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Phishing attacks are the biggest cybersecurity threats in the digital world. Attackers exploit users by impersonating real, authentic websites to obtain sensitive information such as passwords and bank statements. One common technique in these attacks is using malicious URLs. These malicious URLs mimic legitimate URLs, misleading users into interacting with malicious websites. This practice, URL phishing, presents a big threat to internet security, emphasizing the need for advanced detection methods. So we aim to enhance phishing URL detection by using machine learning and deep learning models, leveraging a set of low-level URL features derived from n-gram analysis. In this paper, we present a method for detecting malicious URLs using statistical features extracted from n-grams. These n-grams are extracted from the hexadecimal representation of URLs. We employed 4 experiments in our paper. The first 3 experiments used machine learning with the statistical features extracted from these n-grams, and the fourth experiment used these grams directly with deep learning models to evaluate their effectiveness. Also, we used Explainable AI (XAI) to explore the extracted features and evaluate their importance and role in phishing detection. A key advantage of our method is its ability to reduce the number of features required and reduce the training time by using fewer features after applying XAI techniques. This stands in contrast to the previous study, which relies on high-level URL features and needs pre-processing and a high number of features (87 high-level URL-based features). So our technique only uses statistical features extracted from n-grams and the n-gram itself, without the need for any high-level features. Our method is evaluated across different n-gram lengths (2, 4, 6, and 8), aiming to optimize detection accuracy. We conducted four experiments in our study. In the first experiment, we focused on extracting and using 12 common statistical features like mean, median, etc. In the first experiment, the XGBoost model achieved the highest accuracy using 8-gram features with 82.41%. In the second experiment, we expanded the feature set and extracted an additional 13 features, so our feature count became 25. XGBoost in the second experiment achieved the highest accuracy with 86.40%. Accuracy improvement continued in the third experiment, we extracted an additional 16 features (character count features), and these features increased XGBoost accuracy to 88.15% in the third experiment. In the fourth experiment, we directly fed n-gram representations into deep learning models. The Convolutional Neural Network (CNN) model achieved the highest accuracy of 94.09% in experiment four. Also, we applied XAI techniques, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). Through the explanation provided by XAI methods, we were able to determine the most important features in our feature set, enabling a reduction in feature count. Using fewer features (4, 7, 10, 13, 15), we got good accuracy compared to the 41 features used in experiment three and reduced the models' training times and complexity. This research aimed to enhance phishing URL detection by using machine learning and deep learning models, leveraging a set of low-level URL features derived from n-gram analysis. Our findings show the importance of using minimal statistical features to identify malicious URLs. Notably, the use of CNN had a great advancement, achieving an accuracy rate of 94.09% with using n-grams of URLs, surpassing traditional machine learning models. This achievement not only validates the efficacy of deep learning models in complex pattern recognition tasks but also highlights the efficiency of our feature selection approach, which relies on a lower number of features and is less complex compared to existing high-level feature-based studies. The research outcomes demonstrate a promising pathway toward developing more robust, efficient, and scalable phishing detection systems. [ABSTRACT FROM AUTHOR]

The TAR DNA Binding Protein 43 (TDP-43) has been implicated in the pathogenesis of human neurodegenerative diseases and exhibits hallmark neuropathology in amyotrophic lateral sclerosis (ALS). Here, we explore its tractability as a plasma biomarker of disease and describe its localization and possible functions in the cytosol of platelets. Novel TDP-43 immunoassays were developed on three different technical platforms and qualified for specificity, signal-to-noise ratio, detection range, variation, spike recovery and dilution linearity in human plasma samples. Surprisingly, implementation of these assays demonstrated that biobank-archived plasma samples yielded considerable heterogeneity in TDP-43 levels. Importantly, subsequent investigation attributed these differences to variable platelet recovery. Fractionations of fresh blood revealed that ≥ 95% of the TDP-43 in platelet-containing plasma was compartmentalized within the platelet cytosol. We reasoned that this highly concentrated source of TDP-43 comprised an interesting substrate for biochemical analyses. Additional characterization of platelets revealed the presence of the disease-associated phosphoserine 409/410 TDP-43 proteoform and many neuron- and astrocyte-expressed TDP-43 mRNA targets. Considering these striking similarities, we propose that TDP-43 may serve analogous functional roles in platelets and synapses, and that the study of platelet TDP-43 might provide a window into disease-related TDP-43 dyshomeostasis in the central nervous system. [ABSTRACT FROM AUTHOR]

Nowadays, phishing attacks have grown rapidly, and there is an urgent need to introduce a suitable detection method that has the ability to detect different types of phishing attacks. This paper investigates the capability to use bio-inspired meta-heuristic algorithms to improve the performance of the detection engine for phishing attacks by reducing the number of features. This improvement was practiced by investigating the effectiveness of five meta-heuristic algorithms: Particle Swarm Optimization (PSO), Firefly Optimization Algorithm (FFA), Multi-Verse Optimizer (MVO), Moth-Flame Optimization algorithm (MFO), and BAT optimization algorithm, to select the relevant features that could be affected directly by different types of phishing attacks. The suggested detection model was tested and evaluated using four benchmark phishing attack datasets, and the Apache Spark-based decision tree algorithm was selected as a detection engine. The conducted experiments have demonstrated that the Spark-based MVO algorithm achieved the highest detection rate for detecting different types of phishing attacks within four phishing attack datasets. Moreover, the suggested detection model was able to reduce effectively the feature space, which could enhance the computational efficiency. [ABSTRACT FROM AUTHOR]

Purpose of Review: Atypical parkinsonism is a term usually used to describe three neurodegenerative parkinsonian disorders: progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and multiple system atrophy (MSA). In contrast to Parkinson's disease, these disorders have a poor prognosis and present with a multitude of diverse symptoms, including parkinsonism, dystonia, myoclonus, gait disorders, dysarthria, dysphagia, sleep, cognitive, and behavioral disorders. In the absence of an approved disease-modifying treatment, symptomatic treatment is the mainstay of management. The purpose of this review is to present an overview of the management of these complex disorders, with a particular focus on a holistic and multidisciplinary approach. Recent Findings: In addition to presenting the most significant pharmacological interventions for symptom management, data regarding non-pharmacological interventions are analyzed. Important non-pharmacological clinical practice questions such as breaking the news, palliative care, and end-of-life issues are discussed, in an effort to present an overview of the management of atypical parkinsonism from diagnosis to the most advanced stages of these diseases. Summary: Management of atypical parkinsonian disorders includes symptomatic pharmacological and non-pharmacological interventions, in addition to addressing issues such as informing the patient of the diagnosis, palliative care, and end-of-life issues, which require a multidisciplinary approach. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that is now recognized to involve autonomic dysfunction. The burden of autonomic dysfunction is an important factor in the quality of life and prognosis of ALS patients. This article presents the clinical characteristics of a young female ALS patient with a fused in sarcoma (FUS) gene mutation and notable hyperhidrosis. Method: Detailed clinical characteristics of the patients were collected, and comprehensive examinations such as electrophysiological assessment, neuro-ultrasound, genetic testing, and relevant blood tests were conducted. Result: A 24-year-old female experienced progressive weakness in both lower limbs for over 5 months, along with excessive sweating on both palms and feet. A positive skin iodine-starch test was observed. Electromyography revealed extensive neurogenic damage and prolonged sympathetic skin response (SSR) latency in both lower limbs. Full exon gene sequencing showed a heterozygous mutation c.1574C>T (p.Pro525Leu) in the FUS gene. Conclusion: The pathogenesis of ALS remains unclear at present. This case underscores the presence of autonomic nervous symptoms in ALS associated with FUS mutation and highlights the importance of early diagnosis and timely treatment intervention to enhance patient prognosis. [ABSTRACT FROM AUTHOR]

Phishing, a well-known cyberattack that cannot be completely eradicated from the Internet, has increased dramatically since the COVID-19 pandemic. Despite previous efforts to reduce this prevalent Internet threat, constantly changing attacks make phishing detection a difficult task. The lack of continuous learning support provided by existing solutions and the lack of a systematic knowledge acquisition process make its detection more difficult. SmartiPhish is introduced in this context as the first anti-phishing solution with integrated continuous learning support with an innovative knowledge acquisition process. SmartiPhish combines deep learning and reinforcement learning to have a successful phishing detection solution. The deep learning model predicts a phishing probability for a given web page based on the URL and HTML content, and the probability is then passed to a reinforcement learning environment to make a decision based on the popularity of the web page and prior knowledge of it. SmartiPhish has a detection accuracy of 96.40% and a detection time of 4.3 s. SmartiPhish performs well in an imbalanced environment, and zero-day attack detection is also interesting. Furthermore, SmartiPhish demonstrated a 5.65% performance improvement in just six weeks, in contrast to the existing anti-phishing tools' declining performance trend over time. [ABSTRACT FROM AUTHOR]

