Electromagnetic field and cardiovascular diseases: A state-of-the-art review of diagnostic, therapeutic, and predictive values.

Despite encouraging advances in early diagnosis and treatment, cardiovascular diseases (CVDs) remained a leading cause of morbidity and mortality worldwide. Increasing evidence has shown that the electromagnetic field (EMF) influences many biological processes, which has attracted much attention for its potential therapeutic and diagnostic modalities in multiple diseases, such as musculoskeletal disorders and neurodegenerative diseases. Nonionizing EMF has been studied as a therapeutic or diagnostic tool in CVDs. In this review, we summarize the current literature ranging from in vitro to clinical studies focusing on the therapeutic potential (external EMF) and diagnostic potential (internal EMF generated from the heart) of EMF in CVDs. First, we provided an overview of the therapeutic potential of EMF and associated mechanisms in the context of CVDs, including cardiac arrhythmia, myocardial ischemia, atherosclerosis, and hypertension. Furthermore, we investigated the diagnostic and predictive value of magnetocardiography in CVDs. Finally, we discussed the critical steps necessary to translate this promising approach into clinical practice.

Correlations between geomagnetic field and global occurrence of cardiovascular diseases: evidence from 204 territories in different latitude.

BACKGROUND: The correlation between stable geomagnetic fields and unstable geomagnetic activities with mortality, incidence, and prevalence of cardiovascular diseases (CVDs) remains ambiguous. METHOD: To investigate the correlations between geomagnetic field (GMF) intensity and geomagnetic disturbance (GMD) and CVDs events in global, long-period scale, global and 204 countries and territories were included on the base of 2019 Global Burden of Disease study (GBD 2019). Data of GMF intensity, GMD frequency, CVDs events, weather and health economic indicators from 1996 to 2019 of included locations were collected. Linear regression and panel data modelling were conducted to identify the correlations between GMF intensity and CVDs events, multi-factor panel data analysis was also generated to adjust the effect of confounding factors. RESULTS: For the average data during 1996-2019, linear regression model revealed consistent positive correlations between total GMF (tGMF) intensity and mortality of total CVDs [coef = 0.009, (0.006,0.011 95%CI)], whereas negative correlations were found between horizonal GMF (hGMF) intensity and total CVD mortality [coef = -0.010 (-0.013, -0.007 95%CI)]. When considering the time trend, panel data analysis still demonstrated positive correlation between tGMF and total CVDs mortality [coef = 0.009, (0.008,0.009 95%CI)]. Concurrently, the hGMF negatively correlated with total CVDs mortality [coef = -0.008, (-0.009, -0.007 95%CI)]. When the panel models were adjusted for confounding factors, no reverse of correlation tendency was found between tGMF, hGMF and CVDs events. In high-income territories, positive correlation was found between geomagnetic storm (GMS) frequency and mortality of total CVDs [coef = 14.007,(2.785, 25.229 95%CI)], however, this positive trend faded away gradually with the latitude decreasing from polar to equator. CONCLUSIONS: Stable and long-term horizontal component of GMF may be beneficial to cardiac health. Unstable and short-term GMF called GMD could be a hazard to cardiac health. Our results suggest the importance of regular GMF in maintaining cardio-health state and the adverse impacts of GMD on cardiac health.

Transannular patch repair of tetralogy of Fallot with or without monocusp valve reconstruction: a meta-analysis.

