Los Angeles Mission College

Accurate estimation of galaxy redshifts is crucial for understanding the universe's structure and evolution. Traditionally, redshifts are measured using spectroscopic methods. These methods are precise but become inefficient and costly when dealing with large datasets generated by modern astronomical surveys. Photometric redshift estimation offers a more efficient alternative. However, traditional photometric methods often depend on spectral energy distribution (SED) templates, which may not be suitable for large volumes of data. To address these challenges, we applied machine learning techniques for photometric redshift estimation. Our study used the GalaxiesML dataset, composed of five photometric magnitudes (grizy) and corresponding spectroscopic labels (green, red, near-infrared, far-infrared, infrared at a longer wavelength than the z band). We trained three machine learning models: K-Nearest Neighbors (KNN), Random Forest (RF), and Multi-Layer Perceptron (MLP). The results were promising. KNN achieved a root mean square error (RMSE) of 0.27, RF achieved an RMSE of 0.26, and MLP had the lowest RMSE of 0.22. These findings highlight the potential of machine learning to efficiently manage astronomical datasets. It offers a scalable and cost-effective method for photometric redshift estimation. This advancement not only helps handle the data deluge in modern astronomy but also enhances our ability to explore the universe's distant regions. Overall, it contributes significantly to the field of astrophysics.

Over 97 million individuals worldwide have motor disabilities that hinder communication, yet augmentative and alternative communication (AAC) technology often costs between US$9,000 and US$12,000, with clinical systems reaching US$16,000 or more, leaving few affordable alternatives. As a result, between 34% (high-income countries) and 97% (low-income countries) of individuals worldwide cannot afford current AACs. To address this issue, we developed SpeakEEG, a low-cost noninvasive brain-computer interface (BCI) application using electroencephalography (EEG) signals captured by the consumer-grade Muse S headset connected to a tablet. SpeakEEG employs the P300 signal, which is an event-related potential (ERP) evoked by a target stimulus, to detect user intent. Our system uses a word-based speller that integrates a long short-term memory (LSTM) model and an N-gram to generate next-best word options. The user selects a word by focusing on the app’s flashing grid interface, where our support vector machine (SVM) classifier detects the P300 response corresponding to the flashed target. The model was trained on collected ERP data augmented using synthetic samples via the synthetic minority oversampling technique (SMOTE) and achieved 85.16% classification accuracy. Benchmarking with the “Mother of All BCI Benchmarks” (MOABB)—a robust system used to compare BCI algorithms—our SVM model placed among the top three highest performing models across all datasets. SpeakEEG enables communication at approximately 23 characters per minute (cpm), which is significantly faster than typical P300-based spellers (3–8 cpm), while reducing costs by over 95%. The system offers an accessible and portable AAC solution for motor-impaired users worldwide.

Silicon–carbon composite materials offer an attractive and realistic pathway to lithium-ion batteries with higher energy density. Here, a scalable chemical vapor deposition (CVD) process is used to form micron-sized Si–C composites with uniform few-atomic-layer Si on nanoporous carbon. We find that the optimal deposition temperatures are 450–525 °C. The ultrathin Si layer successfully releases stress in the micron-sized particles, while the small pores eliminate the formation of excessive amounts of solid electrolyte interphase, as validated by elemental mapping. Therefore, the resulting Si–C composites demonstrate a high specific capacity exceeding 1400 mAh/g, stable cycling with a decay rate of <5%/100 cycles in the half cell, an initial Coulombic efficiency of 85.2%, and excellent rate capabilities such as 84% retention at 4C. Moreover, the material demonstrates excellent air and water stability, allowing for similar electrode casting and preparation methods as graphite. The corresponding LiNi0.92Mn0.02Co0.06O2/Si–C full cells show stable cycling with 84% capacity retention over 200 cycles, which is attractive for high-energy-density lithium-ion batteries and compares favorably to other silicon-based anodes in terms of both performance and scalability.

Carceral populations in the United States have a disproportionately high prevalence of mental illness, substance use disorder, and communicable and non-communicable chronic disease in comparison to the general population. A historical prohibition of federal fund usage for incarcerated populations has led to both significant resource constraints for carceral healthcare entities and a lack of formalized and consistently enforced healthcare benchmarks for incarcerated people. This has resulted in subpar healthcare standards and delivery and poor health outcomes, ultimately driving up costs for both carceral health systems and the communities to which people return to upon release. Public health agencies are well suited to help improve carceral health given their mission of health promotion and integral role in public safety, but their historical tendency towards disease and crisis-specific interventions in carceral settings has contributed to a fragmented presence and a longstanding disconnect with carceral entities that has affected the quality of interagency collaboration. The COVID-19 pandemic demonstrated the indispensability of infrastructure for communication and shared information and decision-making, with implications for the need to maintain broad, continuous communication and planning efforts between public health and carceral agencies extending beyond the pandemic and other crisis situations. Accordingly, we argue that the complexity and uniqueness of carceral health warrants its recognition as a public health discipline and that the creation of a public health unit focused on carceral health that regularly interfaces and collaborates with carceral entities utilizing a population medicine perspective is essential to improving carceral healthcare.

