hDirect-MAP: projection-free single-cell modeling of response to checkpoint immunotherapy.

There is a lack of robust generalizable predictive biomarkers of response to immune checkpoint blockade in multiple types of cancer. We develop hDirect-MAP, an algorithm that maps T cells into a shared high-dimensional (HD) expression space of diverse T cell functional signatures in which cells group by the common T cell phenotypes rather than dimensional reduced features or a distorted view of these features. Using projection-free single-cell modeling, hDirect-MAP first removed a large group of cells that did not contribute to response and then clearly distinguished T cells into response-specific subpopulations that were defined by critical T cell functional markers of strong differential expression patterns. We found that these grouped cells cannot be distinguished by dimensional-reduction algorithms but are blended by diluted expression patterns. Moreover, these identified response-specific T cell subpopulations enabled a generalizable prediction by their HD metrics. Tested using five single-cell RNA-seq or mass cytometry datasets from basal cell carcinoma, squamous cell carcinoma and melanoma, hDirect-MAP demonstrated common response-specific T cell phenotypes that defined a generalizable and accurate predictive biomarker.

Influence of Different Ions on Pulse Electrodeposition of CaCO3 Scales in the Simulated Seawater.

In the circulating water system of coastal power plants, various kinds of ions have a great influence on the formation and growth of CaCO3 scales. This paper focuses on investigating the influence of existing ions on the pulse electrodeposition behaviors of CaCO3 scales. Different concentrations of ions, such as Fe3+, Mg2+, PO43- and SiO32-, are introduced to simulate the actual seawater environment, and their influence on the CaCO3 scale deposition behaviors is assessed by linear sweep voltammetry, chronoamperometry, and electrochemical impedance spectroscopy tests. The surface coverage of the CaCO3 scale layer is evaluated through the residual current density and polarization resistance values, while the crystal structure and surface compactness of the layer are confirmed by the scanning electron microscope and X-ray diffractometer tests. Results indicate that high concentrations of Mg2+, Fe3+, and PO43- ions have the most significant inhibitory effect on the pulse electrodeposition of CaCO3 scales, among which the inhibition effect of Mg2+ ions is mainly reflected in the change of crystal morphology of CaCO3, that is, the crystallization growth process is inhibited. The inhibition effect of PO43- ions is mainly reflected in the gradually reduced coverage and density of CaCO3 crystals on the electrode surface, suggesting that the crystallization nucleation process is inhibited, while Fe3+ ions have a certain inhibition effect on both the crystallization nucleation and growth processes. Furthermore, lower concentrations of SiO32- ions also display a significant inhibition effect on the crystallization nucleation and growth process, and the inhibition effect weakens with increased concentration. This study provides a theoretical basis for exploring the removal of ions in the industrial water softening field.

Identification and Functional Investigation of Hub Genes Associated with Follicular Lymphoma.

Follicular lymphoma (FL), the most common type of indolent lymphoma, originates from germinal center B cells within the lymphoid follicle. However, the underlying mechanisms of this disease remain unclear. This study aimed to identify the potential hub genes for FL and evaluate their functional roles in clinical applications. Microarray data and clinical characteristics of patients with FL were obtained from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to explore hub genes for FL. Functional enrichment analysis was performed to investigate the potential roles of these hub genes in FL. Mendelian randomization (MR) analysis was performed to verify the causal effect of the top genes on FL risk. In addition, gene set enrichment analysis (GSEA) and immune cell analysis were performed to elucidate the involved mechanisms of the crucial genes in FL. A total of 1363 differentially expressed genes and 157 central genes were identified by differential expression analysis and WGCNA, respectively, resulting in 117 overlapping genes considered as hub genes for FL. Functional enrichment analysis revealed significant correlations between immune-related pathways and FL. MR analysis revealed a significant association only between zeta chain of T-cell receptor-associated protein kinase 70 (ZAP70) and FL risk, with no significance observed for the other top genes. GSEA and immune cell analysis suggested that ZAP70 may be involved in the development and progression of FL through immune-related pathways. By integrating bioinformatics and MR analyses, ZAP70 was successfully identified and validated as a promising FL biomarker. Functional investigations indicated a significant correlation between immune-related pathways and FL. These findings have important implications for the identification of targets for the diagnosis and treatment of FL and provide valuable insights into the molecular mechanisms underlying FL.

