Stimulus intensity-dependent modulations of hippocampal long-term potentiation by basolateral amygdala priming

There is growing realization that the relationship between memory and stress/emotionality is complicated, and may include both memory enhancing and memory impairing aspects. It has been suggested that the underlying mechanisms involve amygdala modulation of hippocampal synaptic plasticity, such as long-term potentiation (LTP). We recently reported that while in CA1 basolateral amygdala (BLA) priming impaired theta stimulation induced LTP, it enhanced LTP in the dentate gyrus (DG). However, emotional and stressfull experiences were found to activate synaptic plasticity within the BLA, raising the possibility that BLA modulation of other brain regions may be altered as well, as it may depend on the way the BLA is activated or is responding. In previous studies BLA priming stimulation was relatively weak (1 V, 50 μs pulse duration). In the present study we assessed the effects of two stronger levels of BLA priming stimulation (1 V or 2 V, 100 μs pulse duration) on LTP induction in hippocampal DG and CA1, in anesthetized rats. Results show that 1V-BLA priming stimulation enhanced but 2V-BLA priming stimulation impaired DG LTP; however, both levels of BLA priming stimulation impaired CA1 LTP, suggesting that modulation of hippocampal synaptic plasticity by amygdala is dependent on the degree of amygdala activation. These findings suggest that plasticity-induced within the amygdala, by stressful experiences induces a form of metaplasticity that would alter the way the amygdala may modulate memory-related processes in other brain areas, such as the hippocampus

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme

BACKGROUND: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. RESULTS: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. CONCLUSION: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks

Privacy Implications of Retrieval-Based Language Models

Retrieval-based language models (LMs) have demonstrated improved interpretability, factuality, and adaptability compared to their parametric counterparts, by incorporating retrieved text from external datastores. While it is well known that parametric models are prone to leaking private data, it remains unclear how the addition of a retrieval datastore impacts model privacy. In this work, we present the first study of privacy risks in retrieval-based LMs, particularly $k$NN-LMs. Our goal is to explore the optimal design and training procedure in domains where privacy is of concern, aiming to strike a balance between utility and privacy. Crucially, we find that $k$NN-LMs are more susceptible to leaking private information from their private datastore than parametric models. We further explore mitigations of privacy risks. When privacy information is targeted and readily detected in the text, we find that a simple sanitization step would completely eliminate the risks, while decoupling query and key encoders achieves an even better utility-privacy trade-off. Otherwise, we consider strategies of mixing public and private data in both datastore and encoder training. While these methods offer modest improvements, they leave considerable room for future work. Together, our findings provide insights for practitioners to better understand and mitigate privacy risks in retrieval-based LMs. Our code is available at: https://github.com/Princeton-SysML/kNNLM_privacy

An improved EfficientNet model and its applications in pneumonia image classification

Since the outburst of COVID-19, the medical system has been facing great challenges due to the explosive growth in detection and treatment needs within a short period. To improve the working efficiency of doctors, an improved EfficientNet model of Convolutional Neural Network (CNN) was proposed and applied for the diagnosis of pneumonia cases and the classification of relevant images in the present study. First, the acquired images of pneumonia cases were divided to determine the zones with target features, and image size was limited to improve the training speed of the network. Meanwhile, reinforcement learning was performed to the input dataset to further improve the training effect of the model. Second, the preprocessed images were inputted into the improved EfficientNet-B4 model. The depth and width of the model, as well as the resolution of the input images, were determined by optimizing the combination coefficient. On this basis, the model was scaled, and then its ability in extracting the features of deep-layer images was strengthened by introducing the attention mechanism. Third, the learning rate was adjusted by using the Adaptive Momentum (ADAM), and the training efficiency of the model was accelerated. Finally, the test set was inputted into the trained model. Results demonstrate that the improved model could detect 98% of patients with pneumonia and 97% of patients without pneumonia. The accuracy rate, precision rate, and sensitivity of the model were generally improved. Lastly, the training and test results of VGGNet, SqueezeNet-Elus, SqueezeNet-Relu, and the improved EfficientNet-B4 models were compared and evaluated. The improved EfficientNet-B4 model achieved the highest comprehensive accuracy rate, reaching 92.95%. The proposed method provides some references to the application of the CNN model in image classification and recognition

