Identifying modifier genes in SMA model mice

Spinal Muscular Atrophy (SMA) involves the loss of nerve cells called motor neurons in the spinal cord and is classified as a motor neuron disease, it affects 1 in 5000-10000 newborns, one of the leading genetic causes of infant death in USA. Mutations in the SMN1, UBA1, DYNC1H1 and VAPB genes cause spinal muscular atrophy. Extra copies of the SMN2 gene modify the severity of spinal muscular atrophy. Mutations in SMN1 (Motor Neuron 1) mainly causes SMA (Autosomal recessive inheritance). SMN1 gene mutations lead to a shortage of the SMN protein and SMN protein forms SMN complex which take part in snRNP biogenesis and pre-mRNA splicing. Without SMN protein, motor neurons die, and nerve impulses are not passed between the brain and muscles. As a result, some muscles cannot perform their normal functions, leading to weakness and impaired movement. In this research, we used SMA model mice (LL samples and Sever samples) to identify de novo mutations and modifiers operating in SMA model mice

A development potential assessment method for clean energy stations

Clean energy is expected to enter a new stage of large-scale development along with the growing demand for building regional clean energy stations. However, as many regional clean energy stations comprise multiple stations with different output characteristics and complementary coupling, the development potential of these stations cannot be simply based on the superposition of outputs, as this method lacks reasonable assessment results. This study proposes a method of combining Grey relational analysis (GRA), artificial neural network (ANN), and XGBoost algorithm for the potential assessment of clean energy stations. First, GRA and ANN are used for the relational analysis between the output of clean energy stations and meteorological factors. Second, the meteorological factors with high correlation and the existing historical data are used to predict the future outputs of new clean energy stations via XGBoost. Finally, according to the predicted output, an assessment method that includes available capacity coefficient (AOC) and other evaluation indicators is proposed. The case studies in this research prove the effectiveness and applicability of the proposed method

Effects of Astragaloside IV on heart failure in rats

<p>Abstract</p> <p>Background</p> <p>Astragaloside IV (ASI) in <it>Radix Astragali </it>is believed to be the active component in treating heart failure. The present study aims to examine the effects of ASI on cardiovascular parameters in long-term heart failure in rats.</p> <p>Methods</p> <p>Using echocardiographic and haemodynamic measurements, we studied the effects of ASI on congestive heart failure (CHF) induced by ligation of the left coronary artery in rats.</p> <p>Results</p> <p>ASI (0.1, 0.3 and 1.0 mg/kg/day) attenuated the decline of fractional shortening (FS). The peak derivatives of the left ventricle (LV) pressure (dp/dt) in ASI-treated groups significantly increased. Both LV internal diameters in diastole (LVIDd) and in systole (LVIDs) decreased significantly after ASI treatment (0.3 and 1.0 mg/kg/day). ASI (1.0 mg/kg/day) attenuated the decrease of LV systolic pressure (LVSP). ASI treatment inhibited compensatory hypertrophy of myocardial cells and lowered the number of apoptotic myocytes.</p> <p>Conclusion</p> <p>ASI improved cardiac functions as measured by cardiovascular parameters.</p

Condensing Multilingual Knowledge with Lightweight Language-Specific Modules

Incorporating language-specific (LS) modules is a proven method to boost performance in multilingual machine translation. This approach bears similarity to Mixture-of-Experts (MoE) because it does not inflate FLOPs. However, the scalability of this approach to hundreds of languages (experts) tends to be unmanageable due to the prohibitive number of parameters introduced by full-rank matrices in fully-connected layers. In this work, we introduce the Language-Specific Matrix Synthesis (LMS) method. This approach constructs LS modules by generating low-rank matrices from two significantly smaller matrices to approximate the full-rank matrix. Furthermore, we condense multilingual knowledge from multiple LS modules into a single shared module with the Fuse Distillation (FD) technique to improve the efficiency of inference and model serialization. We show that our LMS method significantly outperforms previous LS methods and MoE methods with the same amount of extra parameters, e.g., 1.73 BLEU points over the Switch Transformer on many-to-many multilingual machine translation. Importantly, LMS is able to have comparable translation performance with much fewer parameters.Comment: Accepted at the main conference of EMNLP 202

Inverse Correlation Between Plasma Adropin and ET-1 Levels in Essential Hypertension: A Cross-Sectional Study

Adropin is a recently identified bioactive protein that promotes energy homeostasis by affecting glucose and lipid metabolism. Recently, adropin has also been reported to be associated with endothelial dysfunction. Also, ET-1, as a biomarker for endothelial dysfunction, is a key regulator in hypertension. Accordingly, the aim of the present study was to detect the relationship between plasma adropin and ET-1 levels in hypertension. A total of 123 participants, diagnosed with primary hypertension on the basis of World Health Organization criteria (systolic blood pressure [SBP] ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg), and 58 normotensive subjects were enrolled in the cross-sectional study from October 2011 to December 2013. All study participants were older than 18 years of age. Adropin and ET-1 levels were measured by enzyme-linked immunosorbent assay (ELISA). We found that plasma adropin levels were significantly lower in hypertensives compared with controls (3.18 ± 1.00 vs 4.21 ± 1.14 ng/mL, P \u3c 0.001). Plasma ET-1 levels were higher in hypertensives than controls (2.60 ± 1.14 vs 1.54 ± 0.66 pg/mL, P \u3c 0.001). Adropin had a negative correlation with DBP (r = -0.40, P \u3c 0.001), SBP (r = -0.49, P \u3c 0.001), and adjusted for age, body mass index, SBP, DBP, glucose, TC, TG, LDL, and Cr, there was a negative correlation between ET-1 and adropin (r = -0.20, P = 0.04). In multivariate logistic regression analysis of the variables, ET-1 (odds ratio [OR], 3.84; 95% CI, 2.16-6.81; P \u3c 0.001) and adropin (OR, 0.99; 95% CI, 0.99 -1.0; P \u3c  .001) were found to be independent predictors for hypertension.In conclusion, decreased plasma adropin levels are associated with increased blood pressure in hypertension. Adropin is an independent predictor for hypertension, and may influence blood pressure by protecting endothelial function

A market decision-making model for load aggregators with flexible load

The fast development of renewable energy has resulted in great challenges to the power system, which urgently needs more flexible resources to maintain a system supply/demand balance. This paper established a multi-stage electricity market framework in the presence of a load aggregator (LA) including a day-ahead energy/reserve market and a real-time balanced market. To actively participate in the day-ahead energy market and reserve market, a load profile perception model for LA is proposed to evaluate in detail the response performance of consumers. Meanwhile, a market-bidding model of LA and a market-clearing model of the system operator for the day-ahead market are also established. To actively join the real-time balance market, a market-bidding model of LA for the real-time balance market based on surplus flexible resources is established. The system operator further clears the real-time balance market and dispatches the collected flexible resources according to the system supply-demand state. A modified IEEE 30 bus system is tested and shows that the proposed market framework can effectively promote consumers to respond to system regulation requirements and lowers the system supply-demand imbalance risk