High Levels Of Susceptibility Research Articles

Development and characterization of type I interferon receptor knockout sheep: A model for viral immunology and reproductive signaling.

Type I interferons (IFNs) initiate immune responses to viral infections. Their effects are mediated by the type I IFN receptor, IFNAR, comprised of two subunits: IFNAR1 and IFNAR2. One or both chains of the sheep IFNAR were disrupted in fetal fibroblast lines using CRISPR/Cas9 and 12 lambs were produced by somatic cell nuclear transfer (SCNT). Quantitative reverse transcription-polymerase chain reaction for IFN-stimulated gene expression showed that IFNAR deficient sheep fail to respond to IFN-alpha. Furthermore, fibroblast cells from an IFNAR2 −/− fetus supported significantly higher levels of Zika virus (ZIKV) replication than wild-type fetal fibroblast cells. Although many lambs have died from SCNT related problems or infections, one fertile IFNAR2 −/− ram lived to over 4 years of age, remained healthy, and produced more than 80 offspring. Interestingly, ZIKV infection studies failed to demonstrate a high level of susceptibility. Presumably, these sheep compensated for a lack of type I IFN signaling using the type II, IFN-gamma and type III, IFN-lambda pathways. These sheep constitute a unique model for studying the pathogenesis of viral infection. Historical data supports the concept that ruminants utilize a novel type I IFN, IFN-tau, for pregnancy recognition. Consequently, IFNAR deficient ewes are likely to be infertile, making IFNAR knockout sheep a valuable model for studying pregnancy recognition. A breeding herd of 32 IFNAR2 +/− ewes, which are fertile, has been developed for production of IFNAR2 −/− sheep for both infection and reproduction studies.

Resistance of Streptococcus suis Isolates from the Czech Republic during 2018-2022.

A determination of susceptibility/resistance to antimicrobials via serotype was carried out in 506 field isolates of Streptococcus suis, originating from pig farms in the Czech Republic in the period 2018–2022. A very high level of susceptibility of S. suis isolates was found to amoxicillin, in combination with clavulanic acid and sulfamethoxazole potentiated with trimethoprim. None of the tested isolates were resistant to these antimicrobial substances. Only two isolates were found to be intermediately resistant to enrofloxacin in 2020. With regard to ceftiofur, one isolate was intermediately resistant in 2020 and 2022, and two isolates were intermediately resistant in 2018 and 2021. A low level of resistance was detected to ampicillin (0.6% in 2021) and to florfenicol (1.15% in 2019; 1.3% in 2022). With regard to penicillin, a medium level of resistance was detected in 2018 (10.6%), but a low level of resistance was found in the following years (7.0% in 2019; 3.1% in 2020; 3.3% in 2021; 3.9% in 2022). On the contrary, a high or very high level of resistance was found to tetracycline (66.0% in 2018; 65.1% in 2019; 44.35% in 2020; 46.4% in 2021; 54.0% in 2022). Using molecular and serological methods, serotype 7 (16.4%) was determined to be predominant among S. suis isolates, followed by serotypes 1/2, 2, 9, 4, 3, 1, 29, 16, and 31 (10.7%; 8.5%; 5.7%; 5.5%; 4.5%; 4.3%; 3.6%; 3.4%; 3.4%, respectively). Other serotypes were identified among the investigated strains either rarely (up to 10 cases) or not at all. A relatively high percentage of isolates were detected as non-typeable (79 isolates; 15.6%). Dependence of resistance upon serotype assignment could not be proven in all but serotype 31, wherein all isolates (n = 17) were resistant or intermediately resistant to clindamycin, tilmycosin, tulathromycin, and tetracycline. The resistance to clindamycin and tetracycline may be related to the high consumption of these antibiotics on pig farms at present or in previous years. Macrolides (tilmicosin and tulathromycin) and tiamulin are not suitable for the treatment of streptococcal infections, but are used on pig farms to treat respiratory infections caused by gram-negative bacteria, so they were included in the study.

