Overview of mechanism of electroacupuncture pretreatment for prevention and treatment of cardiovascular and cerebrovascular diseases.

Cardio-cerebrovascular disease (CCVD) is a serious threat to huma strategy to prevent the occurrence and development of disease by giving electroacupuncture intervention before the disease occurs. EAP has been shown in many preclinical studies to relieve ischemic symptoms and improve damage from ischemia-reperfusion, with no comprehensive review of its mechanisms in cardiovascular disease yet. In this paper, we first systematically discussed the meridian and acupoint selection law of EAP for CCVD and focused on the progress of the mechanism of action of EAP for the prevention and treatment of CCVD. As a result, in preclinical studies, AMI and MCAO models are commonly used to simulate ischemic injury in CCVD, while MIRI and CI/RI models are used to simulate reperfusion injury caused by blood flow recovery after focal tissue ischemia. According to the meridian matching rules of EAP for CCVD, PC6 in the pericardial meridian is the most commonly used acupoint in cardiovascular diseases, while GV20 in the Du meridian is the most commonly used acupoint in cerebrovascular diseases. In terms of intervention parameters, EAP intervention generally lasts for 30 min, with acupuncture depths mostly between 1.5 and 5 mm, stimulation intensities mostly at 1 mA, and commonly used frequencies being low frequencies. In terms of molecular mechanisms, the key pathways of EAP in preventing and treating cardiovascular and cerebrovascular diseases are partially similar. EAP can play a protective role in cardiovascular and cerebrovascular diseases by promoting autophagy, regulating Ca2+ overload, and promoting vascular regeneration through anti-inflammatory reactions, antioxidant stress, and anti-apoptosis. Of course, both pathways involved have their corresponding specificities. When using EAP to prevent and treat cardiovascular diseases, it involves the metabolic pathway of glutamate, while when using EAP to prevent and treat cerebrovascular diseases, it involves the homeostasis of the blood-brain barrier and the release of neurotransmitters and nutritional factors. I hope these data can provide experimental basis and reference for the clinical promotion and application of EAP in CCVD treatment.

CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data.

Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data.

BACKGROUND: Accurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH. RESULTS: We introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal . CONCLUSIONS: Joint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.

Genome-wide association studies (GWAS) have identified thousands of variants in the human genome with autoimmune diseases. However, identifying functional regulatory variants associated with autoimmune diseases remains challenging, largely because of insufficient experimental validation data. We adopt the concept of semi-supervised learning by combining labeled and unlabeled data to develop a deep learning-based algorithm framework, sscNOVA, to predict functional regulatory variants in autoimmune diseases and analyze the functional characteristics of these regulatory variants. Compared to traditional supervised learning methods, our approach leverages more variants' data to explore the relationship between functional regulatory variants and autoimmune diseases. Based on the experimentally curated testing dataset and evaluation metrics, we find that sscNOVA outperforms other state-of-the-art methods. Furthermore, we illustrate that sscNOVA can help to improve the prioritization of functional regulatory variants from lead single-nucleotide polymorphisms and the proxy variants in autoimmune GWAS data.

Inferring single-cell copy number profiles through cross-cell segmentation of read counts.

BACKGROUND: Copy number alteration (CNA) is one of the major genomic variations that frequently occur in cancers, and accurate inference of CNAs is essential for unmasking intra-tumor heterogeneity (ITH) and tumor evolutionary history. Single-cell DNA sequencing (scDNA-seq) makes it convenient to profile CNAs at single-cell resolution, and thus aids in better characterization of ITH. Despite that several computational methods have been proposed to decipher single-cell CNAs, their performance is limited in either breakpoint detection or copy number estimation due to the high dimensionality and noisy nature of read counts data. RESULTS: By treating breakpoint detection as a process to segment high dimensional read count sequence, we develop a novel method called DeepCNA for cross-cell segmentation of read count sequence and per-cell inference of CNAs. To cope with the difficulty of segmentation, an autoencoder (AE) network is employed in DeepCNA to project the original data into a low-dimensional space, where the breakpoints can be efficiently detected along each latent dimension and further merged to obtain the final breakpoints. Unlike the existing methods that manually calculate certain statistics of read counts to find breakpoints, the AE model makes it convenient to automatically learn the representations. Based on the inferred breakpoints, we employ a mixture model to predict copy numbers of segments for each cell, and leverage expectation-maximization algorithm to efficiently estimate cell ploidy by exploring the most abundant copy number state. Benchmarking results on simulated and real data demonstrate our method is able to accurately infer breakpoints as well as absolute copy numbers and surpasses the existing methods under different test conditions. DeepCNA can be accessed at: https://github.com/zhyu-lab/deepcna . CONCLUSIONS: Profiling single-cell CNAs based on deep learning is becoming a new paradigm of scDNA-seq data analysis, and DeepCNA is an enhancement to the current arsenal of computational methods for investigating cancer genomics.

