The "Q-Collar" is a device designed to mitigate head injuries, particularly in scenarios such as contact sports and military operations where trauma to the head is a significant risk. It operates by exerting an external compressive force on the jugular veins, which effectively increases blood volume within the brain to counteract the "slosh" effect—that is, the movement of the brain within the skull during sudden impacts. The research documented in this literature review investigates the Q-Collar's efficacy by examining various clinical studies, as well as animal models, to understand its role and proven effects on reducing head trauma. This review includes 21 studies that were identified through a literature search using keywords related to "Jugular Vein Compression Collar" in the PUBMED database. The discussion highlights the physiological mechanism behind the Q-Collar's function: by compressing the jugular veins, it reduces the compliance of the cranial cavity, thereby stabilizing the brain and decreasing the risk of traumatic brain injury. The review finds evidence of the Q-Collar's effect in increasing intracranial and intraocular pressures, which suggests a mechanical countermeasure to the destabilizing effects of brain movement after an impact. Moreover, the findings include significant data from studies on high school athletes and special forces personnel, showing that Q-Collar users exhibited fewer microstructural brain alterations, better maintenance of cognitive functions, and fewer changes in white matter integrity than their non-collared counterparts. Preclinical small animal studies similarly present a reduction in inflammatory biomarkers associated with brain injury, indicating the collar's potential in protecting against histopathological changes. Research on the Q-Collar's use following blast exposure in military training shows additional benefits in memory function protection and auditory processing, as well as reduced auditory injury and tympanic membrane rupture, augmenting the case for the collar's protective effects. Finally, the review also touches on a potential application for patients with orthostatic hypotension, given the collar's influence on carotid baroreceptor-induced sympathetic activity. Hence, while the body of evidence under review supports the notion that the Q-Collar may be a valuable adjunct to helmets in the prevention of traumatic brain injuries, the review calls for further, longer-term studies to fully understand the extent of its benefits and potential limitations. The collective findings so far point towards a positive impact of the Q-Collar in scenarios of head trauma, with the device contributing to protective anatomical and functional changes within the brain. However, the nature of these studies—predominantly short-term and focusing on immediate or season-long effects—highlights the need for future research that extends beyond these temporal boundaries.
Abstract The "Q-Collar" is a device designed to mitigate head injuries, particularly in scenarios such as contact sports and military operations where trauma to the head [...]
The current scientific understanding of adolescent depression is incomplete, requiring further research and attention. This review of Selective Serotonin Reuptake Inhibitors (SSRIs) in the context of adolescent depression seeks to inform adolescents about the mechanisms in which SSRIs affect the body, common side effects associated with this class of antidepressants, and the efficacy rates of SSRIs in their population group. This review also underscores the gaps in research regarding both adolescent depression and adolescent antidepressant usage. When considering the use of prescription drugs, especially ones that have large implications towards neurological function, it is essential to understand all facets of the medication. This review provides adolescents and their families with direct information that will inform their thinking about SSRI use. This review considers academic and popular sources in collating evidence for the impact and efficacy of SSRIs on adolescents. Through careful analysis of the consequences and benefits associated with SSRI usage, this review provides a comprehensive scientific overview of SSRIs. To inform adolescents and their families, this review will first offer a mechanistic overview of the SSRI drug class, next evaluate evidence for the neurological effects of SSRIs on the adolescent brain, and then highlight clinical testing of two specific SSRIs, Fluoxetine and Escitalopram. Both the implications of SSRIs on adolescent brain development and their possible short-term and long-term negative side effects are considered in this review. Most importantly, this review draws attention to the multitude of unknowns surrounding SSRI usage in adolescents and urges researchers to continue evaluating the implications of early antidepressant usage. With an adolescent centered viewpoint and a focus on extensivity, this report provides adolescents and their families with the resources to make personalized decisions about SSRI exposure.
Abstract The current scientific understanding of adolescent depression is incomplete, requiring further research and attention. This review of Selective Serotonin Reuptake Inhibitors [...]
