Effects of Saline and Alkaline Stresses on Growth and Physiological Changes in Oat (Avena sativa L.) Seedlings

Two neutral salts (NaCl and Na2SO4) and alkaline salts (NaHCO3 and Na2CO3) were both mixed in 2:1 ratio, and the effects of saline and alkaline stresses on growth and physiological changes in oat seedlings were explored. The result showed that biomass, water content and chlorophyll content decreased while cell membrane permeability significantly increased under alkaline stress. Saline stress did not have an obvious effect on pH value in tissue fluids of shoot and root, but alkaline stress increased pH value in the root tissue fluid. The contents of Na+, Na+/K+, SO42- increased more, and K+, NO3-, H2PO4- decreased more under alkaline stress, the Cl- content increased obviously under saline stress but had little change under alkaline stress. The increments of proline and organic acid were both greater under alkaline stress, but organic acid content kept the same level under saline stress. Alkaline stress caused more harmful effects on growth and physiological changes in oat seedlings especially broke the pH stability in the root tissue fluid. Physiological adaptive mechanisms of oat seedlings under saline stress and alkaline stress were different, which mainly took the way of accumulating organic acid under alkali stress but accumulating Cl- under saline stress

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.publishedVersio

Origin-Destination Travel Time Oracle for Map-based Services

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.Comment: 15 pages, 12 figures, accepted by SIGMOD International Conference on Management of Data 202

Skill-Based Few-Shot Selection for In-Context Learning

In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each test instance separately -- is important for in-context learning. In this paper, we propose Skill-KNN, a skill-based few-shot selection method for in-context learning. The key advantages of Skill-KNN include: (1) it addresses the problem that existing methods based on pre-trained embeddings can be easily biased by surface natural language features that are not important for the target task; (2) it does not require training or fine-tuning of any models, making it suitable for frequently expanding or changing example banks. The key insight is to optimize the inputs fed into the embedding model, rather than tuning the model itself. Technically, Skill-KNN generates the skill-based descriptions for each test case and candidate example by utilizing a pre-processing few-shot prompting, thus eliminating unimportant surface features. Experimental results across five cross-domain semantic parsing datasets and six backbone models show that Skill-KNN significantly outperforms existing methods.Comment: Accepted by EMNLP 2023 main conferenc

The clinical value of progestin-primed ovarian stimulation protocol for women with diminished ovarian reserve undergoing IVF/ICSI: a systematic review and meta-analysis

BackgroundTo determine whether progestin-primed ovarian stimulation (PPOS) is more effective for women with diminished ovarian reserve (DOR) than clomiphene citrate (CC)/letrozole (LE) plus gonadotropin in IVF or ICSI treatment.MethodsNine databases were searched until May 24, 2023, to identify relevant studies. Forest plots were used to present the results of this meta-analysis. Begg’s and Egger’s tests were applied to estimate publication bias. Subgroup and sensitivity analysis were performed to check the potential sources of heterogeneity and verify the robustness of the pooled results, respectively.ResultsA total of 14 studies with 4182 participants were included for meta-analysis. There was evidence of a statistically notable increase in clinical pregnancy rate (OR = 1.39, 95%CI [1.01, 1.91], p = 0.05), optimal embryos rate (OR = 1.50, 95%CI [1.20, 1.88], p = 0.0004), and cumulative pregnancy rate (OR = 1.73, 95%CI [1.14, 2.60], p = 0.009), the duration and the amount of gonadotropin required (MD = 1.56, 95%CI [0.47, 2.66], p = 0.005; SMD = 1.51, 95%CI [0.90, 2.12], p < 0.00001), along with decrease cycle cancellation rate (OR = 0.78, 95%CI [0.64, 0.95], p = 0.02), luteinizing hormone (LH) level on the day of hCG (SMD = -0.81, 95%CI [-1.10, -0.53], p < 0.00001), and premature LH surge rate (OR = 0.10, 95%CI [0.07, 0.15], p < 0.00001) when PPOS was used. No evidence for publication bias within results was revealed.ConclusionsBased on evidence-based results, PPOS protocol seems to improve IVF/ICSI outcomes for women with DOR. More research with larger sample sizes and rigorous designs are required to further explore the value of PPOS among women diagnosed with DOR.Systematic review registrationwww.crd.york.ac.uk, identifier CRD42023430202

Genetic Mapping of Head Size Related Traits in Common Carp (Cyprinus carpio)

Head size is important economic trait for many aquaculture fish which is directly linked to their carcass yield. The genetic basis of head size trait remains unclear in many widely cultured fish species. Common carp (Cyprinus carpio) is one of the most widely studied fish due to its importance on both economic and environmental aspects. In this study, we performed genome-wide association study using 433 Yellow River carp individuals from multiple families to identify loci and genes potentially associated with head size related traits including head length (HL), head length/body length ratio (HBR), eye diameter (ED), and eye cross (EC). QTL mapping was utilized to filter the effects of population stratification and improve power for the candidates identification in the largest surveyed family with a published genetic linkage map. Twelve SNPs showed significant for head size traits in GWAS and 18 QTLs were identified in QTL mapping. Our study combining both GWAS and QTL mapping could compensate the deficiency from each other and advance our understanding of head size traits in common carp. To acquire a better understanding of the correlation between head size and body growth, we also performed comparisons between QTLs of head size traits and growth-related traits. Candidate genes underlying head size traits were identified surrounding the significant SNPs, including parvalbumin, srpk2, fsrp5, igf1, igf3, grb10, igf1r, notch2, sfrp2. Many of these genes have been identified with potential functions on bone formation and growth. Igf1 was a putative gene associated with both head size and body growth in Yellow River carp. The teleost-specific igf3 was a candidate head size related gene, related to both HL and HBR. Our study also indicated the importance of Igf signaling pathway for both growth and head size determination in common carp, which could be potentially used in future selective breeding in common carp as well as other species