American silk moth, Antheraea polyphemus Cramer 1775 (Lepidoptera: Saturniidae), native to North America, has potential significance in sericulture for food consumption and silk production. To date, the phylogenetic relationship and divergence time of A. polyphemus with its Asian relatives remain unknown. To end these issues, two mitochondrial genomes (mitogenomes) of A. polyphemus from the USA and Canada respectively were determined. The mitogenomes of A. polyphemus from the USA and Canada were 15,346 and 15,345 bp in size, respectively, with only two transitions and five indels. The two mitogenomes both encoded typical mitochondrial 37 genes. No tandem repeat elements were identified in the A+T-rich region of A. polyphemus. The mitogenome-based phylogenetic analyses supported the placement of A. polyphemus within the genus Antheraea, and revealed the presence of two clades for eight Antheraea species used: one included A. polyphemus, A. assamensis Helfer, A. formosana Sonan and the other contained A. mylitta Drury, A. frithi Bouvier, A. yamamai Guérin-Méneville, A. proylei Jolly, and A. pernyi Guérin-Méneville. Mitogenome-based divergence time estimation further suggested that the dispersal of A. polyphemus from Asia into North America might have occurred during the Miocene Epoch (18.18 million years ago) across the Berling land bridge. This study reports the mitogenome of A. polyphemus that provides new insights into the phylogenetic relationship among Antheraea species and the origin of A. polyphemus.

Previous studies have reported comorbidities of autoimmune thyroid disorders (AITD), including Hashimoto’s disease (HT) and Graves’ disease (GD), and celiac disease (CeD), as well as the possible beneficial effects of a gluten-free diet (GFD) on AITD. Nonetheless, it remains uncertain whether there is a genetic causal relationship between AITD and CeD, while the beneficial effects on a GFD are controversial. This study aim to explore the causal relationship between CeD and AITD, particularly with HT, and to determine whether a GFD is beneficial for AITD. We performed a two-sample Mendelian Randomization (MR) analysis on data from the largest meta-analysis summary statistics of AITD, CeD and GFD. Genetic instrumental variables were established by pinpointing single nucleotide polymorphisms (SNPs) that relate to corresponding factors. In assessing sensitivity and heterogeneity, we conducted examinations of MR Egger, weighted median, simple mode, weighted mode, and MR Egger intercept tests. HT was found to play a pathogenic role in increasing the risk of CeD (ORIVW = 1.544 [95%CI 1.153-2.068], p = 0.00355), and our Mendelian randomization study does not support genetic liability related to CeD with GD (Graves’ disease) and GFD with AITD. This study supports the positive correlation between HT risk and CeD risk, while GFD has no protective effect on AITD and may exert its effect through other mechanisms. These findings provide valuable insights into potential targets for disease intervention and treatment at the genetic level.

Automatic precision herbicide application offers significant potential for reducing herbicide use in turfgrass weed management. However, developing accurate and reliable neural network models is crucial for achieving optimal precision weed control. The reported neural network models in previous research have been limited by specific geographic regions, weed species, and turfgrass management practices, restricting their broader applicability. The objective of this research was to evaluate the feasibility of deploying a single, robust model for weed classification across a diverse range of weed species, considering variations in species, ecotypes, densities, and growth stages in bermudagrass turfgrass systems across different regions in both China and the United States. Among the models tested, ResNeXt152 emerged as the top performer, demonstrating strong weed detection capabilities across 24 geographic locations and effectively identifying 14 weed species under varied conditions. Notably, the ResNeXt152 model achieved an F1 score and recall exceeding 0.99 across multiple testing scenarios, with a Matthews correlation coefficient (MCC) value surpassing 0.98, indicating its high effectiveness and reliability. These findings suggest that a single neural network model can reliably detect a wide range of weed species in diverse turf regimes, significantly reducing the costs associated with model training and confirming the feasibility of using one model for precision weed control across different turf settings and broad geographic regions.