Abstract HD 138965 is a young A type star and member of the nearby young Argus association. This star is surrounded by a broad, bright debris disc with two temperature components that was spatially resolved at far-infrared wavelengths by Herschel. Here we present ALMA millimetre-wavelength imaging of the cool outer belt. These reveal its radial extent to be 150$^{+10}_{-7}$ au with a width (σ) of 49$^{+7}_{-6}$ au (ΔR/R = 0.77), at a moderate inclination of 49.○9$^{+3.3}_{-3.7}$. Due to the limited angular resolution, signal-to-noise, and inclination we have no constraint on the disc’s vertical scale height. We modelled the disc emission with both gravitational and radiation forces acting on the dust grains. As the inner belt has not been spatially resolved, we fixed its radius and width prior to modelling the outer belt. We find astronomical silicate is the best fit for the dust composition. However, we could not reject possible scenarios where there are at least 10 % water-ice inclusions. Combining the spatially resolved imaging by ALMA with non-detection at optical wavelengths by HST, we obtain a limit on the scattering albedo ω ≤ 0.09 for the debris dust in the outer belt. Analysis of the outer belt’s architecture in conjunction with simple stirring models places a mass limit of 2.3 ± 0.4 MJup on a companion interior to the belt (a ≤ 78 au), a factor of two improvement over constraints from high contrast imaging.

BackgroundEmotional well-being interventions lead to better mental and physical health. However, most of these interventions have been tested on relatively homogeneous samples, with few interventions large enough to examine whether key sociodemographic factors impact outcomes. In addition, barriers to engagement include access and high participant burden. We developed a brief web-based intervention to address these barriers and tested the effects across sociodemographic groups.ObjectiveThe study aims to examine the effectiveness of a brief, low-burden digital well-being intervention in improving emotional well-being and health-related outcomes across a diverse global sample. It investigates how key sociodemographic factors, such as age, sex, race and ethnicity, education, financial strain, and subjective social status, moderate intervention effects. The goal is to identify which groups benefit most, informing the scalability and public health potential of digital well-being interventions.MethodsWe conducted a single-group pre-post study spanning from 2022 to 2024 using a web-based, multicomponent, week-long well-being intervention requiring 5 to 10 minutes of daily activity (the Big Joy Project). Using a global convenience sample recruited via open web-based enrollment, we assessed pre-post changes in emotional well-being, positive emotions, happiness agency, perceived stress, self-reported health, and sleep quality. At baseline, participants also reported sociodemographic characteristics. We used mixed-effects linear models to examine pre-post changes in the outcomes and sociodemographic moderators.ResultsThe sample (N=17,598) consisted of individuals from 169 countries and territories, with broad representation across sociodemographic groups; however, the sample was predominantly White, female, and had at least a high school or college education. Following the intervention, participants showed significant within-subjects effect size increases in emotional well-being (dz=0.48; P<.001), positive emotions (dz=0.45; P<.001), and happiness agency (dz=0.44; P<.001). Furthermore, participants showed a decrease in perceived stress (dz=–0.35; P<.001) and an increase in self-reported health (dz=0.07; P<.001) and sleep quality (dz=0.15; P<.001). There was a clear dose-response pattern across outcomes: participants who engaged in more daily practices showed greater improvements. There was a strong pattern of social disadvantage moderating these effects, with groups experiencing greater social disadvantage showing larger benefits across most outcomes. For example, those with lower education, greater financial strain, or lower subjective social status and those identifying as individuals from racial or ethnic minority groups (Black or Hispanic) all showed larger improvements across well-being outcomes. Furthermore, younger people had greater increases in emotional well-being and greater decreases in perceived stress compared to older people.ConclusionsA brief, low-intensity intervention showed meaningful improvements in well-being and stress, comparable to those seen in longer, more intensive digital well-being interventions. Sociodemographic groups that are at higher risk of poor mental health benefited more from the intervention, highlighting its potential for scalable public health impact. Testing this intervention with a randomized controlled trial design will be important.

Parkinson's disease (PD) is globally the fastestgrowing neurodegenerative disease in the elderly with unclear causes that impairs individuals' motor usage and may induce dementia. Using machine learning technology has the potential to allow for faster and more accurate diagnoses of Parkinson's, which can allow Parkinson's treatment to reach low-income communities with access to fewer health resources. Our study explores a machine learning and deep learning classification algorithm that uses voice recordings to diagnose whether or not a person is likely to have Parkinson's disease. By using various characteristics of the voice recording, such as pitch, frequency, and amplitude, our model is able to accurately predict the presence of Parkinson's with an accuracy of 94%. Unlike other contemporary methods of diagnosing Parkinson's, our research is more accessible as recording voices can be done universally and is not dependent on access to healthcare technology.

The purpose of this study was to examine how integrating community-related components into student presentations in a human physiology course affects learning by connecting complex biological systems to real-world contexts. Twenty-six students were surveyed to evaluate their Cognitive Engagement, Understanding of Physiological Concepts, Community-Related Engagement, Emotional Engagement, Empowerment, Awareness, and Perception of Community Impact after engaging with peer presentations. The results indicated overall improvements across all measured aspects. This approach demonstrates its effectiveness in deepening students' understanding of physiological mechanisms while also highlighting the broader cultural and societal dimensions of diseases. Los Angeles Mission College, ALRISE This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Two seemingly independent disorders, depression and Alzheimer's disease (AD), are in fact interconnected in complex mechanisms.Although their association remains unclear, depression has been hypothesized to be a risk factor, prodrome, or accelerating factor of AD.This review discusses the bidirectional association between depression and AD, with a specific focus on their shared impact on memory deficits and the potential role of antidepressants in managing both conditions.AD, a neurodegenerative disease, causes cognitive decline, in which early diagnosis and intervention is key for preventing progression.Depression, commonly linked to AD, is associated with hippocampal atrophy, a hallmark of AD, and memory decline in AD, particularly in working memory (WM) and episodic memory.The potential role of antidepressants in managing depression and AD is also discussed, though findings remain inconclusive.While some evidence suggests that antidepressants can alleviate depressive symptoms and delay cognitive decline in AD, the overall impact on cognitive and functional outcomes is not definitive, promoting the necessity for further research.This review underscores the importance of early detection and specific treatments for addressing the common negative implications of depression and AD.