Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm.

Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.

Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Genetically Encoded Lizard Color Divergence for Camouflage and Thermoregulation.

Local adaptation is critical in speciation and evolution, yet comprehensive studies on proximate and ultimate causes of local adaptation are generally scarce. Here, we integrated field ecological experiments, genome sequencing, and genetic verification to demonstrate both driving forces and molecular mechanisms governing local adaptation of body coloration in a lizard from the Qinghai-Tibet Plateau. We found dark lizards from the cold meadow population had lower spectrum reflectance but higher melanin contents than light counterparts from the warm dune population. Additionally, the colorations of both dark and light lizards facilitated the camouflage and thermoregulation in their respective microhabitat simultaneously. More importantly, by genome resequencing analysis, we detected a novel mutation in Tyrp1 that underpinned this color adaptation. The allele frequencies at the site of SNP 459# in the gene of Tyrp1 are 22.22% G/C and 77.78% C/C in dark lizards and 100% G/G in light lizards. Model-predicted structure and catalytic activity showed that this mutation increased structure flexibility and catalytic activity in enzyme TYRP1, and thereby facilitated the generation of eumelanin in dark lizards. The function of the mutation in Tyrp1 was further verified by more melanin contents and darker coloration detected in the zebrafish injected with the genotype of Tyrp1 from dark lizards. Therefore, our study demonstrates that a novel mutation of a major melanin-generating gene underpins skin color variation co-selected by camouflage and thermoregulation in a lizard. The resulting strong selection may reinforce adaptive genetic divergence and enable the persistence of adjacent populations with distinct body coloration.

A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

MOTIVATION: A large number of studies have shown that circular RNA (circRNA) affects biological processes by competitively binding miRNA, providing a new perspective for the diagnosis, and treatment of human diseases. Therefore, exploring the potential circRNA-miRNA interactions (CMIs) is an important and urgent task at present. Although some computational methods have been tried, their performance is limited by the incompleteness of feature extraction in sparse networks and the low computational efficiency of lengthy data. RESULTS: In this paper, we proposed JSNDCMI, which combines the multi-structure feature extraction framework and Denoising Autoencoder (DAE) to meet the challenge of CMI prediction in sparse networks. In detail, JSNDCMI integrates functional similarity and local topological structure similarity in the CMI network through the multi-structure feature extraction framework, then forces the neural network to learn the robust representation of features through DAE and finally uses the Gradient Boosting Decision Tree classifier to predict the potential CMIs. JSNDCMI produces the best performance in the 5-fold cross-validation of all data sets. In the case study, seven of the top 10 CMIs with the highest score were verified in PubMed. AVAILABILITY: The data and source code can be found at https://github.com/1axin/JSNDCMI.

Seawater electrolysis for fuels and chemicals production: fundamentals, achievements, and perspectives.

Seawater electrolysis for the production of fuels and chemicals involved in onshore and offshore plants powered by renewable energies offers a promising avenue and unique advantages for energy and environmental sustainability. Nevertheless, seawater electrolysis presents long-term challenges and issues, such as complex composition, potential side reactions, deposition of and poisoning by microorganisms and metal ions, as well as corrosion, thus hindering the rapid development of seawater electrolysis technology. This review focuses on the production of value-added fuels (hydrogen and beyond) and fine chemicals through seawater electrolysis, as a promising step towards sustainable energy development and carbon neutrality. The principle of seawater electrolysis and related challenges are first introduced, and the redox reaction mechanisms of fuels and chemicals are summarized. Strategies for operating anodes and cathodes including the development and application of chloride- and impurity-resistant electrocatalysts/membranes are reviewed. We comprehensively summarize the production of fuels and chemicals (hydrogen, carbon monoxide, sulfur, ammonia, etc.) at the cathode and anode via seawater electrolysis, and propose other potential strategies for co-producing fine chemicals, even sophisticated and electronic chemicals. Seawater electrolysis can drive the oxidation and upgrading of industrial pollutants or natural organics into value-added chemicals or degrade them into harmless substances, which would be meaningful for environmental protection. Finally, the perspective and prospects are outlined to address the challenges and expand the application of seawater electrolysis.

Osteopontin Depletion in Non-haematopoietic Cells Improves Outcomes in Septic Mice by Enhancing Antimicrobial Peptide Production.

