Optimal Caching Policy of Stochastic Updating Information in Delay Tolerant Networks

To increase the speed of information retrieval, one message may have multiple replicas in Delay Tolerant Networks (DTN). In this paper, we adopt a discrete time model and focus on the caching policy of stochastic updating information. In particular, the source creates new version in every time slot with certain probability. New version is usually more useful than the older one. We use a utility function to denote the availability of different versions. To constrain the number of replicas, we propose a probabilistic management policy and nodes to discard information with certain probability determined by the version of the information. Our objective is to find the best value of the probability to maximize the total utility value. Because new version is created with certain probability, nodes other than the source may not know whether the information stored in them is the latest version. Therefore, they can make decisions only according to the local state and decisions based on the local state can be seen as local-policy. We also explore the global-policy, that is, nodes understand the real state. We prove that the optimal policies in both cases conform to the threshold form. Simulations based on both synthetic and real motion traces show the accuracy of our theoretical model. Surprisingly, numerical results show that local-policy is better than the global-policy in some cases

YOLO-FaceV2: A Scale and Occlusion Aware Face Detector

In recent years, face detection algorithms based on deep learning have made great progress. These algorithms can be generally divided into two categories, i.e. two-stage detector like Faster R-CNN and one-stage detector like YOLO. Because of the better balance between accuracy and speed, one-stage detectors have been widely used in many applications. In this paper, we propose a real-time face detector based on the one-stage detector YOLOv5, named YOLO-FaceV2. We design a Receptive Field Enhancement module called RFE to enhance receptive field of small face, and use NWD Loss to make up for the sensitivity of IoU to the location deviation of tiny objects. For face occlusion, we present an attention module named SEAM and introduce Repulsion Loss to solve it. Moreover, we use a weight function Slide to solve the imbalance between easy and hard samples and use the information of the effective receptive field to design the anchor. The experimental results on WiderFace dataset show that our face detector outperforms YOLO and its variants can be find in all easy, medium and hard subsets. Source code in https://github.com/Krasjet-Yu/YOLO-FaceV

Construction and validation of prognostic risk model for hepatocellular carcinoma based on biological analysis of palmitoyl-associated enzyme long-chain non-coding RNA

Objective·To explore the effect of screening the expression of long non-coding RNA (lncRNA) related to palmitoylation on prognosis of liver cancer based on The Cancer Genome Atlas (TCGA) database and construct a risk prediction model in liver cancer.Methods·The sequencing data and the corresponding clinical information of 374 liver cancer tissues and 50 normal tissue samples were downloaded from TCGA database. The differential zinc finger aspartate-histidine-histidine-cysteine domain (ZDHHC) between liver cancer tissues and normal tissues was used to construct the expression profile of lncRNA related to ZDHHC. Furthermore, the prediction model was constructed by LASSO regression algorithm and the validity of the model prediction was verified to analyze the relationship between high-risk and low-risk groups and immune function and to predict the response to immunotherapy.Results·There were 20 differentially expressed ZDHHCs in hepatocellular carcinoma, among which 656 lncRNAs were correlated with differential ZDHHCs (all P<0.05). Univariate COX analysis showed that 22 lncRNAs were associated with the prognosis of hepatocellular carcinoma (HR 1.47‒13.05, all P<0.05), and LASSO regression analysis included 3 lncRNAs to construct a risk model. The risk score=0.662 6×AC026356.1+0.213 9×AC026401.3+0.405 6×POLH-AS1. In the model, the overall survival (OS) and progression-free survival (PFS) of patients in the high-risk group were significantly lower than those in the low-risk group (all P<0.05). Multivariate COX regression analysis showed that the model as a risk factor was an independent factor affecting survival (HR=1.375, 95%CI 1.208‒1.566). In the risk model, there were significant differences between high-risk and low-risk immune function pathways, and the response level of high-risk patients to immunotherapy was lower (P<0.05).Conclusion·The use of a risk model based on palm acylation related lncRNA expression can independently predict the survival period of liver cancer patients, providing reference for patients receiving immunotherapy

A Novel Direct Factor Xa Inhibitory Peptide with Anti-Platelet Aggregation Activity from Agkistrodon acutus Venom Hydrolysates

Snake venom is a natural substance that contains numerous bioactive proteins and peptides, nearly all of which have been identified over the last several decades. In this study, we subjected snake venom to enzymatic hydrolysis to identify previously unreported bioactive peptides. The novel peptide ACH-11 with the sequence LTFPRIVFVLG was identified with both FXa inhibition and anti-platelet aggregation activities. ACH-11 inhibited the catalytic function of FXa towards its substrate S-2222 via a mixed model with a Ki value of 9.02 μM and inhibited platelet aggregation induced by ADP and U46619 in a dose-dependent manner. Furthermore, ACH-11 exhibited potent antithrombotic activity in vivo. It reduced paralysis and death in an acute pulmonary thrombosis model by 90% and attenuated thrombosis weight in an arterio-venous shunt thrombosis model by 57.91%, both at a dose of 3 mg/kg. Additionally, a tail cutting bleeding time assay revealed that ACH-11 did not prolong bleeding time in mice at a dose of 3 mg/kg. Together, our results reveal that ACH-11 is a novel antithrombotic peptide exhibiting both FXa inhibition and anti-platelet aggregation activities, with a low bleeding risk. We believe that it could be a candidate or lead compound for new antithrombotic drug development

