Population genetic structure and demographic history of small yellow croaker, Larimichthys polyactis (Bleeker, 1877), from coastal waters of China

Small yellow croaker, Larimichthys polyactis (Bleeker, 1877), a commercially important benthopelagic fish, is widely distributed in the Bohai, Yellow and East China Seas. To evaluate the population genetic structure and demographic history of L. polyactis, we sequenced the complete mitochondrial deoxyribonucleic acid (mtDNA) control region (798 to 801 bp) in 127 individuals sampled from seven localities throughout its distribution region in China. A total of 136 polymorphic sites were detected, which defined 125 haplotypes. High haplotype diversity (1.000 ± 0.013 to 1.000 ± 0.034) and moderate nucleotide diversity (0.0112 ± 0.0061 to 0.0141 ± 0.0075) were detected in the species. The neighbor-joining tree of haplotypes was assigned into two closely related clades, but did not appear to have any geographic genealogic structure. Hierarchical molecular variance analysis (AMOVA), pair wise FST comparisons and the nearest-neighbor statistic (Snn) showed no significant genetic differences among populations in the Bohai, Yellow and East China Seas. The demographic history of L. polyactis was examined by using neutrality tests and mismatch distribution analysis, which revealed that the species had undergone a Pleistocene population expansion. The results based on the complete mtDNA control region sequences analysis indicate that within its distribution range, L. polyactis constituted a panmictic mtDNA gene pool. Factors such as dispersal capacity, ocean currents and insufficient evolution time could be responsible for the lack of population genetic differentiation in L. polyactis.Keywords: Larimichthys polyactis, mitochondrial control region, population genetic structure, demographi

Learning to Correct Noisy Labels for Fine-Grained Entity Typing via Co-Prediction Prompt Tuning

Fine-grained entity typing (FET) is an essential task in natural language processing that aims to assign semantic types to entities in text. However, FET poses a major challenge known as the noise labeling problem, whereby current methods rely on estimating noise distribution to identify noisy labels but are confused by diverse noise distribution deviation. To address this limitation, we introduce Co-Prediction Prompt Tuning for noise correction in FET, which leverages multiple prediction results to identify and correct noisy labels. Specifically, we integrate prediction results to recall labeled labels and utilize a differentiated margin to identify inaccurate labels. Moreover, we design an optimization objective concerning divergent co-predictions during fine-tuning, ensuring that the model captures sufficient information and maintains robustness in noise identification. Experimental results on three widely-used FET datasets demonstrate that our noise correction approach significantly enhances the quality of various types of training samples, including those annotated using distant supervision, ChatGPT, and crowdsourcing.Comment: Accepted by Findings of EMNLP 2023, 11 page

A Boundary Offset Prediction Network for Named Entity Recognition

Named entity recognition (NER) is a fundamental task in natural language processing that aims to identify and classify named entities in text. However, span-based methods for NER typically assign entity types to text spans, resulting in an imbalanced sample space and neglecting the connections between non-entity and entity spans. To address these issues, we propose a novel approach for NER, named the Boundary Offset Prediction Network (BOPN), which predicts the boundary offsets between candidate spans and their nearest entity spans. By leveraging the guiding semantics of boundary offsets, BOPN establishes connections between non-entity and entity spans, enabling non-entity spans to function as additional positive samples for entity detection. Furthermore, our method integrates entity type and span representations to generate type-aware boundary offsets instead of using entity types as detection targets. We conduct experiments on eight widely-used NER datasets, and the results demonstrate that our proposed BOPN outperforms previous state-of-the-art methods.Comment: Accepted by Findings of EMNLP 2023, 13 page

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate

Effect of Pulsed Magnetic Field on Spark Plasma Sintering of Iron-Based Powders

Iron-based powders were sintered by spark plasma sintering coupled with different pulsed magnetic field strength ranging from 0 to 3.93MAm1. The effects of pulsed magnetic field on the sintering behavior of the powders as well as the microstructure and mechanical properties of sintered alloys were investigated. The results showed that the sintering temperature field on the cross section of sample was more uniform via coupling a pulsed magnetic field. The density, hardness and bending strength of the alloy sintered by coupling an appropriate pulsed magnetic field, arose to 7.75 gcm3, 55 HRC and 1235MPa, respectively. There was no remarkable change of sintered density with a further increase of pulsed magnetic field strength, while the hardness and bending strength of sintered alloys adversely decreased. The roles of pulsed magnetic field coupled with electric field are explained to accelerate the diffusion and reaction of alloying elements by raising sintering temperature, facilitate powders rearrangement, intensify sparking among powders, improve the growth of sintering neck and the formation of new sintering neck, and reduce the sintering temperature gradient on cross section. [doi:10.2320/matertrans.M2010057

Nanoparticle-Based RNAi Therapeutics Targeting Cancer Stem Cells: Update and Prospective

Cancer stem cells (CSCs) are characterized by intrinsic self-renewal and tumorigenic properties, and play important roles in tumor initiation, progression, and resistance to diverse forms of anticancer therapy. Accordingly, targeting signaling pathways that are critical for CSC maintenance and biofunctions, including the Wnt, Notch, Hippo, and Hedgehog signaling cascades, remains a promising therapeutic strategy in multiple cancer types. Furthermore, advances in various cancer omics approaches have largely increased our knowledge of the molecular basis of CSCs, and provided numerous novel targets for anticancer therapy. However, the majority of recently identified targets remain ‘undruggable’ through small-molecule agents, whereas the implications of exogenous RNA interference (RNAi, including siRNA and miRNA) may make it possible to translate our knowledge into therapeutics in a timely manner. With the recent advances of nanomedicine, in vivo delivery of RNAi using elaborate nanoparticles can potently overcome the intrinsic limitations of RNAi alone, as it is rapidly degraded and has unpredictable off-target side effects. Herein, we present an update on the development of RNAi-delivering nanoplatforms in CSC-targeted anticancer therapy and discuss their potential implications in clinical trials

Fiber-Optic Gyroscope Thermal Calibration through Two-Dimensional N-Order Polynomial for Landslide Displacement Monitoring

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring

Fiber-Optic Gyroscope Thermal Calibration through Two-Dimensional N-Order Polynomial for Landslide Displacement Monitoring

A fiber-optic gyroscope (FOG) with lower precision but higher cost advantage is typically selected according to working conditions and engineering budget. Thermal drift is the main factor affecting FOG precision. External thermal calibration methods by algorithms can effectively weaken the influence of thermal drift. This paper presents a thermal calibration method of a two-dimensional N-order polynomial (TDNP) and compares it with artificial neural network (ANN) methods to determine a software FOG thermal calibration method for landslide displacement monitoring. The TDNP thermal calibration coefficient matrix was established, and the thermal calibration capability of the TDNP method with different orders N was evaluated on the basis of error analysis. The ANN model with 1 to 18 hidden neural layers was established on the basis of LM, BR, and SCG algorithms to choose a suitable ANN. Finally, the mean absolute errors of FOG thermal calibration through the TDNP with different orders and the LM were compared. This method was applied in the Huangtupo landslide area, China. The results highlight that the TDNP method with order 5 had better performance and satisfied the requirements of landslide displacement monitoring. The research results can compensate for the lack of adaptability of the FOG thermal calibration method in landslide displacement monitoring