Osteopontin (Opn) depletion can improve septic outcomes, but the underlying mechanism remains unknown. In this study, we demonstrated that non-haematopoietic but not haematopoietic Opn depletion improved septic outcomes. Compared to wild-type (WT) mice, co-housed Opn-/- mice displayed enhanced production of antibacterial peptides (AMPs), decreased bacterial loads, and a distinct bacterial composition of gut microbiota. Fecal microbiota transplantation (FMT) and OPN neutralization assay showed that Opn depletion could reduce the bacterial loads and improve septic inflammation. By employing an intestinal organoid culture system, we proved that OPN neutralization in WT organoids could inactivate AKT and decrease FOXO3a phosphorylation, resulting in enhanced AMP production, whereas OPN treatment in OPN deficient organoids could activate AKT and increase FOXO3a phosphorylation, leading to reduced AMP production. Our findings identified OPN as a novel regulatory factor of AMP production to modulate bacterial loads and composition of gut microbiota, in turn affecting sepsis outcomes.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules.

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

Structure of complex III with bound antimalarial agent CK-2-68 provides insights into selective inhibition of Plasmodium cytochrome bc1 complexes.

Among the various components of the protozoan Plasmodium mitochondrial respiratory chain, only Complex III is a validated cellular target for antimalarial drugs. The compound CK-2-68 was developed to specifically target the alternate NADH dehydrogenase of the malaria parasite respiratory chain, but the true target for its antimalarial activity has been controversial. Here, we report the cryo-EM structure of mammalian mitochondrial Complex III bound with CK-2-68 and examine the structure-function relationships of the inhibitor's selective action on Plasmodium. We show that CK-2-68 binds specifically to the quinol oxidation site of Complex III, arresting the motion of the iron-sulfur protein subunit, which suggests an inhibition mechanism similar to that of Pf-type Complex III inhibitors such as atovaquone, stigmatellin, and UHDBT. Our results shed light on the mechanisms of observed resistance conferred by mutations, elucidate the molecular basis of the wide therapeutic window of CK-2-68 for selective action of Plasmodium vs. host cytochrome bc1, and provide guidance for future development of antimalarials targeting Complex III.

NH3 Electrosynthesis from N2 Molecules: Progresses, Challenges, and Future Perspectives.

Green ammonia (NH3), made by using renewable electricity to split nearly limitless nitrogen (N2) molecules, is a vital platform molecule and an ideal fuel to drive the sustainable development of human society without carbon dioxide emission. The NH3 electrosynthesis field currently faces the dilemma of low yield rate and efficiency; however, decoupling the overlapping issues of this area and providing guidelines for its development directions are not trivial because it involves complex reaction process and multidisciplinary entries (for example, electrochemistry, catalysis, interfaces, processes, etc.). In this Perspective, we introduce a classification scheme for NH3 electrosynthesis based on the reaction process, namely, direct (N2 reduction reaction) and indirect electrosynthesis (Li-mediated/plasma-enabled NH3 electrosynthesis). This categorization allows us to finely decouple the complicated reaction pathways and identify the specific rate-determining steps/bottleneck issues for each synthesis approach such as N2 activation, H2 evolution side reaction, solid-electrolyte interphase engineering, plasma process, etc. We then present a detailed overview of the latest progresses on solving these core issues in terms of the whole electrochemical system covering the electrocatalysts, electrodes, electrolytes, electrolyzers, etc. Finally, we discuss the research focuses and the promising strategies for the development of NH3 electrosynthesis in the future with a multiscale perspective of atomistic mechanisms, nanoscale electrocatalysts, microscale electrodes/interfaces, and macroscale electrolyzers/processes. It is expected that this Perspective will provide the readers with an in-depth understanding of the bottleneck issues and insightful guidance on designing the efficient NH3 electrosynthesis systems.

Protectin D1 ameliorates non-compressive lumbar disc herniation through SIRT1-mediated CGRP signaling.

