Bi-allelic loss-of-function variants in PPFIBP1 cause a neurodevelopmental disorder with microcephaly, epilepsy, and periventricular calcifications.

PPFIBP1 encodes for the liprin-ß1 protein, which has been shown to play a role in neuronal outgrowth and synapse formation in Drosophila melanogaster. By exome and genome sequencing, we detected nine ultra-rare homozygous loss-of-function variants in 16 individuals from 12 unrelated families. The individuals presented with moderate to profound developmental delay, often refractory early-onset epilepsy, and progressive microcephaly. Further common clinical findings included muscular hyper- and hypotonia, spasticity, failure to thrive and short stature, feeding difficulties, impaired vision, and congenital heart defects. Neuroimaging revealed abnormalities of brain morphology with leukoencephalopathy, ventriculomegaly, cortical abnormalities, and intracranial periventricular calcifications as major features. In a fetus with intracranial calcifications, we identified a rare homozygous missense variant that by structural analysis was predicted to disturb the topology of the SAM domain region that is essential for protein-protein interaction. For further insight into the effects of PPFIBP1 loss of function, we performed automated behavioral phenotyping of a Caenorhabditis elegans PPFIBP1/hlb-1 knockout model, which revealed defects in spontaneous and light-induced behavior and confirmed resistance to the acetylcholinesterase inhibitor aldicarb, suggesting a defect in the neuronal presynaptic zone. In conclusion, we establish bi-allelic loss-of-function variants in PPFIBP1 as a cause of an autosomal recessive severe neurodevelopmental disorder with early-onset epilepsy, microcephaly, and periventricular calcifications.

Deep Brain Stimulation of the Globus Pallidus Internus in a Child with Refractory Dystonia due to L2-Hydroxyglutaric Aciduria.

INTRODUCTION: L-2-hydroxyglutaric aciduria (L2HGA) is a rare neurometabolic disorder marked by progressive and debilitating psychomotor deficits. Here, we report the first patient with L2HGA-related refractory dystonia that was managed with deep brain stimulation to the bilateral globus pallidus internus (GPi-DBS). CASE PRESENTATION: We present a 17-year-old female with progressive decline in cognitive function, motor skills, and language ability which significantly impaired activities of daily living. Neurological exam revealed generalized dystonia, significant choreic movements in the upper extremities, slurred speech, bilateral dysmetria, and a wide-based gait. Brisk deep tendon reflexes, clonus, and bilateral Babinski signs were present. Urine 2-OH-glutaric acid level was significantly elevated. Brain MRI showed extensive supratentorial subcortical white matter signal abnormalities predominantly involving the U fibers and bilateral basal ganglia. Genetic testing identified a homozygous pathogenic mutation in the L-2-hydroxyglutarate dehydrogenase gene c. 164G>A (p. Gly55Asp). Following minimal response to pharmacotherapy, GPi-DBS was performed. Significant increases in mobility and decrease in dystonia were observed at 3 weeks, 6 months, and 12 months postoperatively. CONCLUSION: This is the first utilization of DBS as treatment for L2HGA-related dystonia. The resulting significant improvements indicate that pallidal neuromodulation may be a viable option for pharmaco-resistant cases, and possibly in other secondary metabolic dystonias.

Flexible cyclic-olefin with enhanced dipolar relaxation for harsh condition electrification.

Flexible large bandgap dielectric materials exhibiting ultra-fast charging-discharging rates are key components for electrification under extremely high electric fields. A polyoxafluoronorbornene (m-POFNB) with fused five-membered rings separated by alkenes and flexible single bonds as the backbone, rather than conjugated aromatic structure typically for conventional high-temperature polymers, is designed to achieve simultaneously high thermal stability and large bandgap. In addition, an asymmetrically fluorinated aromatic pendant group extended from the fused bicyclic structure of the backbone imparts m-POFNB with enhanced dipolar relaxation and thus high dielectric constant without sacrificing the bandgap. m-POFNB thereby exhibits an unprecedentedly high discharged energy density of 7.44 J/cm3 and high efficiency at 150 °C. This work points to a strategy to break the paradox of mutually exclusive constraints between bandgap, dielectric constant, and thermal stability in the design of all-organic polymer dielectrics for harsh condition electrifications.

