FIM in NLMEMs accounting for uncertainty in parameters and in models. When introducing uncertainty in the population parameters, we evaluated the robust FIM as the expectation of the FIM computed by MC-HMC over the distribution of these parameters. Then, the compound D-optimality criterion (CD optimality), corresponding to a weighted product of the D-optimality criteria of several candidate models, was used to find a common CD-optimal design for the set of candidate models. Finally, a compound DE-criterion (CDE optimality), corresponding to a weighted product of the normalized determinants of the robust FIMs of all the candidate models accounting for uncertainty in parameters, was calculated to find the CDE-optimal design which was robust on both parameters and model. These methods were applied in a longitudinal Poisson count model. We assumed prior distributions on the population parameters, as well as several candidate models describing the relationship between the logarithm of the event rate parameter and the dose. We found that assuming uncertainty in parameters could lead to different optimal designs, and misspecification of models could induce designs with low efficiencies. The CD- or CDE-optimal designs therefore provided a good compromise for different candidate models. Finally, the proposed approach allows for the first time optimization of designs for repeated discrete data accounting for parameter and model uncertainties.

Variation of Mitochondrial DNA HV1 AND HV2 of the Vietnamese Population.

BACKGROUND: The sequence polymorphism of mitochondrial DNA (mtDNA) hypervariable segment 1 (HV1) and hypervariable segment 2 (HV2) is studied and applied to genetic diversity and human evolution assessment, forensic genetics, consanguinity determination, and mitochondrial disease diagnosis. METHODS: The study identified the variations of HV1 and HV2 of 517 unrelated Vietnamese individuals in Kinh, Muong, Cham, and Khmer ethnic. We performed sequencing of two hypervariable segments of mitochondrial DNA: HV1 and HV2. RESULTS: Fifty haplogroups were identified in which F1a haplogroup frequency was highest at 15.7%, followed by B5a (10.8%), M (8.9%), and M7b1 (7.7%). The most frequently encountered SNPs in this study were A263G (100%), A73G (99.6%), 315insC (96%), 309insC (56%), C16223T (41%), and T16189C (39%). The genetic diversity was calculated at 99.83%, and the probability of random match of two individuals sharing the same mtDNA haplotype was 0.37%. CONCLUSION: We have assessed the genetic polymorphism of mtDNA HV1 and HV2 of 517 Kinh, Muong, Cham, and Khmer ethnic samples. The result will help in better understanding of Vietnamese's mitochondrial genome diversity and aid in population as well as forensic science.

A new model framework for sponge city implementation: Emerging challenges and future developments.

Sponge City concept is emerging as a new kind of integrated urban water systems, which aims to address urban water problems. However, its implementation has encountered a variety of challenges. The lack of an integrated comprehensive model to assist Sponge City planning, implementation and life cycle assessment is one of the most challenging factors. This review briefly analyses the opportunity of existing urban water management models and discusses the limitation of recent studies in the application of current integrated models for Sponge City implementation. Furthermore, it proposes a new Sponge City model framework by integrating four main sub-models including MIKE-URBAN, LCA, W045-BEST, and MCA in which environmental, social, and economic aspects of Sponge City infrastructure options are simulated. The new structure of Sponge City model that includes the sub-model layer, input layer, module layer, output layer, and programing language layer is also illustrated. Therefore, the proposed model could be applied to optimize different Sponge City practices by not only assessing the drainage capacity of stormwater infrastructure but also pays attention to multi-criteria analysis of urban water system (including the possibility of assessing Sponge City ecosystem services for urban areas and watershed areas) as well. Balancing between simplification and innovation of integrated models, increasing the efficiency of spatial data sharing systems, defining the acceptability of model complexity level and improving the corporation of multiple stakeholders emphasizing on possible future directions of a proper Sponge City design and construction model.

Impact of Antibiotic Gut Exposure on the Temporal Changes in Microbiome Diversity.

