Nanomolar detection of hydrogen peroxide at a new polynuclear cluster of tin pentacyanonitrosylferrate nanoparticle-modified carbon ceramic electrode

This work describes a new electrochemical sensor for hydrogen peroxide based on tin pentacyanonitrosylferrate (SnPCNF)-modified carbon ceramic electrode (CCE). The modified electrode was constructed by using a sol-gel technique involving two steps: construction of CCE containing metallic tin (Sn) powder and then electrochemical creation of SnPCNF film on the surface of CCE. The modified electrode was characterized by energy-dispersive X-ray, Fourier transform infrared, scanning electron microscopy, and cyclic voltammetry (CV) techniques. The charge transfer coefficient (α) and charge transfer rate constant (k_s) for the modifying film were calculated. The electrocatalytic activity of the modified electrode toward the reduction of hydrogen peroxide was studied by CV and chronoamperometry. A linear calibration curve was obtained over the hydrogen peroxide concentration range of 0.5 to 69.4 μM using a hydrodynamic amperometric technique. The limit of detection (for a signal-to-noise ratio of 3) and sensitivity were found to be 92 nM and 0.89 μA/μM, respectively. Furthermore, the diffusion coefficient of hydrogen peroxide (D) and catalytic rate constant (k_cat) were calculated.     

A NOVEL APPROACH TO SOLID PHASE CHEMICAL SYNTHESIS OF OLIGONUCLEOTIDE mRNA CAP ANALOGS

A novel approach for the synthesis of 5′-capped 2′-O-methyloligoribonucleotides on a disulfide-tethered solid support is described. The key step of the synthesis is ZnCl₂ promoted coupling of m⁷GDP imidazolide to a fully deprotected oligonucleotide 5′-phosphate on-support. By this methodology m⁷G⁵′pppm²′Apm²′Upm^2′Ap has been prepared.     

Handling class imbalance problem in miRNA dataset associated with cancer

MiRNAs are small (~22nt long) non-coding RNA sequences; binds to the complementarity target sites in 3'' Untranslated Region (UTR) of mRNA sequences but not restricted to other mRNA regions viz., 5'' UTR and Coding sequences (CDS). Complementarity binding of miRNA to mRNA target sites either results in complete degradation of the mRNA itself or it may regulate the mRNA as an oncogene or as a tumor suppressor gene. However, the exact mechanism involved in identifying a miRNA to be associated with cancer is still unclear. Further, with the outburst in the number of miRNAs sequences recorded every year in miRBase, the gap is still widening mainly due to the laborious and economically unfavorable experimental procedures associated with the functional annotation. Motivated by the fact, we constructed a two-step support vector machine-based predictive model - miRSEQ and miRINT. However, the major pitfall during the construction of the model is the class imbalance problem. Hence, in order to overcome class imbalance problem, in the present study we empirically compare the effectiveness of two different methods viz., Synthetic Minority Oversampling Technique (SMOTE) and cost-senstive learning method. Performance measures were evaluated in terms of Precision and Recall. Based on our result, it was observed that for miRNA dataset with high class imbalance utilized for predicting association of cancer, cost-sensitive method outperformed the oversampling method.     

固定化酶的微环境效应—载体离子基团性质的影响

作者合成了阴离子型和阳离子型葡聚糖,以此为载体,用CNBr活化其剩余羟基,固定化了葡萄糖淀粉酶和葡萄糖异构酶。就离子型载体对固定化酶的蛋白载量、最适pH和热稳定性等的影响做了考察。发现固定化酶的蛋白载量不仅与载体的电性质有关,也与酶分子自身的电性质有关。当载体电性质与酶蛋白电性质相反时,固定化酶的蛋白载量增加,热稳定性提高、载体电性质与酶蛋白电性质相同时,固定化酶的蛋白载量不变或下降,其热稳定性不变。作者还发现当离子型载体孔度和体系缓冲液浓度一定时,酶分子能否进入多孔性载体内部,对其最适pH是否变化影响极大。若酶分子仅被连接在载体的外表层,其最适pH不发生变化,反之亦然。作者还观察到当多糖类载体引入氨基或羧基后,大大增强了其抵抗微生物侵蚀的能力。     

Evaluation of polysulfone hollow fibres and ceramic suction samplers as devices for the in situ extraction of soil solution

An investigation was carried out to assess the potential of using polysulfone hollow fibres for the extraction of soil solution. In comparison to ceramic suction samplers the fibres were shown to contain very low levels of potential contaminants and a low exchange capacity. Carry over between individual samples was negligible permitting the accurate monitoring of temporal changes in solution chemistry. Their flexible and root sized nature makes them ideal for sampling small soil volumes. Some concern however remains about retention of colloidal Fe at the fibre interface. When comparing the soil solution of samples extracted with polysulfone fibres with those extracted by ceramic suction samplers, no significant differences in solute concentrations were observed. No sample contamination resulting from dissolution of Al or Si from the ceramic cup was detected. Analysis of soil solutions collected over a number of extractions, and during and after a rainfall event showed concentrations of elements in solution to remain relatively constant with time.     