Manganese (Mn) is a crucial trace element that actively participates in a diverse array of physiological processes. Mn is maintained at appropriate levels in the body by absorption and excretion by the body. Dysregulation of Mn homeostasis can lead to a variety of diseases, especially the accumulation of Mn in the brain, resulting in toxic side effects. We reviewed the metabolism and distribution of Mn at multiple levels, including organ, cellular and sub-cell levels. Mitochondria are the main sites of Mn metabolism and energy conversion in cells. Enhanced Mn superoxide dismutase activity reduces mitochondrial oxidative stress and inhibits cancer development. In addition, Mn enhances anti-cancer immune responses through the cGAS–STING pathway. We introduced various delivery vectors for Mn delivery to cancer sites for Mn supplementation and anti-cancer immunity. This review aims to provide new research perspectives for the application of Mn in the prevention and treatment of human diseases, especially by enhancing anti-cancer immune responses to inhibit cancer progression.

Objectives/Goals: Transmission-blocking vaccines hold promise for malaria elimination by reducing community transmission. But a major challenge that limits the development of efficacious vaccines is the vast parasite’s genetic diversity. This work aims to assess the genetic diversity of the Pfs25 vaccine candidate in complex infections across African countries. Methods/Study Population: We employed next-generation amplicon deep sequencing to identify nonsynonymous single nucleotide polymorphisms (SNPs) in 194 Plasmodium falciparum samples from four endemic African countries: Senegal, Tanzania, Ghana, and Burkina Faso. The individuals aged between 1 and 74 years, but most of them ranged from 1 to 19 years, and all presented symptomatic P. falciparum infection. The genome amplicon sequencing was analyzed using Geneious software and P. falciparum 3D7 as a reference. The SPNs were called with a minimum coverage of 500bp, and for this work, we used a very sensitive threshold of 1% variant frequency to determine the frequency of SNPs. The identified SNPs were threaded to the crystal structure of the Pfs25 protein, which allowed us to predict the impact of the novel SNP in the protein or antibody binding. Results/Anticipated Results: We identified 26 SNPs including 24 novel variants, and assessed their population prevalence and variant frequency in complex infections. Notably, five variants were detected in multiple samples (L63V, V143I, S39G, L63P, and E59G), while the remaining 21 were rare variants found in individual samples. Analysis of country-specific prevalence showed varying proportions of mutant alleles, with Ghana exhibiting the highest prevalence (44.6%), followed by Tanzania (12%), Senegal (11.8%), and Burkina Faso (2.7%). Moreover, we categorized SNPs based on their frequency, identifying dominant variants (>25%), and rare variants (Discussion/Significance of Impact: We identified additional SNPs in the Pfs25 gene beyond those previously reported. However, the majority of these newly discovered display low variant frequency and population prevalence. Further research exploring the functional implications of these variations will be important to elucidate their role in malaria transmission.

We show theoretically and empirically that the cross-section of stock return idiosyncratic volatilities contains useful information about the ICAPM. We construct a proxy cross-sectional bivariate idiosyncratic volatility (CBIV) for the covariance risk between the market and the unobserved hedge portfolio under the ICAPM. Consistent with the ICAPM pricing relation, CBIV is a robust and significant predictor of the equity risk premium. We further show that the return predictability of the tail index in Kelly and Jiang (2014) can be explained by the ICAPM covariance risk.

Recent studies utilizing AI-driven speech-based Alzheimer’s disease (AD) detection have achieved remarkable success in detecting AD dementia through the analysis of audio and text data. However, detecting AD at an early stage of mild cognitive impairment (MCI), remains a challenging task, due to the lack of sufficient training data and imbalanced diagnostic labels. Motivated by recent advanced developments in Generative AI (GAI) and Large Language Models (LLMs), we propose an LLM-based data generation framework, leveraging prior knowledge encoded in LLMs to generate new data samples. Our novel LLM generation framework introduces two novel data generation strategies, namely, the cross-lingual and the counterfactual data generation, facilitating out-of-distribution learning over new data samples to reduce biases in MCI label prediction due to the systematic underrepresentation of MCI subjects in the AD speech dataset. The results have demonstrated that our proposed framework significantly improves MCI Detection Sensitivity and F1-score on average by a maximum of 38% and 31%, respectively. Furthermore, key speech markers in predicting MCI before and after LLM-based data generation have been identified to enhance our understanding of how the novel data generation approach contributes to the reduction of MCI label prediction biases, shedding new light on speech-based MCI detection under low data resource constraint. Our proposed methodology offers a generalized data generation framework for improving downstream prediction tasks in cases where limited and/or imbalanced data have presented significant challenges to AI-driven health decision-making. Future study can focus on incorporating more datasets and exploiting more acoustic features for speech-based MCI detection.