Although the global deleterious impact of antibiotics on the intestinal microbiota is well known, temporal changes in microbial diversity during and after an antibiotic treatment are still poorly characterized. We used plasma and fecal samples collected frequently during treatment and up to one month after from 22 healthy volunteers assigned to a 5-day treatment by moxifloxacin (n = 14) or no intervention (n = 8). Moxifloxacin concentrations were measured in both plasma and feces, and bacterial diversity was determined in feces by 16S rRNA gene profiling and quantified using the Shannon index and number of operational taxonomic units (OTUs). Nonlinear mixed effect models were used to relate drug pharmacokinetics and bacterial diversity over time. Moxifloxacin reduced bacterial diversity in a concentration-dependent manner, with a median maximal loss of 27.5% of the Shannon index (minimum [min], 17.5; maximum [max], 27.7) and 47.4% of the number of OTUs (min, 30.4; max, 48.3). As a consequence of both the long fecal half-life of moxifloxacin and the susceptibility of the gut microbiota to moxifloxacin, bacterial diversity indices did not return to their pretreatment levels until days 16 and 21, respectively. Finally, the model characterized the effect of moxifloxacin on bacterial diversity biomarkers and provides a novel framework for analyzing antibiotic effects on the intestinal microbiome.

Antibiotic-Induced Dysbiosis Predicts Mortality in an Animal Model of Clostridium difficile Infection.

Antibiotic disruption of the intestinal microbiota favors colonization by Clostridium difficile Using a charcoal-based adsorbent to decrease intestinal antibiotic concentrations, we studied the relationship between antibiotic concentrations in feces and the intensity of dysbiosis and quantified the link between this intensity and mortality. We administered either moxifloxacin (n = 70) or clindamycin (n = 60) to hamsters by subcutaneous injection from day 1 (D1) to D5 and challenged them with a C. difficile toxigenic strain at D3 Hamsters received various doses of a charcoal-based adsorbent, DAV131A, to modulate intestinal antibiotic concentrations. Gut dysbiosis was evaluated at D0 and D3 using diversity indices determined from 16S rRNA gene profiling. Survival was monitored until D16 We analyzed the relationship between fecal antibiotic concentrations and dysbiosis at the time of C. difficile challenge and studied their capacity to predict subsequent death of the animals. Increasing doses of DAV131A reduced fecal concentrations of both antibiotics, lowered dysbiosis, and increased survival from 0% to 100%. Mortality was related to the level of dysbiosis (P < 10-5 for the change of Shannon index in moxifloxacin-treated animals and P < 10-9 in clindamycin-treated animals). The Shannon diversity index and unweighted UniFrac distance best predicted death, with areas under the receiver operating curve (ROC) of 0.89 (95% confidence interval [CI], 0.82, 0.95) and 0.95 (0.90, 0.98), respectively. Altogether, moxifloxacin and clindamycin disrupted the diversity of the intestinal microbiota with a dependency on the DAV131A dose; mortality after C. difficile challenge was related to the intensity of dysbiosis in similar manners with the two antibiotics.

Can Population Modelling Principles be Used to Identify Key PBPK Parameters for Paediatric Clearance Predictions? An Innovative Application of Optimal Design Theory.

PURPOSE: Physiologically-based pharmacokinetic (PBPK) models are essential in drug development, but require parameters that are not always obtainable. We developed a methodology to investigate the feasibility and requirements for precise and accurate estimation of PBPK parameters using population modelling of clinical data and illustrate this for two key PBPK parameters for hepatic metabolic clearance, namely whole liver unbound intrinsic clearance (CLint,u,WL) and hepatic blood flow (Qh) in children. METHODS: First, structural identifiability was enabled through re-parametrization and the definition of essential trial design components. Subsequently, requirements for the trial components to yield precise estimation of the PBPK parameters and their inter-individual variability were established using a novel application of population optimal design theory. Finally, the performance of the proposed trial design was assessed using stochastic simulation and estimation. RESULTS: Precise estimation of CLint,u,WL and Qh and their inter-individual variability was found to require a trial with two drugs, of which one has an extraction ratio (ER) ≤ 0.27 and the other has an ER ≥ 0.93. The proposed clinical trial design was found to lead to precise and accurate parameter estimates and was robust to parameter uncertainty. CONCLUSION: The proposed framework can be applied to other PBPK parameters and facilitate the development of PBPK models.

Preparing cuprous oxide nanomaterials by electrochemical method for non-enzymatic glucose biosensor.