Oxide-Based Solid-State Batteries: A Perspective on Composite Cathode Architecture

The garnet-type phase Li₇La₃Zr₂O₁₂ (LLZO) attracts significant attention as an oxide solid electrolyte to enable safe and robust solid-state batteries (SSBs) with potentially high energy density. However, while significant progress has been made in demonstrating compatibility with Li metal, integrating LLZO into composite cathodes remains a challenge. The current perspective focuses on the critical issues that need to be addressed to achieve the ultimate goal of an all-solid-state LLZO-based battery that delivers safety, durability, and pack-level performance characteristics that are unobtainable with state-of-the-art Li-ion batteries. This perspective complements existing reviews of solid/solid interfaces with more emphasis on understanding numerous homo- and heteroionic interfaces in a pure oxide-based SSB and the various phenomena that accompany the evolution of the chemical, electrochemical, structural, morphological, and mechanical properties of those interfaces during processing and operation. Finally, the insights gained from a comprehensive literature survey of LLZO–cathode interfaces are used to guide efforts for the development of LLZO-based SSBs.     

Muscle endurance time estimation during isometric training using electromyogram and supervised learning

Constant-force isometric muscle training is useful for increasing the maximal strength , rehabilitation and work-fatigue assessment. Earlier studies have shown that muscle fatigue characteristics can be used for evaluating muscle endurance limit. Study Objective: To predict muscle endurance time during isometric task using frequency spectrum characteristics of surface electromyography signals along with analysis of frequency spectrum shape and scale during fatigue accumulation. Method: Thirteen subjects performed isometric lateral raise at 60% MVC of deltoid (lateral) till endurance limit. Time windowed sEMG frequency spectrum was modelled using 2-parameter distributions namely Gamma and Weibull for spectrum analysis and endurance prediction. Results: Gamma distribution provided better spectrum fitting (P < 0.001) than Weibull distribution. Spectrum Distribution demonstrated no change in shape but shifted towards lower frequency with increase of magnitude at characteristic mode frequency. Support Vector Regression based algorithm was developed for endurance time estimation using features derived from fitted frequency spectrum. Time taken till endurance limit for acquired dataset 38.53 ± 17.33 s (Mean ± Standard Deviation) was predicted with error of 0.029 ± 4.19 s . R-square: 0.956, training and test sets RMSE was calculated as 3.96 and 4.29 s respectively. The application of the algorithm suggested that model required 70% of sEMG signal from maximum time of endurance for high prediction accuracy. Conclusion: Endurance Limit prediction algorithm was developed for quantification of endurance time for optimizing isometric training and rehabilitation. Our method could help personalize and change conventional training method of same weight and duration for all subjects with optimized training parameters, based upon individual sEMG activity.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Optimized Two-Stage Ensemble Model for Mammography Mass Recognition

ObjectivesMammography mass recognition is considered as a very challenge pattern recognition problem due to the high similarity between normal and abnormal masses. Therefore, the main objective of this study is to develop an efficient and optimized two-stage recognition model to tackle this recognition task.Material and methodsBasically, the developed recognition model combines an ensemble of linear Support Vector Machine (SVM) classifiers with a Reinforcement Learning-based Memetic Particle Swarm Optimizer (RLMPSO) as RLMPSO-SVM recognition model. RLMPSO is used to construct a two-stage of an ensemble of linear SVM classifiers by performing simultaneous SVM parameters tuning, features selection, and training instances selection. The first stage of RLMPSO-SVM recognition model is responsible about recognizing the input ROI mammography masses as normal or abnormal mass pattern. Meanwhile, the second stage of RLMPSO-SVM model used to perform further recognition for abnormal ROIs as malignant or benign masses. In order to evaluate the effectiveness of RLMPSO-SVM, a total of 1187 normal ROIs, 111 malignant ROIs, and 135 benign ROIs were randomly selected from DDSM database images.ResultsReported results indicated that RLMPSO-SVM model was able to achieve performances of 97.57% sensitivity rate with 97.86% specificity rate for normal vs. abnormal recognition cases. For malignant vs. benign recognition performance it was reported of 97.81% sensitivity rate with 96.92% specificity rate.ConclusionReported results indicated that RLMPSO-SVM recognition model is an effective tool that could assist the radiologist during the diagnosis of the presented abnormalities in mammography images. The outcomes indicated that RLMPSO-SVM significantly outperformed various SVM-based models as well as other variants of computational intelligence models including multi-layer perceptron, naive Bayes classifier, and k-nearest neighbor.     

Active and machine learning-based approaches to rapidly enhance microbial chemical production

In order to make renewable fuels and chemicals from microbes, new methods are required to engineer microbes more intelligently. Computational approaches, to engineer strains for enhanced chemical production typically rely on detailed mechanistic models (e.g., kinetic/stoichiometric models of metabolism)—requiring many experimental datasets for their parameterization—while experimental methods may require screening large mutant libraries to explore the design space for the few mutants with desired behaviors. To address these limitations, we developed an active and machine learning approach (ActiveOpt) to intelligently guide experiments to arrive at an optimal phenotype with minimal measured datasets. ActiveOpt was applied to two separate case studies to evaluate its potential to increase valine yields and neurosporene productivity in Escherichia coli. In both the cases, ActiveOpt identified the best performing strain in fewer experiments than the case studies used. This work demonstrates that machine and active learning approaches have the potential to greatly facilitate metabolic engineering efforts to rapidly achieve its objectives.