PDLIM5 links kidney anion exchanger 1 (kAE1) to ILK and is required for membrane targeting of kAE1

Anion exchanger 1 (AE1) mediates Cl$^{-}$/HCO$_{3}$$^{-}$ exchange in erythrocytes and kidney intercalated cells where it functions to maintain normal bodily acid-base homeostasis. AE1’s C-terminal tail (AE1C) contains multiple potential membrane targeting/retention determinants, including a predicted PDZ binding motif, which are critical for its normal membrane residency. Here we identify PDLIM5 as a direct binding partner for AE1 in human kidney, via PDLIM5's PDZ domain and the PDZ binding motif in AE1C. Kidney AE1 (kAE1), PDLIM5 and integrin-linked kinase (ILK) form a multiprotein complex in which PDLIM5 provides a bridge between ILK and AE1C. Depletion of PDLIM5 resulted in significant reduction in kAE1 at the cell membrane, whereas over-expression of kAE1 was accompanied by increased PDLIM5 levels, underscoring the functional importance of PDLIM5 for proper kAE1 membrane residency, as a crucial linker between kidney-AE1 and actin cytoskeleton-associated proteins in polarized cells.This work was supported by the Wellcome Trust (grant ref: 088489/Z/09/Z and Strategic award 100140/Z/12/Z to the Cambridge Institute for Medical Research), and the British Heart Foundation (grant ref: SBAG/120). The Addenbrooke's Human Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from Nature Publishing Group via https://doi.org/10.1038/srep3970

PDLIM5 links kidney anion exchanger 1 (kAE1) to ILK and is required for membrane targeting of kAE1.

Anion exchanger 1 (AE1) mediates Cl-/HCO3- exchange in erythrocytes and kidney intercalated cells where it functions to maintain normal bodily acid-base homeostasis. AE1's C-terminal tail (AE1C) contains multiple potential membrane targeting/retention determinants, including a predicted PDZ binding motif, which are critical for its normal membrane residency. Here we identify PDLIM5 as a direct binding partner for AE1 in human kidney, via PDLIM5's PDZ domain and the PDZ binding motif in AE1C. Kidney AE1 (kAE1), PDLIM5 and integrin-linked kinase (ILK) form a multiprotein complex in which PDLIM5 provides a bridge between ILK and AE1C. Depletion of PDLIM5 resulted in significant reduction in kAE1 at the cell membrane, whereas over-expression of kAE1 was accompanied by increased PDLIM5 levels, underscoring the functional importance of PDLIM5 for proper kAE1 membrane residency, as a crucial linker between kAE1 and actin cytoskeleton-associated proteins in polarized cells.This work was supported by the Wellcome Trust (grant ref: 088489/Z/09/Z and Strategic award 100140/Z/12/Z to the Cambridge Institute for Medical Research), and the British Heart Foundation (grant ref: SBAG/120). The Addenbrooke's Human Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre.This is the final version of the article. It first appeared from Nature Publishing Group via https://doi.org/10.1038/srep3970

Progress and perspectives of perioperative immunotherapy in non-small cell lung cancer

Lung cancer is one of the leading causes of cancer-related death. Lung cancer mortality has decreased over the past decade, which is partly attributed to improved treatments. Curative surgery for patients with early-stage lung cancer is the standard of care, but not all surgical treatments have a good prognosis. Adjuvant and neoadjuvant chemotherapy are used to improve the prognosis of patients with resectable lung cancer. Immunotherapy, an epoch-defining treatment, has improved curative effects, prognosis, and tolerability compared with traditional and ordinary cytotoxic chemotherapy, providing new hope for patients with non-small cell lung cancer (NSCLC). Immunotherapy-related clinical trials have reported encouraging clinical outcomes in their exploration of different types of perioperative immunotherapy, from neoadjuvant immune checkpoint inhibitor (ICI) monotherapy, neoadjuvant immune-combination therapy (chemoimmunotherapy, immunotherapy plus antiangiogenic therapy, immunotherapy plus radiotherapy, or concurrent chemoradiotherapy), adjuvant immunotherapy, and neoadjuvant combined adjuvant immunotherapy. Phase 3 studies such as IMpower 010 and CheckMate 816 reported survival benefits of perioperative immunotherapy for operable patients. This review summarizes up-to-date clinical studies and analyzes the efficiency and feasibility of different neoadjuvant therapies and biomarkers to identify optimal types of perioperative immunotherapy for NSCLC

The rise and demise of the Paleogene Central Tibetan Valley

Reconstructing the Paleogene topography and climate of central Tibet informs understanding of collisional tectonic mechanisms and their links to climate and biodiversity. Radiometric dates of volcanic/sedimentary rocks and paleotemperatures based on clumped isotopes within ancient soil carbonate nodules from the Lunpola Basin, part of an east-west trending band of basins in central Tibet and now at 4.7 km, suggest that the basin rose from 4.0 km by 29 Ma. The height change is quantified using the rates at which wet-bulb temperatures ( ) decline at land surfaces as those surface rise. In this case, fell from ~8°C at ~38 Ma to ~1°C at 29 Ma, suggesting at least ~2.0 km of surface uplift in ~10 Ma under warm Eocene to Oligocene conditions. These results confirm that a Paleogene Central Tibetan Valley transformed to a plateau before the Neogene

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic