To holistically understand the biology of animals, we must unravel the complexities and specificities of host-microbe interactions across animal taxa. Birds represent enigmatic and scientifically compelling hosts in which to understand these interactions. Here, we present a brief summary of a series of conversations among avian microbiome researchers regarding methodological challenges facing the avian microbiome field, where most research to date has focused on bacterial communities of the gut. Collectively, we acknowledged a commonly shared but underreported issue facing the avian microbiome field: that of difficulty in obtaining high-quality and high-yield microbial DNA from avian fecal samples. We discuss some of the potential reasons underlying low DNA yields, such as inhibitory compounds and rapid DNA degradation, and provide recommendations for how researchers in the avian microbiome field might cope with these methodological challenges. Collective and dedicated efforts to address these challenges will be required for a robust understanding of host-microbe interactions in avian systems.
During conversations in the presence of other competing talkers, multiple speech streams compete for listeners' attentional focus. Listeners must segregate speech streams, and selectively attend to the target talker while filtering out irrelevant speech. Research on value-driven attention suggests that perceptual and attentional processes are biased by prior rewarding experi-ences with stimuli-that is, high-valued stimuli win attentional competition. However, it remains unknown whether such effects generalize to speech perception in multitalker environments. Here, the present study investigated whether listeners can better understand speech spoken by talkers associated with higher versus lower values in challenging listening conditions. In three experiments, we used reward-based training paradigms to either explicitly or implicitly induce listeners to associate talkers' voices with varying magnitudes of rewards. Subsequently, listeners performed a speech-on-speech intelligibility task in which the exposed voices were either the target or distractor. We found that explicit talker-reward learning did not influence speech intelligibility. In contrast, we observed that implicitly acquired talker-reward associations improved the intelligibility of high-reward talkers, but only in the most adverse listening condition. However, this effect did not replicate when we further imposed greater attentional demands by presenting target and masker talkers' speech without spatial sepa-ration. Instead, listeners relied primarily on acoustic cues other than reward-associated talkers' voices. Furthermore, the reward values associated with the distractor talker had no effect on interference. These findings suggest that value associ-ated with talkers' voices do not reliably bias listeners' attention to overcome the complex, acoustic constraints imposed in speech-on-speech perception.
Cancer is defined as mutations or abnormal changes in genes that regulate cell growth and maintain their health. When these altered cells continue to divide uncontrollably, they can generate similar cells, ultimately forming a tumor. The interactions among genes can influence the rate of cancer development. Gene expression technology allows simultaneous measurement of thousands of genes in a single experiment. However, analyzing the data is challenging because of its complexity, heterogeneity, and intricate interconnections among genes. Network science offers a promising framework for studying genetic data, by representing gene-gene relationships explicitly, enabling structured information sharing, noise averaging, and improved generalization. Using breast cancer gene expression data, this study systematically constructs gene networks and investigates how different network topologies and properties influence the predictive performance of a Graph Convolutional Neural Network (GCNN) model. Network science analysis inspired the development of two network-aware feature selection approaches, leveraging more sophisticated mathematical techniques based on network science to reduce model complexity. The findings indicate that a correlation threshold of 0.5 acts as an optimal point, consistently yielding the best results for GCNN models. Model performance is influenced by the network topology. While stronger connectivity can improve performance, there may be a threshold beyond which excessive connections actually impede the flow of information.
A new optimized LiFSI-LiPF6 dual-salt controlled-solvation electrolyte (E-DS) is demonstrated to enable practical graphite||LiNi0.8Mn0.1Co0.1O2 cells (approximate to 4.1 mAh cm(-2)) to achieve exceptional performance and safety under extreme conditions. By optimizing anion coordination with the smaller, more dissociating FSI- anion, the E-DS forms ultrathin, dense, and inorganic-rich electrode/electrolyte interphases that dramatically suppress solvent decomposition, transition-metal dissolution, and surface reconstruction compared to the conventional LiPF6/carbonate electrolyte. Consequently, E-DS cells deliver >78% capacity retention after 300 cycles at 60 degrees C, retain fast discharging capacity at 30 degrees C, and operate effectively at -20 degrees C. Most strikingly, fully charged full cells with E-DS, even under overcharging to 4.8 V and elevated temperatures, show a lower heat evolution in stable formulations-transforming a traditionally unstable high-voltage/high-temperature configuration into an intrinsically safe state. This work establishes a new benchmark for carbonate-containing electrolytes, simultaneously achieving high energy density, fast-discharging capability, wide-temperature operation (-20 to 60 degrees C), and outstanding thermal safety in nickel-rich lithium-ion batteries.
Objective/Research Question: The purpose of this study was to measure the relationship between High-Impact Practices (HIPs) and engagement at community colleges. The research question guiding this study was: Accounting for student background and institutional characteristics, how do student-level individual participation and institution-level average participation in HIPs relate to engagement measures of institutional practices and student behaviors at community colleges?Methods: Using data from 79,755 respondents to the Community College Survey of Student Engagement from 455 institutions, the relationship between HIP participation and engagement was tested via hierarchical linear modeling. This analysis measured the relationship for both student participation (student-level) and institutional average (institution-level) with three measures of engagement: academic challenge, support for learners, and student-faculty interaction.Results: Almost all student-level HIPs were statistically, significantly, and positively related to these measures; however, the magnitude of specific practices such as class attendance, tutoring, and service-learning held the highest practical significance. Institution-level measures held similar, positive relationships with engagement; however, these relationships were not as wide-spread across all HIPs.Contributions: For the last decade, researchers have related participation in high-impact practices (HIPs) to engagement among students at 4-year institutions. The same fundamental understanding for community colleges was not addressed in the literature until this research. From these results, the student-level relationships advance a fundamental understanding between HIP participation and engagement, whereas the institutional-level relationships highlight specific experiences to guide community college policy and practice. Equity concerns of underrepresented students' participation are also presented.
