Increasing power for voxel-wise genome-wide association studies : the random field theory, least square kernel machines and fast permutation procedures

Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains

PRE+: dual of proxy re-encryption for secure cloud data sharing service

With the rapid development of very large, diverse, complex, and distributed datasets generated from internet transactions, emails, videos, business information systems, manufacturing industry, sensors and internet of things etc., cloud and big data computation have emerged as a cornerstone of modern applications. Indeed, on the one hand, cloud and big data applications are becoming a main driver for economic growth. On the other hand, cloud and big data techniques may threaten people and enterprises’ privacy and security due to ever increasing exposure of their data to massive access. In this paper, aiming at providing secure cloud data sharing services in cloud storage, we propose a scalable and controllable cloud data sharing framework for cloud users (called: Scanf). To this end, we introduce a new cryptographic primitive, namely, PRE+, which can be seen as the dual of traditional proxy re-encryption (PRE) primitive. All the traditional PRE schemes until now require the delegator (or the delegator and the delegatee cooperatively) to generate the re-encryption keys. We observe that this is not the only way to generate the re-encryption keys, the encrypter also has the ability to generate re-encryption keys. Based on this observation, we construct a new PRE+ scheme, which is almost the same as the traditional PRE scheme except the re-encryption keys generated by the encrypter. Compared with PRE, our PRE+ scheme can easily achieve the non-transferable property and message-level based fine-grained delegation. Thus our Scanf framework based on PRE+ can also achieve these two properties, which is very important for users of cloud storage sharing service. We also roughly evaluate our PRE+ scheme’s performance and the results show that our scheme is efficient and practica for cloud data storage applications.Peer ReviewedPostprint (author's final draft

The association of depression status with menopause symptoms among rural midlife women in China

Objective: This study aims to evaluate the association of depression with menopausal status and some menopause symptoms (vasomotor symptoms and poor sleep).Methods: A total of 743 participants aged 40-60 years were recruited. Depression status was evaluated by using Self-Rating Depression Scale (SDS). Sleep quality and vasomotor symptoms were evaluated by specific symptoms questionnaire.Results: The prevalence of depression among participants was 11.4%. Depression was found more likely to occur in participants with poor sleep (OR, 6.02; 95%CI, 3.61, 10.03) or with vasomotor symptoms (VMS) (OR, 2.03; 95%CI, 1.20, 3.44) after controlling for age, education level, marital status, menopause status, monthly family income and chronic diseases. Menopause status was not associated with depression. Stratification analysis showed a significant association between poor sleep and depression across different menopause stages, while VMS were associated with depression only in premenopausal status.Conclusion: The majority of Chinese rural midlife women do not experience depression. The relationship between depression, VMS and sleep disturbances tends to change with menopausal status in Chinese rural midlife women.Keywords: depression, poor sleep, vasomotor symptoms, menopause, rural wome

Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

In this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs). Different from the conventional KV cache that retains key and value vectors for all context tokens, we conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens. Moreover, with the lightweight attention profiling used to guide the construction of the adaptive KV cache, FastGen can be deployed without resource-intensive fine-tuning or re-training. In our experiments across various asks, FastGen demonstrates substantial reduction on GPU memory consumption with negligible generation quality loss. We will release our code and the compatible CUDA kernel for reproducibility.Comment: ICLR 202