BACKGROUND: Tetralogy of Fallot (TOF) is one of the most common cyanotic congenital heart diseases. Pulmonary regurgitation is the most common and severe comorbidity after transannular patch (TAP) repair of TOF patients. It has not been confirmed whether a TAP repair with monocusp valve reconstruction would benefit TOF patients in perioperative period compared to those without monocusp valve reconstruction. The purpose of the study is to review and analyze all clinical studies that have compared perioperative outcomes of TOF patients undergoing TAP repair with or without monocusp valve reconstruction and conduct a preferable surgery. METHODS: Eligible studies were identified by searching the electronic databases. The year of publication of studies was restricted from 2000 till present. The primary outcome was perioperative mortality, and secondary outcomes included cardiopulmonary bypass time, aortic cross-clamp time, ventilation duration, ICU length of stay, hospital length of stay, perioperative right ventricular outflow tract (RVOT) pressure gradient, and moderate or severe pulmonary regurgitation (PR). The meta-analysis and forest plots were drawn using Review Manager 5.3. Statistically significant was considered when p-value ≤ 0.05. RESULTS: Eight studies were included which consisted of 8 retrospective cohort study and 2 randomized controlled trial. The 10 studies formed a pool of 526 TOF patients in total, in which are 300 undergoing TAP repair with monocusp valve reconstruction (monocusp group) compared to 226 undergoing TAP repair without monocusp valve reconstruction (non-monocusp group). It demonstrated no significant differences between two groups in perioperative mortality (OR = 0.69, 95% CI 0.20-2.41, p = 0.58). It demonstrated significant differences in perioperative cardiopulmonary bypass time (minute, 95% CI 17.93-28.42, p < 0.00001), mean length of ICU stay (day, 95% CI - 2.11-0.76, p < 0.0001), and the degree of perioperative PR (OR = 0.03, 95% CI 0.010.12, p < 0.00001). Significant differences were not found in other secondary outcomes. CONCLUSION: Transannular patch repair with monocusp valve reconstruction have significant advantages on decreasing length of ICU stay and reducing degree of PR for TOF patients. Large, multicenter, randomized, prospective studies which focuse on perioperative outcomes and postoperative differences based on long-term follow-up between TAP repair with and without monocusp valve reconstruction are needed.

Association of lymph node involvement with the prognosis of pathological T1 invasive non-small cell lung cancer.

BACKGROUND: Lymph node involvement could help to predict the prognosis of pathological T1 (pT1, diameters of ≤3 cm) non-small cell lung cancer (NSCLC). This study assessed the clinicopathological factors and associated lymph node involvement in invasive lung adenocarcinoma (IAC) and squamous cell lung cancer (SCC) and the overall and disease-free survival associated with these factors. METHODS: Three hundred and twenty-five patients with pathological T1 NSCLC (253 IAC and 72 SCC) were retrospectively analyzed from a pool of 1094 primary lung cancer patients. The data were assessed using multiple logistic regression, Kaplan-Meier curves and multivariable analyses. RESULTS: Among patients with a ≤30-mm tumor lesion (N = 325), N1 and N2 lymph node involvement was found in 28 (8.6%) and 34 (10.4%) patients, respectively. Lymph node metastasis occurred in 13.0% (33/253) of pT1 IAC patients and 40.3% (29/72) of SCC patients. Carcinoembryonic antigen (CEA) levels, SCC by histology, and tumor lesions larger than 1.0 cm were associated with lymph node involvement (P < 0.0001, <0.0001, and 0.048, respectively). In IAC patients, negative lymph nodes were associated with better overall survival compared with lymph node-positive ones (P = 0.021). No significant difference was observed in SCC patients regardless of lymph node status (P = 0.40). Multivariable Cox analysis revealed that lymph node involvement was an independent prognostic predictor of overall IAC patient survival (P = 0.041), but not of SCC patient survival (P = 0.470). Chemotherapy was administered to 72.2% (52/72) of SCC patients, a significantly higher rate when compared with that of IAC patients (42.3%, 107/253). CONCLUSIONS: Lymph node metastasis was inversely associated with the overall survival of IAP patients, but not with the survival of SCC patients. Patients with pT1 SCC exhibited a significantly higher rate of lymph node involvement when compared with IAC patients. Thus, a systematic lymph node dissection should be performed in pT1 IAC patients, especially in patients with IAC larger than 1.0 cm, for additional treatment selections to improve survival.

Differential Inhibition of Macrophage Activation by Lymphocytic Choriomeningitis Virus and Pichinde Virus Is Mediated by the Z Protein N-Terminal Domain.

Several arenavirus pathogens, such as Lassa and Junin viruses, inhibit macrophage activation, the molecular mechanism of which is unclear. We show that lymphocytic choriomeningitis virus (LCMV) can also inhibit macrophage activation, in contrast to Pichinde and Tacaribe viruses, which are not known to naturally cause human diseases. Using a recombinant Pichinde virus system, we show that the LCMV Z N-terminal domain (NTD) mediates the inhibition of macrophage activation and immune functions.

Oncolytic therapy of a recombinant Newcastle disease virus D90 strain for lung cancer.