Cuprous oxide (Cu2O) nanostructure has been synthesized using an electrochemical method with a two-electrode system. Cu foils were used as electrodes and NH2(OH) was utilized as the reducing agent. The effects of pH and applied voltages on the morphology of the product were investigated. The morphology and optical properties of Cu2O particles were characterized using scanning electron microscopy, x-ray diffraction, and diffuse reflectance spectra. The synthesized Cu2O nanostructures that formed in the vicinity of the anode at 2 V and pH = 11 showed high uniform distribution, small size, and good electrochemical sensing. These Cu2O nanoparticles were coated on an Indium tin oxide substrate and applied to detect non-enzyme glucose as excellent biosensors. The non-enzyme glucose biosensors exhibited good performance with high response, good selectivity, wide linear detection range, and a low detection limit at 0.4 µM. Synthesized Cu2O nanostructures are potential materials for a non-enzyme glucose biosensor.

Protection of Hamsters from Mortality by Reducing Fecal Moxifloxacin Concentration with DAV131A in a Model of Moxifloxacin-Induced Clostridium difficile Colitis.

Lowering the gut exposure to antibiotics during treatments can prevent microbiota disruption. We evaluated the effects of an activated charcoal-based adsorbent, DAV131A, on the fecal free moxifloxacin concentration and mortality in a hamster model of moxifloxacin-induced Clostridium difficile infection. A total of 215 hamsters receiving moxifloxacin subcutaneously (day 1 [D1] to D5) were orally infected at D3 with C. difficile spores. They received various doses (0 to 1,800 mg/kg of body weight/day) and schedules (twice a day [BID] or three times a day [TID]) of DAV131A (D1 to D8). Moxifloxacin concentrations and C. difficile counts were determined at D3, and mortality was determined at D12 We compared mortality rates, moxifloxacin concentrations, and C. difficile counts according to DAV131A regimen and modeled the links between DAV131A regimen, moxifloxacin concentration, and mortality. All hamsters that received no DAV131A died, but none of those that received 1,800 mg/kg/day died. Significant dose-dependent relationships between DAV131A dose and (i) mortality, (ii) moxifloxacin concentration, and (iii) C. difficile count were evidenced. Mathematical modeling suggested that (i) lowering the moxifloxacin concentration at D3, which was 58 µg/g (95% confidence interval [CI] = 50 to 66 µg/g) without DAV131A, to 17 µg/g (14 to 21 µg/g) would reduce mortality by 90%; and (ii) this would be achieved with a daily DAV131A dose of 703 mg/kg (596 to 809 mg/kg). In this model of C. difficile infection, DAV131A reduced mortality in a dose-dependent manner by decreasing the fecal free moxifloxacin concentration.

Individual Bayesian Information Matrix for Predicting Estimation Error and Shrinkage of Individual Parameters Accounting for Data Below the Limit of Quantification.

PURPOSE: In mixed models, the relative standard errors (RSE) and shrinkage of individual parameters can be predicted from the individual Bayesian information matrix (MBF). We proposed an approach accounting for data below the limit of quantification (LOQ) in MBF. METHODS: MBF is the sum of the expectation of the individual Fisher information (MIF) which can be evaluated by First-Order linearization and the inverse of random effect variance. We expressed the individual information as a weighted sum of predicted MIF for every possible design composing of measurements above and/or below LOQ. When evaluating MIF, we derived the likelihood expressed as the product of the likelihood of observed data and the probability for data to be below LOQ. The relevance of RSE and shrinkage predicted by MBF in absence or presence of data below LOQ were evaluated by simulations, using a pharmacokinetic/viral kinetic model defined by differential equations. RESULTS: Simulations showed good agreement between predicted and observed RSE and shrinkage in absence or presence of data below LOQ. We found that RSE and shrinkage increased with sparser designs and with data below LOQ. CONCLUSIONS: The proposed method based on MBF adequately predicted individual RSE and shrinkage, allowing for evaluation of a large number of scenarios without extensive simulations.

A limited sampling strategy based on maximum a posteriori Bayesian estimation for a five-probe phenotyping cocktail.