RepLong - de novo repeat identification using long read sequencing data

Abstract Motivation The identification of repetitive elements is important in genome assembly and phylogenetic analyses. The existing de novo repeat identification methods exploiting the use of short reads are impotent in identifying long repeats. Since long reads are more likely to cover repeat regions completely, using long reads is more favorable for recognizing long repeats. Results In this study, we propose a novel de novo repeat elements identification method namely RepLong based on PacBio long reads. Given that the reads mapped to the repeat regions are highly overlapped with each other, the identification of repeat elements is equivalent to the discovery of consensus overlaps between reads, which can be further cast into a community detection problem in the network of read overlaps. In RepLong, we first construct a network of read overlaps based on pair-wise alignment of the reads, where each vertex indicates a read and an edge indicates a substantial overlap between the corresponding two reads. Secondly, the communities whose intra connectivity is greater than the inter connectivity are extracted based on network modularity optimization. Finally, representative reads in each community are extracted to form the repeat library. Comparison studies on Drosophila melanogaster and human long read sequencing data with genome-based and short-read-based methods demonstrate the efficiency of RepLong in identifying long repeats. RepLong can handle lower coverage data and serve as a complementary solution to the existing methods to promote the repeat identification performance on long-read sequencing data. Availability and implementation The software of RepLong is freely available at https://github.com/ruiguo-bio/replong. Supplementary information Supplementary data are available at Bioinformatics online. </jats:sec

Whole-genome sequencing reveals genomic characterization of Listeria monocytogenes from food in China

IntroductionListeria monocytogenes is a foodborne bacterium that could persist in food and food processing environments for a long time. Understanding the population structure and genomic characterization of foodborne L. monocytogenes is essential for the prevention and control of listeriosis.MethodsA total of 322 foodborne L. monocytogenes isolates from 13 geographical locations and four food sources in China between 2000 and 2018 were selected for whole-genome sequencing.ResultsIn silico subtyping divided the 322 isolates into five serogroups, 35 sequence types (STs), 26 clonal complexes (CCs) and four lineages. Serogroup IIa was the most prevalent serogroup and ST9 was the most prevalent ST of foodborne L. monocytogenes strains isolated in China. The in-depth phylogenetic analysis on CC9 revealed that ST122 clone might be original from ST9 clone. Furthermore, 23 potentially relevant clusters were identified by pair-wised whole-genome single nucleotide polymorphism analysis, indicating that persistent- and/or cross-contamination had occurred in markets in China. ST8 and ST121 were the second and third top STs of L. monocytogenes in China, which had heterogeneity with that of L. monocytogenes isolates from other countries. The antibiotic resistance genes aacA4, tetM, tetS, dfrG carried by different mobile elements were found in L. monocytogenes strains. One lineage II strain carrying Listeria Pathogenicity Island 3 was first reported. In addition, a novel type of premature stop codon in inlA gene was identified in this study.DiscussionThese findings revealed the genomic characteristics and evolutionary relationship of foodborne L. monocytogenes in China on a scale larger than previous studies, which further confirmed that whole-genome sequencing analysis would be a helpful tool for routine surveillance and source-tracing investigation

LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation

Evidences increasingly indicate the involvement of long non-coding RNAs (lncRNAs) in various biological processes. As the mutations and abnormalities of lncRNAs are closely related to the progression of complex diseases, the identification of lncRNA-disease associations has become an important step toward the understanding and treatment of diseases. Since only a limited number of lncRNA-disease associations have been validated, an increasing number of computational approaches have been developed for predicting potential lncRNA-disease associations. However, how to predict potential associations precisely through computational approaches remains challenging. In this study, we propose a novel two-side sparse self-representation (TSSR) algorithm for lncRNA-disease association prediction. By learning the self-representations of lncRNAs and diseases from known lncRNA-disease associations adaptively, and leveraging the information provided by known lncRNA-disease associations and the intra-associations among lncRNAs and diseases derived from other existing databases, our model could effectively utilize the estimated representations of lncRNAs and diseases to predict potential lncRNA-disease associations. The experiment results on three real data sets demonstrate that our TSSR outperforms other competing methods significantly. Moreover, to further evaluate the effectiveness of TSSR in predicting potential lncRNAs-disease associations, case studies of Melanoma, Glioblastoma, and Glioma are carried out in this paper. The results demonstrate that TSSR can effectively identify some candidate lncRNAs associated with these three diseases