Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unfortunately, existing methods for session-based recommendation only aim at capturing user interest preference, while ignoring user price preference. Actually, there are primarily two challenges preventing us from accessing price preference. Firstly, the price preference is highly associated to various item features (i.e., category and brand), which asks us to mine price preference from heterogeneous information. Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling. To handle above challenges, we propose a novel approach Bi-Preference Learning Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation. Specifically, the customized heterogeneous hypergraph networks with a triple-level convolution are devised to capture user price and interest preference from heterogeneous features of items. Besides, we develop a Bi-Preference Learning schema to explore mutual relations between price and interest preference and collectively learn these two preferences under the multi-task learning architecture. Extensive experiments on multiple public datasets confirm the superiority of BiPNet over competitive baselines. Additional research also supports the notion that the price is crucial for the task.Comment: This paper has been accepted by ACM TOI

Sentiment Analysis of Long-term Social Data during the COVID-19 Pandemic

The COVID-19 pandemic has bringing the “infodemic” in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people’s attitudes towards progress of major actions during the pandemic. We explore the main topics during the prolonged pandemic, including information surrounding economics, vaccines, and politics. Besides, we show the differences in gender-based attitudes and propose future research questions refer to the “infodemic”. We believe that our work contributes to attracting public attention to the “infodemic” of the social crisis

KESDT: knowledge enhanced shallow and deep Transformer for detecting adverse drug reactions

Adverse drug reaction (ADR) detection is an essential task in the medical field, as ADRs have a gravely detrimental impact on patients' health and the healthcare system. Due to a large number of people sharing information on social media platforms, an increasing number of efforts focus on social media data to carry out effective ADR detection. Despite having achieved impressive performance, the existing methods of ADR detection still suffer from three main challenges. Firstly, researchers have consistently ignored the interaction between domain keywords and other words in the sentence. Secondly, social media datasets suffer from the challenges of low annotated data. Thirdly, the issue of sample imbalance is commonly observed in social media datasets. To solve these challenges, we propose the Knowledge Enhanced Shallow and Deep Transformer(KESDT) model for ADR detection. Specifically, to cope with the first issue, we incorporate the domain keywords into the Transformer model through a shallow fusion manner, which enables the model to fully exploit the interactive relationships between domain keywords and other words in the sentence. To overcome the low annotated data, we integrate the synonym sets into the Transformer model through a deep fusion manner, which expands the size of the samples. To mitigate the impact of sample imbalance, we replace the standard cross entropy loss function with the focal loss function for effective model training. We conduct extensive experiments on three public datasets including TwiMed, Twitter, and CADEC. The proposed KESDT outperforms state-of-the-art baselines on F1 values, with relative improvements of 4.87%, 47.83%, and 5.73% respectively, which demonstrates the effectiveness of our proposed KESDT

Three-Dimensional Medical Image Fusion with Deformable Cross-Attention

Multimodal medical image fusion plays an instrumental role in several areas of medical image processing, particularly in disease recognition and tumor detection. Traditional fusion methods tend to process each modality independently before combining the features and reconstructing the fusion image. However, this approach often neglects the fundamental commonalities and disparities between multimodal information. Furthermore, the prevailing methodologies are largely confined to fusing two-dimensional (2D) medical image slices, leading to a lack of contextual supervision in the fusion images and subsequently, a decreased information yield for physicians relative to three-dimensional (3D) images. In this study, we introduce an innovative unsupervised feature mutual learning fusion network designed to rectify these limitations. Our approach incorporates a Deformable Cross Feature Blend (DCFB) module that facilitates the dual modalities in discerning their respective similarities and differences. We have applied our model to the fusion of 3D MRI and PET images obtained from 660 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through the application of the DCFB module, our network generates high-quality MRI-PET fusion images. Experimental results demonstrate that our method surpasses traditional 2D image fusion methods in performance metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Importantly, the capacity of our method to fuse 3D images enhances the information available to physicians and researchers, thus marking a significant step forward in the field. The code will soon be available online

Fluorescent and photo-oxidizing TimeSTAMP tags track protein fates in light and electron microscopy.

Protein synthesis is highly regulated throughout nervous system development, plasticity and regeneration. However, tracking the distributions of specific new protein species has not been possible in living neurons or at the ultrastructural level. Previously we created TimeSTAMP epitope tags, drug-controlled tags for immunohistochemical detection of specific new proteins synthesized at defined times. Here we extend TimeSTAMP to label new protein copies by fluorescence or photo-oxidation. Live microscopy of a fluorescent TimeSTAMP tag reveals that copies of the synaptic protein PSD95 are synthesized in response to local activation of growth factor and neurotransmitter receptors, and preferentially localize to stimulated synapses in rat neurons. Electron microscopy of a photo-oxidizing TimeSTAMP tag reveals new PSD95 at developing dendritic structures of immature neurons and at synapses in differentiated neurons. These results demonstrate the versatility of the TimeSTAMP approach for visualizing newly synthesized proteins in neurons