Insights into identifying resistance genes for cold and disease stresses through chromosome-level reference genome analyses of Poncirus polyandra.

Poncirus polyandra, a plant species with extremely small populations in China, has become extinct in the wild. This study aimed to identify functional genes that improve tolerance to abiotic and biotic stresses. Here, we present a high-quality chromosome-scale reference genome of P. polyandra. The reference genome is 315.78 Mb in size, with an N50 scaffold size of 32.07 Mb, and contains nine chromosomes with 20,815 protein-coding genes, covering 97.82% of the estimated gene space. We identified 17 rapidly evolving nucleotide-binding-site (NBS) genes, three C-repeat-binding factors (CBF) genes, 19 citrus greening disease (Huanglongbing, HLB) tolerance genes, 11 citrus tristeza virus (CTV) genes, and one citrus nematode resistance gene. A divergence time of 1.96 million years ago was estimated between P. polyandra and P. trifoliata. This is the first genome-scale assembly and annotation of P. polyandra, which will be useful for genetic, genomic, and molecular research and provide guidance for the development of conservation strategies.

Whole genome sequencing and annotation of Scleroderma yunnanense, the only edible Scleroderma species.

Scleroderma yunnanense, an ectomycorrhizal fungus, is a popular edible mushroom within the Yunnan Province of Southwest China that holds great ecological and economic implications. However, despite its significance, there remains limited information about this species. Therefore, we sequenced S. yunnanense genome to identify the functional genes of S. yunnanense involved in secondary metabolite and carbohydrate production pathways. First, we present the 40.43 Mb high-quality reference genome for S. yunnanense, distributed across 35 contigs; moreover, the N50 contig size was found to reach 3.31 Mb and contained 8877 functional genes. Finally, genome annotation was conducted to compare the functional genes of S. yunnanense with protein sequences from different publicly available databases. Taken together, we identified 12 biosynthetic gene clusters across 10 contigs; among these were 13 key mevalonate (MVA) pathway enzymes, a key tyrosinase enzyme in the 3,4-dihydroxyphenylalanine (DOPA) pathway that is responsible for producing DOPA melanins, and 16 enzymes involved in uridine diphosphate glucose biosynthesis. Overall, this study presents the first genome assembly and annotation of S. yunnanense; ultimately, this information will be important in the elucidation of the biological activities and artificial domestication of this fungus.

Enhancing Zero-Shot Stance Detection with Contrastive and Prompt Learning.

In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance detection in today's digital age, where the absence of training examples for specific models poses significant challenges. This task necessitates models with robust generalization abilities to discern target-related, transferable stance features within training data. Recent advances in prompt-based learning have showcased notable efficacy in few-shot text classification. Such methods typically employ a uniform prompt pattern across all instances, yet they overlook the intricate relationship between prompts and instances, thereby failing to sufficiently direct the model towards learning task-relevant knowledge and information. This paper argues for the critical need to dynamically enhance the relevance between specific instances and prompts. Thus, we introduce a stance detection model underpinned by a gated multilayer perceptron (gMLP) and a prompt learning strategy, which is tailored for zero-shot stance detection scenarios. Specifically, the gMLP is utilized to capture semantic features of instances, coupled with a control gate mechanism to modulate the influence of the gate on prompt tokens based on the semantic context of each instance, thereby dynamically reinforcing the instance-prompt connection. Moreover, we integrate contrastive learning to empower the model with more discriminative feature representations. Experimental evaluations on the VAST and SEM16 benchmark datasets substantiate our method's effectiveness, yielding a 1.3% improvement over the JointCL model on the VAST dataset.

AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion.

Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method based on attention mechanism and multi-scale feature fusion (AM-MSFF). By combining the advantages of attention mechanism and multi-scale feature fusion, this method significantly improves the accuracy of pest recognition. Firstly, we introduce the relation-aware global attention (RGA) module to adaptively adjust the feature weights of each position, thereby focusing more on the regions relevant to pests and reducing the background interference. Then, we propose the multi-scale feature fusion (MSFF) module to fuse feature maps from different scales, which better captures the subtle differences and the overall shape features in pest images. Moreover, we introduce generalized-mean pooling (GeMP) to more accurately extract feature information from pest images and better distinguish different pest categories. In terms of the loss function, this study proposes an improved focal loss (FL), known as balanced focal loss (BFL), as a replacement for cross-entropy loss. This improvement aims to address the common issue of class imbalance in pest datasets, thereby enhancing the recognition accuracy of pest identification models. To evaluate the performance of the AM-MSFF model, we conduct experiments on two publicly available pest datasets (IP102 and D0). Extensive experiments demonstrate that our proposed AM-MSFF outperforms most state-of-the-art methods. On the IP102 dataset, the accuracy reaches 72.64%, while on the D0 dataset, it reaches 99.05%.

FSN: Joint Entity and Relation Extraction Based on Filter Separator Network.

Joint entity and relation extraction methods have attracted an increasing amount of attention recently due to their capacity to extract relational triples from intricate texts. However, most of the existing methods ignore the association and difference between the Named Entity Recognition (NER) subtask features and the Relation Extraction (RE) subtask features, which leads to an imbalance in the interaction between these two subtasks. To solve the above problems, we propose a new joint entity and relation extraction method, FSN. It contains a Filter Separator Network (FSN) module that employs a two-direction LSTM to filter and separate the information contained in a sentence and merges similar features through a splicing operation, thus solving the problem of the interaction imbalance between subtasks. In order to better extract the local feature information for each subtask, we designed a Named Entity Recognition Generation (NERG) module and a Relation Extraction Generation (REG) module by adopting the design idea of the decoder in Transformer and average pooling operations to better capture the entity boundary information in the sentence and the entity pair boundary information for each relation in the relational triple, respectively. Additionally, we propose a dynamic loss function that dynamically adjusts the learning weights of each subtask in each epoch according to the proportionality between each subtask, thus narrowing down the difference between the ideal and realistic results. We thoroughly evaluated our model on the SciERC dataset and the ACE2005 dataset. The experimental results demonstrate that our model achieves satisfactory results compared to the baseline model.

Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS: The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS: A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION: Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Origin-independent analysis links SARS-CoV-2 local genomes with COVID-19 incidence and mortality.

There is an urgent public health need to better understand Severe Acute Respiratory Syndrome (SARS)-CoV-2/COVID-19, particularly how sequences of the viruses could lead to diverse incidence and mortality of COVID-19 in different countries. However, because of its unknown ancestors and hosts, elucidating the genetic variations of the novel coronavirus, SARS-CoV-2, has been difficult. Without needing to know ancestors, we identified an uneven distribution of local genome similarities among the viruses categorized by geographic regions, and it was strongly correlated with incidence and mortality. To ensure unbiased and origin-independent analyses, we used a pairwise comparison of local genome sequences of virus genomes by Basic Local Alignment Search Tool (BLAST). We found a strong statistical correlation between dominance of the SARS-CoV-2 in distributions of uneven similarities and the incidence and mortality of illness. Genomic annotation of the BLAST hits also showed that viruses from geographic regions with severe infections tended to have more dynamic genomic regions in the SARS-CoV-2 receptor-binding domain (RBD) and receptor-binding motif (RBM) of the spike protein (S protein). Dynamic domains in the S protein were also confirmed by a canyon region of mismatches coincident with RBM and RBD, without hits of alignments of 100% matching. Thus, our origin-independent analysis suggests that the dynamic and unstable SARS-CoV-2-RBD could be the main reason for diverse incidence and mortality of COVID-19 infection.

The Golgi microtubules regulate single cell durotaxis.

