IB-UQ: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning

We propose a novel framework for uncertainty quantification via information bottleneck (IB-UQ) for scientific machine learning tasks, including deep neural network (DNN) regression and neural operator learning (DeepONet). Specifically, we incorporate the bottleneck by a confidence-aware encoder, which encodes inputs into latent representations according to the confidence of the input data belonging to the region where training data is located, and utilize a Gaussian decoder to predict means and variances of outputs conditional on representation variables. Furthermore, we propose a data augmentation based information bottleneck objective which can enhance the quantification quality of the extrapolation uncertainty, and the encoder and decoder can be both trained by minimizing a tractable variational bound of the objective. In comparison to uncertainty quantification (UQ) methods for scientific learning tasks that rely on Bayesian neural networks with Hamiltonian Monte Carlo posterior estimators, the model we propose is computationally efficient, particularly when dealing with large-scale data sets. The effectiveness of the IB-UQ model has been demonstrated through several representative examples, such as regression for discontinuous functions, real-world data set regression, learning nonlinear operators for partial differential equations, and a large-scale climate model. The experimental results indicate that the IB-UQ model can handle noisy data, generate robust predictions, and provide confident uncertainty evaluation for out-of-distribution data.Comment: 27 pages, 22figure

Molecular brewing: molecular structural effects involved in barley malting and mashing

Ten barley samples containing varied protein contents were subject to malting followed by mashing to investigate molecular effects of both barley starch and starch- protein interactions on malting and mashing performances, and the underlying mechanism. Starch granular changes were examined using differential scanning calorimetry and scanning electron microscopy. The molecular fine structures of amylose and amylopectin from unmalted and malted grain were obtained using size-exclusion chromatography. The results showed that both amylose and amylopectin polymers were hydrolyzed at the same time during malting. Protein and amylose content in both unmalted and malted barley significant negatively correlated with fermentable sugar content after mashing. While protein content is currently the main criterion for choosing malting varieties, this study shows that information about starch molecular structure is also useful for determining the release of fermentable sugars, an important functional property. This provides brewers with some new methods to choose malting barley

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning

Multimodal tasks in the fashion domain have significant potential for e-commerce, but involve challenging vision-and-language learning problems - e.g., retrieving a fashion item given a reference image plus text feedback from a user. Prior works on multimodal fashion tasks have either been limited by the data in individual benchmarks, or have leveraged generic vision-and-language pre-training but have not taken advantage of the characteristics of fashion data. Additionally, these works have mainly been restricted to multimodal understanding tasks. To address these gaps, we make two key contributions. First, we propose a novel fashion-specific pre-training framework based on weakly-supervised triplets constructed from fashion image-text pairs. We show the triplet-based tasks are an effective addition to standard multimodal pre-training tasks. Second, we propose a flexible decoder-based model architecture capable of both fashion retrieval and captioning tasks. Together, our model design and pre-training approach are competitive on a diverse set of fashion tasks, including cross-modal retrieval, image retrieval with text feedback, image captioning, relative image captioning, and multimodal categorization.Comment: 14 pages, 4 figures. To appear at Conference on Empirical Methods in Natural Language Processing (EMNLP) 202

A Scheme for Verification on Data Integrity in Mobile Multicloud Computing Environment

In order to verify the data integrity in mobile multicloud computing environment, a MMCDIV (mobile multicloud data integrity verification) scheme is proposed. First, the computability and nondegeneracy of verification can be obtained by adopting BLS (Boneh-Lynn-Shacham) short signature scheme. Second, communication overhead is reduced based on HVR (Homomorphic Verifiable Response) with random masking and sMHT (sequence-enforced Merkle hash tree) construction. Finally, considering the resource constraints of mobile devices, data integrity is verified by lightweight computing and low data transmission. The scheme improves shortage that mobile device communication and computing power are limited, it supports dynamic data operation in mobile multicloud environment, and data integrity can be verified without using direct source file block. Experimental results also demonstrate that this scheme can achieve a lower cost of computing and communications

