Generation and Application of Mouse-Rat Allodiploid Embryonic Stem Cells.

Mammalian interspecific hybrids provide unique advantages for mechanistic studies of speciation, gene expression regulation, and X chromosome inactivation (XCI) but are constrained by their limited natural resources. Previous artificially generated mammalian interspecific hybrid cells are usually tetraploids with unstable genomes and limited developmental abilities. Here, we report the generation of mouse-rat allodiploid embryonic stem cells (AdESCs) by fusing haploid ESCs of the two species. The AdESCs have a stable allodiploid genome and are capable of differentiating into all three germ layers and early-stage germ cells. Both the mouse and rat alleles have comparable contributions to the expression of most genes. We have proven AdESCs as a powerful tool to study the mechanisms regulating X chromosome inactivation and to identify X inactivation-escaping genes, as well as to efficiently identify genes regulating phenotypic differences between species. A similar method could be used to create hybrid AdESCs of other distantly related species.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

miR-19a/b-3p promotes inflammation during cerebral ischemia/reperfusion injury via SIRT1/FoxO3/SPHK1 pathway.

BACKGROUND: Stroke affects 3-4% of adults and kills numerous people each year. Recovering blood flow with minimal reperfusion-induced injury is crucial. However, the mechanisms underlying reperfusion-induced injury, particularly inflammation, are not well understood. Here, we investigated the function of miR-19a/b-3p/SIRT1/FoxO3/SPHK1 axis in ischemia/reperfusion (I/R). METHODS: MCAO (middle cerebral artery occlusion) reperfusion rat model was used as the in vivo model of I/R. Cultured neuronal cells subjected to OGD/R (oxygen glucose deprivation/reperfusion) were used as the in vitro model of I/R. MTT assay was used to assess cell viability and TUNEL staining was used to measure cell apoptosis. H&E staining was employed to examine cell morphology. qRT-PCR and western blot were performed to determine levels of miR-19a/b-3p, SIRT1, FoxO3, SPHK1, NF-κB p65, and cytokines like TNF-α, IL-6, and IL-1ß. EMSA and ChIP were performed to validate the interaction of FoxO3 with SPHK1 promoter. Dual luciferase assay and RIP were used to verify the binding of miR-19a/b-3p with SIRT1 mRNA. RESULTS: miR-19a/b-3p, FoxO3, SPHK1, NF-κB p65, and cytokines were elevated while SIRT1 was reduced in brain tissues following MCAO/reperfusion or in cells upon OGD/R. Knockdown of SPHK1 or FoxO3 suppressed I/R-induced inflammation and cell death. Furthermore, knockdown of FoxO3 reversed the effects of SIRT1 knockdown. Inhibition of the miR-19a/b-3p suppressed inflammation and this suppression was blocked by SIRT1 knockdown. FoxO3 bound SPHK1 promoter and activated its transcription. miR-19a/b-3p directly targeted SIRT1 mRNA. CONCLUSION: miR-19a/b-3p promotes inflammatory responses during I/R via targeting SIRT1/FoxO3/SPHK1 axis.

Knockdown of microRNA-17-5p Enhances the Neuroprotective Effect of Act A/Smads Signal Loop After Ischemic Injury.

Cerebral ischemic injury is a leading cause of human mortality and disability, seriously threatening human health in the world. Activin A (Act A), as a well-known neuroprotective factor, could alleviate ischemic brain injury mainly through Act A/Smads signaling. In our previous study, a noncanonical Act A/Smads signal loop with self-amplifying property was found, which strengthened the neuroprotective effect of Act A. However, this neuroprotective effect was limited due to the self-limiting behavior mediated by Smad anchor for receptor activation (SARA) protein. It was reported that microRNA-17-5p (miR-17-5p) could suppress the expression of SARA in esophageal squamous cell carcinoma. Thus we proposed that knockdown of miR-17-5p could strengthen the neuroprotective effect of Act A/Smads signal loop through SARA. To testify this hypothesis, oxygen-glucose deficiency (OGD) was introduced to highly differentiated rattus pheochromocytoma (PC12) cells. After the transfection of miR-17-5p mimic or inhibitor, the activity of Act A signal loop was quantified by the expression of phosphorylated Smad3. The results showed that suppression of miR-17-5p up-regulated the expression of SARA protein, which prolonged and strengthened the activity of Act A signaling through increased phosphorylation of downstream Smad3 and accumulation of Act A ligand. Further luciferase assay confirmed that SARA was a direct target gene of miR-17-5p. These practical discoveries will bring new insight on the endogenous neuroprotective effects of Act A signal loop by interfering a novel target: miR-17-5p.