Endoscopic balloon dilatation for pediatric subglottic stenosis: a meta-analysis of successful outcomes.

OBJECTIVES: The primary goal of this study was to systematically identify all relevant published articles on the use of primary endoscopic balloon dilation for the management of pediatric patients with subglottic stenosis, critically assess the technique's success, and determine which patients are the best candidates for the procedure. METHODOLOGY: This was a systematic review and meta-analysis that aimed to investigate the use and success rate of EBD for treating pediatric SGS. An electronic systematic literature search of three major databases, PubMed, EBSCO, and Web of Science&MEDLINE through Clarivate, was conducted to include the eligible articles. RESULTS: A total of 14 unique studies were included in the final analysis, with 473 cases of pediatric SGS. The pooled success rate of EBD in treatment of pediatric SGS was 76% (k = 14 studies, 95% confidence interval [CI] = 0.65-0.86, P < 0.001, Q test for heterogeneity = 0.03, P < .001, I2 = 91%). CONCLUSIONS: We reported a high success rate of EBD in treating pediatric SGS. The reported complications were uncommon, although they can be serious and life threatening. The intensity of SGS may be related to the likelihood of therapy failure.

Gas Chromatography-Mass Spectrometry Chemical Profiling of Commiphora myrrha Resin Extracts and Evaluation of Larvicidal, Antioxidant, and Cytotoxic Activities.

Plant extracts and essential oils can be alternative environmentally friendly agents to combat pathogenic microbes and malaria vectors. Myrrh is an aromatic oligum resin that is extracted from the stem of Commiphora spp. It is used in medicine as an insecticide, cytotoxic, and aromatic. The current study assessed the effect of Commiphora myrrha resin extracts on the biological potency of the third larval stage of Aedes aegypti, as well as its antioxidant and cytotoxic properties against two types of tumor cells (HepG-2 and Hela cell lines). It also used GC-MS to determine the chemical composition of the C. myrrha resin extracts. Fifty components from the extracted plant were tentatively identified using the GC-MS method, with curzerene (33.57%) typically listed as the primary ingredient, but other compounds also make up a significant portion of the mixture, including 1-Methoxy-3,4,5,7-tetramethylnaphthalene (15.50%), ß-Elemene (5.80%), 2-Methoxyfuranodiene (5.42%), 2-Isopropyl-4,7-Dimethyl-1-Naphthol (4.71%), and germacrene B (4.35%). The resin extracts obtained from C. myrrha exhibited significant efficacy in DPPH antioxidant activity, as evidenced by an IC50 value of 26.86 mg/L and a radical scavenging activity percentage of 75.06%. The 50% methanol extract derived from C. myrrha resins exhibited heightened potential for anticancer activity. It demonstrated substantial cytotoxicity against HepG-2 and Hela cells, with IC50 values of 39.73 and 29.41 µg mL-1, respectively. Notably, the extract showed non-cytotoxic activity against WI-38 normal cells, with an IC50 value exceeding 100 µg mL-1. Moreover, the selectivity index for HepG-2 cancer cells (2.52) was lower compared to Hela cancer cells (3.40). Additionally, MeOH resin extracts were more efficient against the different growth stages of the mosquito A. aegypti, with lower LC50, LC90, and LC95 values of 251.83, 923.76, and 1293.35 mg/L, respectively. In comparison to untreated groups (1454 eggs/10 females), the average daily number of eggs deposited (424 eggs/L) decreases at higher doses (1000 mg/L). Finally, we advise continued study into the possible use of C. myrrha resins against additional pests that have medical and veterinary value, and novel chemicals from this extract should be isolated and purified for use in medicines.

Whole-exome sequencing analyses in a Saudi Ischemic Stroke Cohort reveal association signals, and shows polygenic risk scores are related to Modified Rankin Scale Risk.