PURPOSE: Cocktail approach using a combination of probes to phenotype several cytochromes P450 or transporters is of high interest in anticipating drug­drug interactions and personalized medicine. Its clinical use remains however limited by the intensive sampling scheme required to obtain phenotyping indexes (PI) which consists in calculating the area under the concentration­time curves. We proposed to use maximum a posteriori Bayesian estimation (MAPBE) that incorporates available information from the whole population to derive PI from a few individual observations. The performance of a limited sampling strategy (LSS) based on MAPBE was evaluated for a five-probe cocktail. METHODS: The studied cocktail included midazolam, tolbutamide, caffeine, dextromethorphan, omeprazole and their relevant metabolites. Prior information for MAPBE was obtained by nonlinear mixed-effect modelling of data from a pilot study. Sampling times were chosen based on optimal design theory using the Bayesian Fisher information matrix. Through a simulation study, we investigated the predicted PI in terms of bias and imprecision for varying number and timing of samples. RESULTS: Some three-point Bayesian designs gave mean prediction errors in [−5 %, 5 %], root mean square errors below 30 % for all probes, except dextromethorphan whose model should be consolidated further with additional data. This approach gave overall less outlier predicted values than single-point metrics and was more flexible to the timing of the latest sampling. CONCLUSIONS: MAPBE is accurate for predicting simultaneously several PI while being flexible in terms of integrating clinical constraints. Therefore, LSS based on MAPBE could help reduce the time of presence in hospital for individuals to be phenotyped.

Design optimisation for pharmacokinetic modeling of a cocktail of phenotyping drugs.

Our paper proposes a methodological strategy to select optimal sampling designs for phenotyping studies including a cocktail of drugs. A cocktail approach is of high interest to determine the simultaneous activity of enzymes responsible for drug metabolism and pharmacokinetics, therefore useful in anticipating drug-drug interactions and in personalized medicine. Phenotyping indexes, which are area under the concentration-time curves, can be derived from a few samples using nonlinear mixed effect models and maximum a posteriori estimation. Because of clinical constraints in phenotyping studies, the number of samples that can be collected in individuals is limited and the sampling times must be as flexible as possible. Therefore to optimize joint design for several drugs (i.e., to determine a compromise between informative times that best characterize each drug's kinetics), we proposed to use a compound optimality criterion based on the expected population Fisher information matrix in nonlinear mixed effect models. This criterion allows weighting different models, which might be useful to take into account the importance accorded to each target in a phenotyping test. We also computed windows around the optimal times based on recursive random sampling and Monte-Carlo simulation while maintaining a reasonable level of efficiency for parameter estimation. We illustrated this strategy for two drugs often included in phenotyping cocktails, midazolam (probe for CYP3A) and digoxin (P-glycoprotein), based on the data of a previous study, and were able to find a sparse and flexible design. The obtained design was evaluated by clinical trial simulations and shown to be efficient for the estimation of population and individual parameters.

Mathematical modeling of bacterial kinetics to predict the impact of antibiotic colonic exposure and treatment duration on the amount of resistant enterobacteria excreted.

Fecal excretion of antibiotics and resistant bacteria in the environment are major public health threats associated with extensive farming and modern medical care. Innovative strategies that can reduce the intestinal antibiotic concentrations during treatments are in development. However, the effect of lower exposure on the amount of resistant enterobacteria excreted has not been quantified, making it difficult to anticipate the impact of these strategies. Here, we introduce a bacterial kinetic model to capture the complex relationships between drug exposure, loss of susceptible enterobacteria and growth of resistant strains in the feces of piglets receiving placebo, 1.5 or 15 mg/kg/day ciprofloxacin, a fluoroquinolone, for 5 days. The model could well describe the kinetics of drug susceptible and resistant enterobacteria observed during treatment, and up to 22 days after treatment cessation. Next, the model was used to predict the expected amount of resistant enterobacteria excreted over an average piglet's lifetime (150 days) when varying drug exposure and treatment duration. For the clinically relevant dose of 15 mg/kg/day for 5 days, the total amount of resistant enterobacteria excreted was predicted to be reduced by 75% and 98% when reducing treatment duration to 3 and 1 day treatment, respectively. Alternatively, for a fixed 5-days treatment, the level of resistance excreted could be reduced by 18%, 33%, 57.5% and 97% if 3, 5, 10 and 30 times lower levels of colonic drug concentrations were achieved, respectively. This characterization on in vivo data of the dynamics of resistance to antibiotics in the colonic flora could provide new insights into the mechanism of dissemination of resistance and can be used to design strategies aiming to reduce it.

A TeGM6-4r antigen-based immunochromatographic test (ICT) for animal trypanosomosis.