The recent surge in phishing incidents in the post-COVID era poses a serious threat towards the social and economic well-being of users. The escalation in dependency upon the internet for meeting daily chores has made them vulnerable to falling prey to the ever-evolving menace of phishing. The objective of this article is: to explore different tactics and motivational factors behind phishing, identify the communication mediums through which phishing is circulated and perform a detailed review along with a comparison of the various surveys in this domain. Another objective is to determine the open research challenges in this genre and to identify the scope of research in the future. An extensive literature survey is performed, which includes articles from eminent online research databases. Barring a few initial articles related to phishing, the articles published in Science Citation/Scopus-indexed journals and survey/review articles published in the last ten years are considered. Highly cited works are given preference. The search query returned numerous articles, which were narrowed by title screening. Further screening of articles was performed by reading the abstract and eliminating the articles related to user-oriented phishing interventions. Eventually, 25 survey articles were shortlisted to be surveyed. This article is an effort to provide a novel taxonomy of phishing to academia that would assist in identifying the sections where phishing countermeasures are inadequate. [ABSTRACT FROM AUTHOR]

Background: Tear fluid (TF) production is an important component of normal ocular function. It is regulated by parasympathetic and sympathetic innervation. Because parasympathetic nerve fibers originate in the brainstem, pathology in this brain region may affect TF production. For example, a reduction in TF production has been described in patients with Parkinson's disease (PD). Methods: TF was collected at one center from 772 individuals, 708 of which were patients with different neurological diseases, and 64 healthy controls. Wetting lengths (WL) were recorded using Schirmer test strips with a collection time of 10 min. Results: WL correlated negatively with age and was significantly reduced in subgroups of patients with neurodegenerative diseases (NDDs) (PD, Amyotrophic lateral sclerosis (ALS), other motor neuron diseases (MNDs)), as well as inflammatory/autoimmune/infectious central nervous system (CNS) diseases and vascular CNS diseases (VCDs), even if corrected for age or sex. While temperature had a significant negative effect on TF production, other environmental factors, such as hours of sunlight and humidity, did not. Conclusion: WL was altered in many neurological diseases compared to healthy controls. Most importantly, we observed a reduction of WL in NDDs, independent of age or sex. This study highlights the potential of WL as an easily obtainable parameter and suggests functional alterations in the autonomic innervation in various neurological disorders. [ABSTRACT FROM AUTHOR]

Purpose of review: To describe the clinical presentation, diagnosis, and treatment options of vasculitic neuropathies. Overall, vasculitic neuropathies can be divided into systemic and nonsystemic. Systemic vasculitic neuropathies form part of rheumatological diseases, where in some cases, the vasculitic is the predominant feature, and these are called primary vasculitis, while in others, the vasculitis is secondary to a connective tissue disease. Nonsystemic vasculitic neuropathies involve only the peripheral nervous system, and thus, diagnosis is more challenging as it requires a high degree of suspicion and confirmation with nerve biopsy results. Recent findings: Over the past few years, rituximab has been increasingly used for induction and maintenance treatment of vasculitis due to a more favorable side effect profile and similar efficacy when compared to cyclophosphamide. Summary: Herein, we describe the different types of vasculitic neuropathies. Regarding their treatment, have focused on the indications, dosing, and side effects for the most common options for primary systemic vasculitic neuropathies, as these tend to be similar for the classical nonsystemic vasculitic neuropathy phenotype. [ABSTRACT FROM AUTHOR]

In recent years, security incidents of website occur increasingly frequently, and this motivates us to study websites' security. Although there are many phishing detection approaches to detect phishing websites, the detection accuracy has not been desirable. In this paper, we propose a novel phishing detection model based on a novel neural network classification method. This detection model can achieve high accu-racy and has good generalization ability by design risk minimization principle. Furthermore, the training process of the novel detection model is simple and stable by Monte Carlo algorithm. Based on testing of a set of phishing and benign websites, we have noted that this novel phishing detection model achieves the best Accuracy, True-positive rate (TPR), False-positive rate (FPR), Precision, Recall, F-measure and Matthews Correlation Coefficient(MCC) comparable to other models as Naive Bayes (NB), Logistic Regression(LR), K-Nearest Neighbor (KNN), Decision Tree (DT), Linear Support Vector Machine (LSVM), Radial-Basis Support Vector Machine (RSVM) and Linear Discriminant Analysis (LDA). Furthermore, based upon experiments, we find that the proposed detection model can achieve a high Accuracy of 97.71% and a low FPR of 1.7%. It indicates that the proposed detection model is promising and can be effectively applied to phishing detection. [ABSTRACT FROM AUTHOR]

Website Uniform Resource Locator (URL) spoofing remains one of the ways of perpetrating phishing attacks in the twenty-first century. Hackers continue to employ URL spoofing to deceive naïve and unsuspecting consumers into releasing important personal details in malicious websites. Blacklists and rule-based filters that were once effective at reducing the risks and sophistication of phishing are no longer effective as there are over 1.5 million new phishing websites created monthly. Therefore, research aimed at unveiling new techniques for detecting phishing websites has sparked a lot of interest in both academics and business with machine and deep learning techniques being at the forefront. Among the deep learning techniques that have been employed, Convolutional Neural Network (CNN) remains one of the most widely used with high performance in feature learning. However, CNN has a problem of memorizing contextual relationships in URL text, which makes it challenging to efficiently detect sophisticated malicious URLs in real-time applications. On the contrary, Long Short-Term Memory (LSTM) deep learning model has been successfully employed in complex real-time problems because of its ability to store inputs for a long period of time. This study experiments with the use of hybrid CNN and LSTM deep learning models for spoofing website URL detection in order to exploit the combined strengths of the two approaches for a more sophisticated spoofing URL detection. Two publicly available datasets (UCL spoofing Website and PhishTank Datasets) were used to evaluate the performance of the proposed hybrid model against other models in the literature. The hybrid CNN-LSTM model achieved accuracies of 98.9% and 96.8%, respectively, when evaluated using the UCL and PhishTank datasets. On the other hand, the standalone CNN and LSTM achieved accuracies of 90.4% and 94.6% on the UCL dataset, while their accuracies on the PhishTank dataset were 89.3% and 92.6%, respectively. The results show that the hybrid CNN-LSTM algorithm largely outperformed the standalone CNN and LSTM models, which demonstrates a much better performance. Therefore, the hybrid deep learning technique is recommended for detecting spoofing website URL thereby reducing losses attributed to such attacks. [ABSTRACT FROM AUTHOR]

In the past ten years, due to the rapid growth of the Internet, a huge number of cyber-crimes have been committed on the Internet. One of the crucial obstacles' user's encounters is the phishing website's threat, especially for login credentials and credit card information. According to the statistics, most attackers utilized PayPal (22%), Microsoft (19%), Facebook (15%), eBay (6%), and Amazon (3%) for phishing. Some phishing attempts are designed to get login credentials or infect specific people's computers. Since the stakes are so high, attackers invest a lot of effort into fooling the chosen victims. The feature selection-based Particle Swarm Optimization (PSO) method is applied in this study to detect phishing websites. Feature engineering is critical in phishing website detection systems, and detection accuracy is heavily dependent on prior knowledge of characteristics. PSO iteratively attempts to optimize a problem by improving the proposed model. Many computational methods are applied in this model to evaluate the results. The experimental findings show that the proposed PSO-based feature selection model substantially improved classification accuracy, sensitivity, specificity, f1–score, and Matthew's correlation coefficient in machine learning models. Testing the model on two prominent datasets containing phishing and legitimate website, the accuracy reaches 97.81% and 90.39% for the two datasets using the artificial neural network in both cases. Experimental findings demonstrate that the detection efficiency may be enhanced by selecting the features appropriately. [ABSTRACT FROM AUTHOR]