Revealing the clinical effect and biological mechanism of acupuncture in COPD: A review.

BACKGROUND: To provide new ideas for the clinical and mechanism research of acupuncture in the treatment of chronic obstructive pulmonary disease (COPD), this study systematically reviews clinical research and the progress of basic research of acupuncture in the treatment of COPD. METHODS: PubMed and Web of Science databases were searched using acupuncture and COPD as keywords in the last 10 years, and the included literature was determined according to exclusion criteria. FINDINGS: Acupuncture can relieve clinical symptoms, improve exercise tolerance, anxiety, and nutritional status, as well as hemorheological changes (blood viscosity), reduce the inflammatory response, and reduce the duration and frequency of COPD in patients with COPD. Mechanistically, acupuncture inhibits M1 macrophage activity, reduces neutrophil infiltration, reduces inflammatory factor production in alveolar type II epithelial cells, inhibits mucus hypersecretion of airway epithelial cells, inhibits the development of chronic inflammation in COPD, and slows tissue structure destruction. Acupuncture may control pulmonary COPD inflammation through the vagal-cholinergic anti-inflammatory, vagal-adrenomedullary-dopamine, vagal-dual-sensory nerve fiber-pulmonary, and CNS-hypothalamus-orexin pathways. Furthermore, acupuncture can increase endogenous cortisol levels by inhibiting the HPA axis, thus improving airway antioxidant capacity and reducing airway inflammation in COPD. In conclusion, the inhibition of the chronic inflammatory response is the key mechanism of acupuncture treatment for COPD.

Intervertebral Disk Degeneration and Bone Mineral Density: A Bidirectional Mendelian Randomization Study.

This study aimed to investigate the causal relationship between bone mineral density (BMD) and intervertebral disk degeneration (IVDD) using a two-sample bidirectional Mendelian randomization analysis. Summary-level data from the Genome-Wide Association Study (GWAS) were used. Instrumental variables (IVs) for IVDD were selected from the large-scale Genome-Wide Association Study (GWAS) (20,001 cases and 164,682 controls). Bone mineral density (BMD) at five different sites (heel (n = 426,824), total body (TB) (n = 56,284), forearm (FA) (n = 8143), femoral neck (FN) (n = 32,735), and lumbar spine (LS) (n = 28,498)) was used as a phenotype for OP. Bidirectional causality between IVDD and BMD was assessed using inverse variance weighting (IVW) and other methods. Related sensitivity analyses were performed. Myopia was also analyzed as a negative control result to ensure the validity of IVs. Heel bone mineral density (heel BMD), total body bone mineral density (TB-BMD), femoral neck bone mineral density (FN-BMD), and lumbar spine bone mineral density (LS-BMD) have a direct causal relationship on intervertebral disk degeneration (IVDD) [heel BMD-related analysis: beta = 0.06, p = 0.03; TB-BMD-related analysis: beta = 0.18, p = 8.72E-08; FN-BMD-related analysis: beta = 0.15, p = 4.89E-03; LS-BMD-related analysis: beta = 0.16, p = 1.43E-04]. There was no evidence of a significant causal effect of IVDD on BMD. In conclusion, our study found a significant positive causal effect of lower BMD on IVDD, and we identified significant causal effects of heel, TB-, FN-, and LS-BMD on IVDD, but there was no evidence of a significant causal effect of IVDD on BMD.

Predictive Value of the Lipoprotein(a) to Prealbumin Ratio and of the NT-proBNP to LVEF Ratio for Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome.