Ischemic stroke represents a significant societal burden across the globe. Rare high penetrant monogenic variants and less pathogenic common single nucleotide polymorphisms (SNPs) have been described as being associated with risk of diseases. Genetic studies in Saudi Arabian patients offer a greater opportunity to detect rare high penetrant mutations enriched in these consanguineous populations. We performed whole exome sequencing on 387 ischemic stroke subjects from Saudi Arabian hospital networks with up to 20,230 controls from the Saudi Human Genome Project and performed gene burden analyses of variants in 177 a priori loci derived from knowledge-driven curation of monogenic and genome-wide association studies of stroke. Using gene-burden analyses, we observed significant associations in numerous loci under autosomal dominant and/or recessive modelling. Stroke subjects with modified Rankin Scale (mRSs) above 3 were found to carry greater cumulative polygenic risk score (PRS) from rare variants in stroke genes (standardized PRS mean > 0) compared to the population average (standardized PRS mean = 0). However, patients with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes (OR (95%CI) = 1.79 (1.29-2.49), p = 0.0005), with the means of standardized PRS at or lower than 0. In conclusion, gene burden testing in Saudi stroke populations reveals a number of statistically significant signals under different disease inheritance models. However, interestingly, stroke subjects with mRS of 3 or lower had lower cumulative genetic risk from rare variants in stroke genes and therefore, determining the potential mRS cutoffs to use for clinical significance may allow risk stratification of this population.

Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

Prevalence of bruxism in obstructive sleep apnea syndrome (OSAS) patients: A systematic review conducted according to PRISMA guidelines and the cochrane handbook for systematic reviews of interventions.

BACKGROUND: Sleep bruxism (SB) and obstructive sleep apnea syndrome (OSAS) are two sleep-related conditions that have been associated with significantly conflicting results in literature. Understanding the prevalence of bruxism among OSA patients is crucial for identifying possible comorbidities and optimising treatment strategies. OBJECTIVE: This systematic review aimed at analysing the prevalence of SB in OSAS sufferers and understanding the association between the two. METHODS: Five online databases were searched for relevant articles in accordance with the PRISMA guidelines for conduction of systematic reviews. Studies reporting the prevalence of bruxism in OSAS patients and diagnosed through clinical assessments or polysomnography were included. Data extraction and quality assessment were performed independently by two reviewers. Methodological quality of the included studies was assessed using Risk of Bias In Non-randomised Studies of Interventions (ROBINS-I). RESULTS: A thorough search of literature yielded only two studies were eligible for this review. SB was found to be significantly present in the OSAS group. Despite methodological variations, the majority of studies reported higher rates of bruxism in OSAS patients compared to the general population or control groups. CONCLUSION: The results of this systematic review point to a significant association between bruxism and obstructive sleep apnea. Further research is required to determine a more precise prevalence rate and investigate the potential therapeutic implications of the bruxism-OSAS association that uses a standardised assessment techniques and larger sample sizes.

Higher Expression Levels of SSX1 and SSX2 in Patients with Colon Cancer: Regulated In Vitro by the Inhibition of Methylation and Histone Deacetylation.

Background and Objectives: Colon cancer (CC) has a high mortality rate and is often diagnosed at an advanced stage in Saudi Arabia. Thus, the identification and characterization of potential new cancer-specific biomarkers are imperative for improving the diagnosis of CC by detecting it at an early stage. Cancer-testis (CT) genes have been identified as potential biomarkers for the early diagnosis of various cancers. Among the CT genes are those belonging to the SSX family. In order to assess the usefulness of SSX family genes as cancer biomarkers for the detection of early-stage CC, the goal of this research was to validate the expressions of these genes in patients with CC and in matched patients with normal colons (NCs). Materials and Methods: RT-PCR assays were used to analyze the SSX1, SSX2, and SSX3 family gene expression levels in 30 neighboring NC and CC tissue samples from male Saudi patients. Epigenetic alterations were also tested in vitro using qRT-PCR analysis to determine whether reduced DNA methyltransferase or histone deacetylation could stimulate SSX gene expression via 5-aza-2'-deoxycytidine and trichostatin treatments, respectively. Results: The RT-PCR results showed SSX1 and SSX2 gene expression in 10% and 20% of the CC tissue specimens, respectively, but not in any of the NC tissue specimens. However, no SSX3 expression was detected in any of the examined CC or NC tissue samples. In addition, the qRT-PCR results showed significantly higher SSX1 and SSX2 expression levels in the CC tissue samples than in the NC tissue samples. The 5-aza-2'-deoxycytidine and trichostatin treatments significantly induced the mRNA expression levels of the SSX1, SSX2, and SSX3 genes in the CC cells in vitro. Conclusions: These findings suggest that SSX1 and SSX2 are potentially suitable candidate biomarkers for CC. Their expressions can be regulated via hypomethylating and histone deacetylase treatments, subsequently providing a potential therapeutic target for CC.

