59 research outputs found
SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
Traditional sequential recommendation methods assume that users' sequence
data is clean enough to learn accurate sequence representations to reflect user
preferences. In practice, users' sequences inevitably contain noise (e.g.,
accidental interactions), leading to incorrect reflections of user preferences.
Consequently, some pioneer studies have explored modeling sequentiality and
correlations in sequences to implicitly or explicitly reduce noise's influence.
However, relying on only available intra-sequence information (i.e.,
sequentiality and correlations in a sequence) is insufficient and may result in
over-denoising and under-denoising problems (OUPs), especially for short
sequences. To improve reliability, we propose to augment sequences by inserting
items before denoising. However, due to the data sparsity issue and
computational costs, it is challenging to select proper items from the entire
item universe to insert into proper positions in a target sequence. Motivated
by the above observation, we propose a novel framework--Self-augmented Sequence
Denoising for sequential Recommendation (SSDRec) with a three-stage learning
paradigm to solve the above challenges. In the first stage, we empower SSDRec
by a global relation encoder to learn multi-faceted inter-sequence relations in
a data-driven manner. These relations serve as prior knowledge to guide
subsequent stages. In the second stage, we devise a self-augmentation module to
augment sequences to alleviate OUPs. Finally, we employ a hierarchical
denoising module in the third stage to reduce the risk of false augmentations
and pinpoint all noise in raw sequences. Extensive experiments on five
real-world datasets demonstrate the superiority of \model over state-of-the-art
denoising methods and its flexible applications to mainstream sequential
recommendation models. The source code is available at
https://github.com/zc-97/SSDRec.Comment: ICDE 202
CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network
We present a novel learning method to predict the cloth deformation for
skeleton-based characters with a two-stream network. The characters processed
in our approach are not limited to humans, and can be other skeletal-based
representations of non-human targets such as fish or pets. We use a novel
network architecture which consists of skeleton-based and mesh-based residual
networks to learn the coarse and wrinkle features as the overall residual from
the template cloth mesh. Our network is used to predict the deformation for
loose or tight-fitting clothing or dresses. We ensure that the memory footprint
of our network is low, and thereby result in reduced storage and computational
requirements. In practice, our prediction for a single cloth mesh for the
skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX
3090 GPU. Compared with prior methods, our network can generate fine
deformation results with details and wrinkles.Comment: 13 page
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Top-down instance segmentation framework has shown its superiority in object
detection compared to the bottom-up framework. While it is efficient in
addressing over-segmentation, top-down instance segmentation suffers from
over-crop problem. However, a complete segmentation mask is crucial for
biological image analysis as it delivers important morphological properties
such as shapes and volumes. In this paper, we propose a region proposal
rectification (RPR) module to address this challenging incomplete segmentation
problem. In particular, we offer a progressive ROIAlign module to introduce
neighbor information into a series of ROIs gradually. The ROI features are fed
into an attentive feed-forward network (FFN) for proposal box regression. With
additional neighbor information, the proposed RPR module shows significant
improvement in correction of region proposal locations and thereby exhibits
favorable instance segmentation performances on three biological image datasets
compared to state-of-the-art baseline methods. Experimental results demonstrate
that the proposed RPR module is effective in both anchor-based and anchor-free
top-down instance segmentation approaches, suggesting the proposed method can
be applied to general top-down instance segmentation of biological images. Code
is available
Associations of HLA-DP Variants with Hepatitis B Virus Infection in Southern and Northern Han Chinese Populations: A Multicenter Case-Control Study
) locus has been reported to be associated with hepatitis B virus (HBV) infection in populations of Japan and Thailand. We aimed to examine whether the association can be replicated in Han Chinese populations.â=â0.097âŒ0.697 and 0.198âŒ0.615 in northern Chinese population, respectively). loci were strongly associated with HBV infection in southern and northern Han Chinese populations, but not with HBV progression
Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial
Background
Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population.
Methods
AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged â„18 years) with a clinical diagnosis of acute stroke in the previous 2â15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921.
Findings
Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76â1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months.
Interpretation
Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke
Structural Biology of Nanobodies against the Spike Protein of SARS-CoV-2
Nanobodies are 130 amino acid single-domain antibodies (VHH) derived from the unique heavy-chain-only subclass of Camelid immunogloblins. Their small molecular size, facile expression, high affinity and stability have combined to make them unique targeting reagents with numerous applications in the biomedical sciences. The first nanobody agent has now entered the clinic as a treatment against a blood disorder. The spread of the SARS-CoV-2 virus has seen the global scientific endeavour work to accelerate the development of technologies to try to defeat a pandemic that has now killed over four million people. In a remarkably short period of time, multiple studies have reported nanobodies directed against the viral Spike protein. Several agents have been tested in culture and demonstrate potent neutralisation of the virus or pseudovirus. A few agents have completed animal trials with very encouraging results showing their potential for treating infection. Here, we discuss the structural features that guide the nanobody recognition of the receptor binding domain of the Spike protein of SARS-CoV-2
Chitosan-Based Materials: An Overview of Potential Applications in Food Packaging
Chitosan is a multifunctional biopolymer that is widely used in the food and medical fields because of its good antibacterial, antioxidant, and enzyme inhibiting activity and its degradability. The biological activity of chitosan as a new food preservation material has gradually become a hot research topic. This paper reviews recent research on the bioactive mechanism of chitosan and introduces strategies for modifying and applying chitosan for food preservation and different preservation techniques to explore the potential application value of active chitosan-based food packaging. Finally, issues and perspectives on the role of chitosan in enhancing the freshness of food products are presented to provide a theoretical basis and scientific reference for subsequent research
Combining of transcriptome and metabolome analyses for understanding the utilization and metabolic pathways of Xyloâoligosaccharide in Bifidobacterium adolescentis
A combination of transcriptome and metabolome analyses was applied to understand the utilization and metabolism of Xyloâoligosaccharide (XOS) in Bifidobacterium adolescentis 15703 as well as identifying the key regulatoryârelated genes and metabolites. Samples of cultures grown on either XOS or xylose were collected. The transcript and metabolite profiles were obtained from highâthroughput RNAâsequencing data analysis and UHPLC system. Compared with xylose, XOS highly promoted the growth of B. adolescentis 15703 and resulted in a growth yield about 1.5âfold greater than xylose. The transcriptome analysis showed that XOS could enhance genes, including ABC transporters, galactosidase, xylosidase, glucosidase, and amylase, which were involved in transport and metabolism of carbohydrate compared with xylose. Furthermore, the expression profile of 16 candidate genes using qRTâPCR has validated the accuracy of the RNAâseq data. Also, the metabolomic analyses, particularly those related to metabolic biomarkers of fatty acids, amino acids, and sugars showed a similar trend of result and approved the advantages of XOS as growth medium for B. adolescentis 15703 compared with xylose. The expression and abundance of specific genes and metabolites highlighted the complex regulatory mechanisms involved in utilization of XOS by B. adolescentis 15703. These results are useful in the understanding of the metabolic pathway of XOS in B. adolescentis 15703 and contribute to the optimization of XOS probiotic effects as a food additive
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