This experiment investigated how the sound of stream water affects the growth of ''Cucumis sativus'' plants under drought stress. Plants can recognize vibrations (sounds). Additionally, plants limit their growth when they receive suboptimal amounts of water to preserve resources. The purpose of this study was to determine how the sound of stream water affects plants under drought stress, in order to determine if it will affect their growth. Four groups of 10 plants were germinated, with 3 experimental groups treated with the sound of stream water for different amounts of time (0.5, 2, or 3 hrs; control group had 0 hr). Once plants were germinated, drought stress began in all groups, but each group’s respective amount of treatment stayed the same (0, 0.5, 2, 3 hrs). During all stages of the experiment, the plants were watered during the period of sound treatment to develop a correlation. At the end of the experiment, it was found that growth significantly increased corresponding to increasing treatment time. Group 4 (3 hr) had the largest increase in growth compared to control, while group 2 (0.5 hr) had the least improvement over control. Subsequently, the sound of stream water treatment beneficially impacts plant growth, directly correlated to exposure.
Abstract This experiment investigated how the sound of stream water affects the growth of ''Cucumis sativus'' plants under drought stress. Plants can recognize vibrations [...]
'''This thesis studied the definition of a green building and the elements associated with the construction of sustainability. There are many rating systems available across the country both private and public. The most well-known is the LEED rating system developed by the United States Green Building Council. LEED has several rating programs now available. In this thesis, I will discuss why Green Building is more significant than conventional building and methods of Green Building like construction Phases. Conventional buildings produce high amounts of greenhouse gas, a large quantity of construction and demolition debris (which decrease landfill capacity), disturb natural resources, and consume much energy and water. Implementation of various simple and sustainable techniques practiced individually can help in many ways to create an environmentally friendly structure, consume minimal natural resources and at the same time be economical. This paper tries to study the Green Building construction method and innovative technologies that enable us to create a sustainable structure. It also includes design concepts of Green Building, methodology and materials requirements, and ideas and suggestions to create a checklist for a builder to refer to the various suggestions in every phase of construction of a general structure. '''Key Words: '''Green Building, Construction of Sustainability, LEED, Minimal Natural Resources, and Sustainable Structure.
Abstract '''This thesis studied the definition of a green building and the elements associated with the construction of sustainability. There are many rating systems available [...]
Eutrophication affects 65% of coastal and 53% of freshwater regions in the US but no scalable cyanobacterial degradation methods exist. Piezocatalysis harnesses mechanical energy to produce reactive oxygen species (ROS) in water, which degrade organic pollutants by oxidation without generating toxic residues. This study designed a novel hydro-turbine using bluff bodies that maximize kinetic flow energy available for aquatic remediation via piezocatalysis. MoS2-doped films exhibited first-order ROS chain reaction production when agitated in Anabaena (20 μg/L), and has potential to inhibit cyanobacterial positive feedback growth by converting nitric products into reactive nitrogen species. The inverted piezo-active flag model was computationally optimized to enhance polarization with turbulent energy and ensure rapid charge dispersion with flow-induced vibrations. The 90° upstream triangle yielded maximum coefficients by effective flow separation (12.6 drag, 18.6 lift) and a flag length ratio of 0.75 was optimal while avoiding canceling zones. The optimized piezocatalytic hydro-turbine design yielded 78% remediation under 800gph for 60s (p=0.032). A minimum flow speed was identified by energy/order to be 0.5m/s, with 0.7W/(1g MoS2). Application depth of 0.3m-2m was identified with cyanobacterial concentration, turbulence needed for crossflow effectiveness, and speed; conditions are within industrial scalability standards. Chain reactions allow the remediation of still-water and high-volume downstream regions through tributaries. In the future, the device can be studied in environments with varying meander speeds and upstream ROS exposure times. The device presents a potential industrial eutrophication treatment applicable in ~93% of fluvial environments.
Abstract Eutrophication affects 65% of coastal and 53% of freshwater regions in the US but no scalable cyanobacterial degradation methods exist. Piezocatalysis harnesses mechanical [...]