Feature selection and hyper-parameters optimization (tuning) are two of the most important and challenging tasks in machine learning. To achieve satisfying performance, every machine learning model has to be adjusted for a specific problem, as the efficient universal approach does not exist. In addition, most of the data sets contain irrelevant and redundant features that can even have a negative influence on the model's performance. Machine learning can be applied almost everywhere; however, due to the high risks involved with the growing number of malicious, phishing websites on the world wide web, feature selection and tuning are in this research addressed for this particular problem. Notwithstanding that many metaheuristics have been devised for both feature selection and machine learning tuning challenges, there is still much space for improvements. Therefore, the research exhibited in this manuscript tries to improve phishing website detection by tuning extreme learning model that utilizes the most relevant subset of phishing websites data sets features. To accomplish this goal, a novel diversity-oriented social network search algorithm has been developed and incorporated into a two-level cooperative framework. The proposed algorithm has been compared to six other cutting-edge metaheuristics algorithms, that were also implemented in the framework and tested under the same experimental conditions. All metaheuristics have been employed in level 1 of the devised framework to perform the feature selection task. The best-obtained subset of features has then been used as the input to the framework level 2, where all algorithms perform tuning of extreme learning machine. Tuning is referring to the number of neurons in the hidden layers and weights and biases initialization. For evaluation purposes, three phishing websites data sets of different sizes and the number of classes, retrieved from UCI and Kaggle repositories, were employed and all methods are compared in terms of classification error, separately for layers 1 and 2 over several independent runs, and detailed metrics of the final outcomes (output of layer 2), including precision, recall, f1 score, receiver operating characteristics and precision–recall area under the curves. Furthermore, an additional experiment is also conducted, where only layer 2 of the proposed framework is used, to establish metaheuristics performance for extreme machine learning tuning with all features, which represents a large-scale NP-hard global optimization challenge. Finally, according to the results of statistical tests, final research findings suggest that the proposed diversity-oriented social network search metaheuristics on average obtains better achievements than competitors for both challenges and all data sets. Finally, the SHapley Additive exPlanations analysis of the best-performing model was applied to determine the most influential features. [ABSTRACT FROM AUTHOR]

Referendums allow people to participate in political decision-making. However, they also come with the challenge of presenting complex issues to the electorate in a concise and comprehensible way. In order to simplify the decision-making process, referendum questions are often tailored to yes/no response options. In comparison, the UK European Union membership referendum of 2016, in contrast to the 1975 referendum on the same issue, discarded this option, and a verbal and conceptually more complex alternative response option was brought into play (remain vs. leave). This potentially relevant change in the voting tradition justifies a linguistic consideration of the suitability of the choice of verbs. Verbal response options such as stay, remain and leave might activate different framing effects due to their underlying etymology. Our goal was therefore to examine whether the choice of verbs can have a biasing effect, which might be the case due to their frame-inherent structure. This investigation attempts, through both a linguistic analysis and an experimental analysis using a version of the Implicit Association Test (IAT), to bridge the research gap between the awareness that there are linguistic factors that can influence decision-making processes and the lack of inclusion of framing effects. Overall, the data of two IAT studies (n = 185 and n = 355) suggest that the exact wording of dichotomic response options has the potential to influence response choice based on evaluative associations of the verbs. Specifically, when compared to leave, we found relatively more positive evaluation for stay than for remain. Furthermore - independent of the Brexit referendum - our study raises the question whether verbs are at all suitable to replace yes/no response options due to inherent framing effects. This linguistic aspect requires more attention in the design of response options in future referendums. [ABSTRACT FROM AUTHOR]

Background: Currently, miRNAs are involved in the development of amyotrophic lateral sclerosis (ALS), and identifying circulating miRNAs that are causally associated with ALS risk as biomarkers is imperative. Methods: We performed a two-sample Mendelian randomization study to evaluate the causal relationship between miRNAs and ALS. Our analysis was conducted using summary statistics from miRNA expression quantitative loci (eQTL) data of the Framingham Heart Study and ALS genome-wide association studies data. Another independent miRNA data was used to further validate. Results: We identified eight unique miRNAs that were causally associated with ALS risk. Using expression data of miRNAs from an independent study, we validated three high-confidence miRNAs, namely hsa-miR-27b-3p, hsa-miR-139-5p, and hsa-miR-152-3p, which might have a potential causal effect on ALS risk. Conclusion: We suggested that higher levels of hsa-miR-27b-3p and hsa-miR-139-5p had protective effects on ALS, whereas higher levels of hsa-miR-152-3p might act as a risk factor for ALS. The analytical framework presented in this study helps to understand the role of miRNAs in the development of ALS and to identify the biomarkers for ALS risk. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder. An increasing number of researchers have found extra motor features in ALS, which are also called ALS-plus syndromes. Besides, a great majority of ALS patients also have cognitive impairment. However, clinical surveys of the frequency and genetic background of ALS-plus syndromes are rare, especially in China. Methods: We investigated a large cohort of 1015 patients with ALS, classifying them into six groups according to different extramotor symptoms and documenting their clinical manifestations. Meanwhile, based on their cognitive function, we divided these patients into two groups and compared demographic characteristics. Genetic screening for rare damage variants (RDVs) was also performed on 847 patients. Results: As a result, 16.75% of patients were identified with ALS-plus syndrome, and 49.5% of patients suffered cognitive impairment. ALS-plus group had lower ALSFRS-R scores, longer diagnostic delay time, and longer survival times, compared to ALS pure group. RDVs occurred less frequently in ALS-plus patients than in ALS-pure patients (P = 0.042) but showed no difference between ALS-cognitive impairment patients and ALS-cognitive normal patients. Besides, ALS-cognitive impairment group tends to harbour more ALS-plus symptoms than ALS-cognitive normal group (P = 0.001). Conclusion: In summary, ALS-plus patients in China are not rare and show multiple differences from ALS-pure patients in clinical and genetic features. Besides, ALS-cognitive impairment group tends to harbour more ALS-plus syndrome than ALS-cognitive normal group. Our observations correspond with the theory that ALS involves several diseases with different mechanisms and provide clinical validation. [ABSTRACT FROM AUTHOR]

Purpose: Amyotrophic lateral sclerosis (ALS) is the most common motor neuron disease in adults. There are many patient-reported outcome measures (PROMs) for measuring quality of life (QoL) and health-related QoL (HRQoL) within this population; however, there is limited consensus regarding which are most valid, reliable, responsive, and interpretable. This systematic review assesses the psychometric properties and interpretability of QoL and HRQoL PROMs for individuals with ALS. Methods: This review was conducted following the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) methodology for systematic reviews of PROMs. MEDLINE, EMBASE, and CINAHL databases were searched. Studies were included if their aim was to evaluate one or more psychometric properties or the interpretability of QoL or HRQoL PROMs in individuals with ALS. Results: We screened 2713 abstracts, reviewed 60 full-text articles, and included 37 articles. Fifteen PROMs were evaluated including generic HRQoL (e.g., SF-36), ALS-specific HRQoL (e.g., ALSAQ-40), and individualized QoL (e.g., SEIQoL) measures. Evidence for internal consistency and test–retest reliability were acceptable. For convergent validity, 84% of hypotheses were met. For known-groups validity, outcomes were able to distinguish between healthy cohorts and other conditions. Responsiveness results ranged from low to high correlations with other measures over 3–24 months. Evidence for content validity, structural validity, measurement error, and divergent validity was limited. Conclusion: This review identified evidence in support of the ALSAQ-40 or ALSAQ-5 for individuals with ALS. These findings can guide healthcare practitioners when selecting evidence-based QoL and HRQoL PROMs for patients and provide researchers with insight into gaps in the literature. [ABSTRACT FROM AUTHOR]