OBJECTIVE: To investigate the lipoprotein(a) [Lp(a)] to prealbumin (PA) ratio and the N-terminal pro-brain natriuretic peptide (NT-proBNP) to left ventricular ejection fraction (LVEF) ratio for the prediction of major adverse cardiovascular events (MACE) in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). METHODS: A 1:1 matched case-control study was performed to retrospectively analyze ACS patients who underwent PCI from January 2022 to June 2022. Patients with MACE were selected as the case group (n = 55), and age- and gender-matched patients without MACE were selected as the control group (n = 55). Clinical data for the two groups was compared by univariate and multivariate logistic regression analysis. Risk factors and the odds ratio (OR) for MACE in ACS patients were evaluated, and receiver operating characteristic curve (ROC) were used to evaluate the Lp(a)/PA ratio, the NT-proBNP/LVEF ratio, and their combination for the prediction of MACE in ACS patients. RESULTS: The MACE and non-MACE groups showed statistically significant differences for time from onset to PCI, LVEF, NT-proBNP, white blood cell (WBC), Lp(a), PA, Lp(a)/PA, NT-proBNP/LVEF, number of catheterizations, number of implanted stents >2, and support diameter >3 (p < 0.05). Multivariate logistic regression analysis showed that LVEF, Lp(a)/PA and NT-proBNP/LVEF were independent risk factors for MACE. ROC curve analysis for Lp(a)/PA showed that the area under the curve (AUC) for the prediction of MACE was 0.779 (0.693-0.864), the cut-off point was 1.36, the sensitivity was 69.1%, and the specificity was 74.5%. The AUC for NT-proBNP/LVEF in predicting MACE was 0.827 (0.75-0.904), the cut-off point was 61.04, the sensitivity was 65.5%, and the specificity was 92.7%. For the combination of Lp(a)/PA and NT-proBNP/LVEF, the AUC for the prediction of MACE was 0.889 (0.830-0.947), the cut-off point was 0.37, the sensitivity was 81.8%, and the specificity was 81.8%. CONCLUSION: The combination of Lp(a)/PA and NT-proBNP/LVEF at admission showed good predictive value for the occurrence of MACE in ACS patients after PCI.

Broadband optical nonlinearity and all-optical switching features in low-defect GaN.

GaN is a one of promising materials for nonlinear optical applications. In this work, the broadband nonlinear optical response and potential applications for all-optical switching (AOS) are evaluated in low-defect GaN. In the pump-probe experiments, the ultrafast optical switching times are consistent with pulse widths accompanied with relative weak free-carrier absorption response, and the modulation contrast can reach ∼60% by varying the polarization orientations between the pump and probe lights. In the visible region, the broadband two-photon absorption effect exhibits excellent values for the imaginary part of figure of merit (FOM), providing the possibility of AOS based on nonlinear absorption (magnitude). While in the near-infrared region and under the presence of three-photon absorption, not only the real part of FOM based on Kerr effect is evaluated, but also the maximum light intensity for the usage of AOS based on nonlinear refraction (phase) is determined. The broadband nonlinear optical and AOS features in low-defect GaN will be highly favorable for the applications in the field of integrated nonlinear photonics and photonic circuits.

High-Performance Infrared Detectors Based on Black Phosphorus/Carbon Nanotube Heterojunctions.

Infrared detectors have broad application prospects in the fields of detection and communication. Using ideal materials and good device structure is crucial for achieving high-performance infrared detectors. Here, we utilized black phosphorus (BP) and single-walled carbon nanotube (SWCNT) films to construct a vertical van der Waals heterostructure, resulting in high-performance photovoltaic infrared detectors. In the device, a strong built-in electric field was formed in the heterojunction with a favored energy-band matching between the BP and the SWCNT, which caused a good photovoltaic effect. The fabricated devices exhibited a diode-like rectification behavior in the dark, which had a high rectification ratio up to a magnitude of 104 and a low ideal factor of 1.4. Under 1550 nm wavelength illumination, the 2D BP/SWCNT film photodetector demonstrated an open-circuit voltage of 0.34 V, a large external power conversion efficiency (η) of 7.5% and a high specific detectivity (D*) of 3.1 × 109 Jones. This external η was the highest among those for the photovoltaic devices fabricated with the SWCNTs or the heterostructures based on 2D materials and the obtained D* was also higher than those for most of the infrared detectors based on 2D materials or carbon materials. This work showcases the application potential of BP and SWCNTs in the detection field.

