Protective effect of grifolin against brain injury in an acute cerebral ischemia rat model

Purpose: To evaluate the protective effects of grifolin against brain injury in an acute cerebral ischemia rat model.Methods: Rats were assigned to five groups: control, negative control, and grifolin (50, 100, and 200 mg/kg, p.o.) treated groups, which received the drug for 2 weeks. All the animals were sacrificed at the end of the protocol, and tissue homogenates were prepared from isolated brain tissue. Glutathione peroxidase (GPX), superoxide dismutase (SOD), malondialdehyde (MDA), and nitric oxide (NO), as oxidative stress indicators, were determined for the tissue homogenates of the ischemic rats. Inflammatory mediators (cytokines and nuclear factor kappa B p65, NF κB), DNA damage, and ATP and caspase 3 levels in the tissue homogenates were also assessed.Results: Treatment with grifolin increased SOD and GPX significantly and decreased MDA and NO levels in tissue homogenates of the cerebral ischemic rats compared with those in the negative control group (p < 0.05). Treatment with grifolin also attenuated the altered levels of inflammatory mediators (cytokines and NF-κB), caspase 3, and ATP levels in the tissue homogenate of cerebral ischemic rats (p < 0.05). The results of comet assay on the tissue homogenate suggest that treatment with grifolin reduced or prevented damage.Conclusions: The results show that treatment with grifolin protects against neuronal damage in acute cerebral ischemic rats via its anti-inflammatory and anti-oxidant properties.Keywords: Neuroprotection, Cerebral ischemia, Brain injury, DNA, Grifolin, Anti oxidan

Linearized Relative Positional Encoding

Relative positional encoding is widely used in vanilla and linear transformers to represent positional information. However, existing encoding methods of a vanilla transformer are not always directly applicable to a linear transformer, because the latter requires a decomposition of the query and key representations into separate kernel functions. Nevertheless, principles for designing encoding methods suitable for linear transformers remain understudied. In this work, we put together a variety of existing linear relative positional encoding approaches under a canonical form and further propose a family of linear relative positional encoding algorithms via unitary transformation. Our formulation leads to a principled framework that can be used to develop new relative positional encoding methods that preserve linear space-time complexity. Equipped with different models, the proposed linearized relative positional encoding (LRPE) family derives effective encoding for various applications. Experiments show that compared with existing methods, LRPE achieves state-of-the-art performance in language modeling, text classification, and image classification. Meanwhile, it emphasizes a general paradigm for designing broadly more relative positional encoding methods that are applicable to linear transformers. The code is available at https://github.com/OpenNLPLab/Lrpe.Comment: Reviewed by TMLR, decision pending. Yiran Zhong is the corresponding author. Code is available at https://github.com/OpenNLPLab/Lrp

3,6-Diphenyltetrazine as Cathode Active Material for Sodium Ion Batteries

3, 6-diphenyltetrazine (DPT) is an electron-deficient π-conjugated molecule with a perfectly planar structure and high crystallinity. In this study, discharge-charge tests of crystalline DPT as a cathode material for sodium ion batteries were conducted. DPT showed an initial reversible capacity of 102 mAh/g (theoretical capacity 114 mAh/g), corresponding to one electron reaction. The plateau of the discharge-charge profiles was observed at 1.9–2.1 V vs. Na/Na⁺. According to the ex-situ XRD, FT-IR, and XPS measurements to investigate the discharge-charge mechanism, the redox center was identified as the conjugated tetrazine ring. DPT was in a crystalline form in both the charged and discharged state and indicated the potential as a reversible Na ion host

Rapid appearance quality of rice based on machine vision and convolutional neural network research on automatic detection system

IntroductionIn the process of rice production and storage, there are many defects in the traditional detection methods of rice appearance quality, but using modern high-precision instruments to detect the appearance quality of rice has gradually developed into a new research trend at home and abroad with the development of agricultural artificial intelligence.MethodsIn this study, we independently designed a fast automatic rice appearance quality detection system based on machine vision technology by introducing convolutional neural network and image processing technology. In this study, NIR and RGB images were generated into five-channel image data by superposition function, and image are preprocessed by combining the Watershed algorithm with the Otus adaptive threshold function. Different grains in the samples were labeled and put in the convolutional neural network for training. The rice grains were classified and the phenotype data were analyzed by selecting the optimal training model to realize the detection of rice appearance quality.Results and discussionThe experimental results showed that the resolution of the system could reach 92.3%. In the detection process, the system designed with this method not only reduces the subjectivity problems caused by different detection environments, visual fatigue caused large sample size and the inspector’s personal factors, but also significantly improves the detection time and accuracy, which further enhances the detection efficiency of rice appearance quality, and has positive significance for the development of the rice industry

Early-initiated childhood reading for pleasure : associations with better cognitive performance, mental well-being and brain structure in young adolescence

Childhood is a crucial neurodevelopmental period. We investigated whether childhood reading for pleasure (RfP) was related to young adolescent assessments of cognition, mental health, and brain structure. We conducted a cross-sectional and longitudinal study in a large-scale US national cohort (10 000 + young adolescents), using the well-established linear mixed model and structural equation methods for twin study, longitudinal and mediation analyses. A 2-sample Mendelian randomization (MR) analysis for potential causal inference was also performed. Important factors including socio-economic status were controlled. Early-initiated long-standing childhood RfP (early RfP) was highly positively correlated with performance on cognitive tests and significantly negatively correlated with mental health problem scores of young adolescents. These participants with higher early RfP scores exhibited moderately larger total brain cortical areas and volumes, with increased regions including the temporal, frontal, insula, supramarginal; left angular, para-hippocampal; right middle-occipital, anterior-cingulate, orbital areas; and subcortical ventral-diencephalon and thalamus. These brain structures were significantly related to their cognitive and mental health scores, and displayed significant mediation effects. Early RfP was longitudinally associated with higher crystallized cognition and lower attention symptoms at follow-up. Approximately 12 h/week of youth regular RfP was cognitively optimal. We further observed a moderately significant heritability of early RfP, with considerable contribution from environments. MR analysis revealed beneficial causal associations of early RfP with adult cognitive performance and left superior temporal structure. These findings, for the first time, revealed the important relationships of early RfP with subsequent brain and cognitive development and mental well-being

Emerging roles and potential application of PIWI-interacting RNA in urological tumors

The piRNA (PIWI-interacting RNA) is P-Element induced wimpy testis (PIWI)-interacting RNA which is a small molecule, non-coding RNA with a length of 24-32nt. It was originally found in germ cells and is considered a regulator of germ cell function. It can interact with PIWI protein, a member of the Argonaute family, and play a role in the regulation of gene transcription and epigenetic silencing of transposable factors in the nucleus. More and more studies have shown that piRNAs are abnormally expressed in a variety of cancer tissues and patient fluids, and may become diagnostic tools, therapeutic targets, staging markers, and prognostic evaluation tools for cancer. This article reviews the recent research on piRNA and summarizes the structural characteristics, production mechanism, applications, and its role in urological tumors, to provide a reference value for piRNA to regulate urological tumors

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science