Background: Williams syndrome (WS), a rare neurodevelopmental disorder caused by hemizygous deletion of ~ 25 genes from chromosomal band 7q11.23, affords an exceptional opportunity to study associations between a well-delineated genetic abnormality and a well-characterized neurobehavioral profile. Clinically, WS is typified by increased social drive (often termed "hypersociability") and severe visuospatial construction deficits. Previous studies have linked visuospatial problems in WS with alterations in the dorsal visual processing stream. We investigated the impacts of hemideletion and haplotype variation of LIMK1, a gene hemideleted in WS and linked to neuronal maturation and migration, on the structure and function of the dorsal stream, specifically the intraparietal sulcus (IPS), a region known to be altered in adults with WS. Methods: We tested for IPS structural and functional changes using longitudinal MRI in a developing cohort of children with WS (76 visits from 33 participants, compared to 280 visits from 94 typically developing age- and sex-matched participants) over the age range of 5–22. We also performed MRI studies of 12 individuals with rare, shorter hemideletions at 7q11.23, all of which included LIMK1. Finally, we tested for effects of LIMK1 variation on IPS structure and imputed LIMK1 expression in two independent cohorts of healthy individuals from the general population. Results: IPS structural (p < 10−4 FDR corrected) and functional (p <.05 FDR corrected) anomalies previously reported in adults were confirmed in children with WS, and, consistent with an enduring genetic mechanism, were stable from early childhood into adulthood. In the short hemideletion cohort, IPS deficits similar to those in WS were found, although effect sizes were smaller than those found in WS for both structural and functional findings. Finally, in each of the two general population cohorts stratified by LIMK1 haplotype, IPS gray matter volume (pdiscovery < 0.05 SVC, preplication = 0.0015) and imputed LIMK1 expression (pdiscovery = 10−15, preplication = 10−23) varied according to LIMK1 haplotype. Conclusions: This work offers insight into neurobiological and genetic mechanisms responsible for the WS phenotype and also more generally provides a striking example of the mechanisms by which genetic variation, acting by means of molecular effects on a neural intermediary, can influence human cognition and, in some cases, lead to neurocognitive disorders. [ABSTRACT FROM AUTHOR]

Background: Among eye movements in amyotrophic lateral sclerosis (ALS), we identified the characteristics of square-wave jerks (SWJs) seen during times without visual fixation (VF) and analyzed their relationships with clinical parameters. Materials and Methods: Clinical symptoms were evaluated and eye movements were tested using electronystagmography in 15 patients with ALS (10 men, 5 women; mean age, 66.9 ± 10.5 years). SWJs with and without VF were recorded, and their characteristics were identified. Relationships between each SWJ parameter and clinical symptoms were evaluated. Results were compared with eye movement data from 18 healthy individuals. Results: The frequency of SWJs without VF was significantly higher in the ALS group than in the healthy group (P < 0.001). When the condition was changed from VF to no-VF in the ALS group, the frequency of SWJs was significantly higher in healthy subjects (P = 0.004). A positive correlation was seen between frequency of SWJs and percentage predicted forced vital capacity (%FVC) (R = 0.546, P = 0.035). Conclusion: The frequency of SWJs was higher with VF in healthy people, and was suppressed without VF. In contrast, the frequency of SWJs was not suppressed without VF in ALS patients. This suggests that SWJs without VF have some clinical significance in ALS patients. Moreover, a relationship was noted between the parameters of SWJs without VF in ALS patients and results of pulmonary function tests, suggesting that SWJs during times without VF may offer a clinical parameter of ALS. [ABSTRACT FROM AUTHOR]

Phishing is one of the most dangerous threats in which a hacker imitates a person, company or government agency to lure and deceive their victims. Machine learning anti-phishing solutions are gaining popularity nowadays. However, most anti-phishing solutions rely heavily on features extracted from third-party services such as whois services, DNS search, and web traffic. As a result, they are slow and require a lot of computing resources. This paper introduces a machine-learning-based framework: Phish-Sight that detects phishing websites through a visual inspection strategy. Phish-Sight uses dominant color features and highly targeted popular brand names embedded in URLs' web pages with machine learning techniques to detect phishing web pages. Prediction performance of the dominant color features and popular brand names from web pages was investigated using five machine learning algorithms. The Random Forest algorithm surpassed the others, with a 98.43% true positive rate and 99.13% accuracy in detecting phishing frauds. The prediction run time per web page measured at 7.6 s suggests that Phish-Sight has potential for real-time applications. [ABSTRACT FROM AUTHOR]

Background: Genome-Wide Association Studies (GWAS) have identified numerous risk genes for Amyotrophic Lateral Sclerosis (ALS); however, the mechanisms by which these loci confer ALS risk are uncertain. This study aims to identify novel causal proteins in the brains of patients with ALS using an integrative analytical pipeline. Methods: Using the datasets of Protein Quantitative Trait Loci (pQTL) (NpQTL1 = 376, NpQTL2 = 152), expression QTL (eQTL) (N = 452), and the largest ALS GWAS (NALS=27,205, NControls = 110,881), we performed a systematic analytical pipeline including Proteome-Wide Association Study (PWAS), Mendelian Randomization (MR), Bayesian colocalization, and Transcriptome-Wide Association Study (TWAS) to identify novel causal proteins for ALS in the brain. Results: Using PWAS, we found that the altered protein abundance of 12 genes in the brain was associated with ALS. Three genes (SCFD1, SARM1 and CAMLG) were identified as lead causal genes for ALS with solid evidence (False discovery rate < 0.05, in MR analysis; PPH4 > 80% for Bayesian colocalization). Specifically, an increased abundance of SCFD1 and CAMLG led to an increased risk of ALS, whereas a higher abundance of SARM1 led to a decreased risk of developing ALS. TWAS showed that SCFD1 and CAMLG were related to ALS at the transcriptional level. Conclusions: SCFD1, CAMLG, and SARM1 exhibited robust associations and causality with ALS. The study findings provide novel clues for identifying potential therapeutic targets in ALS. Further studies are required to explore the mechanisms underlying the identified genes. [ABSTRACT FROM AUTHOR]

Background: Delirium is an acute disturbance in attention, awareness and cognition. Immediate detection in older adults is recommended because delirium is associated with adverse outcomes. The 4 'A's Test (4AT) is a short screening instrument for delirium. The aim of this study is to evaluate diagnostic accuracy of the Dutch version of the screening tool 4AT for delirium detection in different settings. Methods: Prospective observational study conducted in two hospitals in patients aged ≥ 65 years in geriatric wards and the Emergency Department (ED). Each participant underwent two assessments; the index test 4AT, followed by the reference standard for delirium performed by a geriatric care specialist. The reference standard delirium is according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria. Results: A total of 71 geriatric inpatients and 49 older ED patients were included. The prevalence of delirium was 11.6% in the acute geriatric ward and 6.1% in the ED. The sensitivity and specificity of the 4AT in the acute geriatric ward were 0.88 and 0.69, respectively. In the ED, the sensitivity and specificity were 0.67 and 0.83, respectively. The area under the receiver operating characteristic curve was 0.80 for the acute geriatric ward setting and 0.74 for the ED setting. Conclusion: The Dutch version of the 4AT is a reliable screening tool for delirium detection in both acute geriatric wards and ED. Due to its brevity and practicality (i.e., no special training is required to administer the tool), it is useful in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Bulbar dysfunction is a cardinal feature of ALS with important quality of life and management implications. The objective of this study is the longitudinal evaluation of a large panel imaging metrics pertaining to bulbar dysfunction, encompassing cortical measures, structural and functional cortico-medullary connectivity indices and brainstem metrics. Methods: A standardised, multimodal imaging protocol was implemented with clinical and genetic profiling to systematically appraise the biomarker potential of specific metrics. A total of 198 patients with ALS and 108 healthy controls were included. Results: Longitudinal analyses revealed progressive structural and functional disconnection between the motor cortex and the brainstem over time. Cortical thickness reduction was an early feature on cross-sectional analyses with limited further progression on longitudinal follow-up. Receiver operating characteristic analyses of the panel of MR metrics confirmed the discriminatory potential of bulbar imaging measures between patients and controls and area-under-the-curve values increased significantly on longitudinal follow-up. C9orf72 carriers exhibited lower brainstem volumes, lower cortico-medullary structural connectivity and faster cortical thinning. Sporadic patients without bulbar symptoms, already exhibit significant brainstem and cortico-medullary connectivity alterations. Discussion: Our results indicate that ALS is associated with multi-level integrity change from cortex to brainstem. The demonstration of significant corticobulbar alterations in patients without bulbar symptoms confirms considerable presymptomatic disease burden in sporadic ALS. The systematic assessment of radiological measures in a single-centre academic study helps to appraise the diagnostic and monitoring utility of specific measures for future clinical and clinical trial applications. [ABSTRACT FROM AUTHOR]

