Down syndrome (DS) is a developmental disability caused by trisomy of chromosome 21 and as with other developmental disabilities, individuals with DS may present with challenging behavior (e.g., aggression, tantrums, self-injurious behavior; Feeley & Jones, 2006). The purpose of this study was to evaluate the presence of challenging behavior in those with DS by surveying caregivers of individuals with DS via Qualtrics. A link to our survey was sent out to national and local organizations that support the DS population with a request to disseminate the link. Given limited response, the survey was modified (shorted and reorganized) and a link to the revised survey was disseminated via Facebook to groups focused on DS. Although both surveys received limited responses, there are preliminary findings worth exploring further. Aggression, noncompliance, and tantrum behaviors were frequently reported behavior and, escape and attention were the most reported perceived functions of behavior. Challenging behavior is reported in DS by caregivers. Future research should be conducted to examine the prevalence and function of challenging behavior in DS to develop effective preventative approaches to challenging behavior while promoting skill acquisition.
{"title":"Challenging Behavior in Down Syndrome: Initial Surveys Evaluating Co-occurrence","authors":"M. Valdovinos","doi":"10.47611/jsr.v12i2.1942","DOIUrl":"https://doi.org/10.47611/jsr.v12i2.1942","url":null,"abstract":"Down syndrome (DS) is a developmental disability caused by trisomy of chromosome 21 and as with other developmental disabilities, individuals with DS may present with challenging behavior (e.g., aggression, tantrums, self-injurious behavior; Feeley & Jones, 2006). The purpose of this study was to evaluate the presence of challenging behavior in those with DS by surveying caregivers of individuals with DS via Qualtrics. A link to our survey was sent out to national and local organizations that support the DS population with a request to disseminate the link. Given limited response, the survey was modified (shorted and reorganized) and a link to the revised survey was disseminated via Facebook to groups focused on DS. Although both surveys received limited responses, there are preliminary findings worth exploring further. Aggression, noncompliance, and tantrum behaviors were frequently reported behavior and, escape and attention were the most reported perceived functions of behavior. Challenging behavior is reported in DS by caregivers. Future research should be conducted to examine the prevalence and function of challenging behavior in DS to develop effective preventative approaches to challenging behavior while promoting skill acquisition.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"43 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85528468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4272
Kyle Wang, Sajeth Dinakaran
Adrenoleukodystrophy (ALD) is a rare, inherited disorder that affects the brain, spinal cord, and adrenal glands. It is caused by mutations in the ABCD1 gene, which provides instructions for making a protein called ABCD1, which is involved in the metabolism of very long-chain fatty acids (VLCFAs). In ALD, the body cannot properly break down and clear VLCFAs, which can lead to the accumulation of these fatty acids in the brain and other tissues. This accumulation can cause inflammation and damage to cells and tissues, leading to various symptoms. Symptoms of ALD may vary depending on the type of ALD and the severity of the condition. Common symptoms include neurological problems, such as difficulty walking, speaking, behavioral changes, and problems with the adrenal gland, such as adrenal insufficiency which is a condition in which the adrenal glands do not produce enough hormones. ALD is a progressive disorder, meaning symptoms may worsen over time if left untreated. Treatment for ALD typically involves medications and supportive care to manage symptoms and prevent complications. Sometimes, a bone marrow transplant may be recommended to replace damaged cells and tissues. Genetic testing is available for ALD and can be used to diagnose the disorder and identify people at risk of developing it. Early diagnosis and treatment can help improve the chances of a full recovery and a good quality of life for people with ALD.