Xiangranggounan is an intensively occupied settlement associated with the Kayue culture on the north-eastern Qinghai-Tibet Plateau. Excavations in 2022 and 2023 revealed five house types with clear stratigraphic relationships that help to expand current understanding of the evolution of prehistoric settlement patterns in harsh plateau environments.

The collapse of an initially spherical cavitation bubble near a free surface leads to the formation of two jets: a downward jet into the liquid, and an upward jet penetrating the free surface. In this study, we examine the surprising interaction of a bubble trapped in a stable cavitating vortex ring approaching a free surface. As a result, a single fast and tall liquid jet forms. We find that this jet is observed only above critical Froude numbers ($Fr$) and Weber numbers ($We$) when ${Fr}^2 (1.6-2.73/{We}) > 1$, illustrating the importance of inertia, gravity and surface tension in accelerating this novel jet and thereby reaching heights several hundred times the radius of the vortex ring. Our experimental results are supported by numerical simulations, revealing that the underlying mechanism driving the vortex ring acceleration is the disruption of the equilibrium of high-pressure regions at the front and rear of the vortex ring caused by the free surface. Quantitative analysis based on the energy relationships elucidates that the velocity ratio between the maximum velocity of the free-surface jet and the translational velocity of the vortex ring is relatively stable yet is attenuated by surface tension when the jet is mild.

In this study, nine isonitrogenous experimental diets containing graded levels of carbohydrates (40 g/kg, 80 g/kg and 120 g/kg) and crude lipids (80 g/kg, 120 g/kg and 160 g/kg) were formulated in a two-factor (3 × 3) orthogonal design. A total of 945 mandarin fish with similar body weights were randomly assigned to twenty-seven tanks, and the experiment diets were fed to triplicate tanks twice daily for 10 weeks. Results showed that different dietary treatments did not significantly affect the survival rate and growth performance of mandarin fish. However, high dietary lipid and carbohydrate levels significantly decreased the protein content of the whole body and muscle of cultured fish. The lipid content of the whole body, liver and muscle all significantly increased with increasing levels of dietary lipid, while only liver lipid level was significantly affected by dietary carbohydrate level. Hepatic glycogen content increased significantly with increasing dietary carbohydrate levels. As to liver antioxidant capacity, malondialdehyde content increased significantly with increasing dietary lipid or carbohydrate content, and catalase activity showed an opposite trend. Superoxide dismutase activity increased significantly with increasing levels of dietary lipid but decreased first and then increased with increasing dietary carbohydrate levels. Additionally, the increase in both dietary lipid and carbohydrate levels resulted in a significant reduction in muscle hardness. Muscle chewiness, gumminess and shear force were only affected by dietary lipid levels and decreased significantly with increasing dietary lipid levels. In conclusion, considering all the results, the appropriate dietary lipids and carbohydrate levels for mandarin fish were 120 g/kg and 80 g/kg, respectively.

Robot pick-and-place for unknown objects is still a very challenging research topic. This paper proposes a multi-modal learning method for robot one-shot imitation of pick-and-place tasks. This method aims to enhance the generality of industrial robots while reducing the amount of data and training costs the one-shot imitation method relies on. The method first categorizes human demonstration videos into different tasks, and these tasks are classified into six types to symbolize as many types of pick-and-place tasks as possible. Second, the method generates multi-modal prompts and finally predicts the action of the robot and completes the symbolic pick-and-place task in industrial production. A carefully curated dataset is created to complement the method. The dataset consists of human demonstration videos and instance images focused on real-world scenes and industrial tasks, which fosters adaptable and efficient learning. Experimental results demonstrate favorable success rates and loss results both in simulation environments and real-world experiments, confirming its effectiveness and practicality.