Alterations in ocular microcirculation and oxygen metabolism in patients with lipemia retinalis.

PURPOSE: The study aims to assess the alterations in retinal oxygen saturation and retinal and choroidal blood flow in lipemia retinalis. METHODS: This was a cross-sectional study on 10 eyes (5 patients) with history of lipemia retinalis. The study comprised 10 eyes with documented history of lipemia retinalis and 10 participants as healthy controls. Patients with a confirmed history of lipemia retinalis were grouped into two cohorts based on their most recent fundus examination: untreated lipemia retinalis (abnormal fundus) and resolved lipemia retinalis (normal fundus). Both retinal arteriolar and venular oxygen saturation were measured using the non-invasive spectrophotometric retinal oximeter (Oxymap T1). The mean blur rate (MBR) of the optic nerve and choroidal blood flow were analyzed using a laser speckle flowgraph (LSFG). RESULTS: Patients with untreated lipemia retinalis had a significantly higher retinal arteriolar and venular oxygen saturation than that of the other two groups (p < 0.001). Moreover, patients with untreated lipemia retinalis had significantly smaller retinal arteriolar and venular diameters (p < 0.001). On LSFG, there was a significant difference in the overall MBR (p = 0.007) and vessel MBR of the optic nerve between the groups (p = 0.011). The patients with history of lipemia retinalis (untreated and resolved) exhibited a high overall MBR and vessel MBR of the optic nerve than that of the control group. There was a significant elevation of the optic nerve (p = 0.002) and choroidal blowout score (p < 0.001), while the resistivity index of the optic nerve (p = 0.001) and choroids (p = 0.002) was significantly lower in patients with resolved and untreated lipemia retinalis. CONCLUSIONS: There was a significant alteration in retinal oximetry, in untreated lipemia retinalis, and in retinal blood flow, in both the resolved and untreated groups. The increase in retinal blood flow and oxygen saturation may elucidate the preservation of visual acuity and function despite the fundus changes observed in lipemia retinalis.

Cloud of Things in Crowd Engineering: A Tile-Map-Based Method for Intelligent Monitoring of Outdoor Crowd Density.

Managing citizen and community safety is one of the most essential services that future cities will require. Crowd analysis and monitoring are also a high priority in the current COVID-19 pandemic scenario, especially because large-scale gatherings can significantly increase the risk of infection transmission. However, crowd tracking presents several complex technical challenges, including accurate people counting and privacy preservation. In this study, using a tile-map-based method, a new intelligent method is proposed which is integrated with the cloud of things and data analytics to provide intelligent monitoring of outdoor crowd density. The proposed system can detect and intelligently analyze the pattern of crowd activity to implement contingency plans, reducing accidents, ensuring public safety, and establishing a smart city. The experimental results demonstrate the feasibility of the proposed model in detecting crowd density status in real-time. It can effectively assist with crowd management tasks such as monitoring, guiding, and managing crowds to ensure safety. In addition, the proposed algorithm provides acceptable performance.

Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization.

The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models.

Postprandial fasting related headache during Ramadan in Saudi Arabia: A cross-sectional study.