Black Phosphorus Field-Effect Transistors with Improved Contact via Localized Joule Heating.

Two-dimensional (2D) black phosphorus (BP) is considered an ideal building block for field-effect transistors (FETs) owing to its unique structure and intriguing properties. To achieve high-performance BP-FETs, it is essential to establish a reliable and low-resistance contact between the BP and the electrodes. In this study, we employed a localized Joule heating method to improve the contact between the 2D BP and gold electrodes, resulting in enhanced BP-FET performance. Upon applying a sufficiently large source-drain voltage, the zero-bias conductance of the device increased by approximately five orders of magnitude, and the linearity of the current-voltage curves was also enhanced. This contact improvement can be attributed to the formation of gold phosphide at the interface of the BP and the gold electrodes owing to current-generated localized Joule heat. The fabricated BP-FET demonstrated a high on/off ratio of 4850 and an on-state conductance per unit channel width of 1.25 µS µm-1, significantly surpassing those of the BP-FETs without electrical annealing. These findings offer a method to achieve a low-resistance BP/metal contact for developing high-performance BP-based electronic devices.

Revealing the biological mechanism of acupuncture in alleviating excessive inflammatory responses and organ damage in sepsis: a systematic review.

Sepsis is a systemic inflammation caused by a maladjusted host response to infection. In severe cases, it can cause multiple organ dysfunction syndrome (MODS) and even endanger life. Acupuncture is widely accepted and applied in the treatment of sepsis, and breakthroughs have been made regarding its mechanism of action in recent years. In this review, we systematically discuss the current clinical applications of acupuncture in the treatment of sepsis and focus on the mechanisms of acupuncture in animal models of systemic inflammation. In clinical research, acupuncture can not only effectively inhibit excessive inflammatory reactions but also improve the immunosuppressive state of patients with sepsis, thus maintaining immune homeostasis. Mechanistically, a change in the acupoint microenvironment is the initial response link for acupuncture to take effect, whereas PROKR2 neurons, high-threshold thin nerve fibres, cannabinoid CB2 receptor (CB2R) activation, and Ca2+ influx are the key material bases. The cholinergic anti-inflammatory pathway of the vagus nervous system, the adrenal dopamine anti-inflammatory pathway, and the sympathetic nervous system are key to the transmission of acupuncture information and the inhibition of systemic inflammation. In MODS, acupuncture protects against septic organ damage by inhibiting excessive inflammatory reactions, resisting oxidative stress, protecting mitochondrial function, and reducing apoptosis and tissue or organ damage.

Hypothyroidism and rheumatoid arthritis: a two-sample Mendelian randomization study.

Background: Meta-analysis of genome-wide association studies (GWAS) data showed that the relationship between hypothyroidism and rheumatoid arthritis (RA) risk remains under debate. This study is conducted to test the causal relationship of hypothyroidism and RA. Methods: A two-sample Mendelian randomization (TSMR) analysis was employed to estimate the causality of hypothyroidism and rheumatoid arthritis in European ancestry and Asian ancestry. Integrating the effects generated by TSMR, functional annotations and noncoding variant prediction framework were applied to analyze and interpret the functional instrument variants (IVs). Results: The results of the inverse variance weighted method showed a strong significant causal relationship between hypothyroidism and risk of RA in European ancestry [odds ratio (OR) = 1.96; 95% confidence interval (CI) 1.49, 2.58; p < 0.001]. The outcomes of MR-Egger, weighted median, weighted mode, and simple mode also showed that hypothyroidism was significantly associated with increased risk of RA in European ancestry. The MR-PRESSO method also showed significant results [Outlier-corrected Causal Estimate = 0.70; standard error (SE) = 0.06; p < 0.001]. An independent dataset and an Asian ancestry dataset were applied to estimate and obtain the coincident results. Furthermore, we integrated the effect of variants in TSMR analysis, functional annotations, and prediction methods to pinpoint the single-nucleotide polymorphism (SNP) rs4409785 as one of the causal variants, which suggested that this variant could impact the binding of CTCF-cohesin and play a vital role in immune cells. Conclusion: In this study, we prove that hypothyroidism is significantly causally associated with increased RA risk, which has not been shown in previous studies. Furthermore, we pinpoint the potential causal variants in RA.