Hirsutane-Type Sesquiterpenes with Inhibitory Activity of Microglial Nitric Oxide Production from the Red Alga-Derived Fungus Chondrostereum sp. NTOU4196.

The marine red alga Pterocladiella capillacea is an economic alga for the food industry in Taiwan, and its associated highly diversified fungi have not been investigated meticulously thus far. The EtOAc extract of the fermented broth of Chondrostereum sp. NTOU4196, a fungal strain isolated from P. capillacea, was found to exhibit significant nitric oxide (NO) production inhibitory activity in lipopolysaccharide-activated murine RAW 264.7 cells at a concentration of 100 µg/mL in the preliminary screening. Therefore, separation of the active principles from the fermented broths was performed, and that has led to the isolation of eight new 5,5,5-tricyclic hirsutane-type sesquiterpenes, namely, chondroterpenes A-H (1-8), together with seven known analogues. They were identified by analyses of spectroscopic data and comparison with literature values. Among the new isolates, chondroterpene A (1) exhibited more significant NO production inhibitory activity in murine BV-2 microglial cells, and of all the isolated compounds, hirsutanol A (9) exerted limited cytotoxic effects and the most potent inhibitory activity on NO production.

PEG Grafted-Nanodiamonds for the Delivery of Gemcitabine.

Carboxyl end-functionalized poly[poly(ethylene glycol) methyl ether methacrylate] [P(PEGMEMA)] and its block copolymer with gemcitabine substituted poly(N-hydroxysuccinimide methacrylate) [PGem-block-P(PEGMEMA)] are synthesized via reversible addition-fragmentation transfer (RAFT) polymerization. Then, two polymers are grafted onto the surface of amine-functionalized nanodiamonds to obtain [P(PEGMEMA)]-grafted nanodiamonds (ND-PEG) and [PGem-block-P(PEGMEMA)]-grafted nanodiamonds (ND-PF). Gemcitabine is physically absorbed to ND-PEG to produce ND-PEG (Gem). Two polymer-grafted nanodiamonds (i.e., with physically absorbed gemcitabine ND-PEG (Gem) and with chemically conjugated gemcitabine ND-PF) are characterized using attenuated total reflectance infrared spectroscopy, dynamic light scattering, and thermogravimetric analysis. The drug release, cytotoxicity (to seed human pancreatic carcinoma AsPC-1 cells), and cellular uptake of ND-PEG (Gem) and ND-PF are also investigated.

Neuroprotection by Carbenoxolone Against Ischemia Injury Involves PI3K/Akt Pathway.

BACKGROUND: Previous experimental studies have shown some protective effects on brain ischemic injures in vivo and in vitro models. However, cotreatment with carbenoxolone (Cbx) and phosatidylinositol 3-kinase (PI3K) inhibitor LY 294002 to a focal cerebral ischemia and reperfusion rat model has not been studied yet. Here we investigate their protective effects on neural cells and examine the function of PI3K/Akt pathway in this protection. METHODS: Both flow cytometry and western blot were used quantitatively and qualitatively to determine cell apoptosis. RESULTS: The neural cell apoptosis is related with Cx43, and Bcl-2/Bax and caspase 3 pathways. The percentage of apoptosis cells following transient middle cerebral artery occlusion (MCAO) in mice decrease with the treatment of Cbx. Our data demonstrated that treatment with Cbx reduced cerebral injury in rats exposed to transient focal ischemia and reperfusion (I/R), and this was mediated by the activation of the PI3K/Akt pathways as well as by blocking the caspase 3 apoptosis pathway. LY294002 blocked the increase in phospho-AKT evoked by Cbx and abolished the associated protective effect. CONCLUSIONS: Taken together, these findings provide evidence that Cbx protects neurons against ischemia injury and this neuroprotective effect involves the PI3K/Akt pathway.