Association of Base Excision Repair Gene Polymorphisms with ESRD Risk in a Chinese Population

The base excision repair (BER) pathway, containing OGG1, MTH1 and MUTYH, is a major protector from oxidative DNA damage in humans, while 8-oxoguanine (8-OHdG), an index of DNA oxidation, is increased in maintenance hemodialysis (HD) patients. Four polymorphisms of BER genes, OGG1 c.977C > G (rs1052133), MTH1 c.247G > A (rs4866), MUTYH c.972G > C (rs3219489), and AluYb8MUTYH (rs10527342), were examined in 337 HD patients and 404 healthy controls. And the 8-OHdG levels in leukocyte DNA were examined in 116 HD patients. The distribution of MUTYH c.972 GG or AluYb8MUTYH differed between the two groups and was associated with a moderately increased risk for end-stage renal disease (ESRD) (P = 0.013 and 0.034, resp.). The average 8-OHdG/106 dG value was significantly higher in patients with the OGG1 c.977G, MUTYH c.972G or AluYb8MUTYH alleles (P < 0.001 via ANOVA). Further analysis showed that combination of MUTYH c.972GG with OGG1 c.977GG or AluYb8MUTYH increased both the risk for ESRD and leukocyte DNA 8-OHdG levels in HD patients. Our study showed that MUTYH c.972GG, AluYb8MUTYH, and combination of OGG1 c.977GG increased the risk for ESRD development in China and suggested that DNA oxidative damage might be involved in such process

Toward Electrically Pumped Organic Lasers: A Review and Outlook on Material Developments and Resonator Architectures

Organic lasers have undergone decades of development. A myriad of materials with excellent optical gain properties, including small molecules, dendrimers, and polymers, have been demonstrated. Various resonator geometries have also been applied. While sharing the advantages of the solution processability and mechanical flexibility features of organic materials, organic optical gain media also offer interesting optical properties, such as emission tunability through chemical functionalization and inherent large optical gain coefficients. They offer prospects for different applications in the fields of bioimaging, medicine, chemo‐ and biosensing, anticounterfeit applications, or displays. However, the realization of electrically pumped organic lasers still remains a challenge due to the inherent drawbacks of organic semiconductors, e.g., modest carrier mobility, long‐lived excited‐state absorption, and extra losses which originate in the device (e.g., absorption from metal electrodes). Herein, the past developments of organic lasers are discussed, highlighting the importance of materials and cavities with regard to the goal of electrically pumped organic lasers. The latest progress and the possible ways to address the challenge are discussed

Assessment of global health risk of antibiotic resistance genes

Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health in the last decade. Many genes can confer resistance, but evaluating the relative health risks of ARGs is complex. Factors such as the abundance, propensity for lateral transmission and ability of ARGs to be expressed in pathogens are all important. Here, an analysis at the metagenomic level from various habitats (6 types of habitats, 4572 samples) detects 2561 ARGs that collectively conferred resistance to 24 classes of antibiotics. We quantitatively evaluate the health risk to humans, defined as the risk that ARGs will confound the clinical treatment for pathogens, of these 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. Our results demonstrate that 23.78% of the ARGs pose a health risk, especially those which confer multidrug resistance. We also calculate the antibiotic resistance risks of all samples in four main habitats, and with machine learning, successfully map the antibiotic resistance threats in global marine habitats with over 75% accuracy. Our novel method for quantitatively surveilling the health risk of ARGs will help to manage one of the most important threats to human and animal health

Clonal expansion in multiple Phyllosticta species causing citrus black spot or similar symptoms in China