High-performance diodes based on black phosphorus/carbon nanomaterial heterostructures.

The performance of diodes, which are the basic building blocks in integrated circuits, highly depends on the materials used. Black phosphorus (BP) and carbon nanomaterials with unique structures and excellent properties can form heterostructures with favorable band matching to fully utilize their respective advantages and thus achieve high diode performance. Here, high-performance Schottky junction diodes based on a two-dimensional (2D) BP/single-walled carbon nanotube (SWCNT) film heterostructure and a BP nanoribbon (PNR) film/graphene heterostructure were investigated for the first time. The fabricated Schottky diode based on the heterostructure with the 10 nm-thick 2D BP stacked on the SWCNT film had a rectification ratio of 2978 and a low ideal factor of 1.5. The Schottky diode based on the heterostructure with the PNR film stacked on the graphene exhibited a high rectification ratio of 4455 and an ideal factor of 1.9. The high rectification ratios for both devices were attributed to the large Schottky barriers formed between the BP and carbon materials, thus leading to a small reverse current. We found that the thickness of the 2D BP in the 2D BP/SWCNT film Schottky diode and the stacking order of the heterostructure in the PNR film/graphene Schottky diode had a significant effect on the rectification ratio. Furthermore, the rectification ratio and breakdown voltage of the resulting PNR film/graphene Schottky diode were larger than those of the 2D BP/SWCNT film Schottky diode, which was attributed to the larger bandgap of the PNRs compared to the 2D BP. This study demonstrates that high-performance diodes can be achieved via the collaborative application of BP and carbon nanomaterials.

rcCAE: a convolutional autoencoder method for detecting intra-tumor heterogeneity and single-cell copy number alterations.

Intra-tumor heterogeneity (ITH) is one of the major confounding factors that result in cancer relapse, and deciphering ITH is essential for personalized therapy. Single-cell DNA sequencing (scDNA-seq) now enables profiling of single-cell copy number alterations (CNAs) and thus aids in high-resolution inference of ITH. Here, we introduce an integrated framework called rcCAE to accurately infer cell subpopulations and single-cell CNAs from scDNA-seq data. A convolutional autoencoder (CAE) is employed in rcCAE to learn latent representation of the cells as well as distill copy number information from noisy read counts data. This unsupervised representation learning via the CAE model makes it convenient to accurately cluster cells over the low-dimensional latent space, and detect single-cell CNAs from enhanced read counts data. Extensive performance evaluations on simulated datasets show that rcCAE outperforms the existing CNA calling methods, and is highly effective in inferring clonal architecture. Furthermore, evaluations of rcCAE on two real datasets demonstrate that it is able to provide a more refined clonal structure, of which some details are lost in clonal inference based on integer copy numbers.

Narrow Directed Black Phosphorus Nanoribbons Produced by A Reformative Mechanical Exfoliation Approach.

Black phosphorus nanoribbons (PNRs) are ideal candidates for constructing electronic and optoelectronic devices owing to their unique structure and high bandgap tunability. However, the preparation of high-quality narrow PNRs aligned along the same direction is very challenging. Here, a reformative mechanical exfoliation approach combining tape and polydimethylsiloxane (PDMS) exfoliations to fabricate high-quality, narrow, and directed PNRs with smooth edges for the first time is developed. In this method, partially-exfoliated PNRs are first formed on thick black phosphorus (BP) flakes via the tape exfoliation and further peeled off to obtain separated PNRs via the PDMS exfoliation. The prepared PNRs have widths from a dozen to hundreds of nanometers (down to 15 nm) and a mean length of 18 µm. It is found that the PNRs can align along a same direction and the length directions of directed PNRs are along the zigzag direction. The formation of PNRs is attributed to that the BP prefers to be unzipped along the zigzag direction and has an appropriate magnitude of interaction force with the PDMS substrate. The fabricated PNR/MoS2 heterojunction diode and PNR field-effect transistor exhibit good device performance. This work provides a new pathway to achieve high-quality, narrow, and directed PNRs for electronic and optoelectronic applications.