Frontotemporal lobar degeneration (FTLD) is an umbrella term describing the neuropathology of a clinically, genetically and pathologically heterogeneous group of diseases, including frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP). Among the major FTLD pathological subgroups, FTLD with TDP-43 positive inclusions (FTLD-TDP) and FTLD with tau-positive inclusions (FTLD-tau) are the most common, representing about 90% of the cases. Although alterations in DNA methylation have been consistently associated with neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease, little is known for FTLD and its heterogeneous subgroups and subtypes. The main goal of this study was to investigate DNA methylation variation in FTLD-TDP and FTLD-tau. We used frontal cortex genome-wide DNA methylation profiles from three FTLD cohorts (142 FTLD cases and 92 controls), generated using the Illumina 450K or EPIC microarrays. We performed epigenome-wide association studies (EWAS) for each cohort followed by meta-analysis to identify shared differentially methylated loci across FTLD subgroups/subtypes. In addition, we used weighted gene correlation network analysis to identify co-methylation signatures associated with FTLD and other disease-related traits. Wherever possible, we also incorporated relevant gene/protein expression data. After accounting for a conservative Bonferroni multiple testing correction, the EWAS meta-analysis revealed two differentially methylated loci in FTLD, one annotated to OTUD4 (5'UTR-shore) and the other to NFATC1 (gene body-island). Of these loci, OTUD4 showed consistent upregulation of mRNA and protein expression in FTLD. In addition, in the three independent co-methylation networks, OTUD4-containing modules were enriched for EWAS meta-analysis top loci and were strongly associated with the FTLD status. These co-methylation modules were enriched for genes implicated in the ubiquitin system, RNA/stress granule formation and glutamatergic synaptic signalling. Altogether, our findings identified novel FTLD-associated loci, and support a role for DNA methylation as a mechanism involved in the dysregulation of biological processes relevant to FTLD, highlighting novel potential avenues for therapeutic development. [ABSTRACT FROM AUTHOR]

During the attempt to steadily fixate at a single spot, sequences of small involuntary fixation saccades (SIFSs, known also as microsaccades οr intrusions) occur which form spatio-temporal patterns such as square wave jerks (SWJs), a pattern characterised by alternating centrifugal and centripetal movements of similar magnitude. In many neurodegenerative disorders, SIFSs exhibit elevated amplitudes and frequencies. Elevated SIFS amplitudes have been shown to favour the occurrence of SWJs ("SWJ coupling"). We analysed SIFSs in different subject groups comprising both healthy controls (CTR) and patients with amyotrophic lateral sclerosis (ALS) and progressive supranuclear palsy (PSP), i.e. two neurodegenerative diseases with completely different neuropathological basis and different clinical phenotypes. We show that, across these groups, the relations between SIFS amplitude and the relative frequency of SWJ-like patterns and other SIFS characteristics follow a common law. As an explanation, we propose that physiological and technical noise comprises a small, amplitude-independent component that has little effect on large SIFSs, but causes considerable deviations from the intended amplitude and direction of small ones. Therefore, in contrast to large SIFSs, successive small SIFSs have a lower chance to meet the SWJ similarity criteria. In principle, every measurement of SIFSs is affected by an amplitude-independent noise background. Therefore, the dependence of SWJ coupling on SIFS amplitude will probably be encountered in almost any group of subjects. In addition, we find a positive correlation between SIFS amplitude and frequency in ALS, but none in PSP, suggesting that the elevated amplitudes might arise at different sites in the two disorders. [ABSTRACT FROM AUTHOR]

The Wobbler mouse is an accepted model of sporadic amyotrophic lateral sclerosis. The spinal cord of clinically symptomatic animals (3–5 months old) shows vacuolar motoneuron degeneration, inflammation, and gliosis accompanied by motor impairment. However, data are not conclusive concerning pathological changes appearing early after birth. To answer this question, we used postnatal day (PND) 6 genotyped Wobbler pups to determine abnormalities of glia and neurons at this early age period in the spinal cord. We found astrogliosis, microgliosis with morphophenotypic changes pointing to active ameboid microglia, enhanced expression of the proinflammatory markers TLR4, NFkB, TNF, and inducible nitric oxide synthase. The astrocytic enzyme glutamine synthase and the glutamate-aspartate transporter GLAST were also reduced in PND 6 Wobbler pups, suggesting excitotoxicity due to impaired glutamate homeostasis. At the neuronal level, PND 6 Wobblers showed swollen soma, increased choline acetyltransferase immunofluorescence staining, and low expression of the neuronal nuclear antigen NeuN. However, vacuolated motoneurons, a typical signature of older clinically symptomatic Wobbler mice, were absent in the spinal cord of PND 6 Wobblers. The results suggest predominance of neuroinflammation and abnormalities of microglia and astrocytes at this early period of Wobbler life, accompanied by some neuronal changes. Data support the non-cell autonomous hypothesis of the Wobbler disorder, and bring useful information with regard to intervening molecular inflammatory mechanisms at the beginning stage of human motoneuron degenerative diseases. [ABSTRACT FROM AUTHOR]

Phishing is a term for unauthorized access to users' confidential information such as password, username, and credit card information. Today, the internet has become an essential need, and most of the requirements are provided through the internet. For this reason, phishers deceive users on the internet in various ways, such as social engineering, and steal their confidential information. Therefore, designing a method to deal with phishing is essential. However, if a phishing detection system only has high accuracy and ignores the time required to detect phishing, it will cause lengthy delays. In this research, phishing, anti-phishing, and feature selection methods are introduced. In the following, a data set containing 30 features of phishing and legal websites was prepared. Then we presented an effective method for feature selection based on Genetic Algorithm to deal with phishing, which can reduce the phishing detection time and maintain accuracy. Finally, the results show that the proposed method can achieve a detection time of about 0.46 s and an accuracy of 96.04%, which is better than existing research. [ABSTRACT FROM AUTHOR]

Background: We analysed the relationship between cerebrospinal fluid (CSF)/serum albumin quotient (Q-Alb) and phenotype in a large cohort of patients with amyotrophic lateral sclerosis (ALS). Methods: Three hundred twenty-eight single-centre consecutive patients with ALS were evaluated for Q-Alb, basic epidemiological and clinical data, motor phenotype, cognitive/behavioural impairment, clinical staging, clinical and neurophysiological indexes of upper (UMN) and lower motor neuron (LMN) dysfunction, and presence of ALS gene mutations. Results: Q-Alb did not correlate with age but was independently associated with sex, with male patients having higher levels than female ones; the site of onset was not independently associated with Q-Alb. Q-Alb was not associated with motor phenotype, cognitive/behavioural impairment, disease stage, progression rate, survival, or genetic mutations. Among measures of UMN and LMN dysfunction, Q-Alb only had a weak positive correlation with an electromyography-based index of active limb denervation. Conclusion: Previous work has documented increased Q-Alb in ALS compared to unaffected individuals. This, together with the absence of associations with nearly all ALS phenotypic features in our cohort, suggests dysfunction of the blood-CSF barrier as a shared, phenotype-independent element in ALS pathophysiology. However, correlation with the active denervation index could point to barrier dysfunction as a local driver of LMN degeneration. [ABSTRACT FROM AUTHOR]

Objective: We explored the potential of neurofilament light chain (NfL) in serum and cerebrospinal fluid as a biomarker for neurodestruction in status epilepticus. Methods: In a retrospective analysis, we measured NfL in serum and cerebrospinal fluid samples of patients with status epilepticus using a highly sensitive single-molecule array technique (Simoa). Status epilepticus was diagnosed according to ILAE criteria. Additionally, we employed an alternative classification with more emphasis on the course of status epilepticus. We used data from three large control groups to compare NfL in status epilepticus versus neurologically healthy controls. Results: We included 28 patients (mean age: 69.4 years, SD: 15 years) with a median status duration of 44 h (IQR: 80 h). Twenty-one patients (75%) suffered from convulsive status epilepticus and seven (25%) from non-convulsive status epilepticus. Six patients died (21%). Cerebrospinal fluid and serum NfL concentrations showed a high correlation (r = 0.73, p < 0.001, Pearson). The main determinant of NfL concentration was the status duration. NfL concentrations did not differ between convulsive status epilepticus and convulsive status epilepticus classified according to the ILAE or to the alternative classification without and with adjusting for status duration and time between status onset and sampling. We found no association of NfL concentration with death, treatment refractoriness, or prognostic scores. Conclusion: The results suggest that neurodestruction in status epilepticus measured by NfL is mainly determined by status duration, not status type nor therapy refractoriness. Therefore, our results suggest that regarding neurodestruction convulsive and non-convulsive status epilepticus are both neurological emergencies of comparable urgency. [ABSTRACT FROM AUTHOR]

Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. Autonomous vehicles (AVs) are set to address major transportation challenges including safety, efficiency, reliability, sustainability, and personalization. The foremost challenge for AVs is to perceive their environments in real-time with the highest possible certainty. Relatedly, connected vehicles (CVs) have been another major driver of innovation in transportation. In this paper, we bring autonomous and connected vehicles together and propose TAAWUN, a novel approach based on the fusion of data from multiple vehicles. The aim herein is to share the information between multiple vehicles about their environments, enhance the information available to the vehicles, and make better decisions regarding the perception of their environments. TAWUN shares, among the vehicles, visual data acquired from cameras installed on individual vehicles, as well as the perceived information about the driving environments. The environment is perceived using deep learning, random forest (RF), and C5.0 classifiers. A key aspect of the TAAWUN approach is that it uses problem specific feature sets to enhance the prediction accuracy in challenging environments such as problematic shadows, extreme sunlight, and mirage. TAAWUN has been evaluated using multiple metrics, accuracy, sensitivity, specificity, and area-under-the-curve (AUC). It performs consistently better than the base schemes. Directions for future work to extend the tool are provided. This is the first work where visual information and decision fusion are used in CAVs to enhance environment perception for autonomous driving. [ABSTRACT FROM AUTHOR]

Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain–computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs. [ABSTRACT FROM AUTHOR]

Purpose of Review: Peripheral nervous system vasculitides (PNSV) are a heterogeneous group of disorders with a clinical subset that may differ in prognosis and therapy. We provide a comprehensive update on the clinical assessment, diagnosis, complications, treatment, and follow-up of PNSV. Recent Findings: Progress in neuroimaging, molecular testing, and peripheral nerve biopsy has improved clinical assessment and decision-making of PNSV, also providing novel insights on how to prevent misdiagnosis and increase diagnostic certainty. Advances in imaging techniques, allowing to clearly display the vessel walls, have also enhanced the possibility to differentiate inflammatory from non-inflammatory vascular lesions, while recent histopathology data have identified the main morphological criteria for more accurate diagnosis and differential diagnoses. Overall, the identification of peculiar morphological findings tends to improve diagnostic accuracy by defining a clearer boundary between systemic and non-systemic neuropathies. Therefore, the definition of epineurium vessel wall damage, type of vascular lesion, characterization of lymphocyte populations, antibodies, and inflammatory factors, as well as the identification of direct nerve damage or degeneration, are the common goals for pathologists and clinicians, who will both benefit for data integration and findings translation. Nevertheless, to date, treatment is still largely empiric and, in some cases, unsatisfactory, thus often precluding precise prognostic prediction. In this context, new diagnostic techniques and multidisciplinary management will be essential in the proper diagnosis and prompt management of PNSV, as highlighted in the present review. Summary: Thirty to fifty percent of all patients with vasculitis have signs of polyneuropathy. Neuropathies associated with systemic vasculitis are best managed according to the guidelines of the underlying disease because appropriate workup and initiation of treatment can reduce morbidity. Steroids, or in severe or progressive cases, cyclophosphamide pulse therapy is the standard therapy in non-systemic vasculitic neuropathies. Some patients need long-term immunosuppression. The use of novel technologies for high-throughput genotyping will permit to determine the genetic influence of related phenotypes in patients with PNSV. [ABSTRACT FROM AUTHOR]

Nowadays, phishing attacks have become one of the major security threats that acquire the personal credentials of Internet users via forged websites for committing fraudulent financial transactions. The traditional phishing detection approaches employ single classification method in which the accuracy is more dependent on specific classification algorithm. A particular classifier may well perform on some dataset and less accurately on others. Hence, the framework for combining the complementary information of different classifiers is required to increase the prediction accuracy. This study assesses the performance of various neural network algorithms for selecting the base classifiers and models an ensemble method for detecting phishing websites. Based on the experimental results, Radial Basis Function (RBF), Generalized Radial Basis Function (GRBF), Probabilistic Neural Network (PNN), and Heteroscedastic Probabilistic Neural Network (HPNN) have been chosen as base classifiers for the proposed ensemble method. The proposed approach is focused on improving the performance of base classifiers individually as well as collaboratively for detecting phishing websites. Our proposed ensemble approach, Deep Ensemble Evidential Neural Network (DeepEEviNNet) is obtained by combining the outcome of base classifiers based on their weights for making the final decision. The optimal weight of each classifier is determined by the distance existing between the fusion result that is calculated using Dempster Shafer Theory (DST) and the ground truth. In addition, a novel categorical clustering algorithm named WEighted Fuzzy condense K-Modes (WEFKM) clustering is proposed to determine the RBF centers and Gaussian kernels of the base classifiers. The performance of DeepEEviNNet has been evaluated on various phishing datasets. The results obtained from the experiments reveal that DeepEEviNNet outperforms the stand-alone classification techniques as well as other ensemble methods for detecting phishing attacks. [ABSTRACT FROM AUTHOR]

Several studies using cryogenic electron microscopy (cryo-EM) techniques recently reported the isolation and characterization of novel protein filaments, composed of a C-terminal fragment (CTF) of the endolysosomal transmembrane protein 106B (TMEM106B), from human post-mortem brain tissue with various neurodegenerative conditions and normal aging. Genetic variation in TMEM106B is known to influence the risk and presentation of several neurodegenerative diseases, especially frontotemporal dementia (FTD) caused by mutations in the progranulin gene (GRN). To further elucidate the significance of TMEM106B CTF, we performed immunohistochemistry with antibodies directed against epitopes within the filament-forming C-terminal region of TMEM106B. Accumulation of TMEM106B C-terminal immunoreactive (TMEM-ir) material was a common finding in all the conditions evaluated, including frontotemporal lobar degeneration with TDP-43 pathology (FTLD-TDP), Alzheimer's disease, tauopathies, synucleinopathies and neurologically normal aging. TMEM-ir material was present in a wide range of brain cell types and in a broad neuroanatomical distribution; however, there was no co-localization of TMEM-ir material with other neurodegenerative proteins in cellular inclusions. In most conditions, the presence and abundance of TMEM-ir aggregates correlated strongly with patient age and showed only a weak correlation with the TMEM106B haplotype or the primary pathological diagnosis. However, all patients with FTD caused by GRN mutations were found to have high levels of TMEM-ir material, including several who were relatively young (< 60 years). These findings suggest that the accumulation of TMEM106B CTF is a common age-related phenomenon, which may reflect lysosomal dysfunction. Although its significance in most neurodegenerative conditions remains uncertain, the consistent finding of extensive TMEM-ir material in cases of FTLD-TDP with GRN mutations further supports a pathomechanistic role of TMEM106B and lysosomal dysfunction in this specific disease population. [ABSTRACT FROM AUTHOR]