Landslide susceptibility analysis based on a PSO-DBN prediction model in an earthquake-stricken area

In recent years, the major geological hazard of landslides has greatly impact normal human life. Deep belief networks (DBN) is a commonly used deep learning model, and the DBN hyperparameter determination problem is the key to its application. To improve the accuracy of regional landslide susceptibility prediction, this paper introduces the particle swarm algorithm (PSO) to determine the hyperparameters of the DBN; this is applied to regional landslide susceptibility prediction. Firstly, PSO is used to optimize the hyperparameters of the DBN and obtain a set of hyperparameters with the optimal fitness function. A landslide susceptibility prediction model based on PSO-DBN is then constructed and the K-fold cross-validation method is used to determine its accuracy. The model is applied to landslide susceptibility prediction in the most impacted area of the Wenchuan earthquake to analyze the model’s accuracy. Finally, model susceptibility analysis is performed. The research results show that the final optimal model accuracy of the PSO-DBN model is 95.52%, which is approximately 28.31% and 15.35% higher than that of the logistic regression (LR) model and the common DBN model, respectively. The Kappa coefficient is 0.883, which is higher than that of the LR model. Compared with the LR model and the common DBN model, Kappa coefficient is improved by approximately 0.542 and 0.269 respectively; the area under the curve (AUC) is 0.951, which is improved by approximately 0.201 and 0.080 compared to the LR model and the common DBN model. The susceptibility of the model to the inertia factor is low, the average change in model accuracy (when the inertia factor changes by 0.1) is approximately 0.1%, and the overall stability of the model is high. The landslide susceptibility level is very high. The area includes 219 landslide points, which account for 39.2% of total landslide points. In the area with a high level of landslide susceptibility are 191 landslide points, accounting for 34.2% of total landslide points. Together, the two contain approximately 73.4% of the landslide points. This indicates that the model prediction results agree well with the spatial distribution characteristics of the landslide.

Susceptibility networks reveal independent patterns of brain iron abnormalities in multiple sclerosis

Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels.We applied ICA to quantitative susceptibility maps for 170 individuals aged 9–81 years without neurological disease (“Healthy Aging” (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the “MS/HC” cohort).Two DGM-associated “susceptibility networks” identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. “robust”) across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects.These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.

Wheat Stem Rust Back in Europe: Diversity, Prevalence and Impact on Host Resistance.

The objective of this study was to investigate the re-emergence of a previously important crop pathogen in Europe, Puccinia graminis f.sp. tritici, causing wheat stem rust. The pathogen has been insignificant in Europe for more than 60 years, but since 2016 it has caused epidemics on both durum wheat and bread wheat in local areas in southern Europe, and additional outbreaks in Central- and West Europe. The prevalence of three distinct genotypes/races in many areas, Clade III-B (TTRTF), Clade IV-B (TKTTF) and Clade IV-F (TKKTF), suggested clonal reproduction and evolution by mutation within these. None of these genetic groups and races, which likely originated from exotic incursions, were detected in Europe prior to 2016. A fourth genetic group, Clade VIII, detected in Germany (2013), was observed in several years in Central- and East Europe. Tests of representative European wheat varieties with prevalent races revealed high level of susceptibility. In contrast, high diversity with respect to virulence and Simple Sequence Repeat (SSR) markers were detected in local populations on cereals and grasses in proximity to Berberis species in Spain and Sweden, indicating that the alternate host may return as functional component of the epidemiology of wheat stem rust in Europe. A geographically distant population from Omsk and Novosibirsk in western Siberia (Russia) also revealed high genetic diversity, but clearly different from current European populations. The presence of Sr31-virulence in multiple and highly diverse races in local populations in Spain and Siberia stress that virulence may emerge independently when large geographical areas and time spans are considered and that Sr31-virulence is not unique to Ug99. All isolates of the Spanish populations, collected from wheat, rye and grass species, were succesfully recovered on wheat, which underline the plasticity of host barriers within P. graminis. The study demonstrated successful alignment of two genotyping approaches and race phenotyping methodologies employed by different laboratories, which also allowed us to line up with previous European and international studies of wheat stem rust. Our results suggest new initiatives within disease surveillance, epidemiological research and resistance breeding to meet current and future challenges by wheat stem rust in Europe and beyond.

