68 research outputs found
One In-Situ Extraction Algorithm for Monitoring Bunch-by-Bunch Profile in the Storage Ring
As the brightness of synchrotron radiation (SR) light sources improves, the
operation stability of light sources is weakened. To explore various beam
instability related issues in light sources, one transverse beam diagnostics
system for bunch-by-bunch (BbB) profile measurement has been established at
Hefei Light Source-II (HLS-II). In this paper, one in-situ extraction algorithm
in the data processing backend of the system is developed for BbB profiles, so
as to provide important beam information of the machine operation in time.Comment: Accepted by the International Conference on Optical Communication and
Optical Information Processing (OCOIP 2023
Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillation
Traditional computer vision models are trained to predict a fixed set of
predefined categories. Recently, natural language has been shown to be a
broader and richer source of supervision that provides finer descriptions to
visual concepts than supervised "gold" labels. Previous works, such as CLIP,
use InfoNCE loss to train a model to predict the pairing between images and
text captions. CLIP, however, is data hungry and requires more than 400M
image-text pairs for training. The inefficiency can be partially attributed to
the fact that the image-text pairs are noisy. To address this, we propose OTTER
(Optimal TransporT distillation for Efficient zero-shot Recognition), which
uses online entropic optimal transport to find a soft image-text match as
labels for contrastive learning. Based on pretrained image and text encoders,
models trained with OTTER achieve strong performance with only 3M image text
pairs. Compared with InfoNCE loss, label smoothing, and knowledge distillation,
OTTER consistently outperforms these baselines in zero shot evaluation on
Google Open Images (19,958 classes) and multi-labeled ImageNet 10K (10032
classes) from Tencent ML-Images. Over 42 evaluations on 7 different
dataset/architecture settings x 6 metrics, OTTER outperforms (32) or ties (2)
all baselines in 34 of them.Comment: 19 pages, 6 figure
Correlation of LC3 and the recruitment of dendritic cell and the formation of TLS in colorectal cancer and its clinical significance
Background and purpose: It has been recognized as a complex problem in tumor therapy to deal with the tumor immune escape, and over-activated autophagy can cause the increase of tumor surface antigen, which participates in anti-tumor immunity. In this study, the expressions of microtubule-associated protein light chain 3 (LC3), mature dendritic cell (mDC) and the formation of tertiary lymphoid structure (TLS), an essential autophagy factor in colorectal cancer, were detected in clinical samples. The results had important clinical implications and provided new insights for enhancing anti-tumor immunity. Methods: Immunohistochemical EnVision method was used to detect the expressions of LC3, DC-lamp and the formation of TLS in cancer tissues and normal mucosal tissues of 179 patients with T2 stage high-risk and T3 stage colorectal cancer who underwent surgical resection at Binzhou Medical University Hospital from January 2016 to June 2017. Western blot and real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) were used to detect the expressions of LC3, NY-ESO-2, lymphotoxin-beta (LTβ), CC chemokine ligand 21 (CCL21), CXC chemokine ligand 13 (CXCL13) and interleukin-17 (IL-17) in TLS+ and TLS- colorectal cancer tissues. Then the correlation and clinical significance were analyzed. Log-rank test was used to compare the prognostic differences between groups, and COX proportional risk regression model was used for multivariate survival analysis. Results: Clinical samples showed that the expressions of LC3 and DC-lamp were higher in colorectal cancer tissues than in normal mucosa tissues (P<0.05), and the expressions of LC3 and DC-lamp were positively correlated (P<0.05). The protein and mRNA expressions of LC3, NY-ESO-2, LTβ, CXCL13 and CCL21 were higher in TLS+ group than in TLS- group. The expression of IL-17 was higher in the TLS- group than in the TLS+ group (P<0.05). The expression of LC3 was positively correlated with TLS/germinal center (GC)+ and TLS/GC- subtypes and positively correlated with the expression of NY-ESO-2, LTβ, CXCL13 and CCL21 (P<0.05). The expression of DC-lamp was higher in TLS/GC+ and TLS/GC- subtype groups than in the other two subgroups (P<0.05), and there was a positive correlation. Kaplan-Meier and COX regression models showed that LC3, DC-lamp, TLS and lymph node metastasis were closely related to the prognosis of patients with colorectal cancer, and they were independent risk factors for the prognosis of colorectal cancer. Conclusion: The abnormal expression of LC3 in colorectal cancer can activate mDC to recruit lymphocytes and promote the expression and maturation of TLS, ultimately affecting the prognosis of patients
Fast Model Debias with Machine Unlearning
Recent discoveries have revealed that deep neural networks might behave in a
biased manner in many real-world scenarios. For instance, deep networks trained
on a large-scale face recognition dataset CelebA tend to predict blonde hair
for females and black hair for males. Such biases not only jeopardize the
robustness of models but also perpetuate and amplify social biases, which is
especially concerning for automated decision-making processes in healthcare,
recruitment, etc., as they could exacerbate unfair economic and social
inequalities among different groups. Existing debiasing methods suffer from
high costs in bias labeling or model re-training, while also exhibiting a
deficiency in terms of elucidating the origins of biases within the model. To
this respect, we propose a fast model debiasing framework (FMD) which offers an
efficient approach to identify, evaluate and remove biases inherent in trained
models. The FMD identifies biased attributes through an explicit counterfactual
concept and quantifies the influence of data samples with influence functions.
