| 1 |
Author(s):
Dwight Qing Teng and Xixian Li .
Page No : 1-8
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Smoothened Inhibitors to Block the Sonic Hedgehog Signaling for Cancer Treatment
Abstract
The Sonic Hedgehog (sHH) pathway is vital for embryonic development and adult tissue maintenance. Aberrant sHH pathway activation is implicated in tumors like basal cell carcinoma (BCC), medulloblastoma, and pancreatic cancer, prompting significant efforts to develop pathway inhibitors. Smoothened (SMO), a pivotal protein in the sHH signaling pathway, is a key drug target for the treatment of tumors. To date, many chemical compounds have been developed to target SMO, including vismodegib and sonidegib which have been approved by FDA to treat advanced BCC with aberrant sHH pathway activation. In this article, we review recent advances in drug development by targeting SMO to inhibit the sHH signaling pathway for tumor treatment.
| 2 |
Author(s):
Nathan Lin Xiao, Wei Cheng.
Page No : 9-23
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Deep Neural Network on Detection of Road Distress Using Mixture of Predicted And Observed Data
Abstract
Roadway distress detection is essential for ensuring a safe and comfortable driving environment. However, given the irregular shape, small area size, and occasionally very large number, of the road distress objects, it is often laborious to label the distress instances during the training process under the fully supervised algorithm. To address this issue, the study strives to apply semi-supervised learning for distress detection that claims to reduce the cost associated with the labeling process, while maintaining or even improving the learning accuracy in some situations. The research features three distinct backbones of Mask R-CNN models, Unmanned Aerial System imagery of two resolutions, three levels of pseudo-labeled data, eleven threshold values and two types of assessment (that is, in-resolution and out-of-resolution). The results demonstrate that semi-supervised Mask R-CNN models are effective in detecting road distress. Nonetheless, the sensitive analysis is recommended in the future research to identify the optimal pseudo ratio that could generate the highest prediction accuracy.
| 3 |
Author(s):
S. Pushpavathi, Sri Ranjani, Shravani A Basapur, Aschelle Tricia Rodrigues, Venkata Krishna Bayineni.
Page No : 24-34
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Harnessing Bacterial Consortia and Green-Synthesized Metal Oxide Nanoparticles for Photocatalytic Dye Degradation
Abstract
This study introduces an innovative approach to degrade hazardous dyes by leveraging eco-friendly nano photocatalysts and microbial consortia. The research assesses the effectiveness of this integrated system in dye degradation for wastewater treatment and environmental remediation. Utilizing a bacterial consortium isolated from drains in the textile dyeing industry, alongside a photocatalytic process (Metal oxide/UV), the study demonstrates significant results. When applied individually, both biological and photochemical methods displayed limited decolorization efficiency. However, after 5 days under specific conditions (37°C, pH 7, and 200 mg/L dye concentration), bacterial degradation achieved a noteworthy 72.38% decolorization rate. In comparison, UV photocatalytic treatment with zinc oxide nanoparticles alone yielded a modest 23.5% decolorization. The combined approach, integrating UV-metal oxide treatment with bacterial consortia, showcased the most promising outcome. Notably, the highest bacterial degradation rate of 83.8% was observed when the dye mix sample underwent pretreatment with zinc oxide nanoparticles synthesized using leaves of Parmentiera cereifera, followed by 77.46% with Hibiscus schizopetalus, and 73.9% with Combretum rotundifolium. These previously unexplored plants exhibit potential as sources for green-synthesized metal oxide nanoparticles for dye degradation applications. Further optimization of degradation parameters could unlock the full potential of these nanoparticles.
| 4 |
Author(s):
Shaarda Krishna.
Page No : 35-51
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A Review on Recent Advances in Vision Prostheses
Abstract
Vision Loss causes both social and economic alterations in a great number of individuals. Vision Loss, or Blindness in particular, incurs a large economic burden on the United States. Moreover, blindness and Vision Loss have several negative effects on its population, including economic instability, as well as psychological strain. Vision loss and blindness may result from damage to the retina, optic nerve, or genetic defects. Traditional treatments of vision loss may target these defects or prevent damage to the region in question but are limited in that several of these conditions progress quickly, irreversibly, and cannot be treated once the patient is completely blind. Therefore, treatments using electrical stimulation have been developed to restore vision in blind patients. Such methods include Intracortical, Retinal, Optic Nerve, and Lateral Geniculate Nucleus Prosthesis. The Intracortical Vision Prosthesis (ICVP) is implanted on the visual cortex, the retinal prosthesis on the retina, the optic nerve prosthesis on the surface of the optic nerve, and the LGN prosthesis through Deep Brain stimulation. All four mechanisms attempt to produce phosphenes through electrical stimulation i.e., perceptions of light that imitate an image, but vary in image characteristics; they pose several challenges, however, such as the necessity to be both biocompatible and suitable for implantation without incurring damage. Moreover, socioeconomic effects of complete blindness also limit the ability to test and implement treatments. Overall, vision prostheses have become increasingly developed, sophisticated, and advanced, and may, in the future, be utilized as a blindness treatment for the common public.
| 5 |
Author(s):
Chris Cheung.
