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Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

This paper introduces a multi-compartment model for microscopic diffusion anisotropy imaging. The aim is to estimate microscopic features specific to the intra- and extra-neurite compartments in nervous tissue unconfounded by the effects of fibre crossings and orientation dispersion, which are ubiquitous in the brain. The proposed MRI method is based on the Spherical Mean Technique (SMT), which factors out the neurite orientation distribution and thus provides direct estimates of the microscopic tissue structure. This technique can be immediately used in the clinic for the assessment of various neurological conditions, as it requires only a widely available off-the-shelf sequence with two b-shells and high-angular gradient resolution achievable within clinically feasible scan times. To demonstrate the developed method, we use high-quality diffusion data acquired with a bespoke scanner system from the Human Connectome Project. This study establishes the normative values of the new biomarkers for a large cohort of healthy young adults, which may then support clinical diagnostics in patients. Moreover, we show that the microscopic diffusion indices offer direct sensitivity to pathological tissue alterations, exemplified in a preclinical animal model of Tuberous Sclerosis Complex (TSC), a genetic multi-organ disorder which impacts brain microstructure and hence may lead to neurological manifestations such as autism, epilepsy and developmental delay. [Display omitted] •The Spherical Mean Technique (SMT) has enabled us to recover the microscopic diffusion process in brain tissue.•This paper introduces a multi-compartment model, which provides estimates of neurite density and intrinsic diffusivity.•These microscopic diffusion-based features are unconfounded by fibre crossings and orientation dispersion.•SMT requires only an off-the-shelf diffusion sequence with two b-shells achievable on standard clinical scanners.•Demonstrator applications: human normative database of the novel biomarkers; preclinical mouse study for the detection of tuberous sclerosis (TSC).

Tuberous sclerosis complex (TSC) is a multisystem disorder that results from heterozygous mutations in either or . The primary organ systems that are affected include the brain, skin, lung, kidney, and heart, all with variable frequency, penetrance, and severity. Neurological features include epilepsy, autism, and intellectual disability. There are more than 1,500 known pathogenic variants for and , including deletion, nonsense, and missense mutations, and all pathogenic mutations are inactivating, leading to loss of function effects on the encoded proteins TSC1 and TSC2. These proteins form a complex to constitutively inhibit mechanistic target of rapamycin (mTOR) signaling cascade, and as a consequence, mTOR signaling is constitutively active within all TSC-associated lesions. The mTOR inhibitors rapamycin (sirolimus) and everolimus have been shown to reduce the size of renal and brain lesions and improve pulmonary function in TSC, and these compounds may also decrease seizure frequency. The clinical application of mTOR inhibitors in TSC has provided one of the first examples of precision medicine in a neurodevelopmental disorder.

Tuberous sclerosis complex (TSC) is a rare multisystem genetic disorder characterized by benign tumor growth in multiple organs, including the brain, kidneys, heart, eyes, lungs, and skin. Pathogenesis stems from mutations in either the TSC1 or TSC2 gene, which encode the proteins hamartin and tuberin, respectively. These proteins form a complex that inhibits the mTOR pathway, a critical regulator of cell growth and proliferation. Disruption of the tuberin–hamartin complex leads to overactivation of mTOR signaling and uncontrolled cell growth, resulting in hamartoma formation. Neurological manifestations are common in TSC, with epilepsy developing in up to 90% of patients. Seizures tend to be refractory to medical treatment with anti-seizure medications. Infantile spasms and focal seizures are the predominant seizure types, often arising in early childhood. Drug-resistant epilepsy contributes significantly to morbidity and mortality. This review provides a comprehensive overview of the current state of knowledge regarding the pathogenesis, clinical manifestations, and treatment approaches for epilepsy and other neurological features of TSC. While narrative reviews on TSC exist, this review uniquely synthesizes key advancements across the areas of TSC neuropathology, conventional and emerging pharmacological therapies, and targeted treatments. The review is narrative in nature, without any date restrictions, and summarizes the most relevant literature on the neurological aspects and management of TSC. By consolidating the current understanding of TSC neurobiology and evidence-based treatment strategies, this review provides an invaluable reference that highlights progress made while also emphasizing areas requiring further research to optimize care and outcomes for TSC patients.

An accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

Tuberous sclerosis complex (TSC) is an autosomal dominant syndrome that presents with diverse and complex clinical features and involves multiple human systems. TSC-related neurological abnormalities and organ dysfunction greatly affect the quality of life and can even result in death in patients with TSC. It is widely accepted that most TSC-related clinical manifestations are associated with hyperactivation of the mammalian target of rapamycin (mTOR) pathway caused by loss‑of‑function mutations in TSC1 or TSC2. Remarkable progress in basic and translational research has led to encouraging clinical advances. Although mTOR inhibitors (rapamycin/everolimus) demonstrate great potential in TSC management, two major concerns hamper their generalized application. One is the frequent manifestation of adverse events, such as stomatitis, infections, and menstrual disorders; and the other is the poor response in certain patients. Thus, indicators are required to effectively predict the efficacy of mTOR inhibitors. Herein, we have summarized the current utilization of mTOR inhibitors in the treatment of TSC and focused on their efficacy and safety, in an attempt to provide a reference to guide the treatment of TSC. Highlights Hyperactivation of mammalian target of rapamycin (mTOR) is essential in the pathogenesis of tuberous sclerosis complex (TSC) and can serve as a therapeutic target. mTOR inhibitors have shown considerable success in multiple clinical trials for the treatment of TSC, including neurological, pulmonary, cardiac, renal, and cutaneous phenotypes. mTOR inhibitors are associated with adverse events, which should be considered during the management of TSC. Indicators to predict mTOR inhibitor efficacy are required to select patients who are likely to benefit from such therapy.

