Enhancing Perovskite Solar Cell Performance through Propylamine Hydroiodide Passivation.

In recent years, the power conversion efficiency of perovskite solar cells has increased rapidly. Perovskites can be prepared using simple and cost-effective solution methods. However, the perovskite films obtained are usually polycrystalline and contain numerous defects. Passivation of these defects is crucial for enhancing the performance of solar cells. Here, we report the use of propylamine hydroiodide (PAI) for defect passivation. We found that PAI can result in higher-efficiency cells by reducing the defects and suppressing non-radiative recombination. Consequently, n-i-p perovskite solar cells with a certificated efficiency of 21% were obtained. In addition, PAI exhibited excellent performance in p-i-n devices by serving as a buried interface layer, leading to an improved efficiency of 23%.

Identifying differentially expressed genes in goat mammary epithelial cells induced by overexpression of SOCS3 gene using RNA sequencing.

The suppressor of cytokine signaling 3 (SOCS3) is a key signaling molecule that regulates milk synthesis in dairy livestock. However, the molecular mechanism by which SOCS3 regulates lipid synthesis in goat milk remains unclear. This study aimed to screen for key downstream genes associated with lipid synthesis regulated by SOCS3 in goat mammary epithelial cells (GMECs) using RNA sequencing (RNA-seq). Goat SOCS3 overexpression vector (PC-SOCS3) and negative control (PCDNA3.1) were transfected into GMECs. Total RNA from cells after SOCS3 overexpression was used for RNA-seq, followed by differentially expressed gene (DEG) analysis, functional enrichment analysis, and network prediction. SOCS3 overexpression significantly inhibited the synthesis of triacylglycerol, total cholesterol, non-esterified fatty acids, and accumulated lipid droplets. In total, 430 DEGs were identified, including 226 downregulated and 204 upregulated genes, following SOCS3 overexpression. Functional annotation revealed that the DEGs were mainly associated with lipid metabolism, cell proliferation, and apoptosis. We found that the lipid synthesis-related genes, STAT2 and FOXO6, were downregulated. In addition, the proliferation-related genes BCL2, MMP11, and MMP13 were upregulated, and the apoptosis-related gene CD40 was downregulated. In conclusion, six DEGs were identified as key regulators of milk lipid synthesis following SOCS3 overexpression in GMECs. Our results provide new candidate genes and insights into the molecular mechanisms involved in milk lipid synthesis regulated by SOCS3 in goats.

Effect of tissue fixatives on the corrosion of biomedical magnesium alloys.

In this work, the corrosion behavior of pure Mg, Mg3Ag, Mg6Ag, and MgZnYNd alloys in different fixatives (ethyl alcohol (EA), 85 % ethyl alcohol (85 % EA), 10 % neutral buffered formalin (10 % NBF), 4 % glutaric dialdehyde (4 % GD), and 4 % paraformaldehyde (4 % PFA)) was investigated to provide a valuable reference for the selection of fixatives during the histological evaluation of Mg implants. Through the hydrogen evolution test, pH test, and corrosion morphology and product characterization, it was found that corrosion proceeded slowest in the EA and 85 % EA groups, slightly faster in 4 % GD, faster in 10 % NBF, and fastest in 4 % PFA. After corrosion, the EA group surface remained unchanged, while the 85%EA group surface developed minor cracks and warping. The 4%GD fixative formed a dense needle-like protective layer on the Mg substrate. The 10%NBF group initially grew a uniform layer, but later developed irregular pits due to accelerated corrosion. In contrast, the 4%PFA solution caused more severe corrosion attributed to chloride ions. The main corrosion products in the EA and 85%EA groups were MgO and Mg(OH)2, while the other fixatives containing diverse ions also yielded phosphates like Mg3(PO4)2 and MgHPO4. In 4 % PFA, AgCl formed on the surface of Mg6Ag alloy after corrosion. Therefore, to minimize Mg alloy corrosion without compromising staining quality, EA or 85 % EA is recommended, while 4 % PFA is not recommended due to its significant impact.

Synchronous liver metastasis at initial diagnosis of adrenal pheochromocytoma by CT: A case report.

Pheochromocytoma is a tumor of the sympathetic nervous system, characterized by atypical symptoms and signs. Pheochromocytoma metastases can be found in various tissues and organs. However, synchronous metastasis at the initial diagnosis of pheochromocytoma is rare. The present study described a case with synchronous liver metastasis at the initial diagnosis of adrenal pheochromocytoma based on imaging findings. A 41-year-old woman presented with liver pain and fatigue for 1 month. Physical examination showed increased blood pressure and heart rate with sinus tachycardia. Laboratory examination revealed normal levels of liver tumor markers and increased levels of serum or urine epinephrine and norepinephrine. CT examination revealed a large cystic solid mass in the right lobe of the liver and right adrenal gland, and the solid part of the mass was enhanced after enhancement. The pathological diagnosis was pheochromocytoma of the right adrenal gland with liver metastasis. The patient underwent right hepatectomy and right adrenal tumor resection. During the postoperative follow-up, the patient's blood pressure and catecholamine levels were within the normal range. Three years after surgery, the CT examination revealed multiple liver metastases. Chemotherapy was administered to the patient. A year later, re-examination revealed an increase and enlargement of the metastases, and the mass of the right adrenal gland remained similar to the previous one. After 6 months of follow-up, the patient succumbed to recurrence and metastasis. Preoperative diagnosis of metastatic pheochromocytoma is challenging. This case mainly emphasizes that imaging findings can help the clinical diagnosis of metastatic pheochromocytoma.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease.

Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

EEG Signal Processing Techniques and Applications.

Electroencephalography (EEG) is a widely recognised non-invasive method for capturing brain electrophysiological activity [...].

Neurosurgical robots in China: State of the art and future prospect.

Neurosurgical robots have developed for decades and can effectively assist surgeons to carry out a variety of surgical operations, such as biopsy, stereo-electroencephalography (SEEG), deep brain stimulation (DBS), and so forth. In recent years, neurosurgical robots in China have developed rapidly. This article will focus on several key skills in neurosurgical robots, such as medical imaging systems, automatic manipulator, lesion localization techniques, multimodal image fusion technology, registration method, and vascular imaging technology; introduce the clinical application of neurosurgical robots in China, and look forward to the potential improvement points in the future based on our experience and research in the field.

A Comparison Between W Score and RYAN Score in Diagnosing Laryngopharyngeal Reflux Disease.

OBJECTIVE: To assess the diagnostic value of the W score in differentiating laryngopharyngeal reflux disease (LPRD) patients from the normal population by pharyngeal pH (Dx-pH) monitoring, compared with the RYAN score. METHODS: One hundred and eight patients with suspected LPRD and complete follow-up results after more than 8 weeks of anti-reflux therapy were enrolled from the Department of Otolaryngology-Head and Neck Surgery, Gastroenterology and Respiratory Medicine of seven hospitals. Their Dx-pH monitoring data before treatment were reanalyzed to obtain the W score in addition to the RYAN score and then the diagnostic sensitivity and specificity were compared and evaluated with reference to the result of anti-reflux therapy. RESULTS: In eighty-seven (80.6%) cases, anti-reflux therapy was effective, and in 21 patients (19.4%), therapy was ineffective. Twenty-seven patients (25.0%) had a positive RYAN score. The W score was positive in 79 (73.1%) patients. There were 52 patients who had a negative RYAN score, but a positive W score. The diagnostic sensitivity, specificity, positive predictive value, and negative predictive value of the RYAN score were 28.7%, 90.5%, 92.6%, and 23.5%, respectively (kappa = 0.092, P = 0.068), whereas those of the W score for LPRD was 83.9%, 71.4%, 92.4%, and 51.7%, respectively (kappa = 0.484, P < 0.001). CONCLUSIONS: W score is much more sensitive for the diagnosis of LPRD. Prospective studies with larger patient populations are necessary to validate and improve diagnostic efficacy. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR1800014931.

Changes in cerebral autoregulation, stroke-related blood biomarkers, and autonomic regulation after patent foramen ovale closure in severe migraine patients.

AIMS: This study aimed to investigate changes in dynamic cerebral autoregulation (dCA), 20 stroke-related blood biomarkers, and autonomic regulation after patent foramen ovale (PFO) closure in severe migraine patients. METHODS: Patent foramen ovale severe migraine patients, matched non-PFO severe migraine patients, and healthy controls were included. dCA and autonomic regulation were evaluated in each participant at baseline, and within 48-h and 30 days after closure in PFO migraineurs. A panel of stroke-related blood biomarkers was detected pre-surgically in arterial-and venous blood, and post-surgically in the arterial blood in PFO migraineurs. RESULTS: Forty-five PFO severe migraine patients, 50 non-PFO severe migraine patients, and 50 controls were enrolled. The baseline dCA function of PFO migraineurs was significantly lower than that of non-PFO migraineurs and controls but was rapidly improved with PFO closure, remaining stable at 1-month follow-up. Arterial blood platelet-derived growth factor-BB (PDGF-BB) levels were higher in PFO migraineurs than in controls, which was immediately and significantly reduced after closure. No differences in autonomic regulation were observed among the three groups. CONCLUSION: Patent foramen ovale closure can improve dCA and alter elevated arterial PDGF-BB levels in migraine patients with PFO, both of which may be related to the preventive effect of PFO closure on stroke occurrence/recurrence.

Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease.

Freezing of gaits (FOG) is a very disabling symptom of Parkinson's Disease (PD), affecting about 50% of PD patients and 80% of advanced PD patients. Studies have shown that FOG is related to a complex interplay between motor, cognitive and affective factors. A full characterization of FOG is crucial for FOG detection/prediction and prompt intervention. A protocol has been designed to acquire multimodal physical and physiological information during FOG, including gait acceleration (ACC), electroencephalogram (EEG), electromyogram (EMG), and skin conductance (SC). Two tasks were designed to trigger FOG, including gait initiation failure and FOG during walking. A total number of 12 PD patients completed the experiments and produced a length of 3 hours and 42 minutes of valid data including 2 hours and 14 minutes of normal gait and 1 hour and 28 minutes of freezing of gait. The FOG episodes were labeled by two qualified physicians. The multimodal data have been validated by a FOG detection task.