Clinical Laboratory Features of Microbes That Cause Neonatal Sepsis: An 8-Year Retrospective Study.

PurposeTo determine the distribution and antibiotic resistance patterns among pathogens that cause neonatal sepsis (NS) and to assess trends in antibiotic resistance.Patients and methodsA total of 864 patients with sepsis admitted to a neonatal intensive care unit (NICU) between 2014 and 2021 were enrolled. Data on neonate age and sex, pathogenic microbes, and antimicrobial susceptibility were collected. Univariate and linear regression analyses were performed to determine the differences and trends in antibiotic resistance rates.ResultsThe overall incidence rate of NS was 4.59 cases per 1000 live births. Of these cases, 255 (29.5%) were early-onset neonatal sepsis (EONS) and 609 (70.5%) were late-onset neonatal sepsis (LONS). A total of 670 (70.5%) gram-positive cocci and 171 (19.8%) gram-negative bacilli were identified. Among the 552 coagulase-negative Staphylococcus (CoNS) strains, the rate of oxacillin resistance was 70.6%, but no strains were resistant to linezolid, vancomycin or tigecycline. Among the antibiotic resistance patterns of the top three gram-negative pathogens, K. pneumoniae showed the highest rates of resistance, with resistance rates of 37.9% and 39.4% to ertapenem and imipenem, respectively, while E. coli and Enterobacter cloacae showed high levels of susceptibility to both. With regard to the trends in resistance among important pathogens, the rates of resistance to rifampicin, ciprofloxacin, levofloxacin, moxifloxacin and clindamycin by Staphylococcus epidermidis significantly decreased (p<0.05) during the study period. E. coli strains exhibited a significant increase in ceftriaxone resistance during the study period (p<0.05).ConclusionCoNS was the main microbe that caused NS, followed by E. coli. The bacterial isolates showed varying levels of resistance to the antimicrobial drugs tested. Thus, periodic surveillance in hospital settings to monitor changes in pathogens and antibiotic resistance is important.

Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

This paper aims to propose an efficient landslide susceptibility prediction (LSP) model based on the frequency ratio method and C5.0 Decision Tree (C5.0 DT) model. Taking Ruijin City as the study area, local landslide inventory and 12 environmental factors are collected. Then the nonlinear correlations between landslide inventory and environmental factors are established by frequency ratio (FR) method. Thirdly, the FR values of these environmental factors are taken as the input variables of the C5.0 DT/SVM models; landslide samples and non-landslide samples are set as the output variables with values of 1 and 0, respectively. The mathematical relationship between input variables and output variables is established by C5.0 DT/SVM models. Finally, the performance of LSP of both models is evaluated by the Area Under Receiver Operation Characteristic Curve (AUC). Results show that: 1) The landslide susceptibility mapping (LSM) of the C5.0 DT and the SVM models are similar on the whole, high and very high susceptibility levels are mainly distributed in the north and in the edge of the study area. 2) The AUC values of C5.0 DT and SVM are 0.886 and 0.819, respectively. Both models have good LSP accuracy, however, the overall LSP accuracy of the C5.0 DT model is better than that of SVM. 3) It is significant and reliable to carry out LSP based on frequency ratio method and C5.0 DT model.

Impact of Calonectria Diseases on Ornamental Horticulture: Diagnosis and Control Strategies.