Combined approaches for fixation of Schatzker type II tibial plateau fractures involving the posterolateral column: a prospective observational cohort study.

INTRODUCTION: This study aimed to investigate the surgical techniques and the clinical efficacy of combined approaches for the treatment of Schatzker type II tibial plateau fractures involving the posterolateral column [lateral and posterolateral columns (LPCs) fractures] in a prospective cohort. MATERIALS AND METHODS: From January 2007 through December 2010, a total of 65 patients with LPCs underwent dual-plate fixation via a combined anterior and posterior approach. The anterior and posterior approaches were the conventional anterolateral approach and a posteromedial inverted L-shaped approach, respectively, with the patients in a floating position. RESULTS: Ultimately, 41 patients were followed up for a mean period of 52.5 months. All fractures healed. The mean time to radiographic bony union was 15.2 weeks and the mean time to full weight-bearing was 18.7 weeks. No parameter associated with knee alignment changed significantly between immediately postoperation and 2 years postoperation. No collapse of the reduced articular surface was detected. Two years postoperation, the mean Hospital for Special Surgery score was 92.3; the mean Short Form-36 score was 90.1, and the mean range of knee motion was 1.7°-123.6° (extension-flexion). Two patients suffered dehiscence of the anterolateral incision and another suffered partial necrosis at the margin of the posteromedial incision postoperatively. All healed in response conservative treatment. Another two patients experienced numbness in the posteromedial inferior region of the calf. No implant loosening, breakage, fixation failure, or other complication was observed during follow-up. CONCLUSIONS: LPCs are not uncommon. Careful preoperative analysis of computed tomography images and impeccable preparation are necessary to avoid neglecting a posterolateral column fracture. It is inappropriate to generalize one scenario for all Schatzker type II fractures: a single approach cannot address all subtypes of these fractures. Dual-plate fixation via a combined approach is an effective treatment for LPCs.

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online at https://github.com/ALEX95GOGO/CAGNN.

BELT: Bootstrapped EEG-to-language Training by Natural Language Supervision.

Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.

Human neural stem cells.

'Human neural stem cells' jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human neural stem cells (hNSCs) in China. This standard specifies the technical requirements, test methods, test regulations, instructions for use, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for hNSCs, which is applicable to the quality control for hNSCs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that publication of the guideline will facilitate institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of hNSCs for clinical development and therapeutic applications.

Human midbrain dopaminergic progenitors.

Human midbrain dopaminergic progenitors (mDAPs) are one of the most representative cell types in both basic research and clinical applications. However, there are still many challenges for the preparation and quality control of mDAPs, such as the lack of standards. Therefore, the establishment of critical quality attributes and technical specifications for mDAPs is largely needed. "Human midbrain dopaminergic progenitor" jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human mDAPs in China. This standard specifies the technical requirements, test methods, inspection rules, instructions for usage, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for human mDAPs, which is applicable to the quality control for human mDAPs. It was originally released by the China Society for Cell Biology on 30 August 2022. We hope that the publication of this guideline will facilitate the institutional establishment, acceptance and execution of proper protocols, and accelerate the international standardization of human mDAPs for clinical development and therapeutic applications.

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.

