Genome-Wide CRISPRi Screening of Key Genes for Recombinant Protein Expression in Bacillus Subtilis.

Bacillus subtilis is an industrially important microorganism that is often used as a microbial cell factory for the production of recombinant proteins due to its food safety, rapid growth, and powerful secretory capacity. However, the lack of data on functional genes related to recombinant protein production has hindered the further development of B. subtilis cell factories. Here, a strategy combining genome-wide CRISPRi screening and targeted CRISPRa activation to enhance recombinant protein expression is proposed. First, a CRISPRi library covering a total of 4225 coding genes (99.7%) in the B. subtilis genome and built the corresponding high-throughput screening methods is constructed. Twelve key genes for recombinant protein expression are identified, including targets without relevant functional annotations. Meanwhile, the transcription of recombinant protein genes by CRISPRa is up-regulated. These screened or selected genes can be easily applied to metabolic engineering by constructing sgRNA arrays. The relationship between differential pathways and recombinant protein expression in engineered strains by transcriptome analysis is also revealed. High-density fermentation and generalisability validation results prove the reliability of the strategy. This method can be extended to other industrial hosts to support functional gene annotation and the design of novel cell factories.

High level food-grade expression of maltogenic amylase in Bacillus subtilis through dal gene auxotrophic selection marker.

As a food-safe microorganism, Bacillus subtilis has been widely utilized in the production of food enzyme, where a food-grade expression system without antibiotic is required. However, there is no mature system for such expression, since the recombinant plasmid in existing food-grade expression system is unstable especially in high-density fermentation. In this study, we constructed a food-grade expression system based on the dal gene auxotrophic selection marker. Specifically, maltogenic amylase (AmyM) was expressed in dal deletion strain without antibiotic, yielding an activity of 519 U/mL. To increase the expression of AmyM, the promoter of amyM (gene encoding AmyM) was optimized. Furthermore, we found that excessive expression of dal gene was detrimental to the stability of plasmid, and the ribosome binding site (RBS) of dal was mutated with the reduced synthesis of D-alanine. After that, AmyM activity increased to 1364 U/mL with the 100 % stability of plasmid. The 3-L fermentor cultivation was performed with the highest value ever reported in food-grade microorganisms, an activity of 2388 U/mL, showing the scale-up production capability of this system. Besides, it is also able to apply the system for other food enzymes, which indicating the great generalizability of this system for different application.

Overexpression of the class A penicillin-binding protein PonA in Bacillus improves recombinant protein production.

The bottleneck of recombinant protein production in microbial cell factories is sometimes determined by limited manipulable targets and the lack of gene annotation related to protein expression. PonA is the major class A penicillin-binding protein in Bacillus, which polymerizes and cross-links peptidoglycan. Here, we described its novel functions during recombinant protein expression in Bacillus subtilis and analyzed the mechanism of its chaperone activity. When PonA was overexpressed, the expression of hyperthermophilic amylase significantly increased 3.96- and 1.26-fold in shake flasks and fed-batch processes, respectively. Increased cell diameter and reinforced cell walls were observed in PonA-overexpressing strains. Furthermore, the FN3 structural domain and the natural dimeric structure of PonA may be critical for exerting its chaperone activity. These data suggest that PonA can be an effective target for modification of the expression of recombinant proteins in B. subtilis.

Liquid exfoliation of ultrasmall zirconium carbide nanodots as a noninflammatory photothermal agent in the treatment of glioma.

Photothermal therapy (PTT), like other clinical translational tumor ablation techniques, requires a temperature increase above 50 °C to cause necrosis and death of tumor cells. Although the tumor can be eliminated rapidly by PTT, the inflammatory response is triggered by the large amounts of released reactive oxygen species (ROS). Therefore, liquid exfoliation was used to create ultrasmall zirconium carbide nanodots (NDs) with an average diameter of approximately 4.5 nm as noninflammatory/anti-inflammatory photosensitizers for PTT of glioma. Ultrasmall ZrC NDs showed excellent photothermal stability and biocompatibility but no obvious toxicity. Moreover, the ultrasmall ZrC NDs effectively ablated glioma at relatively low concentrations and inhibited tumor migration and proliferation in vitro and in vivo. Furthermore, the excellent ROS-scavenging ability of ultrasmall ZrC NDs suppressed the inflammatory response to PTT. Intriguingly, we found that ZrC had the capability of performing CT imaging. We demonstrated that the ultrasmall ZrC NDs created in this study could effectively and safely treat glioma without inflammation.

Optimization of the moving averaging-moving differential algorithm for Φ-OTDR.

The moving averaging-moving differential (MA-MD) algorithm is widely adopted for event recognition and positioning in phase-sensitive optical time-domain reflectometry (Φ-OTDR) vibration sensing. In this paper, two theorems about the two critical parameters of this algorithm, the MA window width and the MD interval, are found, proved, and verified. According to them we can not only obtain an optimal signal-to-noise ratio and avoid missing the vibration signal but also greatly improve the signal-searching efficiency. Work in this paper is of essential importance for understanding the characteristics of the MA-MD algorithm and will remarkably benefit its practical applications.