Animal trypanosomosis is a disease that is distributed worldwide which results in huge economic losses due to reduced animal productivity. Endemic regions are often located in the countryside where laboratory diagnosis is costly or inaccessible. The establishment of simple, effective, and accurate field tests is therefore of great interest to the farming and veterinary sectors. Our study aimed to develop a simple, rapid, and sensitive immunochromatographic test (ICT) for animal trypanosomosis utilizing the recombinant tandem repeat antigen TeGM6-4r, which is conserved amongst salivarian trypanosome species. In the specificity analysis, TeGM6-4r/ICT detected all of Trypanosoma evansi-positive controls from experimentally infected water buffaloes. As expected, uninfected controls tested negative. All sera samples collected from Tanzanian and Ugandan cattle that were Trypanosoma congolense- and/or Trypanosoma vivax-positive by microscopic examination of the buffy coat were found to be positive by the newly developed TeGM6-4r/ICT, which was comparable to results from TeGM6-4r/ELISA (kappa coefficient [κ] = 0.78). TeGM6/ICT also showed substantial agreement with ELISA using Trypanosoma brucei brucei (κ = 0.64) and T. congolense (κ = 0.72) crude antigen, suggesting the high potential of TeGM6-4r/ICT as a field diagnostic test, both for research purposes and on-site diagnosis of animal trypanosomosis.

Comparison between U-shaped structural deep learning models to detect landslide traces.

Landslides endanger lives and public infrastructure in mountainous areas. Monitoring landslide traces in real-time is difficult for scientists, sometimes costly and risky because of the harsh terrain and instability. Nowadays, modern technology may be able to identify landslide-prone locations and inform locals for hours or days when the weather worsens. This study aims to propose indicators to detect landslide traces on the fields and remote sensing images; build deep learning (DL) models to identify landslides from Sentinel-2 images automatically; and apply DL-trained models to detect this natural hazard in some particular areas of Vietnam. Nine DL models were trained based on three U-shaped architectures, including U-Net, U2-Net, and U-Net3+, and three options of input sizes. The multi-temporal Sentinel-2 images were chosen as input data for training all models. As a result, the U-Net, using an input image size of 32 × 32 and a performance of 97 % with a loss function of 0.01, can detect typical landslide traces in Vietnam. Meanwhile, the U-Net (64 × 64) can detect more considerable landslide traces. Based on multi-temporal remote sensing data, a different case study in Vietnam was chosen to see landslide traces over time based on the trained U-Net (32 × 32) model. The trained model allows mountain managers to track landslide occurrences during wet seasons. Thus, landslide incidents distant from residential areas may be discovered early to warn of flash floods.

Oral DAV131, a charcoal-based adsorbent, inhibits intestinal colonization by ß-lactam-resistant Klebsiella pneumoniae in cefotaxime-treated mice.

Antibiotics excreted into the intestinal tract, such as broad-spectrum cephalosporins, disrupt the indigenous microflora, affect colonization resistance (CR), and promote intestinal colonization by resistant bacteria. We tested whether oral DAV131, a charcoal-based adsorbent, would prevent colonization by a cefotaxime (CTX)-resistant Klebsiella pneumoniae strain (PUG-2) in CTX-treated mice. Mice received CTX, saline, CTX and DAV131, or saline and DAV131 for 3 days before oral challenge with 10(6) CFU of PUG-2. The fecal CTX concentrations and counts of PUG-2 were assayed. Fecal CTX disappeared when DAV131 was given concomitantly with CTX (P < 0.05), and the area under the curve of PUG-2 fecal density was significantly reduced (P < 0.01). In conclusion, reducing intestinal antibiotic exposure with DAV131 may reduce colonization by resistant strains during treatment compared to treatment with CTX only. This might open new possibilities for decreasing the impact of antibiotics on the intestinal microbiota during treatments.

A new species of Sterculia (Malvaceae) from Vietnam.

A new species of Sterculia from Vietnam - S.konchurangensis - is described, illustrated, and compared with the similar S.lanceolata. S.konchurangensis differs from S.lanceolata by the length of the petiole (7.0-9.5 vs. 25-35 mm), shape of the leaf blade (obovate or elliptic vs. elliptic, lanceolate or elliptic-lanceolate), length of the leaf blade (6-8 vs. 9-20 cm), and length of the calyx lobe (11-12.5 vs. 4-6 mm). A diagnostic key of the 22 Sterculia species occurring in Vietnam is also provided.