Diseases caused by fungi in the genus Calonectria pose a significant threat to the ornamental horticulture industries in Europe and the United States. Calonectria spp. are particularly challenging pathogens to manage in ornamental production systems and the urban landscape for multiple reasons. A high level of species diversity and poorly resolved taxonomy in the genus makes proper pathogen identification and disease diagnosis a challenge, though recent molecular phylogenetic studies have made significant advances in species delimitation. From a disease management perspective, Calonectria spp. produce long-lived survival structures (microsclerotia) that contaminate nursery production systems and can survive multiple years in the absence of a susceptible plant host. Latent infection of plant material is poorly understood but likely contributes to long-distance dissemination of these fungal pathogens, including the clonal Calonectria spp. responsible for the global emergence of boxwood blight. Breeding for disease resistance represents a sustainable strategy for managing Calonectria diseases but is challenging due to the perennial nature of many ornamental plants and high levels of susceptibility in commercial cultivars. Ultimately, long-term sustainable management of Calonectria diseases will require an improved understanding of pathogen biology as well as integration of multiple disease management strategies.

Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest

Landslide risk assessment is important for risk management and loss–damage reduction. Herein, we assessed landslide susceptibility, hazard, and risk in the urban area of Yan’an City, which is located on the Loess Plateau of China and affected by many loess landslides. Based on 1841 slope units mapped in the study area, a random forest machine learning classifier and eight environmental factors influencing landslides were used for a landslide susceptibility assessment. In addition, differential synthetic aperture radar interferometry (DInSAR) technology was used for a hazard assessment. The accuracy of the random forest is 0.903 and the area under the receiver operating characteristics (ROC) curve is 0.96. The results show that 16% and 22% of the slope units were classified as being at very high and high-susceptibility levels for landslides, respectively, whereas 16% and 24% of the slope units were at very high and high-hazard levels for landslides, respectively. The landslide risk was obtained based on the susceptibility map and hazard map of landslides. The results show that only 26% of the slope units were located at very high and high-risk levels for landslides and these are mainly concentrated in urban centers. Such risk zones should be taken seriously and their dynamics must be monitored. Our landslide risk map is expected to provide information for planners to help them choose appropriate locations for development schemes and improve integrated geohazard mitigation in Yan’an City.

Assessment of gully erosion susceptibility using different DEM-derived topographic factors in the black soil region of Northeast China

As a primary sediment source, gully erosion leads to severe land degradation and poses a threat to food and ecological security. Therefore, identification of susceptible areas is critical to the prevention and control of gully erosion. This study aimed to identify areas prone to gully erosion using four machine learning methods with derived topographic attributes. Eight topographic attributes (elevation, slope aspect, slope degree, catchment area, plan curvature, profile curvature, stream power index, and topographic wetness index) were derived as feature variables controlling gully occurrence from digital elevation models with four different pixel sizes (5.0 m, 12.5 m, 20.0 m, and 30.0 m). A gully inventory map of a small agricultural catchment in Heilongjiang, China, was prepared through a combination of field surveys and satellite imagery. Each topographic attribute dataset was randomly divided into two portions of 70% and 30% for calibrating and validating four machine learning methods, namely random forest (RF), support vector machines (SVM), artificial neural network (ANN), and generalized linear models (GLM). Accuracy (ACC), area under the receiver operating characteristic curve (AUC), root mean square error (RMSE), and mean absolute error (MAE) were calculated to assess the performance of the four machine learning methods in predicting spatial distribution of gully erosion susceptibility (GES). The results suggested that the selected topographic attributes were capable of predicting GES in the study catchment area. A pixel size of 20.0 m was optimal for all four machine learning methods. The RF method described the spatial relationship between the feature variables and gully occurrence with the greatest accuracy, as it returned the highest values of ACC (0.917) and AUC (0.905) at a 20.0 m resolution. The RF was also the least sensitive to resolutions, followed by SVM (ACC = 0.781–0.891, AUC = 0.724–0.861) and ANN (ACC = 0.744–0.808, AUC = 0.649–0.847). GLM performed poorly in this study (ACC = 0.693–0.757, AUC = 0.608–0.703). Based on the spatial distribution of GES determined using the optimal method (RF + pixel size of 20.0 m), 16% of the study area has very high level susceptibility classes, whereas areas with high, moderate, and low levels of susceptibility make up approximately 24%, 30%, and 31% of the study area, respectively. Our results demonstrate that GES assessment with machine learning methods can successfully identify areas prone to gully erosion, providing reference information for future soil conservation plans and land management. In addition, pixel size (resolution) is the key consideration when preparing suitable datasets of feature variables for GES assessment.

Analysis of gully erosion susceptibility and spatial modelling using a GIS-based approach

Gully erosion, a dominant limiting factor in promoting ecosystem services and functions, is caused by a wide range of geo-environmental factors. However, limited information is available on the gully erosion mechanisms at a regional scale. Herein, susceptibility of a particular type of large-scale gullies across southern China were analyzed based on the 2005 National Survey on Soil and Water Loss. Specifically, frequency ratio model was used to determine the thresholds of 18 geo-environmental factors, including topography (elevation, slope gradient, slope aspect, slope length, hypsometric integral), distance from primary and tertiary rivers, climate (average annual temperature, precipitation, and rainfall erosivity), lithology, soil texture, soil moisture, vegetation index (NDVI, FVC, and EVI), as well as human activity (LULC and population density), followed by evaluating their relative importance by Boruta algorithm, and modelling the gully susceptibility modelling by random forest. The gully susceptibility was showed to be determined by comprehensive effect of all the 18 factors, with the top three important factors including average annual precipitation (9.97%), rainfall erosivity (9.60%), and distance from primary rivers (9.43%), whose thresholds were 1701 ∼ 1872 mm, 5967 ∼ 15676 MJ·mm·(ha·h·a)-1, and 20000 ∼ 50000 m, respectively. Moreover, the random forest model with its AUC value ranging from 0.96 to 0.99 performed excellently in the spatial modelling of gully susceptibility. Collectively, the very low, low, moderate, and high susceptibility to these gullies occupied 65.30%, 21.36, 10.61% and 2.73% of southern China, in which the moderate and high susceptibility level were mainly clustered in and around Guangdong Province.

Incidence of Coffee Leaf Rust in Vietnam, Possible Original Sources and Subsequent Pathways of Migration.

This research focused on the incidence and population genetics of coffee leaf rust (CLR) fungus, Hemileia vastatrix, to estimate the possible original source(s) and subsequent migration pathways of wind-borne and human-aided spores in three main coffee production regions (Northwest, Central Highlands, and Southeast) in Vietnam. In southern Vietnam (Central Highlands and Southeast), Coffea canephora covers the majority area, while Catimor lines of C. arabica accounts for 95% of the coffee plantations in northwestern Vietnam. Field surveys conducted at eighty-five plantations, show coffee leaf samples infected by the rust fungus across forty-one plantations. Catimor varieties exhibited high levels of susceptibility with severe rust symptoms, while robusta varieties had varying degrees of susceptibility. We analyzed 863−869 base pairs of internal transcribed spacer (ITS) region from 83 samples (41 sequences from Vietnam, 2 from Thailand, and the remaining 40 from American countries); and fifty-two haplotypes consisting of 123 polymorphic sites were detected. Although the analysis of molecular variance (AMOVA) indicates significant genetic differentiation in the H. vastatrix populations in Vietnam, there was no clear genetic structure with respect to the three geographic areas surveyed. Based on the haplotype network, NeighborNet analysis, and geographical distribution patterns of the haplotypes, five haplotypes were identified as early established, from which most other haplotypes in Vietnam were derived. The early established haplotypes were found in the highest frequency in Northwest Vietnam. This finding corresponds to the earliest record of CLR in Vietnam. The phylogenetic network analysis also illustrated that H. vastatrix had expanded from the northwest to southern Vietnam. Pairwise genetic distance analysis and the geophylogenetic tree also suggests that CLR was first established in the Northwest. In addition, some scattered individuals on the principal coordinate analysis (PCoA) diagram and several separated haplotypes in the phylogenetic networks indicated that other branches of CLR in Vietnam were initiated in the Central Highlands. Hemileia vastatrix from these branches have been spreading in southern Vietnam.

