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Abstract Objective To develop a high‐throughput sequencing panel for the diagnosis of developmental and epileptic encephalopathy in Tunisia and to clarify the frequency of disease‐causing genes in this region. Methods We developed a custom panel for next‐generation sequencing of the coding sequences of 116 genes in individuals with developmental and epileptic encephalopathy from the Tunisian population. Segregation analyses and in silico studies have been conducted to assess the identified variants' pathogenicity. Results We report 12 pathogenic variants in SCN1A, CHD2, CDKL5, SZT2, KCNT1, GNAO1, PCDH19, MECP2, GRIN2A, and SYNGAP1 in patients with developmental and epileptic encephalopathy. Five of these variants are novel: “c.149delA, p.(Asn50MetfsTer26)” in CDKL5; “c.3616C > T, p.(Arg1206Ter)” in SZT2; “c.111_113del, p.(Leu39del)” in GNAO1; “c.1435G>C, p.(Asp479His)” in PCDH19; and “c.2143delC, p.(Arg716GlyfsTer10)” in SYNGAP1. Additionally, for four of our patients, the genetic result facilitated the choice of the appropriate treatment. Significance This is the first report of a custom gene panel to identify genetic variants implicated in developmental and epileptic encephalopathy in the Tunisian population as well as the North African region (Tunisia, Egypt, Libya, Algeria, Morocco) with a diagnostic rate of 30%. This high‐throughput sequencing panel has considerably improved the rate of positive diagnosis of developmental and epileptic encephalopathy in the Tunisian population, which was less than 15% using Sanger sequencing. The benefit of genetic testing in these patients was approved by both physicians and parents.

Abstract This study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.

Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend components, such as regular seasonal fluctuations. To address this issue, algorithms combining stationary wavelet transform (SWT) with deep learning models have been proposed. The denoised series is fitted with various benchmark models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Gated Recurrent Units (Bi-GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Units Long Short-Term Memory (Bi-GRU LSTM) models. The performance of the SWT approach is evaluated using power consumption data at three different time intervals (1 min, 15 min, and 1 h). The performance of these models is evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The SWT/GRU model, utilizing the bior2.4 filter at level 1, has emerged as a highly reliable option for precise power consumption forecasting across various time intervals. It is observed that the bior2.4/GRU model has enhanced accuracy by over 60% compared to the deep learning model alone across all accuracy measures. The findings clearly highlight the success of the SWT denoising technique with the bior2.4 filter in improving the power consumption prediction accuracy.

Research Question: Do firm characteristics affect compliance with IFRS 15 mandatory disclosures? Motivation: IFRS 15 became mandatory for annual periods beginning on or after January 1, 2018. It introduces new revenue recognition rules compared to the legacy standards and sets extensive disclosure requirements. Focusing on compliance with IFRS 15 mandatory disclosures allows us to measure and understand firm’s disclosures on a specific topic such as revenue which represents a key performance indicator for a given firm. Idea: This study examines the association between firm characteristics and compliance with IFRS 15 disclosures. Data: We selected non-financial firms listed on the French stock market index CAC all tradable. 431 firm-year observations operating in different sectors were identified and cover the 2018- 2021 period. Based on a list comprising the disclosures required under IFRS 15, we performed a content analysis of the annual reports to measure compliance level with IFRS 15 mandatory disclosures. An unweighted disclosure index was then computed. We collected data on firm characteristics from DATASTREAM database. Tools: We developed a multiple regression model with panel data including industry and year fixed effects. We used STATA software to estimate the model. Findings: Results show that the degree of compliance with IFRS 15 mandatory disclosures varies from one industry to another as well as within the same industry and firms do not fully comply with IFRS15 disclosure requirements. In addition, firm characteristics such as firm size, leverage, profitability, audit firm size, and ownership concentration seem to be key determinants of compliance with IFRS15 mandatory disclosure requirements. Contribution: Research on how firms comply with IFRS 15 mandatory disclosures is scarce. To the best of our knowledge, apart from Napier and Stadler (2020), Boujelben and Kobbi-Fakhfakh (2020), Karim and Riya (2022) and Krupova and Partac (2022), no study has investigated this research question. While these studies have provided information on the items complied with, they have advanced descriptive analyses. To the best of our knowledge, this is the pioneer study that measures compliance with IFRS 15 mandatory disclosure requirements and provides empirical evidence on firm-level determinants of compliance levels.

Abstract Objective Mutations in the cyclin‐dependent kinase‐like 5 gene (CDKL5) are associated with a wide spectrum of clinical presentations. Early‐onset epileptic encephalopathy (EOEE) is the most recognized phenotype. Here we describe phenotypic features in eight Tunisian patients with CDKL5‐related encephalopathy. Methods We included all cases with clinical features consistent with CDKL5‐related encephalopathy: infantile epileptic spasm, acquired microcephaly, movement disorders and visual impairment. We collected data about seizure types, electroencephalogram, magnetic resonance imaging, and metabolic analysis. The diagnosis of CDKL5 mutation was made thanks to Sanger sequencing with an ABI PRISM 3100‐Avant automated DNA sequencer using a Big Dye Terminator Cycle Sequencing Reaction Kit v1.1. and Next Generation Sequencing (NGS) since the development of a gene panel responsible for DEE within the framework of “Strengthening the Sfax University Expertise for diagnosis and management of epileptic encephalopathies”. Results We collected four boys and four girls aged meanly 6 years old with confirmed mutation on CDKL5 gene. Overall, we identified five de novo CDKL5 mutations including three Frame‐shift mutations, one missense mutation, and a splicing variant. The mean age at first seizure onset was 4 months. The first seizure type was infantile epileptic spasm (4/8) followed by tonic (2/8) and myoclonic seizures (2/8). Out of eight cases, four exhibited two stages epileptic course while epilepsy in three other patients progressed on three stages. Regarding development, most cases (6/8) had psychomotor retardation from the start whilst the two others showed psychomotor regression with the onset of seizures. Additional clinical features included visual impairment (7/8), tone abnormalities (7/8), stereotypies (7/8), and acquired microcephaly (6/8). Significance Our present report delineates an unusual phenotype of CDKL5‐related encephalopathy with male gender predominance and delayed onset epilepsy. It interestingly described new phenotypic features and uncommon benign developmental profiles in boys, different patterns of CDKL5‐epilepsy, neuroimaging findings, and CDKL5 mutational spectrum.

Wireless Sensor Networks (WSNs) have been securing a big position in the new aspect of security network attacks, where these suffer from various serious cyber threats that can play with their data integrity and reliability. Due to the key importance of WSN in a wide spectrum range of applications such as environmental monitoring and military field, building reliable, robust and efficient intrusion detection systems (IDS) is necessary. Although traditional machine learning approaches have been intended to detect these threats, they often lack high accuracy due to the complexity and dimensionality of WSN data. To address these limitations, the study introduces an innovative approach that greatly improves intrusion detection performance in WSNs by combining a high-speed deep learning model with sophisticated feature selection methods. The newly developed system underwent extensive testing using the WSN-DS dataset and applied Gaussian Naive Bayes (GNB) and Stochastic Gradient Descent (SGD) algorithms within the machine learning framework. The outcomes were exceptional, demonstrating a flawless accuracy rate of 100% and representing a significant advancement compared to prior methodologies based solely on traditional machine learning techniques. The study illustrates how the fusion of deep learning and optimized feature selection effectively addresses the distinctive challenges presented by WSN environments. The results not only present a highly precise and effective method for intrusion detection but also lay the groundwork for further research focused on fortifying the security of sensor networks against progressively intricate cyber threats.

ABSTRACT The main objective of this research work was to investigate active constituents and potential biological activities of Stipagrostis ciliata aerial parts, which may re-explore this plant as an important medicinal plant rather than being used only forage for animals. The results indicated that the total phenolic contents of the methanol, hexane and acetate leaf extract vary from 34.45 and 263.16 mg gallic acid equivalent (EAG)/g of extract. The highest concentrations of phenolics were found in the methanol extract. The results were suggestive that methanol extract is very rich in antioxidant compounds. The highest antioxidant activity was obtained in methanol extract. Stipagrostis ciliata ethyl acetate extract has an IC50 from 0.3 μg/mL. The antibacterial activity of S. ciliata extracts against six bacterial strains showed varying degrees of inhibition on the tested strains. The methanol extract was found to be the most potent (8 to 20.41 mm) against most tested strains and Escherichia coli was the most vulnerable bacteria with a MIC value of 15 µgmL−1. The Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC-ESI-MS/MS) analyses of methanol and ethyl acetate fractions allowed the identification of six phenolic acids and six flavonoids. The present study shows that S. ciliata could be regarded as a promising plant source of bioactive phenolic compounds with good antioxidant and antibacterial activities.