{"title":"The Pathogenesis of Adrenoleukodystrophy","authors":"Kyle Wang, Sajeth Dinakaran","doi":"10.47611/jsrhs.v12i2.4272","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4272","url":null,"abstract":"Adrenoleukodystrophy (ALD) is a rare, inherited disorder that affects the brain, spinal cord, and adrenal glands. It is caused by mutations in the ABCD1 gene, which provides instructions for making a protein called ABCD1, which is involved in the metabolism of very long-chain fatty acids (VLCFAs). In ALD, the body cannot properly break down and clear VLCFAs, which can lead to the accumulation of these fatty acids in the brain and other tissues. This accumulation can cause inflammation and damage to cells and tissues, leading to various symptoms. Symptoms of ALD may vary depending on the type of ALD and the severity of the condition. Common symptoms include neurological problems, such as difficulty walking, speaking, behavioral changes, and problems with the adrenal gland, such as adrenal insufficiency which is a condition in which the adrenal glands do not produce enough hormones. ALD is a progressive disorder, meaning symptoms may worsen over time if left untreated. Treatment for ALD typically involves medications and supportive care to manage symptoms and prevent complications. Sometimes, a bone marrow transplant may be recommended to replace damaged cells and tissues. \u0000Genetic testing is available for ALD and can be used to diagnose the disorder and identify people at risk of developing it. Early diagnosis and treatment can help improve the chances of a full recovery and a good quality of life for people with ALD.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"106 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88092867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4296
Shubhi Goyal, Mukul Goyal
There is a common perception that political polarization is increasing in American society and the blame is often assigned to highly partisan traditional media (e.g., TV news channels) and the emergence of social media echo chambers as the major influencers of political opinion. In this paper, we examine the impact of traditional and social media on political polarization in society via simulations. These simulations examine what happens when a population with normally distributed unipolar political views is exposed to social/traditional media espousing very different types of political views. Our simulations reveal that the political polarization in a population is deeply affected by the political views espoused in the media. If the media is primarily unipolar in terms of political views, the population ultimately become politically unipolar as well. On the other hand, if the media is politically bipolar, the population ultimately becomes politically bipolar. Interestingly, the simulations reveal that social media echo chambers can undo the polarizing impact of partisan traditional media if the echo chambers strictly show content matching the current political views of the users. However, if social media echo chambers expose the users to extreme political views, a population that is initially unipolar in political views will ultimately look like two different populations with very different political centers.
{"title":"Impact of Social/Traditional Media on Political Polarization","authors":"Shubhi Goyal, Mukul Goyal","doi":"10.47611/jsrhs.v12i2.4296","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4296","url":null,"abstract":"There is a common perception that political polarization is increasing in American society and the blame is often assigned to highly partisan traditional media (e.g., TV news channels) and the emergence of social media echo chambers as the major influencers of political opinion. In this paper, we examine the impact of traditional and social media on political polarization in society via simulations. These simulations examine what happens when a population with normally distributed unipolar political views is exposed to social/traditional media espousing very different types of political views. Our simulations reveal that the political polarization in a population is deeply affected by the political views espoused in the media. If the media is primarily unipolar in terms of political views, the population ultimately become politically unipolar as well. On the other hand, if the media is politically bipolar, the population ultimately becomes politically bipolar. Interestingly, the simulations reveal that social media echo chambers can undo the polarizing impact of partisan traditional media if the echo chambers strictly show content matching the current political views of the users. However, if social media echo chambers expose the users to extreme political views, a population that is initially unipolar in political views will ultimately look like two different populations with very different political centers.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"8 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88133369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4403
Srinithya Kothapalli, Rajagopal Appavu
This research paper explores the application of artificial intelligence (AI) and machine learning (ML) in anesthesiology. AI and ML have the potential to improve patient outcomes and enhance clinical decision-making by enabling anesthesiologists to monitor patient vital signs in real-time, predict the likelihood of complications, and optimize drug dosages to minimize side effects and enhance efficacy. The Hypotension Prediction Index algorithm is a compelling example of how AI and ML can be utilized to improve intraoperative patient care. However, there is a need for further research and validation to ensure the safety and efficacy of these technologies in clinical practice. Future advancements in AI and ML techniques are likely to result in more sophisticated and accurate predictive models, decision support tools, and monitoring systems that will ultimately benefit patients undergoing anesthesia. Overall, the application of AI and ML in anesthesiology presents a promising avenue for improving patient care and outcomes.