Background. Neuro-inflammatory response promotes the initiation and sustenance of lumbar disc herniation (LDH). Protectin D1 (PD1), as a new type of specialized pro-resolving mediator (SPM), can improve the prognosis of various inflammatory diseases. Recent studies have shown that over representation of calcitonin gene-related peptides (CGRP) may activate nociceptive signaling following nerve injury. Silent information regulator 1 (SIRT1) is ubiquitously expressed in the dorsal horn of the spinal cord and plays a role in the pathogenesis of LDH. In this study, we investigated the analgesic effects of PD1 and elucidated the impact of neurogenic inflammation in the pathogenesis of neuropathic pain induced by non-compressive lumbar disc herniation (NCLDH) in a rat model. Methods. NCLDH models were established by applying protruding autologous nucleus pulposus to the L5 Dorsal root ganglion (DRG). PD1, SIRT1 antagonist or agonist, CGRP or antagonist were administered as daily intrathecal injections for three consecutive days postoperatively. Behavioral tests were conducted to assess mechanical and thermal hyperalgesia. The ipsilateral lumbar (L4-6) segment of the spinal dorsal horn was isolated for further analysis. Alterations in the release of SIRT1 and CGRP were explored using western blot and immunofluorescence. Results. Application of protruded nucleus (NP) materials to the DRG induced mechanical and thermal allodynia symptoms, and deregulated the expression of pro-inflammatory and anti-inflammatory cytokines in rats. Intrathecal delivery of PD1 significantly reversed the NCLDH-induced imbalance in neuro-inflammatory response and alleviated the symptoms of mechanical and thermal hyperalgesia. In addition, NP application to the DGRs resulted the spinal upregulation of CGRP and SIRT1 expression, which was almost restored by intrathecal injection of PD1 in a dose-dependent manner. SIRT1 antagonist or agonist and CGRP or antagonist treatment further confirmed the result. Conclusion. Our findings indicate PD1 has a potent analgesic effect, and can modulate neuro-inflammation by regulating SIRT1-mediated CGRP signaling in NCLDH.

Cognitive enhancing effect of rTMS combined with tDCS in patients with major depressive disorder: a double-blind, randomized, sham-controlled study.

BACKGROUND: Cognitive dysfunction is one of the common symptoms in patients with major depressive disorder (MDD). Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) have been studied separately in the treatment of cognitive dysfunction in MDD patients. We aimed to investigate the effectiveness and safety of rTMS combined with tDCS as a new therapy to improve neurocognitive impairment in MDD patients. METHODS: In this brief 2-week, double-blind, randomized, and sham-controlled trial, a total of 550 patients were screened, and 240 MDD inpatients were randomized into four groups (active rTMS + active tDCS, active rTMS + sham tDCS, sham rTMS + active tDCS, sham rTMS + sham tDCS). Finally, 203 patients completed the study and received 10 treatment sessions over a 2-week period. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) was performed to assess patients' cognitive function at baseline and week 2. Also, we applied the 24-item Hamilton Depression Rating Scale (HDRS-24) to assess patients' depressive symptoms at baseline and week 2. RESULTS: After 10 sessions of treatment, the rTMS combined with the tDCS group showed more significant improvements in the RBANS total score, immediate memory, and visuospatial/constructional index score (all p < 0.05). Moreover, post hoc tests revealed a significant increase in the RBANS total score and Visuospatial/Constructional in the combined treatment group compared to the other three groups but in the immediate memory, the combined treatment group only showed a better improvement than the sham group. The results also showed the RBANS total score increased significantly higher in the active rTMS group compared with the sham group. However, rTMS or tDCS alone was not superior to the sham group in terms of other cognitive performance. In addition, the rTMS combined with the tDCS group showed a greater reduction in HDRS-24 total score and a better depression response rate than the other three groups. CONCLUSIONS: rTMS combined with tDCS treatment is more effective than any single intervention in treating cognitive dysfunction and depressive symptoms in MDD patients. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR2100052122).

Recent Advances in Low-Temperature Liquid Electrolyte for Supercapacitors.