Page No : 52-63
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Spatial and Temporal Synergy: Advanced Autoregression Models for Global Agricultural Development Insights
Abstract
Accurate crop production predictions are crucial for global food security and effective agricultural policymaking. Traditional predictive models often struggle to capture the complex spatiotemporal dynamics qualities in agricultural data. This research aims to improve crop production index predictions by integrating temporal agricultural data from 1990 to 2021 with geospatial information for 162 countries. Advanced time-related architectures, including autoregression (AR), vector autoregression (VAR), spatial temporal autoregressive (STAR), and spatial temporal vector autoregressive (STVAR) models are explored to address the limitations of traditional methods.
The study also uses geospatial data to improve the spatial influences inside the models. By combining temporal data (number of years) with geospatial coordinates (longitude and latitude), the research develops predictive models that could better capture the underlying patterns affecting crop production. Various model training measurements are applied to optimize model performance.
The outcomes demonstrated that incorporating temporal with spatial data significantly increases the precision of crop yield forecasts as compared to conventional models. The research highlights how the inclusion of both temporal and spatial variables in agricultural predictive modeling can provide useful information for policy makers, farmers and the rest of the actors in agriculture. By creating a platform for using advanced autoregression models and spatiotemporal data integration, it would help improve decision making in agriculture as well as resource management.
| 6 |
Author(s):
Jayden P. Chen.
Page No : 64-72
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Fusion of Neural Networks and Logistic Regression for Predictive Maintenance of Vehicle Engines
Abstract
The rise of predictive maintenance models has revolutionized vehicle maintenance, promising significant improvements in performance and lifespan. This research aims to develop a robust predictive maintenance model for automotive engines using a hybrid approach that combines neural networks and logistic regression models. By analyzing patterns within a publicly available dataset of 19,503 engine cases, which includes features such as engine rotations per minute, temperatures, and pressures, the study trains hybrid models to predict when a vehicle requires maintenance.
The methodology involves preprocessing the dataset, training individual models, and integrating them within a stacked ensemble framework. Neural networks are leveraged for their complex pattern recognition capabilities, and logistic regression models offer interpretability and simplicity. Metrics such as accuracy, precision, and recall evaluate the models’ performance.
The ultimate goal is to enable vehicle owners and mechanics to address potential issues proactively, ensuring better vehicle performance and extending engine lifetimes. The hybrid models show enhanced success compared to traditional models, providing potential contributions to predictive analytics, and a new standard for various industries.
| 7 |
Author(s):
Srishti Iyer, Lananh Ho.
Page No : 73-79
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Treatment Approaches For Amyotrophic Lateral Sclerosis: Conventional Therapies And Innovative Solutions
Abstract
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that impairs motor neuron function, causing progressive muscle weakness and paralysis. ALS is extremely rare, affecting only around 30,000 people in the United States. However, it has a very low survival rate; only 10% of patients survive 10 years after disease onset. While the cause is typically unknown, 5-10% of ALS cases are caused by genetic mutations in the SOD1 and C9orf72 genes. Currently, several FDA-approved therapies for ALS exist. These drugs aim to partially correct the SOD1 mutation, or block cellular and neurological pathways related to motor neuron damage. In clinical studies to date, these drugs either unsuccessfully treat ALS, or are only able to prolong a patient's lifespan up to several months. Scientists are exploring novel therapies that target the root cause of ALS, whether it be damaged neurons, genetic factors, or muscle dysfunction. These therapies include gene therapy, monoclonal antibody therapy, and stem cell therapy. While further research is needed, current data show significant promise, as these treatments directly target the underlying mechanisms of ALS, rather than focusing on select symptoms like conventional approaches, ultimately making new approaches to ALS therapy more sustainable for the future. This paper will discuss and evaluate current FDA-approved therapies for ALS and explore the potential efficacy of novel treatments.
| 8 |
Author(s):
Yifan Zhuo.
Page No : 80-92
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A Comparative Analysis of Gain-of-Function Research and Future Perspectives
Abstract
Gain-of-function (GOF) research is both impactful and controversial, as it involves genetically altering a pathogen to enhance its biological functions. One side believes that GOF research can offer knowledge about deadly pathogens and allow scientists to prevent future outbreaks. However, the other side argues that GOF research risks causing pandemics, making it too dangerous. There is clear disagreement in the scientific community regarding GOF research. This paper presents a comprehensive analysis of the GOF research debate by comparing the arguments and presenting the common grounds between them, as well as the limitations of current literature. Both sides prioritize protecting humankind yet emphasize the need for public involvement in the GOF research debate. Due to the pressing and significant need to make a decision regarding the future of GOF research, more objective papers with updated arguments and data are needed for both sides of the debate, and more effort should be put forward to inform the public so they can be involved in the discussions.