Discovery of N-substituted oseltamivir derivatives as novel neuraminidase inhibitors with improved drug resistance profiles and favorable drug-like properties.

To yield potent neuraminidase inhibitors with improved drug resistance and favorable drug-like properties, two series of novel oseltamivir derivatives targeting the 150-cavity of neuraminidase were designed, synthesized, and biologically evaluated. Among the synthesized compounds, the most potent compound 43b bearing 3-floro-4-cyclopentenylphenzyl moiety exhibited weaker or slightly improved inhibitory activity against wild-type neuraminidases (NAs) of H1N1, H5N1, and H5N8 compared to oseltamivir carboxylate (OSC). Encouragingly, 43b displayed 62.70- and 5.03-fold more potent activity than OSC against mutant NAs of H5N1-H274Y and H1N1-H274Y, respectively. In cellular antiviral assays, 43b exerted equivalent or more potent activities against H1N1, H5N1, and H5N8 compared to OSC with no significant cytotoxicity up to 200 µM. Notably, 43b displayed potent antiviral efficacy in the embryonated egg model, in which achieved a protective effect against H5N1 and H5N8 similar to OSC. Molecular docking studies were implemented to reveal the binding mode of 43b in the binding pocket. Moreover, 43b possessed improved physicochemical properties and ADMET properties compared to OSC by in silico prediction. Taken together, 43b appeared to be a promising lead compound for further investigation.

Controlled Preparation and Device Application of Sub-5 nm Graphene Nanoribbons and Graphene Nanoribbon/Carbon Nanotube Intramolecular Heterostructures.

Narrow graphene nanoribbons (GNRs) and GNR/single-walled carbon nanotube (SWNT) intramolecular heterojunctions are ideal candidates to construct next-generation electronic and optoelectronic devices. However, the fabrication of high-quality long sub-5 nm wide GNRs and GNR/SWNT heterojunctions is a great challenge. Here, we report a method to produce high-quality sub-5 nm wide GNRs with smooth edges and GNR/SWNT intramolecular heterostructures via palladium-catalyzed full and partial unzipping of SWNTs, respectively. The resulting GNRs could be as narrow as 2.2 nm and had an average length of over 1 µm. By adjusting the unzipping time and the deposited positions of palladium nanoparticles, controlled multiple GNR/SWNT heterostructures were also fabricated on an individual parent SWNT. A GNR field-effect transistor (FET) constructed by a 3.1 nm wide GNR could simultaneously achieve a high on/off current ratio of 1.1 × 104 and a large mobility of 598 cm2 V-1 s-1. The photovoltaic device based on a single GNR (2.4 nm in width)/SWNT (0.8 nm in diameter) heterojunction exhibited a large open-circuit voltage (Voc) of 0.52 V and a high external power conversion efficiency (η) of 4.7% under the 1550 nm wavelength illumination of 931 mW cm-2. Our method provides a pathway to controllably prepare high-quality sub-5 nm GNRs and GNR/SWNT heterojunctions for fundamental studies and practical applications in the electronic and optoelectronic fields.

Computational Assessment of the Expression-modulating Potential for Noncoding Variants.

Large-scale genome-wide association studies (GWAS) and expression quantitative trait loci (eQTLs) studies have identified multiple noncoding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional noncoding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTLs datasets further pinpoints several causal variants other than reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets and is available online as a web server at http://carmen.gao-lab.org.

REVA as A Well-curated Database for Human Expression-modulating Variants.

More than 90% of disease- and trait-associated human variants are noncoding. By systematically screening multiple large-scale studies, we compiled REVA, a manually curated database for over 11.8 million experimentally tested noncoding variants with expression-modulating potentials. We provided 2424 functional annotations that could be used to pinpoint the plausible regulatory mechanism of these variants. We further benchmarked multiple state-of-the-art computational tools and found that their limited sensitivity remains a serious challenge for effective large-scale analysis. REVA provides high-quality experimentally tested expression-modulating variants with extensive functional annotations, which will be useful for users in the noncoding variant community. REVA is freely available at http://reva.gao-lab.org.