ALBERT

Beschreibung: Background The degradation of intracellular proteins plays an essential role in plant responses to stressful environments. ClpS1 and E3 ubiquitin ligase function as adaptors for selecting target substrates in caseinolytic peptidase (Clp) proteases pathways and the 26S proteasome system, respectively. Currently, the role of E3 ubiquitin ligase in the plant immune response to pathogens is well defined. However, the role of ClpS1 in the plant immune response to pathogens remains unknown. Results Here, wheat (Triticum aestivum) ClpS1 (TaClpS1) was studied and resulted to encode 161 amino acids, containing a conserved ClpS domain and a chloroplast transit peptide (1–32 aa). TaClpS1 was found to be specifically localized in the chloroplast when expressed transiently in wheat protoplasts. The transcript level of TaClpS1 in wheat was significantly induced during infection by Puccinia striiformis f. sp. tritici (Pst). Knockdown of TaClpS1 via virus-induced gene silencing (VIGS) resulted in an increase in wheat resistance against Pst, accompanied by an increase in the hypersensitive response (HR), accumulation of reactive oxygen species (ROS) and expression of TaPR1 and TaPR2, and a reduction in the number of haustoria, length of infection hypha and infection area of Pst. Furthermore, heterologous expression of TaClpS1 in Nicotiana benthamiana enhanced the infection by Phytophthora parasitica. Conclusions These results suggest that TaClpS1 negatively regulates the resistance of wheat to Pst.

Beschreibung: Background Sudden death in epilepsy (SUDEP) is a rare disease in US, however, they account for 8–17% of deaths in people with epilepsy. This disease involves complicated physiological patterns and it is still not clear what are the physio-/bio-makers that can be used as an indicator to predict SUDEP so that care providers can intervene and treat patients in a timely manner. For this sake, UTHealth School of Biomedical Informatics (SBMI) organized a machine learning Hackathon to call for advanced solutions https://sbmi.uth.edu/hackathon/archive/sept19.htm. Methods In recent years, deep learning has become state of the art for many domains with large amounts data. Although healthcare has accumulated a lot of data, they are often not abundant enough for subpopulation studies where deep learning could be beneficial. Taking these limitations into account, we present a framework to apply deep learning to the detection of the onset of slow activity after a generalized tonic–clonic seizure, as well as other EEG signal detection problems exhibiting data paucity. Results We conducted ten training runs for our full method and seven model variants, statistically demonstrating the impact of each technique used in our framework with a high degree of confidence. Conclusions Our findings point toward deep learning being a viable method for detection of the onset of slow activity provided approperiate regularization is performed.

Beschreibung: Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

Beschreibung: Background Convolutional neural network (CNN) has achieved state-of-art performance in many electroencephalogram (EEG) related studies. However, the application of CNN in prediction of risk factors for sudden unexpected death in epilepsy (SUDEP) remains as an underexplored area. It is unclear how the trade-off between computation cost and prediction power varies with changes in the complexity and depth of neural nets. Methods The purpose of this study was to explore the feasibility of using a lightweight CNN to predict SUDEP. A total of 170 patients were included in the analyses. The CNN model was trained using clips with 10-s signals sampled from the original EEG. We implemented Hann function to smooth the raw EEG signal and evaluated its effect by choosing different strength of denoising filter. In addition, we experimented two variations of the proposed model: (1) converting EEG input into an “RGB” format to address EEG channels underlying spatial correlation and (2) incorporating residual network (ResNet) into the bottle neck position of the proposed structure of baseline CNN. Results The proposed baseline CNN model with lightweight architecture achieved the best AUC of 0.72. A moderate noise removal step facilitated the training of CNN model by ensuring stability of performance. We did not observe further improvement in model’s accuracy by increasing the strength of denoising filter. Conclusion Post-seizure slow activity in EEG is a potential marker for SUDEP, our proposed lightweight architecture of CNN achieved satisfying trade-off between efficiently identifying such biomarker and computational cost. It also has a flexible interface to be integrated with different variations in structure leaving room for further improvement of the model’s performance in automating EEG signal annotation.

Beschreibung: Background Sudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization. Methods We address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording. Results We evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES. Conclusion Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES—otherwise difficult to detect due to EEG noise and artifacts—using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.

Beschreibung: Background Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997). Methods This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011). Results The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. Conclusion The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Beschreibung: Enhancer of zeste homolog 2 (EZH2), as a main component of Polycomb Repressive Complex 2, catalyzes histone H3K27me3 to silence its target gene expression. EZH2 upregulation results in cancer development and poor prognosis of cancer patients. Post-translational modifications (PTMs) are important biological events in cancer progression. PTMs regulate protein conformation and diversity functions. Recently, mounting studies have demonstrated that EZH2 stability, histone methyltransferase activity, localization, and binding partners can be regulated by PTMs, including phosphorylation, O-GlcNAcylation, acetylation, methylation and ubiquitination. However, the detailed molecular mechanisms of the EZH2-PTMs and whether other types of PTMs occur in EZH2 remain largely unclear. This review presents an overview of different roles of EZH2 modification and EZH2-PTMs crosstalk during tumorigenesis and cancer metastasis. We also discussed the therapeutic potential of targeting EZH2 modifications for cancer therapy.