SUPPLEMENTARY MATERIALS : FIGURE S1: Symptoms of citrus fruit spots from which the Phyllostitca isolates were obtained; FIGURES S2–S7: Maximum likelihood phylogeny of Phyllostica isolates related to citrus. S2. 194 isolates of ITS tree, S3. 183 isolates of actA tree, S4. 176 isolates of tef1 tree, S5. 166 isolates of gapdh tree, S6. 164 isolates of LSU tree, S7. 116 isolates of rpb2 tree; FIGURE S8. Column chart indicating the average lesion area produced each isolate of Phyllosticta spp. TABLE S1: Isolates information sequenced in this study; TABLE S2: GenBank Accession number of the isolates used for phylogenetic analysis in this study. TABLE S3: Datasets used and statistics resulting from phylogenetic analyses. TABLE S4: Comparison of morphology of two novel Phyllosticta species and their related sister species. TABLE S5: Nucleotide differences observed among P. paracitriasiana and P. citriasiana isolates used in this study. TABLE S6: Nucleotide differences observed among P. paracitrichinaensis and P. citrichinaensis isolates used in this study. TABLE S7: Fst values among provincial or/and host subpopulations of five Phyllostitca spp. in China.Phyllosticta spp. are important pathogens of citrus plants. Several Phyllosticta species associated with Citrus species grown in China have been reported; however, the relative prevalences of individual species and the distributions of their genotypes among host Citrus species remain largely unknown. In this study, we conducted an extensive survey of Phyllosticta species across 11 citrus-producing provinces in southern China. From fruits and leaves with black spots or blackspot- like symptoms, a total of 461 Phyllosticta strains were isolated. Based on molecular (ITS, actA, tef1, gapdh, LSU, and rpb2 sequences) and morphological data, the strains were systematically identified as belonging to five species: P. capitalensis, P. citrichinaensis, P. citriasiana, P. citricarpa, and P. paracitricarpa. To further understand intraspecific genetic diversity and relationships, strains of five species from different geographic and host sources were analyzed based on the multilocus sequence data. Our population genetic analyses revealed that all five Phyllosticta species on citrus showed evidence for clonal dispersals within and among geographic regions. In addition, pathogenicity tests using representative strains showed that all five species can cause disease on the tested Citrus spp. We discuss the implications of our results for the control and management of Citrus Black Spot and related diseases.The Key Research and Development Program of Zhejiang Province and the National Natural Science Foundation of China.https://www.mdpi.com/journal/jofam2024BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyNon

Detection of elevated levels of PINK1 in plasma from patients with idiopathic Parkinson’s disease

BackgroundsNumerous lines of evidence support the intricate interplay between Parkinson’s disease (PD) and the PINK1-dependent mitophagy process. This study aimed to evaluate differences in plasma PINK1 levels among idiopathic PD, PD syndromes (PDs), and healthy controls.MethodsA total of 354 participants were included, consisting of 197 PD patients, 50 PDs patients, and 107 healthy controls were divided into two cohorts, namely the modeling cohort (cohort 1) and the validated cohort (cohort 2). An enzyme-linked immunosorbent assay (ELISA)-based analysis was performed on PINK1 and α-synuclein oligomer (Asy-no). The utilization of the area under the curve (AUC) within the receiver-operating characteristic (ROC) curves served as a robust and comprehensive approach to evaluate and quantify the predictive efficacy of plasma biomarkers alone, as well as combined models, in distinguishing PD patients from controls.ResultsPINK1 and Asy-no were elevated in the plasma of PD and PDs patients compared to healthy controls. The AUCs of PINK1 (0.771) and Asy-no (0.787) were supposed to be potentially eligible plasma biomarkers differentiating PD from controls but could not differentiate PD from PDs. Notably, the PINK + Asy-no + Clinical RBD model showed the highest performance in the modeling cohort and was comparable with the PINK1 + Clinical RBD in the validation cohort. Moreover, there is no significant correlation between PINK1 and UPDRS, MMSE, HAMD, HAMA, RBDQ-HK, and ADL scores.ConclusionThese findings suggest that elevated PINK1 in plasma holds the potential to serve as a non-invasive tool for distinguishing PD patients from controls. Moreover, the outcomes of our investigation lend support to the plausibility of implementing a feasible blood test in future clinical translation