Cooperative Jamming in Wireless Networks - Turning Attacks into Privacy Protection

Generally, collisions between packets are undesired in wireless networks. We design this scheme, Cooperative Jamming in Wireless Networks (CJWN), to make use of collision to protect secret DATA packets from being sniffed by a nearby eavesdropper. We are intending to greatly increase the Packet Error Rate (PER) at the eavesdropper when the PER at the receiver is maintained at an acceptable level. This scheme is not intended to completely take the place of various encryption/decryption schemes which are working based on successfully received packets. Adding CJWN to the popular CSMA/CA adopted in IEEE 802.11 will add more security even the key for encryption/decryption is already exposed. Because the overhead of CJWN is very big, we do not suggest using it on every transmission. When some secret packets have a high requirement of confidentiality, CJWN is worth trying at the cost of throughput performance and power

Efficient Anomaly Detection with Budget Annotation Using Semi-Supervised Residual Transformer

Anomaly Detection is challenging as usually only the normal samples are seen during training and the detector needs to discover anomalies on-the-fly. The recently proposed deep-learning-based approaches could somehow alleviate the problem but there is still a long way to go in obtaining an industrial-class anomaly detector for real-world applications. On the other hand, in some particular AD tasks, a few anomalous samples are labeled manually for achieving higher accuracy. However, this performance gain is at the cost of considerable annotation efforts, which can be intractable in many practical scenarios. In this work, the above two problems are addressed in a unified framework. Firstly, inspired by the success of the patch-matching-based AD algorithms, we train a sliding vision transformer over the residuals generated by a novel position-constrained patch-matching. Secondly, the conventional pixel-wise segmentation problem is cast into a block-wise classification problem. Thus the sliding transformer can attain even higher accuracy with much less annotation labor. Thirdly, to further reduce the labeling cost, we propose to label the anomalous regions using only bounding boxes. The unlabeled regions caused by the weak labels are effectively exploited using a highly-customized semi-supervised learning scheme equipped with two novel data augmentation methods. The proposed method outperforms all the state-of-the-art approaches using all the evaluation metrics in both the unsupervised and supervised scenarios. On the popular MVTec-AD dataset, our SemiREST algorithm obtains the Average Precision (AP) of 81.2% in the unsupervised condition and 84.4% AP for supervised anomaly detection. Surprisingly, with the bounding-box-based semi-supervisions, SemiREST still outperforms the SOTA methods with full supervision (83.8% AP) on MVTec-AD.Comment: 20 pages,6 figure

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature

BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure

Bartter syndrome type III with glomerular dysplasia and chronic kidney disease: A case report

BackgroundBartter syndrome (BS) type III is a rare autosomal recessive genetic disease. Its clinical features are polyuria, hypokalemia, hypochloremia, metabolic alkalosis, and hyperreninaemia. A few BS type III can be complicated with chronic kidney disease.Case presentationWe report a 14-year-old boy with Bartter syndrome caused by a c.1792C > T (p.Q598*) mutation in the CLCNKB gene. He was a no deafness and full-term baby, and he had renal dysplasia and chronic kidney disease (CKD). In addition, we summarize all cases of BS type III complicated with CKD.ConclusionsWe report a case of Bartter syndrome complicated by chronic kidney disease caused by a new mutation of CLCNKB. As we all know, BS type IV is usually combined with chronic kidney disease, and BS type III can also integrate with CKD. We don't find BS type III with glomerular dysplasia in the literature. So renal damage in BS type III is not only FSGS; clinicians must also be aware of glomerular dysplasia

Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.Comment: RecSys 202

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community

Effect of Sow Intestinal Flora on the Formation of Endometritis

Endometritis is the main cause of decreased reproductive performance of sows, while one of the most important factors in the etiology of sow endometritis is an aberration of birth canal microbiota. Therefore, people began to pay attention to the microbiota structure and composition of the birth canal of sows with endometritis. Interestingly, we found that the risk of endometritis was increased in the sows with constipation in clinical practice, which may imply that the intestinal flora is related to the occurrence of endometritis. Therefore, understanding the relationship between birth canal microbiota and intestinal microbiota of the host has become exceptionally crucial. In this study, the microbiota of birth canal secretions and fresh feces of four healthy and four endometritis sows were analyzed via sequencing the V3 + V4 region of bacterial 16S ribosomal (rDNA) gene. The results showed a significant difference between endometritis and healthy sows birth canal flora in composition and abundance. Firmicutes (74.36%) and Proteobacteria were the most dominant phyla in birth canal microbiota of healthy sows. However, the majority of beneficial bacteria that belonging to Firmicutes phylum (e.g., Lactobacillus and Enterococcus) declined in endometritis sow. The abundance of Porphyromonas, Clostridium sensu stricto 1, Streptococcus, Fusobacterium, Actinobacillus, and Bacteroides increased significantly in the birth canal microbiota of endometritis sows. Escherichia–Shigella and Bacteroides were the common genera in the birth canal and intestinal flora of endometritis sows. The abundance of Escherichia–Shigella and Bacteroides in the intestines of sows suffering from endometritis were significantly increased than the intestinal microbiota of the healthy sows. We speculated that some intestinal bacteria (such as Escherichia–Shigella and Bacteroides) might be bound up with the onset of sow endometritis based on intestinal microbiota analysis in sows with endometritis and healthy sows. The above results can supply a theoretical basis to research the pathogenesis of endometritis and help others understand the relationship with the microbiota of sow's birth canal and gut

Prognostic value of the FUT family in acute myeloid leukemia

Genetic abnormalities are more frequently viewed as prognostic markers in acute myeloid leukemia (AML) in recent years. Fucosylation, catalyzed by fucosyltransferases (FUTs), is a post-translational modification that widely exists in cancer cells. However, the expression and clinical implication of the FUT family (FUT1-11) in AML has not been investigated. From the Cancer Genome Atlas database, a total of 155 AML patients with complete clinical characteristics and FUT1-11 expression data were included in our study. In patients who received chemotherapy alone showed that high expression levels of FUT3, FUT6, and FUT7 had adverse effects on event-free survival (EFS) and overall survival (OS) (all P <0.05), whereas high FUT4 expression had favorable effects on EFS and OS (all P <0.01). However, in the allogeneic hematopoietic stem cell transplantation (allo-HSCT) group, we only found a significant difference in EFS between the high and low FUT3 expression subgroups (P = 0.047), while other FUT members had no effect on survival. Multivariate analysis confirmed that high FUT4 expression was an independent favorable prognostic factor for both EFS (HR = 0.423, P = 0.001) and OS (HR = 0.398, P <0.001), whereas high FUT6 expression was an independent risk factor for both EFS (HR = 1.871, P = 0.017) and OS (HR = 1.729, P = 0.028) in patients who received chemotherapy alone. Moreover, we found that patients with low FUT4 and high FUT6 expressions had the shortest EFS and OS (P <0.05). Our study suggests that high expressions of FUT3/6/7 predict poor prognosis, high FUT4 expression indicates good prognosis in AML; FUT6 and FUT4 have the best prognosticating profile among them, but their effects could be neutralized by allo-HSCT

High expression of chaperonin-containing TCP1 subunit 3 may induce dismal prognosis in multiple myeloma

The prognosis role of CCT3 in MM and the possible pathways it involved were studied in our research. By analyzing ten independent datasets (including 48 healthy donors, 2220 MM, 73 MGUS, and 6 PCL), CCT3 was found to express higher in MM than healthy donors, and the expression level was gradually increased from MGUS, SMM, MM to PCL (all P <0.01). By analyzing three independent datasets (GSE24080, GSE2658, and GSE4204), we found that CCT3 was a significant indicator of poor prognosis (all P <0.01). KEGG and GSEA analysis showed that CCT3 expression was associated with JAK-STAT3 pathway, Hippo signaling pathway, and WNT signaling pathway. In addition, different expressed genes analysis revealed MYC, which was one of the downstream genes regulated by JAK-STAT3 pathway, was upregulated in MM. This confirms that JAK-STAT3 signaling pathway may promote the progress of disease which was regulated by CCT3 expression. Our study revealed that CCT3 may play a supporting role at the diagnosis of myeloid, and high expression of CCT3 suggested poor prognosis in MM. CCT3 expression may promote the progression of MM mainly by regulating MYC through JAK-STAT3 signaling pathway