Detection of dynorphin 1-17 biotransformation fragments in human nasal polyps by UPLC-QTOF-MS.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a persistent inflammation of the sinonasal mucosa. CRSwNP treatments are associated with inconsistent efficacy and recurrence of symptoms. Dynorphin 1-17 (DYN 1-17) and its fragments have been shown to modulate the immune response in various inflammatory conditions. This study aimed to investigate the effect of different pH and degrees of inflammation on DYN 1-17 metabolism in human CRSwNP tissues. DYN 1-17 was incubated with grade 3 and grade 4 inflamed tissues of CRSwNP patients at pH 5.5 and pH 7.4 over a range of incubation periods. The resulting fragments were identified using an ultra-performance liquid chromatography (UPLC) system coupled to quadrupole-time of flight (QTOF) mass spectrometry based on their accurate mass. The rate of DYN 1-17 fragmentation was slower at pH 5.5 in comparison to pH 7.4. The extent and rate of metabolism of DYN 1-17 were much lower in grade 3 inflamed tissue (31-32 fragments) than in grade 4 (34-41 fragments). N-Terminal fragments (DYN 1-15, 1-11, 1-10, and 1-6) were metabolized slower at pH 5.5 as compared to pH 7.4. DYN 1-12, 1-8, 2-10, 4-10, 5-10, and 8-14 were only observed under the inflammatory pH while DYN 5-17 and 6-17 were only identified upon incubation with grade 4 CRSwNP tissues. DYN 1-17 metabolism was significantly affected by the pH level and the severity of the inflammation of CRSwNP tissues, indicating the potential roles of DYN 1-17 and its fragments in modulating the inflammatory response and their avenue as therapeutics in future studies.

An integrated investigation of hydrocarbon pollution in Ahoada area, Niger Delta Region, Nigeria.

This study investigates hydrocarbon pollution in the Ahoada community of the Niger Delta region of Nigeria. The study uses a geographic information system (GIS) for mapping oil spill hotspots in the region. The resistivity method was used to delineate the extent of hydrocarbon pollution to a depth of 19.7 m in the Ahoada area of the region. Three categories of soil samples, impacted soil (IMS), remediated soil (RS), and control soil (CS), were collected and analyzed for the presence of BTEX, PAH, TPH, TOC, and TOG. The concentrations of the samples from the IMS and RS were compared to that of the CS to determine the extent of pollution. The GIS mapping shows that the most polluted areas in the Niger Delta Region are Rivers, Bayelsa, and Delta states. Results of the geophysical images revealed contaminants' presence to depths beyond 20 m at some locations in the study area. The highest depth of contaminant travel was at Ukperede. Soil samples' analysis showed that the range of concentrations of TPH in IMS at Oshie was 17.27-58.36 mg/kg; RS was 11.73-50.78 mg/kg which were higher than the concentrations of 0.68 mg/kg in the CS. PAHs are more prevalent in Ukperede, ranging from 54.56 to 77.54 mg/kg. BTEX concentrations ranged from 0.02 to 0.38 mg/kg for IMP and 0.01-2.7 mg/kg for RS against a CS value of 0.01 mg/kg. The study revealed that there are characteristically high resistivity values in the samples which were corroborated by the findings from the resistivity survey. TOC was found to be higher in the IMS and RS than in the CS, demonstrating that a significant quantity of the hydrocarbon has undergone appreciable decomposition.

Importance of green roof criteria for residential and governmental buildings: a multi-criteria decision analysis.

Megacities recently are experiencing a shortage of green spaces basically due to the rapid growth of urbanization and increasing demand for different building types. Consideration of sustainable urban development is essential since the expansion of city facilities should be in line with social, economic, and environmental aspects. In this regard, green roof technology has been recommended as an effective solution for the growth of green spaces per capita and improving sustainability means of urban developments due to its diverse advantages. This study thus aimed at prioritizing sustainability indicators and relative sub-criteria of adopting green roof technology for residential and governmental buildings in the city of Mashhad, Iran, which has a dry climate. For this purpose, thirteen sub-criteria, which are extracted from the existing literature, are classified into three main sustainability indicators (environmental, economic, and social). Also, the best-worth method (BWM) as a multi-criteria decision-making technique was implemented to prioritize indicators and sub-criteria by analyzing the expert's opinion. The results indicated that respective economic and environmental indicators attract the highest priority in residential and governmental buildings. Additionally, the most important sub-criteria in environmental, economic, and social groups are air quality, roof longevity, and public health in both building types, respectively. However, when all criteria were considered, the respective highest priorities belong to roof longevity and air quality in residential and governmental buildings, while biodiversity conservation is the least important one in both building types. The results of this research can be beneficial in other cities with similar economic and climate conditions.

A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran.

We mapped landslide susceptibility in Kamyaran city of Kurdistan Province, Iran, using a robust deep-learning (DP) model based on a combination of extreme learning machine (ELM), deep belief network (DBN), back propagation (BP), and genetic algorithm (GA). A total of 118 landslide locations were recorded and divided in the training and testing datasets. We selected 25 conditioning factors, and of these, we specified the most important ones by an information gain ratio (IGR) technique. We assessed the performance of the DP model using statistical measures including sensitivity, specificity, accuracy, F1-measure, and area under-the-receiver operating characteristic curve (AUC). Three benchmark algorithms, i.e., support vector machine (SVM), REPTree, and NBTree, were used to check the applicability of the proposed model. The results by IGR concluded that of the 25 conditioning factors, only 16 factors were important for our modeling procedure, and of these, distance to road, road density, lithology and land use were the four most significant factors. Results based on the testing dataset revealed that the DP model had the highest accuracy (0.926) of the compared algorithms, followed by NBTree (0.917), REPTree (0.903), and SVM (0.894). The landslide susceptibility maps prepared from the DP model with AUC = 0.870 performed the best. We consider the DP model a suitable tool for landslide susceptibility mapping.