{"title":"The Application of Artificial Intelligence and Machine Learning to Anesthesiology","authors":"Srinithya Kothapalli, Rajagopal Appavu","doi":"10.47611/jsrhs.v12i2.4403","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4403","url":null,"abstract":"This research paper explores the application of artificial intelligence (AI) and machine learning (ML) in anesthesiology. AI and ML have the potential to improve patient outcomes and enhance clinical decision-making by enabling anesthesiologists to monitor patient vital signs in real-time, predict the likelihood of complications, and optimize drug dosages to minimize side effects and enhance efficacy. The Hypotension Prediction Index algorithm is a compelling example of how AI and ML can be utilized to improve intraoperative patient care. However, there is a need for further research and validation to ensure the safety and efficacy of these technologies in clinical practice. Future advancements in AI and ML techniques are likely to result in more sophisticated and accurate predictive models, decision support tools, and monitoring systems that will ultimately benefit patients undergoing anesthesia. Overall, the application of AI and ML in anesthesiology presents a promising avenue for improving patient care and outcomes.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"16 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77985393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4229
Leila Zak, Arianna D Cascone
Cognitive decline exists on a spectrum, ranging from mild cognitive impairment to dementia. These two diagnoses encompass a group of symptoms rooted in the progressive decline of cognitive function, impairing aspects like memory, decision-making, language use, and locomotion. This review centers upon the premise of cognitive reserve, an intangible measure of the brain’s resilience and capacity to compensate for damage, and its relationship with the preservation of cognitive function later in life. Bilingualism constitutes one of many contributing factors to a higher cognitive reserve; however, this term fails to reflect the unique linguistic profile intrinsic to every individual—including whether a second language was acquired during childhood or later in life. Therefore, a distinction between “acquired” and “lifelong” bilingualism is made. Through the analysis of task-based and neuroimaging data, this review article elucidates the impact both forms of bilingualism have on cognitive reserve as a protectant against cognitive decline, revealing that resultant neuroprotective advantage is most salient when both languages are used regularly, in diverse environments, and in an active manner. These practices are observed in both acquired and lifelong bilinguals to varying degrees, which are contextualized and explored in detail within the review.
{"title":"The Impact of Bilingualism on Cognitive Reserve as a Protectant Against Cognitive Decline","authors":"Leila Zak, Arianna D Cascone","doi":"10.47611/jsrhs.v12i2.4229","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4229","url":null,"abstract":"Cognitive decline exists on a spectrum, ranging from mild cognitive impairment to dementia. These two diagnoses encompass a group of symptoms rooted in the progressive decline of cognitive function, impairing aspects like memory, decision-making, language use, and locomotion. This review centers upon the premise of cognitive reserve, an intangible measure of the brain’s resilience and capacity to compensate for damage, and its relationship with the preservation of cognitive function later in life. Bilingualism constitutes one of many contributing factors to a higher cognitive reserve; however, this term fails to reflect the unique linguistic profile intrinsic to every individual—including whether a second language was acquired during childhood or later in life. Therefore, a distinction between “acquired” and “lifelong” bilingualism is made. Through the analysis of task-based and neuroimaging data, this review article elucidates the impact both forms of bilingualism have on cognitive reserve as a protectant against cognitive decline, revealing that resultant neuroprotective advantage is most salient when both languages are used regularly, in diverse environments, and in an active manner. These practices are observed in both acquired and lifelong bilinguals to varying degrees, which are contextualized and explored in detail within the review.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"36 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81369171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4397
Christina Kim
The El Niño–Southern Oscillation (ENSO) is the most dominant natural variability in the Earth system, which represents seasonal-to-interannual variations of the surface equatorial Pacific Ocean temperatures and subsurface ocean interior. Impacting the physical upper ocean characteristics, ENSO exerts significant influences on the marine ecosystem, such as oxygen and phytoplankton concentrations via strong quasiperiodic oscillation between El Niño (warm phase) and La Niña (cold phase) events. The present study uses observational reanalysis and satellite data to investigate seasonal variations of ENSO and their impacts on marine biogeochemical processes. The results show that the oxygen and chlorophyll anomalies in the upper ocean exhibit different seasonal responses to ENSO. While both the summer and winter season biological responses significantly lag ENSO, the concentration of oxygen and phytoplankton during summer (winter) has no (large) concurrent covariability with ENSO. Given a strong negative correlation between chlorophyll-based indices and El Niño events, increasing mean ocean temperatures and ocean extreme events may induce lower upper-ocean oxygen levels, leading to possible risks in the ecosystem over the tropical Pacific Ocean.