With the origin of smart homes, smart cities, and smart everything, smart phones came up as an area of magnificent growth and development. These devices became a part of daily activities of human life. This impact and growth have made these devices more vulnerable to attacks than other devices such as desktops or laptops. Text messages or SMS (Short Text Messages) are a part of smartphones through which attackers target the users. Smishing (SMS Phishing) is an attack targeting smartphone users through the medium of text messages. Though smishing is a type of phishing, it is different from phishing in many aspects like the amount of information available in the SMS, the strategy of attack, etc. Thus, detection of smishing is a challenge in the context of the minimum amount of information shared by the attacker. In the case of smishing, we have short text messages which are often in short forms or in symbolic forms. A single text message contains very few smishing-related features, and it consists of abbreviations and idioms which makes smishing detection more difficult. Detection of smishing is a challenge not only because of features constraint but also due to the scarcity of real smishing datasets. To differentiate spam messages from smishing messages, we are evaluating the legitimacy of the URL (Uniform Resource Locator) in the message. We have extracted the five most efficient features from the text messages to enable the machine learning classification using a limited number of features. In this paper, we have presented a smishing detection model comprising of two phases, Domain Checking Phase and SMS Classification Phase. We have examined the authenticity of the URL in the SMS which is a crucial part of SMS phishing detection. In our system, Domain Checking Phase scrutinizes the authenticity of the URL. SMS Classification Phase examines the text contents of the messages and extracts some efficient features. Finally, the system classifies the messages using Backpropagation Algorithm and compares results with three traditional classifiers. A prototype of the system has been developed and evaluated using SMS datasets. The results of the evaluation achieved an accuracy of 97.93% which shows the proposed method is very efficient for the detection of smishing messages. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is one of the progressive neurological diseases which affect around 10 million population worldwide. The clinical manifestation of motor symptoms in PD patients appears later when most dopaminergic neurons have degenerated. Thus, for better management of PD, the development of accurate biomarkers for the early prognosis of PD is imperative. The present work will discuss the potential biomarkers from various attributes covering biochemical, microRNA, and neuroimaging aspects (α-synuclein, DJ-1, UCH-L1, β-glucocerebrosidase, BDNF, etc.) for diagnosis, recent development in PD management, and major limitations with current and conventional anti-Parkinson therapy. This manuscript summarizes potential biomarkers and therapeutic targets, based on available preclinical and clinical evidence, for better management of PD. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the solid tumors investigated for gene expression. Objective: Our research aimed to investigate the roles of transmembrane protein 106C (TMEM106C) on breast cancer and the underlying mechanisms. Results: The results from GEPIA website indicated that TMEM106C was up-regulated in breast cancer and the TMEM106C over-expression was concerned with poor outcomes in breast cancer patients. Moreover, western blotting and qRT-PCR assay also showed that TMEM106C level was up-regulated in breast cancer cells. Our results showed that over-expression of TMEM106C accelerated the malignant phenotypes of MCF7 cells, while TMEM106C silencing displayed the opposite outcomes in MDA-MB-231 cells. Furthermore, TMEM106C over-expression activated PI3K/AKT/mTOR signaling, which reversed by Wortmannin. Similarly, TMEM106C silencing inhibited PI3K/AKT/mTOR signaling, which abolished by 740Y-P. Moreover, we also confirmed that 740Y-P significantly reversed the function of TMEM106C silencing on the malignant phenotypes of MDA-MB-231 cells. Conclusion: This study indicated that TMEM106C could promote the malignant phenotypes of breast cancer cells by activating PI3K/AKT/mTOR signaling. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis, a fatal neurodegeneration disease affecting motor neurons in the brain and spinal cord, is difficult to diagnose and treat. The objective of this study is to identify novel candidate genes related to ALS. Transcriptome-wide association study of ALS was conducted by integrating the genome-wide association study summary data (including 1234 ALS patients and 2850 controls) and pre-computed gene expression weights of different tissues. The ALS-associated genes identified by TWAS were further compared with the differentially expressed genes detected by the mRNA expression profiles of the sporadic ALS. Functional enrichment and annotation analysis of identified genes were performed by an R package and the functional mapping and annotation software. TWAS identified 761 significant genes (PTWAS < 0.05), 627 Gene ontology terms, and 8 Kyoto Encyclopedia of Genes and Genomes pathways for ALS, such as C9orf72, with three expression quantitative trait loci were found significantly: rs2453554 (PTWAS CBRS = 4.68 × 10–10, PTWAS CBRS = 2.54 × 10–9), rs10967976 (PTWAS CBRS = 7.85 × 10–10, PTWAS CBRS = 8.91 × 10–9, PTWAS CBRS = 1.49 × 10–7, PTWAS CBRS = 5.59 × 10–7), rs3849946 (PTWAS CBRS = 7.69 × 10–4, PTWAS YBL = 4.02 × 10–2), Mitochondrion (Padj = 4.22 × 10–16), and Cell cycle (Padj = 2.04 × 10–3). Moreover, 107 common genes, 4 KEGG pathways and 41 GO terms were detected by integrating mRNA expression profiles of sALS, such as CPVL (FC = 2.06, PmRNA = 6.99 × 10–6, PTWAS CBR = 2.88 × 10–2, PTWAS CBR = 4.37 × 10–2), Pyrimidine Metabolism (Padj = 2.43 × 10–2), and Cell Activation (Padj = 5.54 × 10–3). Multiple candidate genes and pathways were detected for ALS. Our findings may provide novel clues for understanding the genetic mechanism of ALS. [ABSTRACT FROM AUTHOR]

Current treatments for dysphagia in ALS do not target the underlying tongue weakness and denervation atrophy that is prevalent in spinal and bulbar ALS cases. To address this clinical gap, we studied the low copy number SOD1-G93A (LCN-SOD1) mouse model of ALS to quantify the impact of limb phenotype on tongue denervation atrophy, dysphagia penetrance, and survival time in preparation for future treatment-based studies. Two male LCN-SOD1 breeders and 125 offspring were followed for limb phenotype inheritance, of which 52 (30 LCN-SOD1 and 22 wild-type/WT, both sexes) underwent characterization of dysphagia penetrance (via videofluoroscopic swallow study; VFSS) and survival time at disease end-stage (15–20% body weight loss). From these, 16 mice (8/genotype) underwent postmortem histological analysis of the genioglossus for evidence of denervation atrophy. Results revealed that both breeders displayed a mixed (hindlimb and forelimb) ALS phenotype and sired equal proportions of hindlimb vs. mixed phenotype offspring. Dysphagia penetrance was complete for mixed (100%) versus incomplete for hindlimb (64%) phenotype mice; yet survival times were similar. Regardless of limb phenotype, LCN-SOD1 mice had significantly smaller genioglossus myofibers and more centralized myonuclei compared to WT mice (p < 0.05). These biomarkers of denervation atrophy were significantly correlated with VFSS metrics (lick and swallow rates, p < 0.05) but not survival time. In conclusion, both LCN-SOD1 phenotypes had significant tongue denervation atrophy, even hindlimb phenotype mice without dysphagia. This finding recapitulates human ALS, providing robust rationale for using this preclinical model to explore targeted treatments for tongue denervation atrophy and ensuing dysphagia. [ABSTRACT FROM AUTHOR]

The development of digitization over the globe has made digital security inescapable. As every single article on this planet is being digitalized quickly, it is more important to protect those items. Numerous cyber threats effectively deceive ordinary individuals to take away their identifications. Phishing is a kind of social engineering attack where the hackers are using this kind of attack in modern days to steal the user's credentials. After a systematic research analysis of phishing technique and email scam, an intrusion detection system in chrome extension is developed. This technique is used to detect real-time phishing by examining the URL, domain, content and page attributes of an URL prevailing in an email and any web page portion. Considering the reliability, robustness and scalability of an efficient phishing detection system, we designed a lightweight and proactive rule-based incremental construction approach to detect any unknown phishing URLs. Due to the computational intelligence and nondependent of the blacklist signatures, this application can detect the zero-day and spear phishing attacks with a detection rate of 89.12% and 76.2%, respectively. The true positive values acquired in our method is 97.13% and it shows less than 1.5% of false positive values. Thus the application shows the precision level higher than the previous model developed and other phishing techniques. The overall results indicate that our framework outperforms the existing method in identifying phishing URLs. [ABSTRACT FROM AUTHOR]

More than half a century ago Martin Gardner popularized a question leading to the benchmark problem of determining the minimum side length of a square into which the squares of sizes 1 , 2 , ⋯ , n can be packed without overlap. Constructions are known for a certain range of n, and summing up the areas yields that a packing in a square of size smaller than N : = n (n + 1) (2 n + 1) / 6) is not possible. Here we prove that an asymptotically minimal packing exists in a square of size N + c n + O (n) with c < 1 , and such a packing is achievable with guillotine-cuts. An improved construction is also given for the case where the constraint of guillotine cutting is dropped. [ABSTRACT FROM AUTHOR]

Introduction: Progranulin (PGRN) is a secreted glycoprotein encoded in humans by the GRN gene, located on chromosome 17q21. Several nonsense and missense pathogenetic GRN mutations have been described. Objective: We herein describe two sisters carrying a rare GRN mutation with extremely different clinical features and family history of dementia and behavioral disorders, with a novel presentation with stridor and dysphonia. Methods: Patients underwent a multidimensional assessment including neurological and neuropsychological evaluation, structural and functional imaging, and genetic screening. Results: The younger sister presented at the age of 64 with inspiratory stridor, dysphonia and exercise-induced dyspnea. Transnasal fiberoptic laryngoscopy showed bilateral adduction of the vocal cords at rest and paradoxical further adduction of the vocal cords during forced inspiration, suggesting the hypothesis of an adductor laryngeal dystonia. The older sister presented at the age of 63 with a rapidly progressive corticobasal syndrome. The only clinical feature common to both sisters was a dysexecutive syndrome. The c.893G > A mutation in exon 9 of GRN was found in heterozygosis in both sisters, causing a missense Arginine to Histidine substitution in position 298 of the protein (p.R298H). Conclusions: Our report supports the pathogenicity of the GRN p.R298H mutation, which is first detected in two members from the same family, showing an extremely different phenotypes. Moreover, we report the first case of an FTD-associated mutation presenting with inspiratory stridor and dysphonia linked to adductor laryngeal dystonia, thus expanding the clinical spectrum of GRN-related disorders. [ABSTRACT FROM AUTHOR]

