Biallelic variants in the ectonucleotidase ENTPD1 cause a complex neurodevelopmental disorder with intellectual disability, distinct white matter abnormalities, and spastic paraplegia.

OBJECTIVE: Human genomics established that pathogenic variation in diverse genes can underlie a single disorder. For example, hereditary spastic paraplegia (HSP) is associated with over 80 genes with frequently only few affected individuals described for each gene. Herein, we characterize a large cohort of individuals with biallelic variation in ENTPD1, a gene previously linked to spastic paraplegia 64 (MIM# 615683). METHODS: Individuals with biallelic ENTPD1 variants were recruited worldwide. Deep phenotyping and molecular characterizations were performed. RESULTS: A total of 27 individuals from 17 unrelated families were studied; additional phenotypic information was collected from published cases. Twelve novel pathogenic ENTPD1 variants are described: c.398_399delinsAA; p.(Gly133Glu), c.540del; p.(Thr181Leufs* 18), c.640del; p.(Gly216Glufs* 75), c.185T>G; p.(Leu62*), c.1531T>C; p.(*511Glnext* 100), c.967C>T; p.(Gln323*), c.414-2_414-1del, and c.146 A>G; p.(Tyr49Cys) including four recurrent variants c.1109T>A; p.(Leu370* ), c.574-6_574-3del, c.770_771del; p.(Gly257Glufs*18), and c.1041del; p.(Ile348Phefs*19). Shared disease traits include: childhood-onset, progressive spastic paraplegia, intellectual disability (ID), dysarthria, and white matter abnormalities. In vitro assays demonstrate that ENTPD1 expression and function are impaired and that c.574-6_574-3del causes exon skipping. Global metabolomics demonstrates ENTPD1 deficiency leads to impaired nucleotide, lipid, and energy metabolism. INTERPRETATION: The ENTPD1 locus trait consists of childhood disease-onset, ID, progressive spastic paraparesis, dysarthria, dysmorphisms, and white matter abnormalities with some individuals showing neurocognitive regression. Investigation of an allelic series of ENTPD1: i) expands previously described features of ENTPD1-related neurological disease, ii) highlights the importance of genotype-driven deep phenotyping, iii) documents the need for global collaborative efforts to characterize rare AR disease traits, and iv) provides insights into the disease trait neurobiology. This article is protected by copyright. All rights reserved

TCT innovation taxonomy and open foresight innovation paradigm

This paper presents a novel taxonomy for the review of literature specifically in the field of Science Fiction Prototyping (SFP) and Foresight Innovation. The taxonomy builds upon the ideas behind Socio-Technical Theory through a three-set Venn diagram that encompasses Academic Theory, Context, and Technologies (TCT). This taxonomy enables the observation for the existence of a new Open Foresight Innovation Paradigm (OFIP) that combines the previously established paradigms of Open Foresight and Open Innovation. The need to conduct research to validate the benefits of OFIP is presented by arguing that the paradigm has the potential to strengthen an organisations ability to successfully adopt SFP approaches to drive successful innovation outcomes via sustained university to business collaboration

A risk model for assessing exposure factors influence oil price fluctuations

The impact of oil price volatility on the global economy is considerable. However, the uncertainty of crude oil prices is affected by many risk factors. Several prior studies have examined the factors that impact oil price fluctuations, but these methods are unable to indicate their dynamic non-fundamental factors. To address this issue, we propose a risk model inspired by the Mean-Variance Portfolio theory. The model can automatically construct optimal portfolios that seek to maximize returns with the lowest level of risk without needing human intervention. The results demonstrate a significant asymmetric cointegrating correlation between oil price volatility and non-fundamental factors

A novel enhanced SOC estimation method for lithium-ion battery cells using cluster-based LSTM models and centroid proximity selection