El Niño-Southern涛动(ENSO)是地球系统中最主要的自然变率，它代表了赤道太平洋表层和海底海洋内部温度的季节-年际变化。ENSO通过El Niño(暖相)和La Niña(冷相)之间强烈的准周期振荡，影响海洋上层物理特征，对海洋生态系统的氧和浮游植物浓度产生显著影响。本研究利用观测再分析和卫星资料研究ENSO的季节变化及其对海洋生物地球化学过程的影响。结果表明，上层海洋氧和叶绿素异常对ENSO有不同的季节响应。夏季和冬季的生物响应都明显滞后于ENSO，但夏季(冬季)的氧和浮游植物浓度与ENSO没有(大)的同步协变性。鉴于叶绿素指数与El Niño事件之间存在强烈的负相关关系，平均海洋温度升高和海洋极端事件可能导致海洋上层氧含量降低，从而可能导致热带太平洋生态系统的风险。
{"title":"Impacts of seasonal variations of the Tropical Pacific ocean temperatures on upper ocean oxygen response","authors":"Christina Kim","doi":"10.47611/jsrhs.v12i2.4397","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4397","url":null,"abstract":"The El Niño–Southern Oscillation (ENSO) is the most dominant natural variability in the Earth system, which represents seasonal-to-interannual variations of the surface equatorial Pacific Ocean temperatures and subsurface ocean interior. Impacting the physical upper ocean characteristics, ENSO exerts significant influences on the marine ecosystem, such as oxygen and phytoplankton concentrations via strong quasiperiodic oscillation between El Niño (warm phase) and La Niña (cold phase) events. The present study uses observational reanalysis and satellite data to investigate seasonal variations of ENSO and their impacts on marine biogeochemical processes. The results show that the oxygen and chlorophyll anomalies in the upper ocean exhibit different seasonal responses to ENSO. While both the summer and winter season biological responses significantly lag ENSO, the concentration of oxygen and phytoplankton during summer (winter) has no (large) concurrent covariability with ENSO. Given a strong negative correlation between chlorophyll-based indices and El Niño events, increasing mean ocean temperatures and ocean extreme events may induce lower upper-ocean oxygen levels, leading to possible risks in the ecosystem over the tropical Pacific Ocean.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"43 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88389359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4430
Scott Shin, Juliana Caulkins, Leya Joykutty
As society is becoming more reliant on fuels, a more sustainable form of energy must be investigated. Another presiding issue is the output of cellulose left over from other plants that are used for biofuels, such as corn. However, the microalgae, Chlamydomonas Reinhardtii, a unicellular organism, is an ideal source of energy, as there is evidence that it contains the genes that are responsible for the encoding of Cellulases, which allow for the degradation of cellulose, such as endoglucanases. Chlamydomonas Reinhardtii typically lives in both soil and water environments, a photosynthetic organism that utilizes light as an energy source. The uncommon trait for microalgae to express cellulase allows for external sources of carbon to be utilized by the microorganism, which could affect the biological output of macromolecules common in biofuels such as lipids and carbohydrates. The study aims to compare not only the cellulase expression levels of Chlamydomonas Reinhardtii, but also see how the lipid output of the microalgae compares to other microorganisms used in the biofuel industry such as Chlorella Vulgaris, another phototrophic microalgae, which is used for direct fuel. Additionally, Trichoderma Reesei will also be compared, which is another microorganism that is used for biofuel production. However, the industry utilizes Trichoderma Reesei’s ability to produce cellulase, rather than just taking directly from the microorganism. The conclusions unfortunately did not show any cellulase expression, and biofuel output favored algae
{"title":"The Potential of microalgae for cellulose degradation and utilization for biofuel application","authors":"Scott Shin, Juliana Caulkins, Leya Joykutty","doi":"10.47611/jsrhs.v12i2.4430","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4430","url":null,"abstract":"As society is becoming more reliant on fuels, a more sustainable form of energy must be investigated. Another presiding issue is the output of cellulose left over from other plants that are used for biofuels, such as corn. However, the microalgae, Chlamydomonas Reinhardtii, a unicellular organism, is an ideal source of energy, as there is evidence that it contains the genes that are responsible for the encoding of Cellulases, which allow for the degradation of cellulose, such as endoglucanases. Chlamydomonas Reinhardtii typically lives in both soil and water environments, a photosynthetic organism that utilizes light as an energy source. The uncommon trait for microalgae to express cellulase allows for external sources of carbon to be utilized by the microorganism, which could affect the biological output of macromolecules common in biofuels such as lipids and carbohydrates. The study aims to compare not only the cellulase expression levels of Chlamydomonas Reinhardtii, but also see how the lipid output of the microalgae compares to other microorganisms used in the biofuel industry such as Chlorella Vulgaris, another phototrophic microalgae, which is used for direct fuel. Additionally, Trichoderma Reesei will also be compared, which is another microorganism that is used for biofuel production. However, the industry utilizes Trichoderma Reesei’s ability to produce cellulase, rather than just taking directly from the microorganism. The conclusions unfortunately did not show any cellulase expression, and biofuel output favored algae","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"12 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86602946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4231