Current understandings on cell motility and directionality rely heavily on accumulated investigations of the adhesion-actin cytoskeleton-actomyosin contractility cycles, while microtubules have been understudied in this context. Durotaxis, the ability of cells to migrate up gradients of substrate stiffness, plays a critical part in development and disease. Here, we identify the pivotal role of Golgi microtubules in durotactic migration of single cells. Using high-throughput analysis of microtubule plus ends/focal adhesion interactions, we uncover that these non-centrosomal microtubules actively impart leading edge focal adhesion (FA) dynamics. Furthermore, we designed a new system where islands of higher stiffness were patterned within RGD peptide coated polyacrylamide gels. We revealed that the positioning of the Golgi apparatus is responsive to external mechanical cues and that the Golgi-nucleus axis aligns with the stiffness gradient in durotaxis. Together, our work unveils the cytoskeletal underpinning for single cell durotaxis. We propose a model in which the Golgi-nucleus axis serves both as a compass and as a steering wheel for durotactic migration, dictating cell directionality through the interaction between non-centrosomal microtubules and the FA dynamics.

AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network.

Micro-expressions are the small, brief facial expression changes that humans momentarily show during emotional experiences, and their data annotation is complicated, which leads to the scarcity of micro-expression data. To extract salient and distinguishing features from a limited dataset, we propose an attention-based multi-scale, multi-modal, multi-branch flow network to thoroughly learn the motion information of micro-expressions by exploiting the attention mechanism and the complementary properties between different optical flow information. First, we extract optical flow information (horizontal optical flow, vertical optical flow, and optical strain) based on the onset and apex frames of micro-expression videos, and each branch learns one kind of optical flow information separately. Second, we propose a multi-scale fusion module to extract more prosperous and more stable feature expressions using spatial attention to focus on locally important information at each scale. Then, we design a multi-optical flow feature reweighting module to adaptively select features for each optical flow separately by channel attention. Finally, to better integrate the information of the three branches and to alleviate the problem of uneven distribution of micro-expression samples, we introduce a logarithmically adjusted prior knowledge weighting loss. This loss function weights the prediction scores of samples from different categories to mitigate the negative impact of category imbalance during the classification process. The effectiveness of the proposed model is demonstrated through extensive experiments and feature visualization on three benchmark datasets (CASMEII, SAMM, and SMIC), and its performance is comparable to that of state-of-the-art methods.

Antagonists targeting eEF2 kinase rescue multiple aspects of pathophysiology in Alzheimer's disease model mice.

It is imperative to develop novel therapeutic strategies for Alzheimer's disease (AD) and related dementia syndromes based on solid mechanistic studies. Maintenance of memory and synaptic plasticity relies on de novo protein synthesis, which is partially regulated by phosphorylation of eukaryotic elongation factor 2 (eEF2) via its kinase eEF2K. Abnormally increased eEF2 phosphorylation and impaired mRNA translation have been linked to AD. We recently reported that prenatal genetic suppression of eEF2K is able to prevent aging-related cognitive deficits in AD model mice, suggesting the therapeutic potential of targeting eEF2K/eEF2 signaling in AD. Here, we tested two structurally distinct small-molecule eEF2K inhibitors in two different lines of AD model mice after the onset of cognitive impairments. Our data revealed that treatment with eEF2K inhibitors improved AD-associated synaptic plasticity impairments and cognitive dysfunction, without altering brain amyloid ß (Aß) and tau pathology. Furthermore, eEF2K inhibition alleviated AD-associated defects in dendritic spine morphology, post-synaptic density formation, protein synthesis, and dendritic polyribosome assembly. Our results may offer critical therapeutic implications for AD, and the proof-of-principle study indicates translational implication of inhibiting eEF2K for AD and related dementia syndromes. Cover Image for this issue: https://doi.org/10.1111/jnc.15392.

Brain-specific suppression of AMPKα2 isoform impairs cognition and hippocampal LTP by PERK-mediated eIF2α phosphorylation.

The AMP-activated protein kinase (AMPK) is a molecular sensor to maintain energy homeostasis. The two isoforms of the AMPK catalytic subunit (AMPKα1 and α2) are both expressed in brains, but their roles in cognition are unknown. We generated conditional knockout mice in which brain AMPKα isoforms are selectively suppressed (AMPKα1/α2 cKO), and determined the isoform-specific effects in mice of either sex on cognition and synaptic plasticity. AMPKα2 cKO but not AMPKα1 cKO displayed impaired cognition and hippocampal late long-term potentiation (L-LTP). Further, AMPKα2 cKO mice exhibited decreased dendritic spine density and abnormal spine morphology in hippocampus. Electron microscope imaging demonstrated reduced postsynaptic density formation and fewer dendritic polyribosomes in hippocampi of AMPKα2 cKO mice. Biochemical studies revealed unexpected findings that repression of AMPKα2 resulted in increased phosphorylation of mRNA translational factor eIF2α and its kinase PERK. Importantly, L-LTP failure and cognitive impairments displayed in AMPKα2 cKO mice were alleviated by suppressing PERK activity pharmacologically or genetically. In summary, we demonstrate here that brain-specific suppression of AMPKα2 isoform impairs cognition and hippocampal LTP by PERK-mediated eIF2α phosphorylation, providing molecular mechanisms linking metabolism, protein synthesis, and cognition.

Blockchain Private File Storage-Sharing Method Based on IPFS.

Under the current national network environment, anyone can participate in publishing. As an important information resource, knowledge files reflect the workload of publishers. Moreover, high-quality knowledge files can promote the progress of society. However, pirated inferior files have the opposite effect. At present, most organizations use centralized servers to centrally manage the knowledge files released by users. In addition, it is necessary to introduce an untrusted third party to examine and encrypt the contents of files, which leads to an opaque process of file storage transactions, tampering with intellectual copyright, and the inability to have consistent systems of file management among institutions due to the lack of uniform standards for the same intellectual files. The purpose of this paper is to ensure the safe storage of knowledge files on the one hand and to realize efficient sharing of copyrighted files on the other hand. Therefore, this paper combines NDN (Named Data Network) technology with a distributed blockchain and an Interplanetary File System (IPFS) and proposes a blockchain knowledge file storage and sharing method based on an NDN. The method uses the NDN itself for the file content signature and encryption, thereby separating the file security and transmission process. At the same time, the method uses a flexible NDN reverse path forwarding and routing strategy, combining an IPFS private storage network to improve the safety of the encrypted data storage security. Finally, the method takes advantage of all participating nodes consensus and shares files in the synchronized blockchain to ensure traceability. This paper introduces the structure and principles of the method and describes the process of file upload and transfer. Finally, the performance of the method is compared and evaluated, and the advantages and disadvantages of the method and the future research direction are summarized.

Unsupervised Person Re-Identification with Attention-Guided Fine-Grained Features and Symmetric Contrast Learning.

Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challenging. Existing studies tend to ignore the optimisation of feature extractors in the feature-extraction stage of this task, while the use of traditional losses in the unsupervised learning stage severely affects the performance of the model. Additionally the use of a contrast learning framework in the latest methods uses only a single cluster centre or all instance features, without considering the correctness and diversity of the samples in the class, which affects the training of the model. Therefore, in this paper, we design an unsupervised person-re-identification framework called attention-guided fine-grained feature network and symmetric contrast learning (AFF_SCL) to improve the two stages in the unsupervised person-re-identification task. AFF_SCL focuses on learning recognition features through two key modules, namely the Attention-guided Fine-grained Feature network (AFF) and the Symmetric Contrast Learning module (SCL). Specifically, the attention-guided fine-grained feature network enhances the network's ability to discriminate pedestrians by performing further attention operations on fine-grained features to obtain detailed features of pedestrians. The symmetric contrast learning module replaces the traditional loss function to exploit the information potential given by the multiple samples and maintains the stability and generalisation capability of the model. The performance of the USL and UDA methods is tested on the Market-1501 and DukeMTMC-reID datasets by means of the results, which demonstrate that the method outperforms some existing methods, indicating the superiority of the framework.

Protein expression alteration in hippocampus upon genetic repression of AMPKα isoforms.