In line with the global mission in achieving the net zero target through deployment of renewable energy technologies and electrifying the transportation sector; precise and adaptable State of Charge (SOC) estimation for Lithium-ion batteries has emerged as a critical need. The paper introduces a novel Cluster-Based Learning Model (CBLM) framework that integrates the strengths of K-Means and Fuzzy C-Means clustering with the predictive power of Long Short-Term Memory (LSTM) networks. This approach aims to enhance the precision and reliability of battery SOC estimations, adapting to the dynamic and complex operational conditions characteristic of Li-ion batteries. The key contributions of this study is the development and validation of the CBLM framework, which was proven to outperform state-of-art standalone deep learning techniques particularly under diverse operational conditions. Additionally, the introduction of a centroid proximity selection mechanism within the CBLM framework, which dynamically selects the most appropriate cluster model in real-time based on the proximity of the operational data to the cluster centroids. The performance of the proposed CBLM approach is evaluated using a Tesla Model 3 2170 Li-ion battery dataset. Results demonstrate the model's enhanced performance, with reductions in Root Mean Square Error (RMSE) to as low as 0.65% and Mean Absolute Error (MAE) to 0.51%, reducing state-of-art benchmark model errors by margins of 61.8% and 68.5% respectively. Additionally, the maximum error using CBLM was lower than benchmark, emphasising the model's reliability in worst-case-scenarios. The study also conducted comprehensive ablation tests on the proposed novel framework to further optimise its performance

Multi View Face Detection in Cattle Using Infrared Thermography

© 2020, Springer Nature Switzerland AG. Face detection in thermal imaging has been used widely in human for different purposes such as surveillance, obtaining physiological reading: respiratory and heart rate from face region via thermal imaging. Physiological reading via infrared thermal imaging used in emotion and stress detection as well as polygraph analysis. In animal as general and cattle in specific, face region localized manually in order to obtain temperature for eyes, nose and mouth, which used for stress, diseases and inflammation detection. In order to develop a future automated system for monitoring health conditions in cattle, it required to detect the face region automatically. Based on author knowledge, there is no research done regarding face detection in cattle using infrared thermal images. Unlike the human, cattle keep roaming, which lead to a change in the face and body orientation. The main objective of this paper is proposing a new method for Multi-view face detection in cattle with accuracy enhancement by using three classifiers and temperature thresholding. Classifiers are established by using Histogram Oriented Gradient (HOG) as features and Support vector machine (SVM) for classification. The results show that the proposed algorithm is performing well in term of Specificity, Recall and F-measure and detection rate compare to the currently used method in the literature

Automatic eyes localization in thermal images for temperature measurement in cattle

© 2017 IEEE. Infrared thermography technology (IRT) is a non-invasive method that has been used to measure the temperature from Infrared images. The measured temperature can be correlated to evaluate health status. Due to the ability of IRT for measuring temperature remotely, IRT has succeeded widely in detecting a different kind of diseases and inflammations in the animal. Many studies pointed out that eye is the best region for measuring body temperature. Recent work deals with automation of the measurement as a part of the computer-aided diagnosis system. However, such system still based on localization of the eye manually. This paper proposes a novel automated method for eyes localization in cattle. The proposed algorithm is work based on masking the face by modified ellipse detection algorithm. It also extends to remove the consideration for the position and orientation of target animal. Experimental results show that proposed algorithm is working well for localization purpose and temperature measurement as the final result

Phenotypic and molecular detection of biofilm production by P. aeruginosa isolated from different clinical cases

This research was performed in the aim of detection the ability of  Pseudomonas aeruginosa to form a biofilm. So 100 sample were collected from hospitalized and reviewed patients with a different clinical cases including (urinary tract infections, respiratory tract infections, burns, andotitis), both sexes and different ages,at Al-Sadiq hospital in BabilGovernorate, as well as Al‑Diwaniyah Teaching hospital, Burns hospital, Women & Childrenhospital in Al‑Diwaniyah Governorate.Samples were collected from October 2021 until January 2022. 20 isolate of Pseudomonas aeruginosa were obtained. The detection of biofilm formation was performed by using two conventional methods, and all isolates was able to form a biofilms. Thena genetic detection was performed throughthe search for the presenceofalgD, pelF, and pslD genes thatencode for a biofilm production. The results showed the presence of algDgene in a percentage of 65%, while the presence of pelF gene was equal to psld gene, which reached a percentage of 70%, while some isolates contained both genes, others contained pelF gene and lake ofpslD gene or vice versa, the percentage of isolates that contained all three genes was 35%, and the percentage of isolates that contained only one gene was 30%