Prevalence and severity of Bertolottis syndrome in Malaysia: A common under diagnosis.

Introduction: Bertolotti's syndrome (BS) is defined as the presence of low back pain (LBP), radiculopathy or both with a dysplastic transverse process (TP) of the fifth lumbar vertebra that is articulated or fused with the sacral base or iliac crest. This study aimed to investigate the prevalence and severity of BS to promote awareness of this disease. Method: A retrospective review of anteroposterior lumbosacral plain radiographs was conducted between 1 January and 31 December 2017. Patients were recruited via systematic randomised sampling and were then interviewed and examined. The severity of BS was measured objectively using the numerical pain rating scale (NPRS) and Oswestry disability questionnaire (ODQ). Data were analysed using IBM SPSS for Windows version 22. Results: The prevalence of BS was 9.6% (16/166). Age significantly affected the severity of BS. The older and younger groups had a mean ODQ score of 42.86% and 24.08%, respectively (P=0.006). There was no significant relationship found between the prevalence of BS and age (P=0.126). Only one patient was diagnosed with BS during medical consultation. The mean NPRS score was 5.5. The majority of the BS cases were of moderate severity (43.8%), followed by those of minimal severity (31.2%) and severe disability (25%). Conclusion: Early diagnosis of BS and orthopaedic referral are crucial to halt its progression. BS should be considered in patients presenting with LBP during assessments of lumbosacral radiographs.

Assessment of DSM Based on Radiometric Transformation of UAV Data.

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

Short-Term Outcome of Early Primary Total Knee Arthroplasty for Fractures Around the Knee in the Elderly Population: The Experience of a Secondary Healthcare Centre in Malaysia.

BACKGROUND: The management of fractures around the knee in the elderly population can be challenging due to the complexity of the patients and the fracture characteristics. In this study, we aimed to investigate the short-term outcome of elderly patients who had fractures around the knee and who were treated with primary total knee arthroplasty. The study included patients who were at least 70 years old with poor bone quality and who presented with a fracture around the knee that would be difficult to treat with open reduction and internal fixation (ORIF) as well as patients who were at least 55 years old presenting with severe concomitant knee osteoarthritis. METHODS: This is a cross-sectional study in which all the elderly patients who underwent early primary total knee replacement due to trauma around the knee at the Segamat Hospital between January 2015 and June 2019 were identified. Data were collected from clinical and operative notes. The clinical outcomes of these patients were evaluated by the range of motion of the knee and the Knee Society Score (KSS). RESULTS: Ten patients were identified to have undergone this procedure. Six patients sustained supracondylar femur fractures, two patients had tibial plateau fractures and two patients had concurrent supracondylar femur and tibial plateau fractures. The mean follow-up duration was 22.3 ± 13.9 months, the mean knee score was 87.7 ± 10.0 and the mean functional knee score was 56 ± 41.9. CONCLUSION: In this cohort, good short-term outcomes close to pre-fracture condition was noted in patients who did not suffer from any complications during the post-operative period. Two patients who had surgical site infection had lower functional knee scores. Another two patients with lower knee scores experienced surgical site infection of the distal tibia and contralateral fixed flexion deformity of the knee. Early primary total knee replacement remains a viable option in treating fractures around the knee in the elderly. Infection, which in this study affected 20% of the patients, is the main deterring factor in performing this procedure.

Evaluating Water Level Changes at Different Tidal Phases Using UAV Photogrammetry and GNSS Vertical Data.

Evaluating water level changes at intertidal zones is complicated because of dynamic tidal inundation. However, water level changes during different tidal phases could be evaluated using a digital surface model (DSM) captured by unmanned aerial vehicle (UAV) with higher vertical accuracy provided by a Global Navigation Satellite System (GNSS). Image acquisition using a multirotor UAV and vertical data collection from GNSS survey were conducted at Kilim River, Langkawi Island, Kedah, Malaysia during two different tidal phases, at high and low tides. Using the Structure from Motion (SFM) algorithm, a DSM and orthomosaics were produced as the main sources of data analysis. GNSS provided horizontal and vertical geo-referencing for both the DSM and orthomosaics during post-processing after field observation at the study area. The DSM vertical accuracy against the tidal data from a tide gauge was about 12.6 cm (0.126 m) for high tide and 34.5 cm (0.345 m) for low tide. Hence, the vertical accuracy of the DSM height is still within a tolerance of ±0.5 m (with GNSS positioning data). These results open new opportunities to explore more validation methods for water level changes using various aerial platforms besides Light Detection and Ranging (LiDAR) and tidal data in the future.

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping.

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.

Coordinate systems integration for development of malaysian craniofacial database.

This study presents a data registration method for craniofacial spatial data of different modalities. The data consists of three dimensional (3D) vector and raster data models. The data is stored in object relational database. The data capture devices are Laser scanner, CT (Computed Tomography) scan and CR (Close Range) Photogrammetry. The objective of the registration is to transform the data from various coordinate systems into a single 3-D Cartesian coordinate system. The standard error of the registration obtained from multimodal imaging devices using 3D affine transformation is in the ranged of 1-2 mm. This study is a step forward for storing the spatial craniofacial data in one reference system in database.