Boosting-Crystal Graph Convolutional Neural Network for Predicting Highly Imbalanced Data: A Case Study for Metal-Insulator Transition Materials.

Applying machine-learning techniques for imbalanced data sets presents a significant challenge in materials science since the underrepresented characteristics of minority classes are often buried by the abundance of unrelated characteristics in majority of classes. Existing approaches to address this focus on balancing the counts of each class using oversampling or synthetic data generation techniques. However, these methods can lead to loss of valuable information or overfitting. Here, we introduce a deep learning framework to predict minority-class materials, specifically within the realm of metal-insulator transition (MIT) materials. The proposed approach, termed boosting-CGCNN, combines the crystal graph convolutional neural network (CGCNN) model with a gradient-boosting algorithm. The model effectively handled extreme class imbalances in MIT material data by sequentially building a deeper neural network. The comparative evaluations demonstrated the superior performance of the proposed model compared to other approaches. Our approach is a promising solution for handling imbalanced data sets in materials science.

Elucidating the Role of Alkali Metal Carbonates in Impact on Oxygen Vacancies for Efficient and Stable Perovskite Solar Cells.

Effectively suppressing nonradiative recombination at the SnO2/perovskite interface is imperative for perovskite solar cells. Although the capabilities of alkali salts at the SnO2/perovskite interface have been acknowledged, the effects and optimal selection of alkali metal cations remain poorly understood. Herein, a novel approach for obtaining the optimal alkali metal cation (A-cation) at the interface is investigated by comparatively analyzing different alkali carbonates (A2CO3; Li2CO3, Na2CO3, K2CO3, Rb2CO3, and Cs2CO3). Theoretical calculations demonstrate that A2CO3 coordinates with undercoordinated Sn and O on the surface, effectively mitigating oxygen vacancy (VO) defects with increasing A-cation size, whereas Cs2CO3 exhibits diminished preferability owing to enhanced steric hindrance. The experimental results highlight the crucial role of Rb2CO3 in actively passivating VO defects, forming a robust bond with SnO2, and facilitating Rb+ diffusion into the perovskite layer, thereby enhancing charge extraction, alleviating deep-level trap states and structural distortion in the perovskite film, and significantly suppressing nonradiative recombination. X-ray absorption spectroscopy analyses further reveal the effect of Rb2CO3 on the local structure of the perovskite film. Consequently, a Rb2CO3-treated device with aperture area of 0.14 cm2 achieves a notable efficiency of 22.10%, showing improved stability compared to the 20.11% achieved for the control device.

Genome-wide association study comparison analysis based on Hanwoo full-sib family.

Objective: The improvement of carcass traits is essential for the Hanwoo industry because of the Hanwoo grade determination system, and Genome-Wide Association Study (GWAS) analysis is an instrumental tool for identifying the genetic factors that impact these traits. While GWAS analysis utilizing family data offers advantages in minimizing genetic bias, research on family-based GWAS in Hanwoo is currently lacking. Methods: This study classified Group A using both parental and offspring genetic information, and Group B based solely on offspring genetic information, to compare GWAS analysis results of Hanwoo carcass traits. Results: 16 significant SNP markers (carcass weight (CWT) 7, back fat thickness (BFT) 3, marbling score (MS) 6) were identified in Group A, and 7 significant SNP markers (CWT 3, eye muscle area (EMA) 1, BFT 1, MS 2) were identified in Group B. Functional annotation analysis revealed only one common function related to carcass traits between the groups, while Protein-protein interaction (PPI) analysis indicated more gene interactions in Group A. The reliability of estimated values for common SNP markers identified between the groups was higher in Group A. Conclusion: GWAS analysis utilizing parental genetic information holds greater potential for application, owing to its higher reliability of estimated values and the ability to explore numerous candidate genes.

Microcurrent Therapy Mitigates Neuronal Damage and Cognitive Decline in an Alzheimer's Disease Mouse Model: Insights into Mechanisms and Therapeutic Potential.

Alzheimer's disease (AD) presents a significant challenge due to its multifaceted nature, characterized by cognitive decline, memory loss, and neuroinflammation. Though AD is an extensively researched topic, effective pharmacological interventions remain elusive, prompting explorations into non-pharmacological approaches. Microcurrent (MC) therapy, which utilizes imperceptible currents, has emerged as a potent clinical protocol. While previous studies have focused on its therapeutic effects, this study investigates the impact of MC on neuronal damage and neuroinflammation in an AD mouse model, specifically addressing potential side effects. Utilizing 5xFAD transgenic mice, we examined the effects of MC therapy on neuronal integrity and inflammation. Our findings suggest that MC therapy attenuates memory impairment and reduces neurodegeneration, as evidenced by improved performance in memory tests and the preservation of the neuronal structure. Additionally, MC therapy significantly decreases amyloid-beta (Aß) plaque deposition and inhibits apoptosis, indicating its potential to mitigate AD pathology. This study determined that glial activation is effectively reduced by using MC therapy to suppress the TLR4-MyD88-NFκB pathway, which consequently causes the levels of inflammatory factors TNF-α, IL-1ß, and IL-6 to decrease, thus implicating TLR4 in neurodegenerative disease-related neuroinflammation. Furthermore, while our study did not observe significant adverse effects, a further clinical trial into potential side effects and neuroinflammatory responses associated with MC therapy is warranted.

Microcurrent therapy as the nonpharmacological new protocol against Alzheimer's disease.

Introduction: Alzheimer's disease (AD) poses an increasing global health challenge and is marked by gradual cognitive deterioration, memory impairment, and neuroinflammation. Innovative therapeutic approaches as non-pharmacological protocol are urgently needed with side effect risk of drugs. Microcurrent therapy, a non-invasive modality involving low-level electrical currents, has emerged as a potential solution to address AD's complex pathogenesis. This study investigates the optimal application of microcurrent therapy as a clinical protocol for AD, utilizing a comprehensive approach that integrates behavioral assessments and neuroinflammation evaluation in a mouse model of dementia. Methods and results: The results reveal that microcurrent therapy holds promise in ameliorating memory impairment and reducing neuroinflammation in AD. Behavioral assessments, including the Novel Object Recognition Test (NOR) and Radial Arm Maze Test (RAM), demonstrated improved cognitive function following microcurrent therapy. Furthermore, microcurrent therapy inhibited expression of neuroinflammatory proteins, including ionized calcium binding adaptor molecule 1 (Iba1), and glial fibrillary acidic protein (GFAP) in current-treated group. Mechanistic insights suggest that microcurrent therapy may modulate neuroinflammation through the regulation of MAPK signaling pathways. Conclusion: This study emphasizes the prospect of microcurrent therapy as a safe and efficacious non-pharmacological strategy for Alzheimer's disease (AD), providing optimism to the countless individuals impacted by this debilitating ailment. These results contribute to the developments of an innovative clinical protocol for AD and recovery from neurological injury, underscoring the significance of investigating unconventional therapeutic approaches for addressing this complex condition.