Association Mapping for Common Bunt Resistance in Wheat Landraces and Cultivars

Common bunt is a seed borne disease of wheat whose importance is likely to increase due to the growing organic seed market, which, in addition to seed phytosanitary measures, relies on genetic resistances towards the disease. Genome wide association studies in wheat have been proven to be a useful tool in the detection of genetic polymorphisms underlying phenotypic trait variation in wheat. Here 248 wheat landraces and cultivars representing 130 years of breeding history were screened for two years in the field for their resistance reactions towards common bunt. The majority of lines exhibited high levels of susceptibility towards common bunt, while 25 accessions had less than 10% infection. Using Diversity Array Technology (DArT) markers for genotyping and correcting for population stratification by using a compressed mixed linear model, we identified two significant marker trait associations (MTA) for common bunt resistance, designated QCbt.cph-2B and QCbt.cph-7A, located on wheat chromosomes 2B and 7A, respectively. This shows that genome wide association studies (GWAS) are applicable in the search for genetic polymorphisms for resistance towards less studied plant diseases such as common bunt in the context of an under representation of resistant lines.

Socioeconomic influence on community preparedness in the city of land disaster in Sukanagara Subdistrict, Cianjur District

This research is based on Sukanagara District which has a high level of landslide susceptibility. This study aims to analyze the effect of education level, age, employment status and income, and housing conditions on community preparedness in dealing with landslides in Sukanagara District, Cianjur Regency. This research is a descriptive research with a quantitative approach. Based on the results of the study, the variables that affect the level of readiness in education. Meanwhile, age, employment status, and income, as well as living conditions had no effect. Suggestions for research related to efforts to increase community preparedness are to increase the level of public education and provide supporting facilities for preparedness by the government.

Antibiogram Profile of Enteric Pathogens Isolated from Fomites in Cross River University of Technology Medical Centre, Calabar, Nigeria

Background: Healthcare environments are considered as potential reservoirs for pathogenic microorganisms especially those responsible for nosocomial infections. Such microorganisms often present with varying degrees of drugs resistance. This study was aimed at evaluating the Cross River University of Technology (CRUTECH) Medical Center Environment for the presence of pathogenic bacterial contaminants and antibiotics susceptibility profile of such isolates.&#x0D; Materials and Methodology: A total of 72 swab samples were collected from nineteen frequently touched hospital surfaces and processed using the standard bacteriological procedures. The emergent bacterial colonies were identified using phenotypic and biochemical tests. Antibiotic susceptibility testing of the presumptively identified isolates was carried out using Kirby-Bauer’s method.&#x0D; Results: Out of 72 swab samples collected, 44 (61.1%) were positive for Enteric bacterial pathogens. The mean viable count ranged from 1.8 x 106 Cfu/cm2 (weighing scales) to 2.41 x 107 Cfu/cm2 (wash sinks). The most prevalent isolate was E. coli (48 of 127, 37.8%) followed by Klebseilla sp (27 of 127, 21.3%), Salmonella sp (19 of 127, 14.9%), Proteus sp (12 of 127, 9.4%), Citrobacter sp (11 of 127, 8.7%), Enterobacter sp (7 of 127, 5.5%) while Shigella sp (3 of 127, 2.4%) was least isolated. Isolates demonstrated high level of susceptibility to Norfloxacin 124 (97.6%), Imipenem 116 (91.3%) and Chloramphenicol 105 (82.7%). Resistance to Erythromycin was 95 (74.8%), Gentamycin 84 (66.1%) and Amikacin 82 (64.6%).&#x0D; Conclusion: Contamination of healthcare surfaces by multi-drugs resistant pathogens is a potential risk, especially to hospitalized patients and health care workers. Thus, it is therefore imperative that appropriate hygienic measures be implemented to suppress any potential microbial cross-contamination.