BACKGROUND: Lung cancer is one of the leading causes of deaths from cancer worldwide. Tumor virotherapy using naturally oncolytic Newcastle disease virus (NDV) has been shown to be safe and effective in preclinical studies and clinical trials. Previously, we have reported the NDV D90 strain that was isolated from natural source has an antiproliferative effect in human lung cancer cell line A549. METHODS AND RESULTS: In this study, we constructed a reverse genetics system based on the oncolytic NDV D90 strain and generated a recombinant NDV carrying a gene encoding enhanced green fluorescent protein (rNDV-GFP). The rescued virus rNDV-D90 and rNDV-GFP showed the similar characteristics of replication and apoptotic ability in lung cancer A549 cells, which suggested that the recombinant viruses sustained the property of tumor-selective replication and induced apoptosis of tumor cells. The athymic mice bearing implanted lung cancer were treated with the parental D90 virus, the rescued rNDV-D90 and rNDV-GFP via intratumoral injections, respectively. The results showed that the recombinant viruses as well as the parental D90 virus significantly suppressed the loss of body weight and tumor growth. CONCLUSIONS: The study provides a new platform to develop effective therapeutic agents for tumor treatment. The availability of the reverse genetics system for NDV D90 strain will make it possible to develop novel recombinant oncolytic viruses based on the NDV D90 strain for improving the efficacy of tumor treatment.

A SYBR Green-based real-time RT-PCR assay for simple and rapid detection and differentiation of highly pathogenic and classical type 2 porcine reproductive and respiratory syndrome virus circulating in China.

SYBR Green coupled to melting curve analysis has been suggested to detect RNA viruses showing high genomic variability. Here, a SYBR Green-based real-time RT-PCR assay was developed for simultaneous detection and differentiation of highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) and classical type 2 PRRSV (C-PRRSV). The different strains were identified by their distinctive melting temperatures: 82.98 ± 0.25 °C and 85.95 ± 0.24 °C for HP-PRRSVs or 82.74 ± 0.26 °C for C-PRRSVs. Specificity was tested using nine other viral and bacterial pathogens of swine. The detection limit was 1 TCID(50) for HP- or C-PRRSV. Furthermore, the detection results for samples from an animal trial with HP- or C-PRRSV infections showed that the SYBR Green-based real-time RT-PCR was more sensitive than the conventional RT-PCR. Additionally, an analysis of 319 field samples from North China, Central China and Northeast China showed that HP- and C-PRRSVs co-circulated in pig herds. Thus, the SYBR Green-based real-time RT-PCR, which can be performed within one hour, is a rapid, sensitive and low-cost diagnostic tool for rapid differential detection and routine surveillance of HP- and classical type 2 PRRSVs in China.

Variational Progressive-Transfer Network for Soft Sensing of Multirate Industrial Processes.

Deep-learning-based soft sensors have been extensively developed for predicting key quality or performance variables in industrial processes. However, most approaches assume that data are uniformly sampled while the multiple variables are often acquired at different rates in practical processes. This article designed a progressive transfer strategy, based on which a variational progressive-transfer network (VPTN) method is proposed for the soft sensor development of industrial multirate processes. In VPTN, the multirate data are first separated into multiple data chunks where the variables within each chunk are acquired at a uniform rate. Then, a variational multichunk data modeling framework is developed to model the multiple chunks in a unified fashion through deep variational structures. The base models, including the unsupervised ones with only partial process variables and the supervised soft sensor model share a similar network structure, such that the subsequent transfer strategy can be readily implemented. Finally, a progressive transfer learning strategy is designed to transfer the model parameters from the fastest sampled data chunk to the slowest one in a progressive manner. Thus, the knowledge from various data chunks can be sequentially explored and transferred to enhance the performance of the terminal soft sensor model. Case studies on both a debutanizer column dataset and a real coal mill dataset in a thermal power plant validate the performance of the proposed method.

Multisource-Refined Transfer Network for Industrial Fault Diagnosis Under Domain and Category Inconsistencies.

Unsupervised cross-domain fault diagnosis has been actively researched in recent years. It learns transferable features that reduce distribution inconsistency between source and target domains without target supervision. Most of the existing cross-domain fault diagnosis approaches are developed based on the consistency assumption of the source and target fault category sets. This assumption, however, is generally challenged in practice, as different working conditions can have different fault category sets. To solve the fault diagnosis problem under both domain and category inconsistencies, a multisource-refined transfer network is proposed in this article. First, a multisource-domain-refined adversarial adaptation strategy is designed to reduce the refined categorywise distribution inconsistency within each source-target domain pair. It avoids the negative transfer trap caused by conventional global-domainwise-forced alignments. Then, a multiple classifier complementation module is developed by complementing and transferring the source classifiers to the target domain to leverage different diagnostic knowledge existing in various sources. Different classifiers are complemented by the similarity scores produced by the adaptation module, and the complemented smooth predictions are used to guide the refined adaptation. Thus, the refined adversarial adaptation and the classifier complementation can benefit from each other in the training stage, yielding target-faults-discriminative and domain-refined-indistinguishable feature representations. Extensive experiments on two cases demonstrate the superiority of the proposed method when domain and category inconsistencies coexist.