This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions. The proposed models consist of two distinct graph-based convolutional neural networks, each leveraging different types of brain connectivities to enhance classification performance and gain a deeper understanding of brain connections. The first model, Adjacency-based Convolutional Neural Network Model (Adj-CNNM), utilizes a graph representation based on structural brain connectivity to embed spatial information, distinguishing it from prior spatial filtering approaches dependent on subjects and tasks. Extensive tests on a benchmark dataset-IV-2a demonstrate that an accuracy of 72.77% is achieved by the Adj-CNNM, surpassing baseline and state-of-the-art methods. The second model, Phase Locking Value Convolutional Neural Network Model (PLV-CNNM), incorporates functional connectivity to overcome structural connectivity limitations and identifies connections between distinct brain regions. The PLV-CNNM achieves an overall accuracy of 75.10% across the 1-51 Hz frequency range. In the preferred 8-30 Hz frequency band, known for motor imagery data classification (including α, µ, and ß waves), individual accuracies of 91.9%, 90.2%, and 85.8% are attained for α, µ, and ß, respectively. Moreover, the model performs admirably with 84.3% accuracy when considering the entire 8-30 Hz band. Notably, the PLV-CNNM reveals robust connections between different brain regions during motor imagery tasks, including the frontal and central cortex and the central and parietal cortex. These findings provide valuable insights into brain connectivity patterns, enriching the comprehension of brain function. Additionally, the study offers a comprehensive comparative analysis of diverse brain connectivity modeling methods.

Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition.

Identifying meaningful brain activities is critical in brain-computer interface (BCI) applications. Recently, an increasing number of neural network approaches have been proposed to recognize EEG signals. However, these approaches depend heavily on using complex network structures to improve the performance of EEG recognition and suffer from the deficit of training data. Inspired by the waveform characteristics and processing methods shared between EEG and speech signals, we propose Speech2EEG, a novel EEG recognition method that leverages pretrained speech features to improve the accuracy of EEG recognition. Specifically, a pretrained speech processing model is adapted to the EEG domain to extract multichannel temporal embeddings. Then, several aggregation methods, including the weighted average, channelwise aggregation, and channel-and-depthwise aggregation, are implemented to exploit and integrate the multichannel temporal embeddings. Finally, a classification network is used to predict EEG categories based on the integrated features. Our work is the first to explore the use of pretrained speech models for EEG signal analysis as well as the effective ways to integrate the multichannel temporal embeddings from the EEG signal. Extensive experimental results suggest that the proposed Speech2EEG method achieves state-of-the-art performance on two challenging motor imagery (MI) datasets, the BCI IV-2a and BCI IV-2b datasets, with accuracies of 89.5% and 84.07% , respectively. Visualization analysis of the multichannel temporal embeddings show that the Speech2EEG architecture can capture useful patterns related to MI categories, which can provide a novel solution for subsequent research under the constraints of a limited dataset scale.

Classification of inattentional blindness using brain dynamics of ERPs.

Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources. To the best of our knowledge, however, past research has not fully uncovered the neurological characteristics of IB nor classified these characteristics in life-alike virtual situations. Therefore, the purpose of this study is to determine whether ERP dynamics in the brain may be utilised as a neural feature to predict the occurrence of IB using machine learning (ML) algorithms. In a virtual reality simulation of an IB experiment, 30 participants' behaviour and Electroencephalography (EEG) measurements were obtained. Participants were given a target detection task in the IB experiment without knowing the unattended shapes displayed on the background building. The targets were presented in three different sensory modalities (auditory, visual, and visual-auditory). On the post-experiment questionnaire, participants who claimed not to have noticed the unattended shapes were assigned to the IB group. Subsequently, the Aware group was formed from individuals who reported seeing the unattended shapes. Using EEGNet to classify IB and Aware groups demonstrated a high classification performance. According to the research, ERP brain dynamics are associated with the awareness of unattended shapes and have the potential to serve as a reliable indication for predicting the visual consciousness of unexpected objects.(p/)(p)Clinical relevance- This research offers a potential brain marker for the mixed-reality and BCI systems that will be used in the future to identify cognitive deterioration, maintain attentional capacity, and prevent disasters.

[Characteristics and Ecological Risk Assessment of Pharmaceutical Active Compounds in Guangdong Province].