Beschreibung: Natural coagulants from plants resources have gained a lot of attention as it is renewable, biodegradable, non-hazardous, lower cost, and less sludge generated compared to chemical coagulants. However there are still some drawbacks, namely long settling time and possible increase of dissolved organic carbon in the treated water. In this paper we tried to address these drawbacks by utilizing citrate modified Fe3O4 to adsorb protein from Leucaena leucocephala as the active coagulating agent. The effect of trisodium citrate concentration and protein adsorption pH to the adsorbed protein was investigated. It was found that the trisodium citrate concentration of 0.5 M and pH 4.0 gave the highest protein adsorption. The obtained magnetic coagulant was furthermore characterized using Scanning Electron Microscopy, X-ray Diffraction, Fourier Transform Infrared Spectroscopy, and Transmission Electron Microscopy to observe the characteristics before and after protein adsorption. Furthermore, the effect of pH (2 to 10) and coagulant dosage (60 to 600 mg L− 1) to the removal of synthetic Congo red wastewater and sludge volume formation was investigated. It was found that pH 3 was the best pH for coagulation due to charge neutralization mechanism of leucaena protein. Furthermore the highest removal was obtained at dosage 420 mg L− 1 with 80% removal. This result was comparable with crude extract of leucaena with half settling time (20 min) and lower increase of permanganate value, indicating lower increase of dissolved organics in the treated water.

Beschreibung: Background Skeletal muscle is a complex and heterogeneous tissue accounting for approximately 40% of body weight. Excessive ectopic lipid accumulation in the muscle fascicle would undermine the integrity of skeletal muscle in humans but endow muscle with marbling-related characteristics in farm animals. Therefore, the balance of myogenesis and adipogenesis is of great significance for skeletal muscle homeostasis. Significant DNA methylation occurs during myogenesis and adipogenesis; however, DNA methylation pattern of myogenic and adipogenic precursors derived from skeletal muscle remains unknown yet. Methods In this study, reduced representation bisulfite sequencing was performed to analyze genome-wide DNA methylation of adipogenic and myogenic precursors derived from the skeletal muscle of neonatal pigs. Integrated analysis of DNA methylation and transcription profiles was further conducted. Based on the results of pathway enrichment analysis, myogenic precursors were transfected with CACNA2D2-overexpression plasmids to explore the function of CACNA2D2 in myogenic differentiation. Results As a result, 11,361 differentially methylated regions mainly located in intergenic region and introns were identified. Furthermore, 153 genes with different DNA methylation and gene expression level between adipogenic and myogenic precursors were characterized. Subsequently, pathway enrichment analysis revealed that DNA methylation programing was involved in the regulation of adipogenic and myogenic differentiation potential through mediating the crosstalk among pathways including focal adhesion, regulation of actin cytoskeleton, MAPK signaling pathway, and calcium signaling pathway. In particular, we characterized a new role of CACNA2D2 in inhibiting myogenic differentiation by suppressing JNK/MAPK signaling pathway. Conclusions This study depicted a comprehensive landmark of DNA methylome of skeletal muscle-derived myogenic and adipogenic precursors, highlighted the critical role of CACNA2D2 in regulating myogenic differentiation, and illustrated the possible regulatory ways of DNA methylation on cell fate commitment and skeletal muscle homeostasis.

Beschreibung: Background In Morocco, consanguinity rate is very high; which lead to an increase in the birth prevalence of infants with autosomal recessive disorders. Previously, it was difficult to diagnose rare autosomal recessive diseases. Next Generation Sequencing (NGS) techniques have considerably improved clinical diagnostics. A genetic diagnosis showing biallelic causative mutations is the requirement for targeted carrier testing in parents, prenatal and preimplantation genetic diagnosis in further pregnancies, and also for targeted premarital testing in future couples at risk of producing affected children by a known autosomal recessive disease. Methods In this report, we present our strategy to advise a future couple of first cousins, whose descendants would risk cystinosis; an autosomal recessive lysosomal disease caused by mutations in the CTNS gene. Indeed, our future husband’s sister is clinically and biochemically diagnosed with cystinosis in early childhood. First, we opted to identify the patient’s CTNS gene abnormality by using (NGS), then we searched for heterozygosity in the couple’s DNA, which allows us to predict the exact risk of this familial disease in the future couple’s offspring. Results We have shown that the future husband, brother of the patient is heterozygous for the familial mutation. On the other hand, his future wife did not inherit the familial mutation. Therefore, genetic counseling was reassuring for the risk of familial cystinosis in this couple’s offspring. Conclusions We report in this study, one of the major applications of (NGS), an effective tool to improve clinical diagnosis and to provide the possibility of targeted premarital carrier testing in couples at risk.