Frequency-dependent alterations in functional connectivity in patients with Alzheimer’s Disease spectrum disorders

BackgroundIn the spectrum of Alzheimer’s Disease (AD) and related disorders, the resting-state functional magnetic resonance imaging (rs-fMRI) signals within the cerebral cortex may exhibit distinct characteristics across various frequency ranges. Nevertheless, this hypothesis has not yet been substantiated within the broader context of whole-brain functional connectivity. This study aims to explore potential modifications in degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) among individuals with amnestic mild cognitive impairment (aMCI) and AD, while assessing whether these alterations differ across distinct frequency bands.MethodsThis investigation encompassed a total of 53 AD patients, 40 aMCI patients, and 40 healthy controls (HCs). DC and VMHC values were computed within three distinct frequency bands: classical (0.01–0.08 Hz), slow-4 (0.027–0.073 Hz), and slow-5 (0.01–0.027 Hz) for the three respective groups. To discern differences among these groups, ANOVA and subsequent post hoc two-sample t-tests were employed. Cognitive function assessment utilized the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation analysis was applied to investigate the associations between MMSE and MoCA scores with DC and VMHC.ResultsSignificant variations in degree centrality (DC) were observed among different groups across diverse frequency bands. The most notable differences were identified in the bilateral caudate nucleus (CN), bilateral medial superior frontal gyrus (mSFG), bilateral Lobule VIII of the cerebellar hemisphere (Lobule VIII), left precuneus (PCu), right Lobule VI of the cerebellar hemisphere (Lobule VI), and right Lobule IV and V of the cerebellar hemisphere (Lobule IV, V). Likewise, disparities in voxel-mirrored homotopic connectivity (VMHC) among groups were predominantly localized to the posterior cingulate gyrus (PCG) and Crus II of the cerebellar hemisphere (Crus II). Across the three frequency bands, the brain regions exhibiting significant differences in various parameters were most abundant in the slow-5 frequency band.ConclusionThis study enhances our understanding of the pathological and physiological mechanisms associated with AD continuum. Moreover, it underscores the importance of researchers considering various frequency bands in their investigations of brain function

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Automatic Identification of Landslides Based on Deep Learning

A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification

Automatic Identification of Landslides Based on Deep Learning

A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification

Protective Effect of Irisin on Atherosclerosis via Suppressing Oxidized Low Density Lipoprotein Induced Vascular Inflammation and Endothelial Dysfunction.

Irisin, a newly discovered myokine, is considered as a promising candidate for the treatment of metabolic disturbances and cardiovascular diseases. In the present study, we used two animal models, apolipoprotein E-deficient mice fed on a high-cholesterol diet and a mouse carotid partial ligation model to test the anti-atherosclerotic effect of irisin. Irisin treatment (0.5 μg/g body weight/day) significantly reduced the severity of aortic atherosclerosis in apolipoprotein E-deficient mice fed on a high-cholesterol diet and suppressed carotid neointima formation in a carotid partial ligation model. It was associated with decreased inflammation and cell apoptosis in aortic tissues. In addition, in a cell culture model, irisin restored ox-LDL-induced human umbilical vein endothelial cell dysfunction by reducing the levels of inflammatory genes via inhibiting the reactive oxygen species (ROS)/ p38 MAPK/ NF-κB signaling pathway activation and inhibiting cell apoptosis via up-regulating Bcl-2 and down-regulating Bax and caspase-3 expression. Our study demonstrated that irisin significantly reduced atherosclerosis in apolipoprotein E-deficient mice via suppressing ox-LDL-induced cell inflammation and apoptosis, which might have a direct therapeutic effect on atherosclerotic diseases

Effect of irisin on migration of HUVEC.

<p>(A) After HUVEC were incubated with 20 nM irisin, 5 μg/ml mitomycin C (MMC), or 20 nM irisin with 5 ug/ml MMC for different time periods (0, 6, 12, 24, and 48 h), MTT assay was used to detect the viability of the cells. * P< 0.05 vs. irisin (20 nM) +MMC group, determined by unpaired two-tailed Student’s t-test. (B) Images of wound-healing assays of HUVEC at 0, 12 and 24 h after scratch wound. (C) Percent wound closure at 12 and 24 h after scratch is shown (n = 5). (D) Images of Transwell assays of HUVEC treated with or without irisin (10 and 20 nM). (E) The number of HUVEC that migrated to the lower side of the membranes after incubation for 24 h are shown and the relative migration was quantified using Image-Pro Plus software (n = 5). Data are mean± SE. ** P< 0.01 vs. MMC treated group, determined by unpaired two-tailed Student’s t-test.</p

Primers for qPCR.

<p>Primers for qPCR.</p

Effect of U0126 on the irisin-induced expression and gelatinolytic activity of MMP2/9 in HUVEC.

<p>HUVEC were pretreated with the ERK inhibitor U0126 for 30 min followed by irisin treatment (20 nM) for an additional 24 h, then the MMP-2, MMP-9 and β-actin protein expression were analyzed by Western blot (A) and MMP-2/9 activities were analyzed by gelatin zymography (C). (B) and (D) Protein bands were quantitated by densitometric analysis. Data are presented as the mean± SE of triplicates. ** P< 0.01,* P< 0.05, determined by unpaired two-tailed Student’s t-test.</p