Aishwaroopa Narayanan
In this paper, we show that it is possible to use EEG data to detect eye movement using machine learning. By recognizing eye movement through EEG results, our goal is to help individuals with disabilities better control object movement and perform daily activities independently. This is especially important as many disabled individuals rely on assistance from others for their daily needs, which can be burdensome for the person providing help. To achieve these objectives, we trained different machine learning models using a data set of eye-state classification from Kaggle. We analyzed the results to assess the accuracy of a KNN (K Nearest Neighbors) model. With the model achieved an accuracy of 95.23% in detecting eye movement in patients. These findings suggest that the model could be effectively utilized in the future, with further research to assist individuals with disabilities. Overall, our research suggests that it is possible to recognize eye movement through EEG results reliably. Further research in this area could lead to the development of more effective and personalized interventions for individuals with poor hand-eye coordination.
{"title":"Using EEG Data to Detect Eye Movement","authors":"Aishwaroopa Narayanan","doi":"10.47611/jsrhs.v12i2.4231","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4231","url":null,"abstract":"In this paper, we show that it is possible to use EEG data to detect eye movement using machine learning. By recognizing eye movement through EEG results, our goal is to help individuals with disabilities better control object movement and perform daily activities independently. This is especially important as many disabled individuals rely on assistance from others for their daily needs, which can be burdensome for the person providing help. To achieve these objectives, we trained different machine learning models using a data set of eye-state classification from Kaggle. We analyzed the results to assess the accuracy of a KNN (K Nearest Neighbors) model. With the model achieved an accuracy of 95.23% in detecting eye movement in patients. These findings suggest that the model could be effectively utilized in the future, with further research to assist individuals with disabilities. Overall, our research suggests that it is possible to recognize eye movement through EEG results reliably. Further research in this area could lead to the development of more effective and personalized interventions for individuals with poor hand-eye coordination.","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"27 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89417743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4347
Ann Song
Alzheimer's Disease (AD) affects approximately 50 million individuals worldwide and is estimated to rise to 152 million by 2050. There is currently no treatment for AD that halts the progression from cognitively normal (CN) and/or mild cognitive impairment (MCI) to AD. The ability to predict disease progression will allow for early treatment. While Machine Learning (ML) has been successful in diagnosing the cognitive state, further improvement is necessary for predicting progression. In this study, Random Forest and Bagging Decision Tree Recursive Feature Elimination (RFE) was utilized to ascertain the cognitive state and forecast progression. Clinical diagnoses, demographics, and post-processed PET and MRI scans used in this study were obtained from the Open Access Series of Imaging Studies (OASIS). The findings suggest that aging and lower levels of education are associated with higher risk. The study found that ML using post-processed MRI and PET scans, particularly RFE ML, is effective in diagnosing cognitive states with 90% accuracy. It can predict progression from CN to MCI or AD with 85% accuracy, which is significantly higher than the average reported in literature. Patients with progression from CN to AD were distinguished by elevated amyloid deposition, hippocampus and amygdala atrophy, left accumbens atrophy, thinning of the left hemisphere temporal, and enlarged inferior lateral ventricles. The study demonstrated that RFE ML is effective in diagnosing and predicting the progression of AD. Future studies will concentrate on identifying the specific regions of amyloid plaque that have the most significant impact on cognitive state and progression.