Healthy progression of human pregnancy relies on cytotrophoblast (CTB) progenitor self-renewal and its differentiation toward multinucleated syncytiotrophoblasts (STBs) and invasive extravillous trophoblasts (EVTs). However, the underlying molecular mechanisms that fine-tune CTB self-renewal or direct its differentiation toward STBs or EVTs during human placentation are poorly defined. Here, we show that Hippo signaling cofactor WW domain containing transcription regulator 1 (WWTR1) is a master regulator of trophoblast fate choice during human placentation. Using human trophoblast stem cells (human TSCs), primary CTBs, and human placental explants, we demonstrate that WWTR1 promotes self-renewal in human CTBs and is essential for their differentiation to EVTs. In contrast, WWTR1 prevents induction of the STB fate in undifferentiated CTBs. Our single-cell RNA sequencing analyses in first-trimester human placenta, along with mechanistic analyses in human TSCs revealed that WWTR1 fine-tunes trophoblast fate by directly regulating WNT signaling components. Importantly, our analyses of placentae from pathological pregnancies show that extreme preterm births (gestational time ≤28 wk) are often associated with loss of WWTR1 expression in CTBs. In summary, our findings establish the critical importance of WWTR1 at the crossroads of human trophoblast progenitor self-renewal versus differentiation. It plays positive instructive roles in promoting CTB self-renewal and EVT differentiation and safeguards undifferentiated CTBs from attaining the STB fate.

Background Most evidence for TSC-associated neuropsychiatric disorders (TAND) to date have come from small studies and case reports, and very little is known about TAND in adults. We explored baseline TAND data from the large-scale international TOSCA natural history study to compare childhood and adult patterns, describe age-based patterns, and explore genotype-TAND correlations. Results The study enrolled 2216 eligible participants with TSC from 170 sites across 31 countries at the data cut-off for the third interim analysis (data cut-off date: September 30, 2015). The most common behavioural problems (reported in > 10% of participants) were overactivity, sleep difficulties, impulsivity, anxiety, mood swings, severe aggression, depressed mood, self-injury, and obsessions. Psychiatric disorders included autism spectrum disorder (ASD, 21.1%), attention deficit hyperactivity disorder (ADHD, 19.1%), anxiety disorder (9.7%), and depressive disorder (6.1%). Intelligence quotient (IQ) scores were available for 885 participants. Of these, 44.4% had normal IQ, while mild, moderate, severe, and profound degrees of intellectual disability (ID) were observed in 28.1, 15.1, 9.3, and 3.1%, respectively. Academic difficulties were identified in 58.6% of participants, and neuropsychological deficits (performance <5th percentile) in 55.7%. Significantly higher rates of overactivity and impulsivity were observed in children and higher rates of anxiety, depressed mood, mood swings, obsessions, psychosis and hallucinations were observed in adults. Genotype-TAND correlations showed a higher frequency of self-injury, ASD, academic difficulties and neuropsychological deficits in TSC2 . Those with no mutations identified (NMI) showed a mixed pattern of TAND manifestations. Children and those with TSC2 had significantly higher rates of intellectual disability, suggesting that age and genotype comparisons should be interpreted with caution. Conclusions These results emphasize the magnitude of TAND in TSC and the importance of evaluating for neuropsychiatric comorbidity in all children and adults with TSC, across TSC1 and TSC2 genotypes, as well as in those with no mutations identified. However, the high rates of unreported or missing TAND data in this study underline the fact that, even in expert centres, TAND remains underdiagnosed and potentially undertreated.

Purpose To perform comprehensive genotyping of TSC1 and TSC2 in a cohort of 94 infants with tuberous sclerosis complex (TSC) and correlate with clinical manifestations. Methods Infants were enrolled at age <4 months, and subject to intensive clinical monitoring including electroencephalography (EEG), brain magnetic resonance imaging (MRI), and neuropsychological assessment. Targeted massively parallel sequencing (MPS), genome sequencing, and multiplex ligation-dependent probe amplification (MLPA) were used for variant detection in TSC1 / TSC2 . Results Pathogenic variants in TSC1 or TSC2 were identified in 93 of 94 (99%) subjects, with 23 in TSC1 and 70 in TSC2 . Nine (10%) subjects had mosaicism. Eight of 24 clinical features assessed at age 2 years were significantly less frequent in those with TSC1 versus TSC2 variants including cortical tubers, hypomelanotic macules, facial angiofibroma, renal cysts, drug-resistant epilepsy, developmental delay, subependymal giant cell astrocytoma, and median seizure-free survival. Additionally, quantitative brain MRI analysis showed a marked difference in tuber and subependymal nodule/giant cell astrocytoma volume for TSC1 versus TSC2 . Conclusion TSC2 pathogenic variants are associated with a more severe clinical phenotype than mosaic TSC2 or TSC1 variants in TSC infants. Early assessment of gene variant status and mosaicism might have benefit for clinical management in infants and young children with TSC.