Previous studies show that plants can recognize and respond to different sounds, such as insect-chewing and running water. Prior research demonstrated that plants can learn and respond to stimuli like touch; it is unclear if plants can have a conditioned response to sound. Our former study found that plants can recognize the sound of stream water (SW) and continue growing throughout drought. Experimentation aimed to investigate if Cucumis sativus plants can grow throughout drought when conditioned to associate non-water sounds to the sound of water. The hypothesis was that cucumber plants would respond to sounds besides SW after auditory conditioning. Some groups were given a training phase (TP) to help them develop an association between their chosen sound and watering session, while others were not given a TP. After the TP was concluded, all groups went into drought stress, while still being exposed to sound. Control groups had no exposure to sound at all. Shoot lengths, leaf lengths, and chlorophyll contents were collected throughout and at the end of the experiment. Overall, groups with rock music (RM) and TP and the sound of water with or without TP had a visible increase in growth, while RM without TP had comparable results to control. Future research will determine if the ability of plants to respond to auditory conditioned learning is altered depending on the time of training relative to the stages of a plant’s life cycle, similarly to a person’s capability to learn and develop at different stages of life.
Abstract Previous studies show that plants can recognize and respond to different sounds, such as insect-chewing and running water. Prior research demonstrated that plants can learn [...]
Depression is a global mental health issue generalized by a decrease in mood and satisfaction. Treatments for individuals afflicted with depressive symptoms include prescribed medications that require diagnosis to acquire. The purpose of this investigation was to accurately assist psychiatrists in diagnosis procedures to prevent both false positive and false negative conclusions by utilizing machine learning on social media messages. This was done by training a machine learning algorithm which accurately predicted and detected depressive behaviors and communications. As social media messages encompass individual’s general communications among long periods of time with high consistency and frequency, I hypothesized that social media messages could be used as a method to both train an accurate and consistent machine learning model for the detection of depression. Social media dataset messages rely on self-reported diagnoses. Based on F1 accuracy normalization across machine learning HyperTuning, average accuracy indicated 97% [+/-0.5%] among a ~7600 sample dataset. Utilizing generalized sentimental analysis has shown less accurate results (~80%) but needs further research.
Abstract Depression is a global mental health issue generalized by a decrease in mood and satisfaction. Treatments for individuals afflicted with depressive symptoms include prescribed [...]
Phytoplankton play a major role in marine ecosystem health. They form the base of aquatic food webs, but under conditions of nutrient loading and high stratification, they can develop into harmful algal blooms that produce toxins harmful to humans and wildlife. Ongoing phytoplankton blooms have been observed in Long Island’s (LI) coastal waters for the past half-century, but there is a lack of a comprehensive view of phytoplankton spatiotemporal distribution and their driving factors due to the analysis of specific sampling sites, species, and years. Thus, this study obtained 20 years of chlorophyll-a, climate, and nutrient remote sensing and in situ data from the ERDDAP data server and the CTDEEP Long Island Sound Water Monitoring Program to establish phytoplankton phenology using the threshold criterion and cumulative sum of anomalies methods and to investigate regional differences, influencing factors, and interannual trends using correlation and linear trend analysis. The phenology of summer-autumn blooms in Long Island Sound (LIS) was associated with high sea surface temperatures (r = -0.46, p < 0.01). In contrast, winter-spring blooms were most strongly correlated with low salinity (r = -0.52, p < 0.01), indicating P-rich Connecticut river discharges as the dominant nutrient source. However, phytoplankton in LI’s southern shores lack access to river outlets, so phytoplankton production was driven by deep winter mixings from low SST that replenish surface water nutrient levels (r = -0.71, p < 0.05). Finally, our results showed strong decreasing interannual trends of autumn chlorophyll-a levels from 2003-2022 (r < -0.66, p < 0.05), potentially due to heightened N-limitation. Hence, the effects of declining phytoplankton productivity on LI’s fisheries and marine ecosystems should be further investigated.
Abstract Phytoplankton play a major role in marine ecosystem health. They form the base of aquatic food webs, but under conditions of nutrient loading and high stratification, they [...]