Moreover, we design a machine unlearning-based strategy to efficiently and
effectively remove the bias in a trained model with a small counterfactual
dataset. Experiments on the Colored MNIST, CelebA, and Adult Income datasets
along with experiments with large language models demonstrate that our method
achieves superior or competing accuracies compared with state-of-the-art
methods while attaining significantly fewer biases and requiring much less
debiasing cost. Notably, our method requires only a small external dataset and
updating a minimal amount of model parameters, without the requirement of
access to training data that may be too large or unavailable in practice
Towards Distribution-Agnostic Generalized Category Discovery
Data imbalance and open-ended distribution are two intrinsic characteristics
of the real visual world. Though encouraging progress has been made in tackling
each challenge separately, few works dedicated to combining them towards
real-world scenarios. While several previous works have focused on classifying
close-set samples and detecting open-set samples during testing, it's still
essential to be able to classify unknown subjects as human beings. In this
paper, we formally define a more realistic task as distribution-agnostic
generalized category discovery (DA-GCD): generating fine-grained predictions
for both close- and open-set classes in a long-tailed open-world setting. To
tackle the challenging problem, we propose a Self-Balanced Co-Advice
contrastive framework (BaCon), which consists of a contrastive-learning branch
and a pseudo-labeling branch, working collaboratively to provide interactive
supervision to resolve the DA-GCD task. In particular, the contrastive-learning
branch provides reliable distribution estimation to regularize the predictions
of the pseudo-labeling branch, which in turn guides contrastive learning
through self-balanced knowledge transfer and a proposed novel contrastive loss.
We compare BaCon with state-of-the-art methods from two closely related fields:
imbalanced semi-supervised learning and generalized category discovery. The
effectiveness of BaCon is demonstrated with superior performance over all
baselines and comprehensive analysis across various datasets. Our code is
publicly available.Comment: Accepted at NeurIPS 202
68Ga-PSMA-11 PET/CT versus 68Ga-PSMA-11 PET/MRI for the detection of biochemically recurrent prostate cancer: a systematic review and meta-analysis
PurposeOur aim was to conduct a meta-analysis and systematic review in order to compare the diagnostic efficacy of 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI in patients with biochemically recurrent after radical prostatectomy and biochemically recurrent prostate cancers (BCR) after hybrid RT and RP.MethodsUp until February 2023, we searched PubMed, Embase, and Web of Science for pertinent papers. Studies examining the utility of 68Ga-PSMA-11 PET/CT or PET/MRI as a screening tool for biochemically recurrent prostate cancer were included. To measure heterogeneity, we employed the I2 statistic. In cases of substantial heterogeneity (I2 > 50%), we used the random effect model to produce a forest plot. In other cases, we utilized the fixed model. Furthermore, we assessed the quality of the studies included using the Quality Assessment of Diagnostic Performance Studies (QUADAS-2) method.ResultsIn total, 37 studies involving 8409 patients were examined. For 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI, the combined total detection rate was 0.70 (95% CI: 0.65-0.75) and 0.71 (95% CI:0.67-0.75), respectively. 68Ga-PSMA-11 PET/CT and 68Ga-PSMA-11 PET/MRI did not substantially differ in terms of the overall detection rate for BCR (P = 0.58). The detection rate was unaffected by the PSA values (all P > 0.05).ConclusionThe diagnostic efficacy of 68Ga-PSMA-11 PET/CT appears to be equivalent to that of 68Ga-PSMA-11 PET/MRI in detecting biochemically recurrent prostate cancer. Nonetheless, it should be noted that not all studies have used pathological biopsies as the gold standard. Therefore, additional larger prospective studies are needed to address this issue.Systematic review registrationidentifier CRD42023410039