{"title":"Using Machine Learning to Forecast Progression from Cognitively Normal to Alzheimer's Disease","authors":"Ann Song","doi":"10.47611/jsrhs.v12i2.4347","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4347","url":null,"abstract":"Alzheimer's Disease (AD) affects approximately 50 million individuals worldwide and is estimated to rise to 152 million by 2050. There is currently no treatment for AD that halts the progression from cognitively normal (CN) and/or mild cognitive impairment (MCI) to AD. The ability to predict disease progression will allow for early treatment. While Machine Learning (ML) has been successful in diagnosing the cognitive state, further improvement is necessary for predicting progression. In this study, Random Forest and Bagging Decision Tree Recursive Feature Elimination (RFE) was utilized to ascertain the cognitive state and forecast progression. Clinical diagnoses, demographics, and post-processed PET and MRI scans used in this study were obtained from the Open Access Series of Imaging Studies (OASIS). The findings suggest that aging and lower levels of education are associated with higher risk. The study found that ML using post-processed MRI and PET scans, particularly RFE ML, is effective in diagnosing cognitive states with 90% accuracy. It can predict progression from CN to MCI or AD with 85% accuracy, which is significantly higher than the average reported in literature. Patients with progression from CN to AD were distinguished by elevated amyloid deposition, hippocampus and amygdala atrophy, left accumbens atrophy, thinning of the left hemisphere temporal, and enlarged inferior lateral ventricles. The study demonstrated that RFE ML is effective in diagnosing and predicting the progression of AD. Future studies will concentrate on identifying the specific regions of amyloid plaque that have the most significant impact on cognitive state and progression. \u0000 ","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91041863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-31DOI: 10.47611/jsrhs.v12i2.4380
Wudi Ding, Roger Worthington, Bridget Hamill
With the invention of technology devices such as wireless headphones and portable Bluetooth speakers, teenagers have gradually become heavily reliant on music during exercising/practicing. To find the underlying reason for this habit, an anonymous survey was sent out to 50 high school boarding students in America on 9th December 2022 regarding the purpose of listening to music during exercising/practicing, and their music preferences. Music supports both mental and physical aspects during athletic training. A slight correlation was found between music preferences specifically for the purpose of athletic training, and a slight correlation was found between preferences for genres of music and preference for each aspect of music.
{"title":"Non-Athlete High School Students' Music Usage During Exercise","authors":"Wudi Ding, Roger Worthington, Bridget Hamill","doi":"10.47611/jsrhs.v12i2.4380","DOIUrl":"https://doi.org/10.47611/jsrhs.v12i2.4380","url":null,"abstract":"With the invention of technology devices such as wireless headphones and portable Bluetooth speakers, teenagers have gradually become heavily reliant on music during exercising/practicing. To find the underlying reason for this habit, an anonymous survey was sent out to 50 high school boarding students in America on 9th December 2022 regarding the purpose of listening to music during exercising/practicing, and their music preferences. Music supports both mental and physical aspects during athletic training. A slight correlation was found between music preferences specifically for the purpose of athletic training, and a slight correlation was found between preferences for genres of music and preference for each aspect of music. ","PeriodicalId":46753,"journal":{"name":"Journal of Student Affairs Research and Practice","volume":"11 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81156161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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