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This chapter calls for urgent institutional changes to address structural inequalities through advocacy and legislative action. The authors discuss macro practice methods to address racial injustice through advocacy efforts such as fostering policies eliminating anti-Asian hate and violence, advocating for nondiscriminative policies, improving language access, campaigning for narrative change, building coalitions with social justice groups, encouraging civic engagement, strengthening links with social justice organizations, and promoting policies and programs on Asian American, Native Hawai’ian, and Pacific Islander history education and awareness. Policy advocacy to protect Asian Americans against racial hate crimes is lacking but much needed. Macro social workers’ efforts can pressure policymakers to directly address anti-Asian racism and violence, provide targeted assistance, and call on national, state, and local organizations to ensure investments in culturally appropriate services to Asian American communities.

This chapter begins with a review of the history of anti-Asian racism in the United States. Beginning in the mid-19th century, Asian immigrants played a vital role in the development of the country. However, Asian Americans have faced a long legacy of exclusion and inequality, particularly during periods of economic recession, disease outbreaks, or war throughout US history. Adopting the framework of “othering,” this chapter analyzes the major events in US history related to Asian Americans, such as the Chinese Exclusion Act of 1882, the Immigration Act of 1924, the Japanese internment camps during World War II, and the anti-Asian immigration policies adopted by the Trump administration. Through this, the authors illustrate how historic racism and xenophobia at both individual and institutional levels have operated to marginalize Asian Americans and reproduce inequality, and they demonstrate the common roots of racism that lie in White supremacy.

Studying ADA accessibility at library websites of top universities selected from the U.S. News and World Report, the authors used WAVE and AChecker to assess data in compliance with WCAG 2.0 standards. Almost 8 out of 10 public university academic libraries reported accessibility errors as one of the major findings. Low color contrast was becoming a more commonly occurring accessibility issue, making it difficult for people with vision impairments to perceive the color of the image. The outcomes of the study suggest that academic libraries around the world should continue improving their website accessibility.

The purpose of this study is to learn more about virtual reality (VR) and augmented reality (AR) practices at the United States’ top one hundred university libraries, as well as how they are engaging with the metaverse. We conducted qualitative and descriptive analysis on the websites of the top one hundred university libraries in the United States to determine the application fields and application proportions of VR and AR technologies and found good practice examples of using VR and AR technologies in this field. The findings show that 86 percent of the top one hundred US university libraries have implemented VR and AR technologies, with practice areas focused on: VR/AR studio and VR/AR makerspace; immersive learning services and virtual exhibitions/conference services; visual geographic information system and VR navigation services; virtual reading services and visual retrieval services; and VR reference services. The study provides university library administrators and professionals with the most up-to-date information and best practices of VR and AR engagement areas and the proportion of use, which can aid in the development of strategies to leverage VR and AR technologies to improve patron service and embrace the metaverse for the communities they serve.

Convolutional neural network (CNN) based deep learning is used to analyze spectral data collected by visible resonance Raman (VRR) spectroscopy to distinguish human glioma tumors from healthy brain tissues using binary classification and identify the cancer grades of the glioma tumors using multi-class classification. Classification was performed using both raw spectral data and baseline-subtracted data for comparison. The classification using both datasets yielded high accuracy, with the results obtained from baseline subtracted spectra slightly better than that obtained from raw spectra. The study showed VRR combined with deep learning provides a robust molecular diagnostic tool for accurately distinguishing glioma tumors from normal tissues and glioma tumor tissues at different cancer grades. Deep learning aided VRR technique may be used for in-situ intraoperative diagnosis of brain cancer. It may help a surgeon to identify cancer margins and even cancer grades during surgery. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Currently, liquid biopsy method is mainly used for tumor detection based on genomic molecular alterations in vitro. Liquid biopsy is superior to traditional tissue biopsy techniques and its diagnosis time of disease and repeated diagnosis of liquid biopsy are new breakthroughs in clinical application. Liquid biopsy method can be used to detect most human disease based on genetic biomarkers from body fluids, among which, special biomarkers in blood and cerebrospinal fluid (CSF) samples are the main research objects, and have made good achievements in preliminary clinical applications. The application of optical spectroscopy in the field of liquid biopsy has aroused great interest among researchers and demonstrated the potential of its clinical application for oncology. The aim of this study is to reveal the optical spectroscopic characteristics of the main biochemical components of CSF of brain tumor using visible resonance Raman (VRR) spectroscopy ex vivo. Tumor-associated proteins, glucose, lactate and other metabolites released to CSF can be used as markers for liquid biopsy. We studied the VRR spectra of CSF samples from 7 types of brain tumor patients. The characteristic VRR modes that were found and may be used as a combination of multiple analyte biomarkers include amyloid-β and tau protein, excess neurotransmitters such as glutamic acid derived from the exchange with interstitial fluid (ISF), DNA, glucose, lactate, etc. for optical liquid biopsy analyses. Another interesting finding was that CSF of different types of tumors showed different images similar to the crystallization of water under the optical microscope. Considering our previous study, the current study on CSF provides another proof that the VRR system can provide a complete scan region of 200 - 4000cm-1 as a clinical tool for non-invasive diagnosis of brain disease. © 2024 SPIE.

College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross-validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data-driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well-being of diverse student populations through counseling.