Pharmaceutically active compounds(PhACs) have become a class of new pollutants in the environment after extensive production and use of PhACs in China. To investigate the pollution characteristics of PhACs in Guangdong Province, raw sewage was collected from 186 sewage treatment plants in 21 cities, including 178 townships and administrative districts in Guangdong Province. The pollution levels of ten typical PhACs in influent water of sewage treatment plants were analyzed using automatic solid phase extraction and high performance liquid chromatography-triple quadrupole mass spectrometry. The spatial distribution characteristics of PhACs in Guangdong Province were fully revealed, and the potential ecological risks of PhACs were evaluated. The results showed that PhACs were detected in all wastewater plants, and the mass concentration of PhACs ranged from 21.00 to 9558.25 ng·L-1. Metoprolo, acetaminophen, bezafibrate, and caffeine were the main pollutants. In terms of spatial distribution, the average mass concentration of ΣPhACs in various regions of Guangdong Province was in the following order:Pearl River Delta>North Guangdong>East Guangdong≈West Guangdong. When the mass concentration of ΣPhACs was over 2500 ng·L-1 in the influent water of sewage treatment plants, the concentration of PhACs in effluent was estimated according to the sewage disposal technology. The ecological risk of PhACs was carried out based on the effluent. The results revealed that the ecological risk of PhACs was low in Guangdong Province, and the risk of bezafibrate was moderate in the cities of Shaoguan, Jiangmen, and Shenzhen. The highest ecological risk of ΣPhACs was located in Shaoguan.

Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions.

Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature. Although there is a high interest in the BCI topic, the performance of existing systems is still far from ideal, due to the difficulty of performing pattern recognition tasks in EEG signals. This difficulty lies in the selection of the correct EEG channels, the signal-to-noise ratio of these signals, and how to discern the redundant information among them. BCI systems are composed of a wide range of components that perform signal preprocessing, feature extraction, and decision making. In this article, we define a new BCI framework, called enhanced fusion framework, where we propose three different ideas to improve the existing MI-based BCI frameworks. First, we include an additional preprocessing step of the signal: a differentiation of the EEG signal that makes it time invariant. Second, we add an additional frequency band as a feature for the system: the sensorimotor rhythm band, and we show its effect on the performance of the system. Finally, we make a profound study of how to make the final decision in the system. We propose the usage of both up to six types of different classifiers and a wide range of aggregation functions (including classical aggregations, Choquet and Sugeno integrals, and their extensions and overlap functions) to fuse the information given by the considered classifiers. We have tested this new system on a dataset of 20 volunteers performing MI-based brain-computer interface experiments. On this dataset, the new system achieved 88.80% accuracy. We also propose an optimized version of our system that is able to obtain up to 90.76%. Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery.

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients' impairment.

Mechanistic insight into the function of the C-terminal PKD domain of the collagenolytic serine protease deseasin MCP-01 from deep sea Pseudoalteromonas sp. SM9913: binding of the PKD domain to collagen results in collagen swelling but does not unwind the collagen triple helix.

Deseasin MCP-01 is a bacterial collagenolytic serine protease. Its catalytic domain alone can degrade collagen, and its C-terminal PKD domain is a collagen-binding domain (CBD) that can improve the collagenolytic efficiency of the catalytic domain by an unknown mechanism. Here, scanning electron microscopy (SEM), atomic force microscopy (AFM), zeta potential, and circular dichroism spectroscopy were used to clarify the functional mechanism of the PKD domain in MCP-01 collagenolysis. The PKD domain observably swelled insoluble collagen. Its collagen-swelling ability and its improvement to the collagenolysis of the catalytic domain are both temperature-dependent. SEM observation showed the PKD domain swelled collagen fascicles with an increase of their diameter from 5.3 mum to 8.8 mum after 1 h of treatment, and the fibrils forming the fascicles were dispersed. AFM observation directly showed that the PKD domain bound collagen, swelled the microfibrils, and exposed the monomers. The PKD mutant W36A neither bound collagen nor disturbed its structure. Zeta potential results demonstrated that PKD treatment increased the net positive charges of the collagen surface. PKD treatment caused no change in the content or the thermostability of the collagen triple helix. Furthermore, the PKD-treated collagen could not be degraded by gelatinase. Therefore, though the triple helix monomers were exposed, the PKD domain could not unwind the collagen triple helix. Our study reveals the functional mechanism of the PKD domain of the collagenolytic serine protease MCP-01 in collagen degradation, which is distinct from that of the CBDs of mammalian matrix metalloproteases.

Adaptive Initialization Method for K-Means Algorithm.

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.