Effect Of Type And Quantity Of Inherent Alkali Cations On Alkali-silica Reaction

In this study, the macroscopical expansion induced by alkali-silica reaction (ASR) and its corresponding ASR products are investigated using ordinary Portland cement (OPC) mortar specimens with a gradient of boosted alkalis. Experimental results show that the expansion increases with the concentration of inherent alkalis. Sodium-boosted samples expand approximately three times as much as potassium-boosted samples. ASR gels that are present in aggregate veins are calcium-free and amorphous; the atomic ratios of ASR gels are nearly independent of the type and quantity of alkali cations. Aggregate ASR gel exudation occurs in high (≥2.5 %) sodium cases and produces potential Na-shlykovite. Crystalline and amorphous calcium-containing ASR products are present in aggregate vicinities in either Na- or K-boosted samples. The higher hydrophilicity of Na-gel in aggregate veins accounts for the larger expansion. Boosted alkali cations are more effective in ASR products formation than in exposing solution. A new observation that NaOH exposure inhibits ASR in K-boosted samples (zero expansion) is reported

Effect of Exogenous Phosphate on the Lability and Phytoavailability of Arsenic in Soils

The effect of exogenous phosphate (P, 200 mg·kg-1 soil) on the lability and phyto-availability of arsenic (As) was studied using the diffusive gradients in thin films (DGT) technique. Lettuce were grown on the As-amended soils following the stabilization of soil labile As after 90 day’s incubation. Phosphate (P) application generally facilitated plant growth except one grown on P-sufficient soil. Soil labile As concentration increased in all the soils after P application due to a competition effect. Plant As concentration increased in red soils collected from Hunan Province, while decreases were observed in the other soils. Even though, an overall trend of decrease was obtained in As phytoavailability along with the increase of DGT-measured soil labile P/As molar ratio. The functional equation between P/As and As phytoavailability provided a critical value of 1.7, which could be used as a guidance for rational P fertilization, thus avoiding overfertilization

Subject-independent EEG classification based on a hybrid neural network

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

IntroductionBrain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.MethodsThis paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.ResultsA classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.DiscussionThis approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery

The prognostic value of whole-genome DNA methylation in response to Leflunomide in patients with Rheumatoid Arthritis

ObjectiveAlthough Leflunomide (LEF) is effective in treating rheumatoid arthritis (RA), there are still a considerable number of patients who respond poorly to LEF treatment. Till date, few LEF efficacy-predicting biomarkers have been identified. Herein, we explored and developed a DNA methylation-based predictive model for LEF-treated RA patient prognosis.MethodsTwo hundred forty-five RA patients were prospectively enrolled from four participating study centers. A whole-genome DNA methylation profiling was conducted to identify LEF-related response signatures via comparison of 40 samples using Illumina 850k methylation arrays. Furthermore, differentially methylated positions (DMPs) were validated in the 245 RA patients using a targeted bisulfite sequencing assay. Lastly, prognostic models were developed, which included clinical characteristics and DMPs scores, for the prediction of LEF treatment response using machine learning algorithms.ResultsWe recognized a seven-DMP signature consisting of cg17330251, cg19814518, cg20124410, cg21109666, cg22572476, cg23403192, and cg24432675, which was effective in predicting RA patient’s LEF response status. In the five machine learning algorithms, the support vector machine (SVM) algorithm provided the best predictive model, with the largest discriminative ability, accuracy, and stability. Lastly, the AUC of the complex model(the 7-DMP scores with the lymphocyte and the diagnostic age) was higher than the simple model (the seven-DMP signature, AUC:0.74 vs 0.73 in the test set).ConclusionIn conclusion, we constructed a prognostic model integrating a 7-DMP scores with the clinical patient profile to predict responses to LEF treatment. Our model will be able to effectively guide clinicians in determining whether a patient is LEF treatment sensitive or not

Semiconductor Multimaterial Optical Fibers for Biomedical Applications

Integrated sensors and transmitters of a wide variety of human physiological indicators have recently emerged in the form of multimaterial optical fibers. The methods utilized in the manufacture of optical fibers facilitate the use of a wide range of functional elements in microscale optical fibers with an extensive variety of structures. This article presents an overview and review of semiconductor multimaterial optical fibers, their fabrication and postprocessing techniques, different geometries, and integration in devices that can be further utilized in biomedical applications. Semiconductor optical fiber sensors and fiber lasers for body temperature regulation, in vivo detection, volatile organic compound detection, and medical surgery will be discussed

Kinetic release of arsenic after exogenous inputs into two different types of soil

The mobility of arsenic (As) in soil depends on its sorption/desorption processes on soil particles. Plant uptake locally lowers As concentration in soil pore water, which would trigger resupplies of As from soil solid phase. To better understand the fate of As in soil system after its inputs into soil and its subsequent dynamic processes, diffusive gradients in thin films (DGT) technique along with DGT-induced fluxes in soils (DIFS) model were introduced to study the kinetic information of As in soils, including its response time (T-C) and resupply rate constant (k(-1)). To achieve a series of soils with gradient As level, two different types of soils with similar As level (total As in soil JL is 7.4 mg kg(-1), while in soil BJ is 6.5 mg kg(-1)) were collected and amended with exogenous As. Then, DGT deployments were carried out following a period of 90-day soil incubation. The simulated T-C values in non-amended soil JL and soil BJ were 0.036 and 0.001 s(-1), respectively. The difference may due to the properties of these two soils, including pH values and contents of adsorption materials, such as Fe and Al compounds. After As inputs into soils, the intrinsic rate of As release from the solid phase to the solution phase in As-amended JL soil was much higher than that in non-amended soil. While for soil BJ, a decreasing trend was observed after As spiking. The redistribution of As may responsible for the different variation trends of As kinetics in these two soils after As spiking. The results indicated that the distribution coefficient of As (K-d) in soil was mainly affected by soil Olsen-P content due to an ubiquitous competition between P and As on soil particles