This study introduces the Epilet Band, an innovative wristwatch-like device employing machine learning algorithms to detect Convulsive Epileptic Seizures (CES) in real-time. Distinguished by its use of Federated Machine Learning (FML), it ensures maximum data privacy and minimal data transfer. The Epilet band uses the DAP (detection, avoidance and prevention) method and incorporates a carefully selected array of sensors– accelerometer, gyroscope, temperature sensor, light intensity sensor and a digital microphone– all integrated into the Arduino Nano 33 BLE Sense. This study developed a seizure generator that replicates the movement patterns observed during epileptic seizures. Utilizing the Edge Impulse training platform, a machine learning model is trained to recognize these seizures, continually refining its accuracy through retraining and remodeling processes. The Epilet Band works by differentiating epileptic seizures from day-to-day activities by detecting muscle movement patterns produced by the two actions. Moreover, the Epilet Band actively detects ambient temperature, humidity, noise levels and light intensity (for example flashing lights over a period of 5 seconds) and alerts the user’s caretaker in case these conditions align with those which trigger epileptic seizures, with a message that an attack could be triggered. Research findings demonstrate the efficacy of the proposed Epilet band in detecting uncontrollable convulsive seizures timely. The machine learning model allows for improved accuracy of the detection algorithm as the number of trials and sample size increase with time. Being alerted of a potential seizures or triggers affords caretakers the opportunity to act fast to reduce fatalities or unfortunate accidents caused by a sudden onset of seizure. This exhaustive study underscores the innovation and scientific diligence captured by the Epilet Band, illustrating a future where epilepsy management is significantly empowered through technology trained by existing data, offering new horizons for individuals afflicted with this condition.
Abstract This study introduces the Epilet Band, an innovative wristwatch-like device employing machine learning algorithms to detect Convulsive Epileptic Seizures (CES) in real-time. [...]
The dentate gyrus is a unique part of the brain because it is known for housing neurogenesis in the adult brain, a process which normally stops early in development. This makes the dentate gyrus an area of great interest, especially in combating neurodegenerative diseases. This area, as well as the entire nervous system, is composed of both neurons, which send and receive signals, and glial cells, which are responsible for supporting neurons. Glial cells including astrocytes, oligodendrocytes, and ependymal cells are present in both the central nervous system and peripheral nervous system, while Schwann Cells are present only in the peripheral nervous system, and microglia are present only in the central nervous system. Apoptosis and autophagy are both processes which degrade and recycle materials. Autophagy degrades materials inside the cell, such as organelles and proteins, and uses lysosomes to carry out the process. Apoptosis consists of degrading old or damaged cells, and is known as programmed cell death. Many proteins affect these processes including Beclin-1, UC3, p62, and the BCL-2 family of proteins. Some studies have used machine learning algorithms in conjunction with simulation and statistical software to study these proteins and the processes they are involved in. Several areas of research remain unfilled, especially in the area of glial cells and the proteins involved in autophagy and apoptosis, as not many machine learning studies have examined this.
Abstract The dentate gyrus is a unique part of the brain because it is known for housing neurogenesis in the adult brain, a process which normally stops early in development. [...]
Editor-in-Chief, Milan Toma, Ph.D., SMIEEE, Assistant Professor, Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology; Senior IEEE Member.
Thomas Pham, M.H.R., M.P.A., Reading Partners VISTA; Americorps Member (Volunteer and Organizer); CEO of Clarity Consultants, LLC.
Bonnie A.B. Blackwell, Ph.D., F.G.A.C., F.G.S.A., Research Scientist in the Chemistry Dept. at Williams College, Williamstown, MA. Director with the RFK Science Research Institute, Glenwood Landing, NY.
Joel Blickstein, Ph.D., Co-founder and co-director of the RFK Science Research Institute, Glenwood Landing, NY.
Raymond K.F. Lam, Sc.D., Assistant Professor, Department of Engineering Technology, Queensborough Community College, NY.
Michael Nizich, Ph.D., Director, Entrepreneurship & Technology Innovation Center; Director, NSA/DHS CAE Cyber Defense Education Program; Adjunct Associate Professor, Department of Computer Science, New York Institute of Technology.
Yusui Chen, Ph.D., Assistant Professor, Department of Physics, College of Arts & Sciences, New York Institute of Technology.