High-accuracy wearable detection of freezing of gait in Parkinson's disease based on pseudo-multimodal features.

OBJECTIVE: Freezing of gait (FoG) is a serious symptom of Parkinson's disease and prompt detection of FoG is crucial for fall prevention. Although multimodal data combining electroencephalography (EEG) benefit accurate FoG detection, the preparation, acquisition, and analysis of EEG signals are time-consuming and costly, which impedes the application of multimodal information in FoG detection. This work proposes a wearable FoG detection method that merges multimodal information from acceleration and EEG while avoiding the acquisition of real EEG data. METHODS: A proxy measurement (PM) model based on long-short-term-memory (LSTM) network was proposed to measure EEG features from accelerations, and pseudo-multimodal features, i.e., pseudo-EEG and acceleration, could be extracted using a highly wearable inertial sensor for FoG detection. RESULTS: Based on a self-collected FoG dataset, the performance of different feature combinations were compared in terms of subject-dependent and cross-subject settings. In both settings, pseudo-multimodal features achieved the most promising performance, with a geometric mean of 91.0 ± 5.0% in subject-dependent setting and 91.0 ± 3.5% in cross-subject setting. CONCLUSION: Our study suggests that wearable FoG detection can be enhanced through leveraging cross-modal information fusion. SIGNIFICANCE: The new method provides a promising path for multimodal information fusion and the long-term monitoring of FoG in living environments.

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson's Disease.

BACKGROUND: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson's disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. METHODS: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. RESULTS: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2-3%. CONCLUSIONS: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.

A Noninvasive BCI System for 2D Cursor Control Using a Spectral-Temporal Long Short-Term Memory Network.

Two-dimensional cursor control is an important and challenging problem in the field of electroencephalography (EEG)-based brain computer interfaces (BCIs) applications. However, most BCIs based on categorical outputs are incapable of generating accurate and smooth control trajectories. In this article, a novel EEG decoding framework based on a spectral-temporal long short-term memory (stLSTM) network is proposed to generate control signals in the horizontal and vertical directions for accurate cursor control. Precisely, the spectral information is used to decode the subject's motor imagery intention, and the error-related P300 information is used to detect a deviation in the movement trajectory. The concatenated spectral and temporal features are fed into the stLSTM network and mapped to the velocities in vertical and horizontal directions of the 2D cursor under the velocity-constrained (VC) strategy, which enables the decoding network to fit the velocity in the imaginary direction and simultaneously suppress the velocity in the non-imaginary direction. This proposed framework was validated on a public real BCI control dataset. Results show that compared with the state-of-the-art method, the RMSE of the proposed method in the non-imaginary directions on the testing sets of 2D control tasks is reduced by an average of 63.45%. Besides, the visualization of the actual trajectories distribution of the cursor also demonstrates that the decoupling of velocity is capable of yielding accurate cursor control in complex path tracking tasks and significantly improves the control accuracy.

Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction.

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both the characterizations in EEGs themselves and correlations among different electrodes simultaneously, generally neglect the spatial or temporal dependencies in an epileptic brain and, thus, produce suboptimal seizure prediction performance consequently. To tackle this issue, in this article, a patient-specific EEG seizure predictor is proposed by using a novel spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme (STS-HGCN-AL). Specifically, since the epileptic activities in different brain regions may be of different frequencies, the proposed STS-HGCN-AL framework first infers a hierarchical graph to concurrently characterize an epileptic cortex under different rhythms, whose temporal dependencies and spatial couplings are extracted by a spectral-temporal convolutional neural network and a variant self-gating mechanism, respectively. Critical intrarhythm spatiotemporal properties are then captured and integrated jointly and further mapped to the final recognition results by using a hierarchical graph convolutional network. Particularly, since the preictal transition may be diverse from seconds to hours prior to a seizure onset among different patients, our STS-HGCN-AL scheme estimates an optimal preictal interval patient dependently via a semisupervised active learning strategy, which further enhances the robustness of the proposed patient-specific EEG seizure predictor. Competitive experimental results validate the efficacy of the proposed method in extracting critical preictal biomarkers, indicating its promising abilities in automatic seizure prediction.

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

High-Throughput Analysis from Complex Matrices: Acoustic Ejection Mass Spectrometry from Phase-Separated Fluid Samples.

Acoustic ejection mass spectrometry is a novel high-throughput analytical technology that delivers high reproducibility without carryover observed. It eliminates the chromatography step used to separate analytes from matrix components. Fully-automated liquid-liquid extraction is widely used for sample cleanup, especially in high-throughput applications. We introduce a workflow for direct AEMS analysis from phase-separated liquid samples and explore high-throughput analysis from complex matrices. We demonstrate the quantitative determination of fentanyl from urine using this two-phase AEMS approach, with a LOD lower than 1 ng/mL, quantitation precision of 15%, and accuracy better than ±10% over the range of evaluation (1-100 ng/mL). This workflow offers simplified sample preparation and higher analytical throughput for some bioanalytical applications, in comparison to an LC-MS based approach.