Through their role in energy generation and regulation of several vital pathways, including apoptosis and inflammation, mitochondria are critical for the life of eukaryotic organisms. Mitochondrial dysfunction is a major problem implicated in the etiology of many pathologies, including neurodegenerative diseases, such as Parkinson’s disease (PD), Alzheimer’s disease (AD), Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS), diabetes, cardiovascular diseases, and many others. Proteotoxic stress, here defined as a reduction in bioenergetic activity induced by the accumulation of aberrant proteins in the mitochondria, is likely to be implicated in disease-linked mitochondrial and cellular decline. Various quality control pathways, such as mitochondrial unfolded protein response (mtUPR), the ubiquitin (Ub)-dependent degradation of aberrant mitochondrial proteins, and mitochondria-specific autophagy (mitophagy), respond to proteotoxic stress and eliminate defective proteins or dysfunctional mitochondria. This work provides a concise review of mechanisms by which disease-linked aberrant proteins affect mitochondrial function and an overview of mitochondrial quality control pathways that counteract mitochondrial proteotoxicity. We focus on mitochondrial quality control mechanisms relying on the Ub-mediated protein degradation, such as mitochondria-specific autophagy and the mitochondrial arm of the Ub proteasome system (UPS). We highlight the importance of a widening perspective of how these pathways protect mitochondria from proteotoxic stress to better understand mitochondrial proteotoxicity in overlapping pathophysiological pathways. Implications of these mechanisms in disease development are also briefly summarized. [ABSTRACT FROM AUTHOR]

Since the initial identification of TMEM106B as a risk factor for frontotemporal lobar degeneration (FTLD), multiple genetic studies have found TMEM106B variants to modulate disease risk in a variety of brain disorders and healthy aging. Neurodegenerative disorders are typically characterized by inclusions of misfolded proteins and since lysosomes are an important site for cellular debris clearance, lysosomal dysfunction has been closely linked to neurodegeneration. Consequently, many causal mutations or genetic risk variants implicated in neurodegenerative diseases encode proteins involved in endosomal–lysosomal function. As an integral lysosomal transmembrane protein, TMEM106B regulates several aspects of lysosomal function and multiple studies have shown that proper TMEM106B protein levels are crucial for maintaining lysosomal health. Yet, the precise function of TMEM106B at the lysosomal membrane is undetermined and it remains unclear how TMEM106B modulates disease risk. Unexpectedly, several independent groups recently showed that the C-terminal domain (AA120-254) of TMEM106B forms amyloid fibrils in the brain of patients with a diverse set of neurodegenerative conditions. The recognition that TMEM106B can form amyloid fibrils and is present across neurodegenerative diseases sheds new light on TMEM106B as a central player in neurodegeneration and brain health, but also raises important new questions. In this review, we summarize current knowledge and place a decade's worth of TMEM106B research into an exciting new perspective. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to clarify the relationship between progressive medial temporal atrophy and onset subtype in patients with amyotrophic lateral sclerosis (ALS). Methods: Medial temporal atrophy, ALS functional rating scale (ALSFRS), and cognitive function were assessed in 119 patients who were grouped into three ALS subtypes: bulbar, upper limb, and lower limb onset. Medial temporal atrophy, represented by a Z-score, was determined using an analysis software of magnetic resonance images known as the voxel-based specific regional analysis system for Alzheimer's disease (VSRAD). Among 119 patients, 60 underwent follow-up VSRAD, ALSFRS, and cognitive testing. The sequential data were compared among onset subtypes. Furthermore, TDP-43 pathology was assessed in 20 autopsied patients (including seven who underwent VSRAD before death) to examine the relationships among medial temporal atrophy, onset subtypes, and severity of the hippocampal TDP-43 pathology. Results: Multiple regression analysis revealed that the Z-score at baseline was associated with the age of onset and duration of illness. A high Z-score at baseline and the bulbar/upper limb subtypes affected the progression rate of Z-score. Pathological examination revealed increased hippocampal TDP-43 pathology score associated with bulbar and upper limb subtypes. Moreover, the Z-score before death correlated with the hippocampal TDP-43 pathology score. Conclusion: Medial temporal atrophy in ALS is associated with bulbar and upper limb onset subtypes. This progression may be related to the extent of TDP-43 pathology. [ABSTRACT FROM AUTHOR]

Genistein (GE) or 4′,5,7-trihydroxyflavone, a plant derived isoflavone, is a biologically active compound having several beneficial properties. Studies showed that GE possesses anti-neoplastic, anti-tumor, anti-helminthic, anti-oxidant, and anti-inflammatory activities. Herein, we investigated the neuroprotective effects of GE in a mouse model of hypoxia-induced amnesia. Mice were exposed to hypoxic conditions (10% O2) in a designated hypoxia chamber and co-treated with GE (10, 20, or 30 mg/kg) for 4 weeks. Following this, behavioral tests were performed to evaluate memory performance. We assessed microglial activation in the hippocampus, amygdala, and pre-frontal cortex (PFC) regions by evaluating the Iba-1 and GFAP transcript levels, and MIP-1β, Cox-2, and IL6 protein levels. Apoptosis was assessed by evaluating Bax, BAD, and Bcl-2 mRNA levels, and caspase-3 activity. To uncover the underlying molecular mechanism, we evaluated the levels of Nrf2, HO-1, and NQO1 in different brain regions of mice from all groups. Results showed that hypoxia-exposed mice have reduced performance in the behavioral tests and GE treatment enhanced the memory performance in hypoxia-exposed mice. Moreover, hypoxia-exposed mice showed increased expression of microglial activation markers and enhanced apoptosis in the hippocampus, amygdala, and PFC. GE treatment suppressed microglial activation and prevented apoptosis in the brain of hypoxia-exposed mice. Furthermore, hypoxia-exposure reduced the expression of Nrf2, NQO1, and HO-1 while GE treatment ameliorated this decrease in different regions of hypoxia-exposed mice brain. In conclusion, GE prevents cognitive dysfunction by suppressing microglial activation and inhibiting apoptosis in the hypoxia-exposed mice brain. [ABSTRACT FROM AUTHOR]

The neural correlates of action language processing are still debated within embodied cognition research and little is known about the flexible involvement of modality-specific pre-motor system and multimodal high-level temporo-parietal regions as a function of explicit and implicit tasks. A systematic review and the Activation likelihood estimation (ALE) meta-analyses on functional neuroimaging studies were performed to identify neural correlates of action language processing activated during explicit and implicit tasks. The contrast ALE meta-analysis revealed activation of modality-specific premotor area and inferior frontal areas during explicit action language tasks while a greater activation of posterior temporo-occipital areas emerged for implicit tasks. The conjunction analysis revealed overlap in the temporo-parietal multimodal high-level regions for both types of tasks. Functional specialization of the middle temporal gyrus was found where the more posterior-occipital part resulted activated during implicit action language tasks whereas the antero-lateral part was involved in explicit tasks. Our findings were discussed within a conceptual flexibility perspective about the involvement of both the modality-specific and multimodal brain system during action language processing depending on different types of tasks. [ABSTRACT FROM AUTHOR]

Phishing is an attack characterized by attempted fraud against users. The attacker develops a malicious page that is a trusted environment, inducing its victims to submit sensitive data. There are several platforms, such as PhishTank and OpenPhish, that maintain databases on malicious pages to support anti-phishing solutions, such as, for example, block lists and machine learning. A problem with this scenario is that many of these databases are disorganized, inconsistent, and have some limitations regarding integrity and balance. In addition, because phishing is so volatile, considerable effort is put into preserving temporal information from each malicious page. To contribute, this article built a phishing database with consistent and balanced data, temporal information, and a significant number of occurrences, totaling 942,471 records over the 5 years between 2016 and 2021. Of these records, 135,542 preserve the page's source code, 258,416 have the attack target brand detected, 70,597 have the hosting service identified, and 15,008 have the shortener service discovered. Additionally, 123,285 records store WHOIS information of the domain registered in 2021. The data is available on the website https://piracema.io/repository. [ABSTRACT FROM AUTHOR]