Geomorphological Spatial Model of Risk Analysis for Natural Disasters in Heritage Railway Tunnel

Flood event in Ijo Tunnel led to disruption to schedules and train routes. The objectives of this research are to determine the susceptibility, vulnerability, capacity, and risk, to analyze the geophysical and geotechnical condition, and to compile the strategy of disaster mitigation of Ijo Tunnel. A spatial approach based on landform analysis was performed to establish the disaster components. Geophysical and geotechnical investigation was conducted to describe the condition of the affected region. A field survey was accomplished to collect data and to study supporting works of literature. The descriptive analysis was completed to determine strategy and recommendation in flood and potential landslide mitigation. The result depicts that Ijo Tunnel holds multi-disaster risk such as floods and landslides. The occurrence of floods is induced by extreme rainfall and inability of drainage channels to retain water. The landslide potential is influenced by morphological factors and composting materials in Ijo Tunnel’s region. The existence of spring fracture on the north slope also contributes to increasing the slopes’ landslide potential. The flood mitigation measure can be carried out by building a well-prepared drainage system and watershed and surface runoff management. Areas with a high level of landslide susceptibility can be overcome by building permanent retaining walls and pipes on slopes to reduce the soil saturation level.

Hydrocarbon Degrading Potentials and Antibiotic Susceptibility Test of Bacteria Isolated from Amended Soil in Ilorin

This study used bacteria from hydrocarbon-contaminated soil that had been treated. Organic amendments included cassava peels, plantain peels, eggshells, and poultry droppings, while inorganic amendments included NPK and Urea. In this study, three (3) bacteria were characterized and dealt with: Bacillus amyloliquefaciens, Bacillus proteolyticus, and Providencia rettgeri. To determine their biodegradative capabilities, these isolates were tested on a variety of hydrocarbons. In addition, the antibacterial activity of the bacteria was tested in vitro against extracts of Moringa oleifera, Vernonia amygdalina (Bitter leaf), Ocimum gratissimum (Scent lLeaf), and Telifairia occidentalis (Ugwu) at different concentrations (20%, 13%, 10%, 7%, and 3%). Using the maceration procedure, the plant materials were extracted with ethanol and methanol. The bacteria were resistant to the various concentrations of plant extracts, according to the results. The methanolic and ethanolic extracts of Vernonia amygdalina (bitter leaf) had the maximum inhibition zone, measuring 6.00 ± 0.00 mm. Furthermore, all isolates were resistant to crude oil, engine oil, heptane, and benzene in antimicrobial tests on hydrocarbons, with Bacillus amyloliquefaciens having the highest level of susceptibility (18 mm) to pyridine. However, the bacteria all demonstrated broad spectrum susceptibility to the commercial antibiotics used as a control, with the exception of Providencia rettgeri, which proved resistant (5 mm) to Ampiclox (APX), Zinnacef (Z) and Amoxacillin (AM).