Enhanced extracellular α-amylase production in Brevibacillus choshinensis by optimizing extracellular degradation and folding environment.

A strategy for optimizing the extracellular degradation and folding environment of Brevibacillus choshinensis has been used to enhance the extracellular production of recombinant α-amylase. First, a gene (bcp) encoding an extracellular protease and another encoding an extracellular chaperone (prsC) were identified in the genome of B. choshinensis HPD31-SP3. Then, the effect of extracellular protein degradation on recombinant α-amylase production was investigated by establishing a CRISPR/Cas9n system to knock out bcp. The effect of extracellular folding capacity was investigated separately by coexpressing extracellular chaperones genes from different sources (prsA, prsC, prsL, prsQ) in B. choshinensis. The final recombinant strain (BCPPSQ), which coexpressed prsQ in a genetic background lacking bcp, produced an extracellular α-amylase activity of 6940.9 U/ml during shake-flask cultivation. This was 2.1-fold greater than that of the original strain BCWPS (3367.9 U/ml). Cultivation of BCPPSQ in a 3-l fermenter produced an extracellular α-amylase activity of 17925.6 U/ml at 72 h, which was 7.6-fold greater than that of BCWPS (2358.1 U/ml). This strategy demonstrates its great potential in enhancing extracellular α-amylase production in B. choshinensis. What's more, this study provides a strategic reference for improving the extracellular production of other recombinant proteins in B. choshinensis.

Using DFB laser self-injection locked to an optical waveguide ring resonator as a light source of Φ-OTDR.

A distributed feedback (DFB) semiconductor laser self-injection locked to an optical waveguide ring resonator (OWRR) is used in a phase-sensitive optical time-domain reflectometry (Φ-OTDR) vibration sensing system as its light source. A frequency-hopping-free period of more than 100 s is realized. A spatial resolution of 13 m at 4700 m and simultaneous measurement of two vibration sources are realized. The measurable vibration frequency is from 8 Hz to the upper limit of the sampling theorem. These satisfactory performances demonstrate that the DFB laser locked to an OWRR is not only cost effective, but also stable and reliable for Φ-OTDR sensing.

Sudden unexpected death due to spontaneous pneumothorax caused by ruptured bilateral pulmonary bullae.

Sudden unexpected death due to pneumothoraces caused by spontaneous rupture of bilateral pulmonary bullae is rare. This article reports the case of a 16-year-old girl who experienced this rare phenomenon without any precipitating factors. The patient did not have a history of chest pains or smoking but experienced chest tightness in the early morning and collapsed and died 4 h later. Autopsy identified the cause of death to be bilateral pneumothoraces and massive bilateral pulmonary collapse (atelectasis) due to ruptured apical bullae of the bilateral lungs. No injuries or other significant pathological findings were identified. A low body mass index (16.5) may have been a risk factor for the spontaneous tension pneumothoraces. In some situations, genetic counseling and testing may be helpful in identifying a heritable process associated with spontaneous pneumothoraces.

Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset.

The accurate identification of an attention deficit hyperactivity disorder (ADHD) subject has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, the traditional methods concerning the classification model and feature extraction usually depend on the single-channel model and static measurements (i.e., functional connectivity, FC) in the small, homogenous single-site dataset, which is limited and may cause the loss of intrinsic information in functional MRI (fMRI). In this study, we proposed a new two-stage network structure by combing a separated channel convolutional neural network (SC-CNN) with an attention-based network (SC-CNN-attention) to discriminate ADHD and healthy controls on a large-scale multi-site database (5 sites and n = 1019). To utilize both intrinsic temporal feature and the interactions of temporal dependent in whole-brain resting-state fMRI, in the first stage of our proposed network structure, a SC- CNN is used to learn the temporal feature of each brain region, and an attention network in the second stage is adopted to capture temporal dependent features among regions and extract fusion features. Using a "leave-one-site-out" cross-validation framework, our proposed method obtained a mean classification accuracy of 68.6% on five different sites, which is higher than those reported in previous studies. The classification results demonstrate that our proposed network is robust to data variants and is also replicated across sites. The combination of the SC-CNN with the attention network is powerful to capture the intrinsic fMRI information to discriminate ADHD across multi-site resting-state fMRI data.

EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations.

OBJECTIVE: Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition. METHODS: We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition. RESULTS: Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing. SIGNIFICANCE: The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.

Improved Graph Embedding for Robust Recognition with outliers.

Artifacts in biomedical signal recordings, such as gene expression, sonar image and electroencephalogram, have a great influence on the related research because the artifacts with large value usually break the neighbor structure in the datasets. However, the conventional graph embedding (GE) used for dimension reduction such as linear discriminant analysis, principle component analysis and locality preserving projections is essentially defined in the L2 norm space and is prone to the presence of artifacts, resulting in biased sub-structural features. In this work, we defined graph embedding in the L1 norm space and used the maximization strategy to solve this model with the aim of restricting the influence of outliers on the dimension reduction of signals. The quantitative evaluation with different outlier conditions demonstrates that an L1 norm-based GE structure can estimate hyperplanes, which are more stable than those of conventional GE-based methods. The applications to a variety of datasets also show that the proposed L1 GE is more robust to outlier influence with higher classification accuracy estimated. The proposed L1 GE may be helpful for capturing reliable mapping information from the datasets that have been contaminated with outliers.