A Deep Probabilistic Transfer Learning Framework for Soft Sensor Modeling With Missing Data.

Soft sensors have been extensively developed and applied in the process industry. One of the main challenges of the data-driven soft sensors is the lack of labeled data and the need to absorb the knowledge from a related source operating condition to enhance the soft sensing performance on the target application. This article introduces deep transfer learning to soft sensor modeling and proposes a deep probabilistic transfer regression (DPTR) framework. In DPTR, a deep generative regression model is first developed to learn Gaussian latent feature representations and model the regression relationship under the stochastic gradient variational Bayes framework. Then, a probabilistic latent space transfer strategy is designed to reduce the discrepancy between the source and target latent features such that the knowledge from the source data can be explored and transferred to enhance the target soft sensor performance. Besides, considering the missing values in the process data in the target operating condition, the DPTR is further extended to handle the missing data problem utilizing the strong generation and reconstruction capability of the deep generative model. The effectiveness of the proposed method is validated through an industrial multiphase flow process.

A Single-Side Neural Network-Aided Canonical Correlation Analysis With Applications to Fault Diagnosis.

Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships among variables. These assumptions may be improper in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal. With the aid of neural networks, this work proposes a new nonlinear counterpart called a single-side CCA (SsCCA) to enhance FD performance. The contributions of this work are four-fold: 1) an objective function for the nonlinear CCA is first reformulated, based on which a generalized solution is presented; 2) for the practical implementation, a particular solution of SsCCA is developed; 3) an SsCCA-based FD algorithm is designed for nonlinear systems, whose optimal FD ability is illustrated via theoretical analysis; and 4) based on the difference in FD results between two test statistics, fault diagnosis can be directly achieved. The studies on a nonlinear three-tank system are carried out to verify the effectiveness of the proposed SsCCA method.

Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning.

With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.

Toward Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework.

While graph neural networks (GNNs) are popular in the deep learning community, they suffer from several challenges including over-smoothing, over-squashing, and gradient vanishing. Recently, a series of models have attempted to relieve these issues by first augmenting the node features and then imposing node-wise functions based on multilayer perceptron (MLP), which are widely referred to as graph-augmented MLP (GA-MLP) models. However, while GA-MLP models enjoy deeper architectures for better accuracy, their efficiency largely deteriorates. Moreover, popular acceleration techniques such as stochastic-version or data-parallelism cannot be effectively applied due to the dependency among samples (i.e., nodes) in graphs. To address these issues, in this article, instead of data parallelism, we propose a parallel graph deep learning Alternating Direction Method of Multipliers (pdADMM-G) framework to achieve model parallelism: parameters in each layer of GA-MLP models can be updated in parallel. The extended pdADMM-G-Q algorithm reduces communication costs by introducing the quantization technique. Theoretical convergence to a (quantized) stationary point of the pdADMM-G algorithm and the pdADMM-G-Q algorithm is provided with a sublinear convergence rate o(1/k) , where k is the number of iterations. Extensive experiments demonstrate the convergence of two proposed algorithms. Moreover, they lead to a more massive speedup and better performance than all state-of-the-art comparison methods on nine benchmark datasets. Last but not least, the proposed pdADMM-G-Q algorithm reduces communication overheads by up to 45% without loss of performance. Our code is available at https://github.com/xianggebenben/pdADMM-G.

Adeno-associated virus-mediated expression of activated factor V (FVa) for hemophilia phenotypic correction.

Adeno-associated virus (AAV) gene therapy has been successfully applied in hemophilia patients excluding patients with inhibitors. During the coagulation pathway, activated factor V (FVa) functions downstream as a cofactor of activated factor X (FXa) to amplify thrombin generation. We hypothesize that the expression of FVa via gene therapy can improve hemostasis of both factor IX and FVIII deficiencies, regardless of clotting factor inhibitor. A human FVa (hFVa) expression cassette was constructed, and AAV8 vectors encoding hFVa (AAV8/TTR-hFVa) were intravenously administrated into mice with hemophilia A and B with or without FVIII inhibitors. Hemostasis, including hFVa level, activated partial thromboplastin time (aPTT), tail clip, and the saphenous vein bleeding assay (SVBA), was evaluated. In hemophilia B mice, a dose of 4 × 1013 vg/kg AAV8/TTR-hFVa vectors achieved a complete phenotypic correction over 28 weeks. In hemophilia A mice, hemostasis improvement was also achieved, regardless of FVIII inhibitor development. In vivo hemostasis efficacy was confirmed by tail clip and SVBA. Interestingly, while minimal shortening of aPTT was observed at a lower dose of AAV8 vectors, hemostasis improvement was still achieved via in vivo bleeding assays. Collectively, FVa-based AAV gene therapy shows promise for hemostasis correction in hemophilia, regardless of inhibitor development and no potential risk for thrombosis.