BACKGROUND: Headache disorders are classified as primary or secondary; however, among the secondary headaches, those attributed to food ingestion are not well understood. Therefore, we conducted this study to describe and characterize a new headache entity that occurred during the holy month of Ramadan. This headache occurred within 4 h of breaking the fast. METHODS: This is a nationwide descriptive community-based cross-sectional study conducted during the last 10 days of Ramadan, based on a random sample of adults living in Saudi Arabia. The demographic data, headache symptomatology, nature and distribution of the pain, possible triggering and relieving factors, and patient management programs were analyzed. RESULTS: Completed questionnaires were obtained from 16,031 participants. Of those, 3147 (19.6%) reported headaches after breaking the fast in Ramadan. In 84.1% of cases, there was no previous diagnosis of headache or migraine. The characteristics of these postprandial fasting-related headaches mostly was episodic in nature (72%). The nature of the headache was variable, mostly heaviness or tightness (53.9%). Triggering factors included ingestion of fried food in (45%) and coffee (26.3%). Lying down and sleeping was found to be an important relieving factor (61%). CONCLUSION: A new headache entity is being described. Appears to be quite common, occurs less than 2 h following the first meal, and is mostly of the heaviness and tension type.

Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia.

In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.

Detection of Iris Presentation Attacks Using Feature Fusion of Thepade's Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features.

Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images.

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the "black-box" nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.

IoT-Based Geotechnical Monitoring of Unstable Slopes for Landslide Early Warning in the Darjeeling Himalayas.

In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitored continuously during three monsoon seasons (2017-2019), and the data from the sensors were compared with the field observations and rainfall data for the evaluation. The relationship between change in tilt rate, volumetric water content, and rainfall are explored in the study, and the records prove the significance of considering long-term rainfall conditions rather than immediate rainfall events in developing rainfall thresholds for the region.

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques.

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.

Cross-linking of COOH-containing polymers using Ag(i)-catalyzed oxidative decarboxylation in aqueous solution.

Cross-linking that defines the three-dimensional networks in hydrogels has a significant impact on their physiochemical properties. The cross-linking of hydrophilic polymers via post-polymerization reactions is an ideal way to manufacture hydrogels with high reproducibility and without monomer residuals. We herein report the use of Ag(i)-catalyzed oxidative decarboxylation to cross-link poly(acrylic acid) (PAA) and a family of COOH-containing hydrophilic polymers. Our method is based on the radical-mediated elimination reaction to remove COOH group(s) and generate alkyl radical(s) simultaneously, in the presence of AgNO3 and persulfates. The further intermolecular radical coupling is demonstrated to be very effective in inducing cross-linking and gelation of COOH-containing hydrophilic polymers. The cross-linking reaction can be readily achieved by simply mixing a small amount of AgNO3 (as low as 0.03 wt%) and persulfates with polymers at room temperature in air. Rheological measurements show that the gelation occurs in 20-30 min. The applications of oxidative decarboxylation in the preparation of hydrogels of COOH-containing hydrophilic copolymers and their interpenetrating polymer network (IPN) hydrogels are further validated. Finally, the residual Ag(i) ions in hydrogels are discussed in terms of how Ag(i) ions further change the mechanical and optical properties of hydrogels by photoreduction of Ag(i) to Ag nanoparticles. We expect that this Ag(i)-catalyzed oxidative decarboxylation chemistry can not only serve as a facile and general strategy to produce hydrogels through post-polymerization, but also enrich the toolbox of cross-linking chemistries of COOH-containing polymers in all forms (e.g. films, colloids and dispersions).

Basilar artery vasospasm after pretuncal non-aneurysmal subarachnoid hemorrhage responding to milrinone.

Pretruncal (perimesencephalic) non-aneurysmal subarachnoid hemorrhage (PNSAH) is uniformly associated with an excellent outcome. Although cerebral vasospasm remains a common complication of SAH and constitutes an important predictor of outcome, in the setting of PNSAH, it is extremely rare. Preturnal non-aneurysmal subarac refers to a subset of SAH patients with a characteristic pattern of localized blood on CT of the head, normal cerebral angiography, and benign course when compared to the aneurysmal SAH population. The presence of radiological or even clinical vasospasm does not exclude the diagnosis of PNSAH. To our knowledge, this is the first case of symptomatic cerebral vasospasm due to PNSAH that responded to milrinone.