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Attraction and mating between male and female animals depend on effective communication between conspecifics. However, in mosquitoes, we have only a rudimentary understanding of the sensory cues and receptors critical for the communication that is essential for reproductive behavior. While it is known that male Aedes aegypti use sound to help them identify females, it is not unclear whether sound detection is absolutely required since other cues such as vision may also participate in mating behavior. To determine the effect of eliminating hearing on mating success, we knocked out the Ae. aegypti TRPVa channel, which is a protein expressed in chordotonal neurons in the Johnstons organ (JO) that respond to sound-induced movements in the antenna. Loss of trpVa eradicated sound-induced responses from the JO, thereby abolishing hearing. Strikingly, mutation of trpVa eliminated mating behavior in males. In contrast, trpVa-null females mated, although this behavior was slightly delayed relative to wild-type females. Males and females produce sounds as they beat their wings at distinct frequencies during flight. Sound mimicking the female wingbeat induced flight, attraction, and copulatory-like behavior in wild-type males without females present, but not in trpVa-null males. Males are known to modulate their wingbeat frequencies before mating in the air, which is a phenomenon referred to as rapid frequency modulation (RFM). We found that RFM was absent in mosquitoes lacking TRPVa. We conclude that the requirement for trpVa and hearing for male reproductive behavior in Aedes is absolute, as mating in the deaf males is eliminated.

As foundational models in specialized fields like sound art have rapidly advanced, the convergence of artificial intelligence with music creation and performance has become increasingly pronounced. A novel framework for music creation and performance called the parallel music system was proposed. Grounded in parallel systems theory and the artificial system, computational experiments, parallel execution (ACP) approach, this system stimulates the real-world music creation and performance environments to build highly authentic virtual scenarios. It facilitates real-time, seamless interactions between the virtual and real music systems and outlines the technological strategies for music creation and performance in the era of large models, incorporating a hybrid team of humans, digital beings, and robots. Additionally, the theoretical framework of parallel music and its critical technologies are discussed, the innovative contributions and potential in the modern music field are evaluated, the applications in scenarios like music generation and music therapy are examined. The objective is to harness the emerging AI technologies of the large model era to enhance traditional paths in music creation, drive technological advancements in music performance, unlock the creative potential of musicians, and offer fresh perspectives and inspirations for music creation and performance in the new era.

BackgroundThe neurotrophic factor Artemin (ARTN) is involved in tumor proliferation and metastasis. Nonetheless, ARTN's significance in cervical cancer (CC) has not been studied. In our study, we propose to investigate the biological function of ARTN in CC as well as its particular regulatory mechanism.MethodsImmunohistochemistry (IHC) was used to examine the degree of ARTN protein expression in CC patient tissue. Real-time PCR and Western blotting were performed to reveal related genes' levels in CC cells. The CCK-8 test, the colony formation assay, the wound-healing assay, and the transwell assay were utilized to determine the proliferation, migration, and invasion capabilities, respectively. To generate lung metastasis models, stable ARTN-expressing SiHa cells were injected into the caudal tail vein of mice. IHC was used to examine the protein levels in CC mice model tissues.ResultsARTN was overexpressed in CC tissues relative to normal cervical tissues and linked positively with lymph node metastases (P=0.012) and recurrence (P=0.015) in CC patients. In vitro, ARTN overexpression promoted the proliferation, invasion, and migration of CC cells. In contrast, the consequences of depleting endogenous ARTN were the opposite. Moreover, overexpression of ARTN increased lung metastasis of CC cells in vivo and shortened the lifespan of mice models. In addition, ARTN overexpression significantly enhanced AKT phosphorylation on Ser473 and mTOR phosphorylation on Ser2448 and promoted the epithelial-mesenchymal transition (EMT) cascade. In addition, rapamycin, a selective inhibitor of mTORC1, might rescue the EMT phenotype caused by ARTN.ConclusionOur findings suggested that ARTN may enhance CC metastasis through the AKT/mTORC1 pathway. ARTN is anticipated to be a novel potential therapeutic target for the treatment of CC metastases.

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

In most experimental animals, it is challenging to combine mutations and rescue transgenes and to use bipartite systems to assess gene expression. To circumvent the difficulties in combining multiple genetic elements, we developed the DREaMR (Drug-on, REporter, Mutant, Rescue) system. Using Drosophila white as the initial model, we demonstrated that introduction of a single insertion by CRISPR/Cas9 created a null mutation, a tagged rescue construct, which could be induced with doxycycline, and which allowed assessment of protein expression. To create a DREaMR in an organism in which combining multiple genetic elements is more problematic than in Drosophila, we tested the mosquito, Aedes aegypti-the insect vector for dengue, yellow fever, Zika, and other viral diseases. We generated a DREaMR allele in the kh gene, which permitted us to induce expression of the rescue construct, and detect expression of Kh. Thus, this system avoids the need to perform genetic crosses to introduce an inducible rescue transgene in a mutant background, or to combine driver and reporter lines to examine expression of the targeted protein. We propose that DREaMR provides a system that can be applied to additional mosquito vectors as well as other organisms in which CRISPR/Cas9 is effective.

This paper uses a user-based collaborative filtering algorithm to predict the ratings of items by target users and to recommend items that target users have not interacted with. The red music and cultural resources were ranked according to their values, and the Bartholomew coefficient was introduced to calculate the similarity of items from the perspective of a probability distribution, and the content recommendation algorithm was used to construct a model of ecological civilization education concept. It is found that more than 50% of undergraduate college students pay regular attention to ecological environment information, and 30.56% of them will actively learn ecological civilization knowledge, which is not thorough and comprehensive enough for ecological civilization concept establishment and cultivation. It is suggested that universities should integrate red music culture into ecological civilization education to create an ecological civilization cultivation atmosphere.