Customized blood-brain barrier shuttle peptide to increase AAV9 vector crossing the BBB and augment transduction in the brain.

Recombinant adeno-associated virus (rAAV) vectors have been widely used as favored delivery vehicles for the treatment of inherited diseases in clinical trials, including neurological diseases. However, the noninvasive systemic delivery of rAAV to the central nervous system is severely hampered by the blood-brain barrier (BBB). Several approaches have been exploited to enhance AAV vector brain transduction after systemic administration, including genetic modification of AAV capsids and physical methods. However, these approaches are not always predictive of desirable outcomes in humans and induce complications. It is imperative to explore novel strategies to increase the ability of AAV9 to cross the BBB for enhanced brain transduction. Herein, we have conducted a combinatorial in vivo/in vitro phage display library screening in mouse brains and purified AAV9 virions to identify a customized BBB shuttle peptide, designated as PB5-3. The PB5-3 peptide specifically bound to AAV9 virions and enhanced widespread transduction of AAV9 in mouse brains, especially in neuronal cells, after systemic administration. Further study demonstrated that systemic administration of AAV9 vectors encoding IDUA complexed with PB5-3 increased the phenotypic correction in the brains of MPS I mice. Mechanistic studies revealed that the PB5-3 peptide effectively increased AAV9 trafficking and transcytosis efficiency in the human BBB model hCMEC/D3 cell line but did not interfere with AAV9 binding to the receptor terminal N-linked galactosylated glycans. Additionally, the PB5-3 peptide slowed the clearance of AAV9 from blood without hepatic toxicity. This study highlights, for the first time, the potential of this combinatorial approach for the isolation of peptides that interact with specific AAV vectors for enhanced and targeted AAV transduction. This promising approach will open new combined therapeutic avenues and shed light on the potential applications of peptides for the treatment of human diseases in future clinical trials with AAV vector-mediated gene delivery.

Dexamethasone Transiently Enhances Transgene Expression in the Liver When Administered at Late-Phase Post Long-Term Adeno-Associated Virus Transduction.

Glucocorticoids have anti-inflammatory and immunosuppressive functions and have commonly been used for preventing liver toxicity after the systemic application of a high dose of adeno-associated virus (AAV) vector for gene therapy. Clinical studies have reported that glucocorticoids have rescued factor IX (FIX) expression in patients with hemophilia B who showed a reduced FIX expression at 6 to 10 weeks post-AAV vector administration. In this study, we explored whether glucocorticoids could affect transgene expression in AAV targeted livers in animal models. When dexamethasone was applied before AAV9/FIX vector administration in the wild-type C57BL/6 mice, FIX expression was much higher than that of the control mice at any time point. More importantly, FIX expression transiently increased after dexamethasone was administered at week 6 or later post-AAV injection regardless of the various dexamethasone treatments applied. The transient enhancement in transgene expression was observed once there were one to several consecutive dexamethasone treatments completed. A similar result was also achieved in other wild-type BALB/c and hemophilia B mice that were treated with AAV9/FIX and dexamethasone. This mechanism study demonstrated that the administration of dexamethasone did not change either AAV genome copy number or transgene expression at the transcription level but transiently decreased interferon beta (IFN-ß) and tumor necrosis factor alpha (TNF-α) expression in the livers of mice at a later time after AAV injection. Next, we studied the effect of dexamethasone on late transgene expression in hemophilia B dogs. Dexamethasone was administered 1 year after AAV9/FIX injection. Inconsistent with the results in mice, no significant change of FIX expression was observed in hemophilia B dogs. In summary, the results from this study indicate that dexamethasone may have various effects on transgene expression in AAV-transduced livers in different species, which provides valuable information about the rational application of dexamethasone in future clinical studies.

Novel Phage-Derived Depolymerase with Activity against Proteus mirabilis Biofilms.