Use of Hypoxia Combined with Acid Stress to Enrich GABA in Adzuki /Mung Beans, and Optimization of GABA-rich Sprouted Bean/Rice Mixture Processing Conditions
In order to study the effect of hypoxia combined with acid stress on GABA enrichment in adzuki bean and mung bean, germination time, germination temperature, hypoxia time and L-glutamic acid concentration were investigated by single factor. Based on the stress conditions identified for high GABA sprouted beans, GABA-enriched adzuki and mung beans were mixed with rice, and D-mixture design was used to optimize processing conditions for these sprouted bean/rice mixtures. Results showed that hypoxia combined with acid stress promoted GABA enrichment in adzuki and mung beans. In sprouted adzuki beans, GABA reached levels as high as 158.32±3.24 mg/100 g under the germination time of 48 h, germination temperature of 40 ℃, 15 h exposure to hypoxia, and L-glutamic acid concentration of 2.5 mg/mL. Mung bean stress conditions were: Germination time of 24 h, germination temperature of 35 ℃, 15 h exposure to hypoxia, and L-glutamic acid concentration of 2.5 mg/mL, the content of GABA was 141.57±4.35 mg/100 g. On this basis, the sprouted beans and rice formulation was optimized by D-mixture design as follows: Rice 76%, sprouted mung beans 11%, and sprouted adzuki beans 13%. Under this composition, the GABA content of sprouted bean/rice mixture was 23.73±1.03 mg/100 g, and the average sensory score was 88.76±2.47. The taste, color, and aroma of the sprouted bean/rice mixture fell within acceptable ranges, and the active ingredient GABA was enriched, enhancing the nutritional and functional properties of sprouted beans/rice. This study provided a theoretical reference for the further development of rice/grain mixtures
Shewanella Phage Encoding a Putative Anti-crispr-Like Gene Represents a Novel Potential Viral Family
Shewanella is a prevalent bacterial genus in deep-sea environments including marine sediments, exhibiting diverse metabolic capabilities that indicate its significant contributions to the marine biogeochemical cycles. However, only a few Shewanella phages were isolated and deposited in the NCBI database. In this study, we report the isolation and characterization of a novel Shewanella phage, vB_SbaS_Y11, that infects Shewanella KR11 and was isolated from the sewage in Qingdao, China. Transmission electron microscopy revealed that vB_SbaS_Y11 has an icosahedral head and a long tail. The genome of vB_SbaS_Y11 is a linear, double-stranded DNA with a length of 62,799 bp and a G+C content of 46.9%, encoding 71 putative open reading frames. No tRNA genes or integrase-related feature genes were identified. An uncharacterized anti-CRISPR AcrVA2 gene was detected in its genome. Phylogenetic analysis based on the amino acid sequences of whole genomes and comparative genomic analyses indicate that vB_SbaS_Y11 has a novel genomic architecture and shares low similarity to Pseudomonas virus H66 and Pseudomonas phage F116. vB_SbaS_Y11 represents a potential new family-level virus cluster with eight metagenomic assembled viral genomes named Ranviridae. IMPORTANCE
The Gram-negative Shewanella bacterial genus currently includes about 80 species of mostly aquatic Gammaproteobacteria, which were isolated around the globe in a multitude of environments, such as freshwater, seawater, coastal sediments, and the deepest trenches. Here, we present a Shewanella phage vB_SbaS_Y11 that contains an uncharacterized anti-CRISPR AcrVA2 gene and belongs to a potential virus family, Ranviridae. This study will enhance the knowledge about the genome, diversity, taxonomic classification, and global distribution of Shewanella phage populations
State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event
The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field
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