The AMP-activated protein kinase (AMPK) is a molecular sensor to help maintain cellular energy homeostasis. AMPK is a heterotrimeric complex and its enzymatic catalytic subunit includes two isoforms: α1 and α2. Dysregulation of AMPK signaling is linked to neuronal diseases characterized with cognitive impairments. Emerging evidence also suggest isoform-specific roles of AMPK in the brain. AMPK regulates protein synthesis, which is critical for memory formation and neuronal plasticity. However, the consequence of altering AMPK activity on the translation of specific proteins in the brain is unknown. Here, we use unbiased mass spectrometry-based proteomics approach to analyze protein profile alterations in hippocampus and prefrontal cortex of transgenic mice in which the genes for the two AMPKα isoforms are conditionally deleted. The study revealed identities of proteins whose expression is sensitive to suppression of AMPKα1 and/or α2 isoform. These data may serve as a basis for future in-depth study. Elucidation of the functional relevance of the alteration of specific proteins could provide insights into identification of novel therapeutic targets for neuronal disorders characterized with AMPK signaling dysregulation and impaired cellular energy metabolism.

Genetic removal of eIF2α kinase PERK in mice enables hippocampal L-LTP independent of mTORC1 activity.

Characterization of the molecular signaling pathways underlying protein synthesis-dependent forms of synaptic plasticity, such as late long-term potentiation (L-LTP), can provide insights not only into memory expression/maintenance under physiological conditions but also potential mechanisms associated with the pathogenesis of memory disorders. Here, we report in mice that L-LTP failure induced by the mammalian (mechanistic) target of rapamycin complex 1 (mTORC1) inhibitor rapamycin is reversed by brain-specific genetic deletion of PKR-like ER kinase, PERK (PERK KO), a kinase for eukaryotic initiation factor 2α (eIF2α). In contrast, genetic removal of general control non-derepressible-2, GCN2 (GCN2 KO), another eIF2α kinase, or treatment of hippocampal slices with the PERK inhibitor GSK2606414, does not rescue rapamycin-induced L-LTP failure, suggesting mechanisms independent of eIF2α phosphorylation. Moreover, we demonstrate that phosphorylation of eukaryotic elongation factor 2 (eEF2) is significantly decreased in PERK KO mice but unaltered in GCN2 KO mice or slices treated with the PERK inhibitor. Reduction in eEF2 phosphorylation results in increased general protein synthesis, and thus could contribute to the mTORC1-independent L-LTP in PERK KO mice. We further performed experiments on mutant mice with genetic removal of eEF2K (eEF2K KO), the only known kinase for eEF2, and found that L-LTP in eEF2K KO mice is insensitive to rapamycin. These data, for the first time, connect reduction in PERK activity with the regulation of translation elongation in enabling L-LTP independent of mTORC1. Thus, our findings indicate previously unrecognized levels of complexity in the regulation of protein synthesis-dependent synaptic plasticity. Read the Editorial Highlight for this article on page 119. Cover Image for this issue: doi: 10.1111/jnc.14185.

Dural Sinus Malformation Imaging in the Fetus: Based on 4 Cases and Literature Review.

BACKGROUND: This study aims to describe the imaging characteristic of dural sinus malformation in 4 fetuses with different locations. MATERIALS AND METHODS: We report a series of 4 fetuses with dural sinus malformation in Hubei Maternal and Children's Hospital from July 2013 to February 2016. All the mothers undertook the prenatal magnetic resonance (MRI) imaging because of the intracranial space-occupying lesions discovered by prenatal ultrasound. RESULTS: Two of the 4 cases demonstrated typical MRI of dural sinus malformation with thrombosis in the vicinity of torcular herophili (also known as sinus confluence), whereas the other 2 cases showed lesions in the superior sagittal sinus and transverse sinus separately. Three pregnancies were terminated, whereas the other one was delivered in the local hospital. CONCLUSIONS: Sonographer should realize the sign of dural sinus malformation. Atypical location of the dural sinus malformation, such as superior sagittal sinus and transverse sinus, should be paid special attention to. Further, prenatal MRI is necessary to identify the lesion.