Lp (p ≤ 1) Norm Partial Directed Coherence for Directed Network Analysis of Scalp EEGs.

Partial directed coherence (PDC), which is capable of estimating directed brain networks in the frequency domain, has been widely used in various physiological recordings such as electroencephalograms (EEGs) and functional magnetic resonance imaging. However, clinical data from EEGs are inevitably contaminated with unexpected outlier artifacts. This will result in biased networks, which are different from the original physiological mechanism because of the L2 norm structure utilized in PDC to estimate the directed links. In this work, we define a new PDC model in the Lp norm (p ≤ 1) space to restrict outlier influence and use a feasible iteration procedure to solve this model for directed network construction. The quantitative evaluation using a predefined simulation network demonstrates that Lp-PDC is more consistent with the predefined networks than LS-PDC and Lasso-PDC under various simulated outlier conditions. Applying the Lp-PDC model to resting-state EEGs with ocular artifacts also show that the proposed PDC can effectively restrict the ocular artifacts to recover the networks, which is also more consistent with the physiological basis. Both simulation and real-life EEG applications demonstrate the efficiency of the proposed PDC in suppressing the influence of outliers in EEG signals, and the proposed Lp-PDC may be helpful to capture reliable causal relationships for related studies contaminated with outlier artifacts.

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which outperforms other feature models.

Salicylato titanocene complexes as cooperative organometallic Lewis acid and Brønsted acid catalysts for three-component mannich reactions.

A binary acid system has been developed that features an air-stable organometallic precursor, titanocene dichloride, and simple organic cooperative Brønsted acids, which allowed for mild and highly efficient Mannich reactions of both aryl and alkyl ketones with excellent yields and satisfactory diastereoselectivity. Mechanistic studies, including (1) H NMR titration, X-ray structure analyses as well as isolation of catalytically active species, elucidated the dramatic synergistic effects of this new binary acid system.

[Evaluating on recognition impairment after traumatic brain injury with WCST].

OBJECTIVE: To explore the value of Wisconsin Card Sorting Test (WCST) in evaluating the recognition impairment after traumatic brain injury (TB1). METHODS: WCST and Wechsler Adult Intelligence Scale Revised in China (WAIS-RC) were adopted to assess the cognitive function in 186 patients with brain injury (experimental group) and 180 healthy volunteers (control group), respectively. The cognition between the experimental group and the control group was compared. The correlation between WCST and IQ was analyzed. The correlation between age, education level, the severity of TBI and the executive function were also analyzed. RESULTS: (1) The performances of WAIS-RC and WCST in the experimental group were significantly worse than that of the control group. (2) There were significantly negative correlation between the performance of total errors, percentage of preservative errors, percentage of random errors, response number on first category and IQ (P < 0.05). While there were significantly positive correlation between the categories completed, percentage of conceptual level and IQ (P < 0.05). (3) There was significantly negative correlation between percentage of conceptual level and the severity of TBI. While there were significantly positive correlation between percentage of preservative errors, percentage of random errors, response number on first category and the severity of TBI. CONCLUSION: WCST could be an important method in evaluating cognition of patients with brain injury.

[Bcl-2 expression following the brain concussion in rats].

OBJECTIVE: To evaluate the expression of Bcl-2 protein after brain concussion. METHODS: Expression levels of Bel-2 protein in cortex, pontine and cerebellum of rats were investigated using immunohistochemistry. RESULTS: There was no expression of Bcl-2 protein in control group seen. The expression of Bcl-2 protein in brain concussion groups was detected at l hour, and the expression level reached its peak 4 days after the concussion and then declined gradually. CONCLUSION: Our findings suggest that the detection of Bel-2 protein could be an indicator for diagnosis of brain concussion and for estimation of the post injury time interval.

[Application of spiral CT 3D reconstruction of rib fracture in clinical forensic practice].

OBJECTIVE: To assess the clinical application of spiral CT 3D reconstruction in forensic diagnosis of rib fracture. METHODS: Thirty-five cases of rib fractures were collectively studied by X-ray, regular CT scan, and adoptive 3D reconstruction with spiral CT. RESULTS: Clear and stereoscopic images of rib fractures without interference of the surrounding soft tissue were obtained in all of 35 cases examined. CONCLUSION: Spiral CT 3D reconstruction could be a valuable means in forensic diagnosis of rib fracture.

[Integrating the healthcare enterprise: an overview].

Integrating the healthcare enterprise (IHE) is a developing general norm in medical informatics especially in the field of radiology, which is independent of the manufacturers and has been accepted by a number of manufacturers and systems. In this paper, the authors provide a brief overview of IHE with the integration profiles of IHE technical framework being particularly elaborated.