The Effect of Arbuscular Mycorrhiza Fungi on Iron and Manganese Concentration of Berssem Clover by Cadmium Stress

In this study, a factorial experiment was performed in completely randomized design (CRD) with threefactors: arbuscularmycorrhizal fungi with two levels (inoculated and non-inoculated soil) and cadmium with six levels( , , , , and ppm). The results showed that effect of cadmium levels on iron and manganese concentrationwas significant in one percent level of statistical. In ppm cadmium concentration in soil, a reducediron were onconcentration of Iron ( and ) and manganese ( % and %) in root and aerial respectively.Butarbuscularmycorrhiza fungi increasediron concentration % and % in theroot and aerial, and manganeseconcentration % and . % in root and aerial plant respectively

A New Framework to Estimate Breathing Rate from Electrocardiogram, Photoplethysmogram, and Blood Pressure Signals

Breathing Rate (BR) is a key physiological parameter measured in a wide range of clinical settings. However, it is still widely measured manually. In this paper, a novel framework is proposed to estimate the BR from an electrocardiogram (ECG), a photoplethysmogram (PPG), or a blood pressure (BP) signal. The framework uses Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) methods to extract respiratory signals, taking advantage of both time and frequency domain information. An Extended Kalman Filter (EKF), incorporating a Signal Quality Index (SQI), enabled our method to achieve acceptable performance even for significantly distorted periods of the signals. Using state vector fusion, the output signals are combined and finally the BR is estimated. The framework was tested on two publicly available clinical databases: the MIT-BIH Polysomnographic and BIDMC databases. Performance was evaluated using the mean absolute percentage error (MAPE). The results indicated high accuracy: MAPEs on the two databases of 3.9% and 3.6% for ECG signals, 6.0% for PPG, and 5.0% for BP signals. The results also indicated high robustness to noise down to 0dB. Therefore, this framework may have utility for BR monitoring in high noise settings

Extracting Prior Knowledge from Data Distribution to Migrate from Blind to Semi-Supervised Clustering

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised one. To estimate the density distribution of data, Wiebull Mixture Model (WMM) is utilized due to its high flexibility. Another contribution of this study is to propose a new hill and valley seeking algorithm to find the constraints for semi-supervise algorithm. It is assumed that each density peak stands on a cluster center; therefore, neighbor samples of each center are considered as must-link samples while the near centroid samples belonging to different clusters are considered as cannot-link ones. The proposed approach is applied to a standard image dataset (designed for clustering evaluation) along with some UCI datasets. The achieved results on both databases demonstrate the superiority of the proposed method compared to the conventional clustering methods

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier

Bi-allelic variants in RNF170 are associated with hereditary spastic paraplegia.

Alterations of Ca2+ homeostasis have been implicated in a wide range of neurodegenerative diseases. Ca2+ efflux from the endoplasmic reticulum into the cytoplasm is controlled by binding of inositol 1,4,5-trisphosphate to its receptor. Activated inositol 1,4,5-trisphosphate receptors are then rapidly degraded by the endoplasmic reticulum-associated degradation pathway. Mutations in genes encoding the neuronal isoform of the inositol 1,4,5-trisphosphate receptor (ITPR1) and genes involved in inositol 1,4,5-trisphosphate receptor degradation (ERLIN1, ERLIN2) are known to cause hereditary spastic paraplegia (HSP) and cerebellar ataxia. We provide evidence that mutations in the ubiquitin E3 ligase gene RNF170, which targets inositol 1,4,5-trisphosphate receptors for degradation, are the likely cause of autosomal recessive HSP in four unrelated families and functionally evaluate the consequences of mutations in patient fibroblasts, mutant SH-SY5Y cells and by gene knockdown in zebrafish. Our findings highlight inositol 1,4,5-trisphosphate signaling as a candidate key pathway for hereditary spastic paraplegias and cerebellar ataxias and thus prioritize this pathway for therapeutic interventions

A combinatorial deep learning structure for precise depth of anesthesia estimation from EEG signals

Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. However, precise and real-time estimation of DOA index for painful surgical operations is challenging due to problems such as postoperative complications and accidental awareness. To tackle these problems, we propose a new combinatorial deep learning structure involving convolutional neural networks (inspired by the inception module), bidirectional long short-term memory, and an attention layer. The proposed model uses the EEG signal to continuously predicts the bispectral index (BIS). It is trained over a large dataset, mostly from those under general anesthesia with few cases receiving sedation/analgesia and spinal anesthesia. Despite the imbalance distribution of BIS values in different levels of anesthesia, our proposed structure achieves convincing root mean square error of 5.59 ± 1.04 and mean absolute error of 4.3 ± 0.87, as well as improvement in area under the curve of 15% on average, which surpasses state-of-the-art DOA estimation methods. The DOA values are also discretized into four levels of anesthesia and the results demonstrate strong inter-subject classification accuracy of 88.7% that outperforms the conventional methods

ERP detection based on smoothness priors

Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under the Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in post-stimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method performs better than the competing algorithms and improves the classification accuracy. Conclusion: SGLRT can be employed as a powerful means for different ERP detection schemes. Significance: The proposed scheme is opening a new direction in ERP identification which provides better classification results compared to several popular ERP detection methods

Expanding the clinical phenotype of IARS2-related mitochondrial disease.

BACKGROUND: IARS2 encodes a mitochondrial isoleucyl-tRNA synthetase, a highly conserved nuclear-encoded enzyme required for the charging of tRNAs with their cognate amino acid for translation. Recently, pathogenic IARS2 variants have been identified in a number of patients presenting broad clinical phenotypes with autosomal recessive inheritance. These phenotypes range from Leigh and West syndrome to a new syndrome abbreviated CAGSSS that is characterised by cataracts, growth hormone deficiency, sensory neuropathy, sensorineural hearing loss, and skeletal dysplasia, as well as cataract with no additional anomalies. METHODS: Genomic DNA from Iranian probands from two families with consanguineous parental background and overlapping CAGSSS features were subjected to exome sequencing and bioinformatics analysis. RESULTS: Exome sequencing and data analysis revealed a novel homozygous missense variant (c.2625C > T, p.Pro909Ser, NM_018060.3) within a 14.3 Mb run of homozygosity in proband 1 and a novel homozygous missense variant (c.2282A > G, p.His761Arg) residing in an ~ 8 Mb region of homozygosity in a proband of the second family. Patient-derived fibroblasts from proband 1 showed normal respiratory chain enzyme activity, as well as unchanged oxidative phosphorylation protein subunits and IARS2 levels. Homology modelling of the known and novel amino acid residue substitutions in IARS2 provided insight into the possible consequence of these variants on function and structure of the protein. CONCLUSIONS: This study further expands the phenotypic spectrum of IARS2 pathogenic variants to include two patients (patients 2 and 3) with cataract and skeletal dysplasia and no other features of CAGSSS to the possible presentation of the defects in IARS2. Additionally, this study suggests that adult patients with CAGSSS may manifest central adrenal insufficiency and type II esophageal achalasia and proposes that a variable sensorineural hearing loss onset, proportionate short stature, polyneuropathy, and mild dysmorphic features are possible, as seen in patient 1. Our findings support that even though biallelic IARS2 pathogenic variants can result in a distinctive, clinically recognisable phenotype in humans, it can also show a wide range of clinical presentation from severe pediatric neurological disorders of Leigh and West syndrome to both non-syndromic cataract and cataract accompanied by skeletal dysplasia