Advanced Geological Prediction

Due to the particularity of the tunnel project, it is difficult to find out the exact geological conditions of the tunnel body during the survey stage. Once it encounters unfavorable geological bodies such as faults, fracture zones, and karst, it will bring great challenges to the construction and will easily cause major problems, economic losses, and casualties. Therefore, it is necessary to carry out geological forecast work in the tunnel construction process, which is of great significance for tunnel safety construction and avoiding major disaster accident losses. This lecture mainly introduces the commonly used methods of geological forecast in tunnel construction, the design principles, and contents of geological forecast and combines typical cases to show the implementation process of comprehensive geological forecast. Finally, the development direction of geological forecast theory, method, and technology is carried out. Prospects provide a useful reference for promoting the development of geological forecast of tunnels

Evaluation of cloned cells, animal model, and ATRA sensitivity of human testicular yolk sac tumor

The testicular yolk sac tumor (TYST) is the most common neoplasm originated from germ cells differentiated abnormally, a major part of pediatric malignant testicular tumors. The present study aimed at developing and validating the in vitro and vivo models of TYST and evaluating the sensitivity of TYST to treatments, by cloning human TYST cells and investigating the histology, ultra-structure, growth kinetics and expression of specific proteins of cloned cells. We found biological characteristics of cloned TYST cells were similar to the yolk sac tumor and differentiated from the columnar to glandular-like or goblet cells-like cells. Chromosomes for tumor identification in each passage met nature of the primary tumor. TYST cells were more sensitive to all-trans-retinoic acid which had significantly inhibitory effects on cell proliferation. Cisplatin induced apoptosis of TYST cells through the activation of p53 expression and down-regulation of Bcl- expression. Thus, we believe that cloned TYST cells and the animal model developed here are useful to understand the molecular mechanism of TYST cells and develop potential therapies for human TYST

A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images

Pathologists need to combine information from differently stained pathology slices for accurate diagnosis. Deformable image registration is a necessary technique for fusing multi-modal pathology slices. This paper proposes a hybrid deep feature-based deformable image registration framework for stained pathology samples. We first extract dense feature points via the detector-based and detector-free deep learning feature networks and perform points matching. Then, to further reduce false matches, an outlier detection method combining the isolation forest statistical model and the local affine correction model is proposed. Finally, the interpolation method generates the deformable vector field for pathology image registration based on the above matching points. We evaluate our method on the dataset of the Non-rigid Histology Image Registration (ANHIR) challenge, which is co-organized with the IEEE ISBI 2019 conference. Our technique outperforms the traditional approaches by 17% with the Average-Average registration target error (rTRE) reaching 0.0034. The proposed method achieved state-of-the-art performance and ranked 1st in evaluating the test dataset. The proposed hybrid deep feature-based registration method can potentially become a reliable method for pathology image registration.Comment: 22 pages, 12 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Glucose Metabolism in Turner Syndrome

Turner syndrome (TS) is one of the most common female chromosomal disorders. The condition is caused by complete or partial loss of a single X chromosome. Adult patients with TS have a high prevalence of diabetes mellitus (DM). Deranged glucose metabolism in this population seems to be genetically triggered. The traditional risk factors for DM in the general population may not play a major role in the pathogenesis of DM in patients with TS. This review focuses on the latest research studies pertaining to abnormalities of glucose metabolism in TS. We extensively review the available evidence pertaining to the influence of insulin secretion and sensitivity, obesity, autoimmunity, lifestyle, growth hormone, and sex hormone replacement therapy on the occurrence of DM in these patients

C. elegans SIRT6/7 Homolog SIR-2.4 Promotes DAF-16 Relocalization and Function during Stress

FoxO transcription factors and sirtuin family deacetylases regulate diverse biological processes, including stress responses and longevity. Here we show that the Caenorhabditis elegans sirtuin SIR-2.4—homolog of mammalian SIRT6 and SIRT7 proteins—promotes DAF-16–dependent transcription and stress-induced DAF-16 nuclear localization. SIR-2.4 is required for resistance to multiple stressors: heat shock, oxidative insult, and proteotoxicity. By contrast, SIR-2.4 is largely dispensable for DAF-16 nuclear localization and function in response to reduced insulin/IGF-1-like signaling. Although acetylation is known to regulate localization and activity of mammalian FoxO proteins, this modification has not been previously described on DAF-16. We find that DAF-16 is hyperacetylated in sir-2.4 mutants. Conversely, DAF-16 is acetylated by the acetyltransferase CBP-1, and DAF-16 is hypoacetylated and constitutively nuclear in response to cbp-1 inhibition. Surprisingly, a SIR-2.4 catalytic mutant efficiently rescues the DAF-16 localization defect in sir-2.4 null animals. Acetylation of DAF-16 by CBP-1 in vitro is inhibited by either wild-type or mutant SIR-2.4, suggesting that SIR-2.4 regulates DAF-16 acetylation indirectly, by preventing CBP-1-mediated acetylation under stress conditions. Taken together, our results identify SIR-2.4 as a critical regulator of DAF-16 specifically in the context of stress responses. Furthermore, they reveal a novel role for acetylation, modulated by the antagonistic activities of CBP-1 and SIR-2.4, in modulating DAF-16 localization and function