LANDSLIDE SUSCEPTIBILITY ZONATION, KABILA BONE SUB-DISTRICT, BONE BOLANGO DISTRICT, GORONTALO PROVINCE

The research area is administratively located in the Kabila Bone Sub-district, Bone Bolango District, Gorontalo Province which has an area of 143,51 km 2 . The purpose of the research is determine landslide susceptibility zonation of Kabila Bone Sub-district , Bone Bolango District, Gorontalo Province. The method used in this research is geological survey method and analysis of geographic information system. The parameters used refers to Peraturan Menteri Pekerjaan Umum No. 22/PRT/M/2007, which includes of slope, soil condition, lithology, rainfall, slope water system, seismicity, and vegetation. The result of research show that Kabila Bone Sub-district consists of a zone with a low level of susceptibility which has an area of 1258,76 ha, a zone with a medium level of susceptibility which has an area of 4026,90 ha, and a zone with a high level of susceptibility which has an area of 502,03 ha. Zones that have a high level of susceptibility are distributed in several villages, namely Botubarani Village with an area of 117,92 ha, Huangobotu Village with an area of 89,35 ha, Bintalahe Village with an area of 64,22 ha, Oluhuta Village with an area of 119,98 ha, and Olele Village with an are of 104,01 ha.

LANDSLIDE SUSCEPTIBILITY MAPPING IN BERASTAGI DISTRICT KARO REGENCY BASED ON GEOGRAPHIC INFORMATION SYSTEMS

This study aims to describe the spatial distribution of landslide prone areas in Berastagi District, Karo Regency with a Geographic Information System approach. Determination of the level of landslide susceptibility is obtained based on the method of scoring and weighting and overlaying of parameters including slope, rainfall, land use, soil type, rock type, and landform. The results showed that there were 4 levels of landslide susceptibility in Berastagi District, Karo Regency, namely low level of susceptibility 1,036.76 Ha (33.16%), medium level of susceptibility 772.02 Ha (24.69%), high level of susceptibility 1,055 .53 Ha (33.76 %), and very high level of susceptibility 262.13 Ha (8.38 %). The low level of susceptibility is dominated by Guru Singa Village (10.83 %), Raya Village (8.66 %), and Rumah Berastagi Village (3.72%). The medium and high level of susceptibility were dominated by Sempajaya Village at 4.51% and 3.68%. The very high level of susceptibility to landslides is dominated by Doulu Village, which is 17.40%. Meanwhile, the other 5 (five) villages have varying levels of susceptibility to landslides. Thus, 33.16% of the Berastagi District area is still safe from landslides, while the remaining 66.83% is prone to landslides.

Probiotic Characterization of Indigenous Kocuria flava Y4 Strain Isolated from Dioscorea villosa Leaves.

This aim of the study was to isolate and screen potential probiotics from Dioscorea villosa leaves. The potential isolate Y4 was obtained from the Dioscorea villosa leaves, and its ability to grow in a medium containing high NaCl concentrations (2-10%) indicated its negative hemolytic activity. Furthermore, Y4 demonstrated inhibitory activity against human pathogens, such as Klebsiella pneumonia, Staphylococcus aureus, Citrobacter koseri, and Vibrio cholerae, as well as towards a plant pathogen isolate OR-2 (obtained from Citrus sinensis). Some biologically important functional groups of Y4 metabolites, such as sulfoxide; aliphatic ether; 1, 2, 3-trisubstituted, tertiary alcohol: vinyl ether; aromatic amine; carboxylic acid; nitro compound; alkene mono-substituted; and alcohol, were identified through FTIR analysis. The 16S rRNA sequencing and subsequent phylogenetic tree analysis indicated that Y4 and OR-2 are the closest neighbors to Kocuria flava (GenBank accession no. MT773277) and Pantoea dispersa (GenBank accession no. MT766308), respectively. The potential isolate Y4 was found to exhibit adhesion, auto-aggregation, co-aggregation, and weak biofilm activity. It also exhibited a high level of antimicrobial activity and antibiotic susceptibility. The safety of K. flava Y4 isolate, which is proposed to be a probiotic, was evaluated through acute oral toxicity test and biogenic amine production test. Moreover, the preservation potential of isolate Y4 was assessed through application on fruits under different temperatures. Thus, our results confirmed that Kocuria flava Y4 is a prospective probiotic and could also be used for the preservation of fruits.