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.

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.

The migration of Mongolian gazelles (Procapra gutturosa) poses a potential risk of outbreak for zoonotic intestinal protozoan parasite infections. This study aims to investigate the infection status of zoonotic intestinal protozoan parasites in these migratory Mongolian gazelles. We collected 120 fecal samples from Mongolian gazelles during their migration from Mongolia to China in December 2023. These samples were analysed using amplification and sequencing of partial SSU rRNA genes to detect the 4 presence of zoonotic intestinal protozoan parasites and characterize their genotypes. Our analysis revealed the presence of several zoonotic intestinal protozoan parasites in the sampled Mongolian gazelles. Cryptosporidium spp. was detected in 14.17% (17/120) of the samples, followed by Cystoisospora belli in 13.33% (16/120), Blastocystis sp. in 16.67% (20/120) and Cyclospora cayetanensis in 30.00% (36/120). Moreover, we identified novel host-adapted genotypes of Cryptosporidium spp. and C. belli, as well as the presence of ST2 and ST13 Blastocystis sp. subtypes, while distinct genotypes were found in Blastocystis sp. and C. cayetanensis. This study revealed the status of 4 prevalent zoonotic intestinal protozoan parasite infections in Mongolian gazelles and provided crucial insights into their characteristics. The prevalence of these parasites in the population highlights the potential risk of cross-border transmission of infectious diseases associated with long-distance migration. Furthermore, the identification of novel genotypes contributes to our understanding of the genetic diversity and adaptation of these parasites. These findings can inform the development of protective measures to mitigate the impact of these infections on the health and survival of Mongolian gazelles.

This paper presents a three-stage E-band low-noise amplifier (LNA) fabricated in a 28-nm Complementary Metal Oxide Semiconductor High-Performance Compact Plus process. The proposed E-band LNA achieves a peak gain of 16.8 dB, exhibiting a gain variation of less than ±0.5 dB across the frequency range of 67.8–90.4 GHz. The measured 3-dB gain bandwidth spans from 64 to 93.8 GHz, and the minimum measured noise figure (NF) is 3.8 dB. By employing a one-stage common-source with a two-stage cascode topology, the proposed E-band LNA demonstrates competitiveness in terms of gain flatness and NF when compared to recently published E-band CMOS LNAs.

We reported on an efficient high-power continuous-wave laser operation on the 3H4 → 3H5 transition of Tm3+ ions in a diffusion-bonded composite YVO4/Tm:GdVO4 crystal. Pumped by a laser diode at 794 nm, a maximum output power of 7.5 W was obtained from a YVO4/Tm:GdVO4 laser at 2.29 μm, corresponding to a slope efficiency of 40.3% and exceeding the Stokes limit. To the best of our knowledge, this result represents the maximum power ever achieved from a Tm laser at 2.3 μm.

Accurately converting satellite instantaneous evapotranspiration (λETi) over time to daily evapotranspiration (λETd) is crucial for estimating regional evapotranspiration from remote sensing satellites, which plays an important role in effective water resource management. In this study, four upscaling methods based on the principle of energy balance, including the evaporative fraction method (Eva-f method), revised evaporative fraction method (R-Eva-f method), crop coefficient method (Kc-ET0 method) and direct canopy resistance method (Direct-rc method), were validated based on the measured data of the Bowen ratio energy balance system (BREB) in maize fields in northwestern (NW) and northeastern (NE) China (semi-arid and semi-humid continental climate regions) from 2021 to 2023. Results indicated that Eva-f and R-Eva-f methods were superior to Kc-ET0 and Direct-rc methods in both climatic regions and performed better between 10:00 and 11:00, with mean absolute errors (MAE) and coefficient of efficiency (ɛ) reaching <10 W/m2 and > 0.91, respectively. Comprehensive evaluation of the optimal upscaling time using global performance indicators (GPI) showed that the Eva-f method had the highest GPI of 0.59 at 12:00 for the NW, while the R-Eva-f method had the highest GPI of 1.18 at 11:00 for the NE. As a result, the Eva-f approach is recommended as the best way for upscaling evapotranspiration in NW, with 12:00 being the ideal upscaling time. The R-Eva-f method is the optimum upscaling method for the Northeast area, with an ideal upscaling time of 11:00. The comprehensive results of this study could be useful for converting λETi to λETd.