As one of the key components of supercapacitors, electrolyte is intensively investigated to promote the fast development of the energy supply system under extremely cold conditions. However, high freezing point and sluggish ion transport kinetics for routine electrolytes hinder the application of supercapacitors at low temperatures. Resultantly, the liquid electrolyte should be oriented to reduce the freezing point, accompanied by other superior characteristics, such as large ionic conductivity, low viscosity and outstanding chemical stability. In this review, the intrinsically physical parameters and microscopic structure of low-temperature electrolytes are discussed thoroughly, then the previously reported strategies that are used to address the associated issues are summarized subsequently from the aspects of aqueous and non-aqueous electrolytes (organic electrolyte and ionic liquid electrolyte). In addition, some advanced spectroscopy techniques and theoretical simulation to better decouple the solvation structure of electrolytes and reveal the link between the key physical parameters and microscopic structure are briefly presented. Finally, the further improvement direction is put forward to provide a reference and guidance for the follow-up research.

Microscopic-Level Insights into P-O-Induced Strong Electronic Coupling Over Nickel Phosphide with Efficient Benzyl Alcohol Electrooxidation.

Electrooxidation of biomass into fine chemicals coupled with energy-saving hydrogen production for a zero-carbon economy holds great promise. Advanced anode catalysts determine the cell voltage and electrocatalytic efficiency greatly, further the rational design and optimization of their active site coordination remains a challenge. Herein, a phosphorus-oxygen terminals-rich species (Ni2 P-O-300) via an anion-assisted pyrolysis strategy is reported to induce strong electronic coupling and high valence state of active nickel sites over nickel phosphide. This ultimately facilitates the rapid yet in-situ formation of high-valence nickel with a high reaction activity under electrochemical conditions, and exhibits a low potential of 1.33 V vs. RHE at 10 mA cm-2 , exceeding most of reported transition metal-based catalysts. Advanced spectroscopy, theoretical calculations, and experiments reveal that the functional P-O species can induce the favorable local bonding configurations for electronic coupling, promoting the electron transfer from Ni to P and the adsorption of benzyl alcohol (BA). Finally, the hydrogen production efficiency and kinetic constant of BA electrooxidation by Ni2 P-O-300 are increased by 9- and 2.8- fold compared with the phosphorus-oxygen terminals-deficient catalysts (Ni2 P-O-500). This provides an anion-assisted pyrolysis strategy to modulate the electronic environment of the Ni site, enabling a guideline for Ni-based energy/catalysis systems.

FeOOH with Low Spin State Iron as Electron Acceptors for High Yield Rate Electrosynthesis of Urea from Nitrate and Carbon Dioxide.

Co electroreduction of carbon dioxide and nitrate to synthesize urea provides an alternative strategy to high energy-consumption traditional methods. However, the complexity of the reaction mechanism and the high energy barrier of nitrate reduction result in a diminished production of urea. Herein, a convenient electrodeposition technique to prepare the FeOOH with low spin state iron that increases the yield rate of urea efficiently is employed. According to soft X-ray Absorption Spectroscopy and theoretical calculations, the unique configuration of low spin state iron as electron acceptors can effectively induce electron pair transfer from the occupied σ orbitals of intermediate * NO to empty d orbitals of iron. This σ→d donation mechanism leads to a reduction in the energy barrier associated with the rate-determining step (* NOOH→* NO + * OH), hence augmenting the urea generation. The low spin state iron presents a high urea yield rate of 512 µg h-1  cm-2 , representing approximately two times compared to the medium spin state iron. The key intermediates (* NH2 and * CO) in the formation of C─N bond are detected with in situ Fourier transform infrared spectroscopy. The coupling of * NH2 and * CO contributes to the formation of * CONH2 , which subsequently endures multi-step proton-coupled electron transfer to generate urea.

Growth of Size-Tunable Ag2O Polyhedra and Revelation of Their Bulk and Surface Lattices.

By primarily adjusting the reagent amounts, particularly the volume of AgNO3 solution introduced, Ag2O cubes with decreasing sizes from 440 to 79 nm, octahedra from 714 to 106 nm, and rhombic dodecahedra from 644 to 168 nm are synthesized. 733 nm cuboctahedra are also prepared for structural analysis. With in-house X-ray diffraction (XRD) peak calibration, shape-related peak shifts are recognizable. Synchrotron XRD measurements at 100 K reveal the presence of bulk and surface layer lattices. Bulk cell constants also deviate slightly. They show a negative thermal expansion behavior with shrinking cell constants at higher temperatures. The Ag2O crystals exhibit size- and facet-dependent optical properties. Bandgaps red-shift continuously with increasing particle sizes. Optical facet effect is also observable. Moreover, synchrotron XRD peaks of a mixture of Cu2O rhombicuboctahedra and edge- and corner-truncated cubes exposing all three crystal faces can be deconvoluted into three components with the bulk and the [111] microstrain phase as the major component. Interestingly, while the unheated Cu2O sample shows clear diffraction peak asymmetry, annealing the sample to 450 K yields nearly symmetric peaks even when returning the sample to room temperature, meaning even moderately high temperatures can permanently change the crystal lattice.

A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.

Drug-drug interactions (DDIs) prediction is a challenging task in drug development and clinical application. Due to the extremely large complete set of all possible DDIs, computer-aided DDIs prediction methods are getting lots of attention in the pharmaceutical industry and academia. However, most existing computational methods only use single perspective information and few of them conduct the task based on the biomedical knowledge graph (BKG), which can provide more detailed and comprehensive drug lateral side information flow. To this end, a deep learning framework, namely DeepLGF, is proposed to fully exploit BKG fusing local-global information to improve the performance of DDIs prediction. More specifically, DeepLGF first obtains chemical local information on drug sequence semantics through a natural language processing algorithm. Then a model of BFGNN based on graph neural network is proposed to extract biological local information on drug through learning embedding vector from different biological functional spaces. The global feature information is extracted from the BKG by our knowledge graph embedding method. In DeepLGF, for fusing local-global features well, we designed four aggregating methods to explore the most suitable ones. Finally, the advanced fusing feature vectors are fed into deep neural network to train and predict. To evaluate the prediction performance of DeepLGF, we tested our method in three prediction tasks and compared it with state-of-the-art models. In addition, case studies of three cancer-related and COVID-19-related drugs further demonstrated DeepLGF's superior ability for potential DDIs prediction. The webserver of the DeepLGF predictor is freely available at http://120.77.11.78/DeepLGF/.

A novel circRNA-miRNA association prediction model based on structural deep neural network embedding.

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.

Quantum squeezing-induced quantum entanglement and EPR steering in a coupled optomechanical system.

We propose a theoretical project in which quantum squeezing induces quantum entanglement and Einstein-Podolsky-Rosen steering in a coupled whispering-gallery-mode optomechanical system. Through pumping the χ(2)-nonlinear resonator with the phase matching condition, the generated squeezed resonator mode and the mechanical mode of the optomechanical resonator can generate strong quantum entanglement and EPR steering, where the squeezing of the nonlinear resonator plays the vital role. The transitions from zero entanglement to strong entanglement and one-way steering to two-way steering can be realized by adjusting the system parameters appropriately. The photon-photon entanglement and steering between the two resonators can also be obtained by deducing the amplitude of the driving laser. Our project does not need an extraordinarily squeezed field, and it is convenient to manipulate and provides a novel and flexible avenue for diverse applications in quantum technology dependent on both optomechanical and photon-photon entanglement and steering.

A familial missense variant in the Alzheimer's disease gene SORL1 impairs its maturation and endosomal sorting.

The SORL1 gene has recently emerged as a strong Alzheimer's Disease (AD) risk gene. Over 500 different variants have been identified in the gene and the contribution of individual variants to AD development and progression is still largely unknown. Here, we describe a family consisting of 2 parents and 5 offspring. Both parents were affected with dementia and one had confirmed AD pathology with an age of onset > 75 years. All offspring were affected with AD with ages at onset ranging from 53 years to 74 years. DNA was available from the parent with confirmed AD and 5 offspring. We identified a coding variant, p.(Arg953Cys), in SORL1 in 5 of 6 individuals affected by AD. Notably, variant carriers had severe AD pathology, and the SORL1 variant segregated with TDP-43 pathology (LATE-NC). We further characterized this variant and show that this Arginine substitution occurs at a critical position in the YWTD-domain of the SORL1 translation product, SORL1. Functional studies further show that the p.R953C variant leads to retention of the SORL1 protein in the endoplasmic reticulum which leads to decreased maturation and shedding of the receptor and prevents its normal endosomal trafficking. Together, our analysis suggests that p.R953C is a pathogenic variant of SORL1 and sheds light on mechanisms of how missense SORL1 variants may lead to AD.