The adherence of Proteus mirabilis to the surface of urinary catheters leads to colonization and eventual blockage of the catheter lumen by unique crystalline biofilms produced by these opportunistic pathogens, making P. mirabilis one of the leading causes of catheter-associated urinary tract infections. The Proteus biofilms reduce efficiency of antibiotic-based treatment, which in turn increases the risk of antibiotic resistance development. Bacteriophages and their enzymes have recently become investigated as alternative treatment options. In this study, a novel Proteus bacteriophage (vB_PmiS_PM-CJR) was isolated from an environmental sample and fully characterized. The phage displayed depolymerase activity and the subsequent genome analysis revealed the presence of a pectate lyase domain in its tail spike protein. The protein was heterologously expressed and purified; the ability of the purified tail spike to degrade Proteus biofilms was tested. We showed that the application of the tail spike protein was able to reduce the adherence of bacterial biofilm to plastic pegs in a MBEC (minimum biofilm eradication concentration) assay and improve the survival of Galleria mellonella larvae infected with Proteus mirabilis. Our study is the first to successfully isolate and characterize a biofilm depolymerase from a Proteus phage, demonstrating the potential of this group of enzymes in treatment of Proteus infections.

Giant Ferroelectric Resistance Switching Controlled by a Modulatory Terminal for Low-Power Neuromorphic In-Memory Computing.

Ferroelectrics have been demonstrated as excellent building blocks for high-performance nonvolatile memories, including memristors, which play critical roles in the hardware implementation of artificial synapses and in-memory computing. Here, it is reported that the emerging van der Waals ferroelectric α-In2 Se3 can be used to successfully implement heterosynaptic plasticity (a fundamental but rarely emulated synaptic form) and achieve a resistance-switching ratio of heterosynaptic memristors above 103 , which is two orders of magnitude larger than that in other similar devices. The polarization change of ferroelectric α-In2 Se3 channel is responsible for the resistance switching at various paired terminals. The third terminal of α-In2 Se3 memristors exhibits nonvolatile control over channel current at a picoampere level, endowing the devices with picojoule read-energy consumption to emulate the associative heterosynaptic learning. The simulation proves that both supervised and unsupervised learning manners can be implemented in α-In2 Se3 neutral networks with high image recognition accuracy. Moreover, these heterosynaptic devices can naturally realize Boolean logic without an additional circuit component. The results suggest that van der Waals ferroelectrics hold great potential for applications in complex, energy-efficient, brain-inspired computing systems and logic-in-memory computers.

Multiclass Oblique Random Forests With Dual-Incremental Learning Capacity.

Oblique random forests (ObRFs) have attracted increasing attention recently. Their popularity is mainly driven by learning oblique hyperplanes instead of expensively searching for axis-aligned hyperplanes in the standard random forest. However, most existing methods are trained in an off-line mode, which assumes that the training data are given as a batch. Efficient dual-incremental learning (DIL) strategies for ObRF have rarely been explored when new inputs from the existing classes or unseen classes come. The goal of this article is to provide an ObRF with DIL capacity to perform classification on-the-fly. First, we propose a batch multiclass ObRF (ObRF-BM) algorithm by using a broad learning system and a multi-to-binary method to obtain an optimal oblique hyperplane in a higher dimensional space and then separate the samples into two supervised clusters at each node, which provides the basis for the following incremental learning strategy. Then, the DIL strategy for ObRF-BM, termed ObRF-DIL, is developed by analytically updating the parameters of all nodes on the classification route of the increment of input samples and the increment of input classes so that the ObRF-BM model can be effectively updated without laborious retraining from scratch. Experimental results using several public data sets demonstrate the superiority of the proposed approach in comparison with several state-of-the-art methods.

Bound Protein- and Peptide-Based Strategies for Adeno-Associated Virus Vector-Mediated Gene Therapy: Where Do We Stand Now?

Recombinant adeno-associated virus (rAAV) vectors have become one of the most promising and efficacious delivery vehicles for human gene therapy; however, low infectivity remains a major ongoing obstacle in the clinical application of rAAV vectors. Multiple strategies, including rAAV capsid modification and the application of pharmacological reagents, have been explored to enhance rAAV vector gene delivery. Recently, a new strategy using native proteins or various peptides has shown promise for increasing rAAV transduction locally or globally. This review summarizes the current status of protein- and peptide-based strategies and mechanisms to modulate rAAV transduction. We also provide a potential insight regarding the design of effective approaches for rAAV transduction enhancement in future clinical studies.