α-Klotho: the hidden link between dietary inflammatory index and accelerated ageing.

Recent studies suggest an association between greater dietary inflammatory index (DII) and higher biological ageing. As α-Klotho has been considered as a longevity protein, we examined whether α-Klotho plays a role in the association between DII and ageing. We included 3054 participants from the National Health and Nutrition Examination Survey. The associations of DII with biological and phenotypic age were assessed by multivariable linear regression, and the mediating role of α-Klotho was evaluated by mediation analyses. Participants' mean age was 58·0 years (sd 11·0), with a median DII score of 1·85 and interquartile range from 0·44 to 2·79. After adjusting for age, sex, race/ethnicity, BMI, education, marital status, poverty income ratio, serum cotinine, alcohol, physical activity, a higher DII was associated with both older biological age and phenotypic age, with per DII score increment being associated with a 1·01-year increase in biological age (1·01 (95 % CI: 1·005, 1·02)) and 1·01-year increase in phenotypic age (1·01 (1·001, 1·02)). Negative associations of DII with α-Klotho (ß = -1·01 pg/ml, 95 % CI: -1·02, -1·006) and α-Klotho with biological age (ß= -1·07 years, 95 % CI: -1·13, -1·02) and phenotypic age (ß= -1·03 years, 95 % CI: -1·05, -1·01) were found. Furthermore, α-Klotho mediated 10·13 % (P < 0·001) and 9·61 % (P < 0·001) of the association of DII with biological and phenotypic age, respectively. Higher DII was associated with older biological and phenotypic age, and the potential detrimental effects could be partly mediated through α-Klotho.

Detection of Road Crack Images Based on Multistage Feature Fusion and a Texture Awareness Method.

Structural health monitoring for roads is an important task that supports inspection of transportation infrastructure. This paper explores deep learning techniques for crack detection in road images and proposes an automatic pixel-level semantic road crack image segmentation method based on a Swin transformer. This method employs Swin-T as the backbone network to extract feature information from crack images at various levels and utilizes the texture unit to extract the texture and edge characteristic information of cracks. The refinement attention module (RAM) and panoramic feature module (PFM) then merge these diverse features, ultimately refining the segmentation results. This method is called FetNet. We collect four public real-world datasets and conduct extensive experiments, comparing FetNet with various deep-learning methods. FetNet achieves the highest precision of 90.4%, a recall of 85.3%, an F1 score of 87.9%, and a mean intersection over union of 78.6% on the Crack500 dataset. The experimental results show that the FetNet approach surpasses other advanced models in terms of crack segmentation accuracy and exhibits excellent generalizability for use in complex scenes.

Basolateral amygdala input to the medial prefrontal cortex controls obsessive-compulsive disorder-like checking behavior.

Obsessive-compulsive disorder (OCD) affects ∼1 to 3% of the world's population. However, the neural mechanisms underlying the excessive checking symptoms in OCD are not fully understood. Using viral neuronal tracing in mice, we found that glutamatergic neurons from the basolateral amygdala (BLAGlu) project onto both medial prefrontal cortex glutamate (mPFCGlu) and GABA (mPFCGABA) neurons that locally innervate mPFCGlu neurons. Next, we developed an OCD checking mouse model with quinpirole-induced repetitive checking behaviors. This model demonstrated decreased glutamatergic mPFC microcircuit activity regulated by enhanced BLAGlu inputs. Optical or chemogenetic manipulations of this maladaptive circuitry restored the behavioral response. These findings were verified in a mouse functional magnetic resonance imaging (fMRI) study, in which the BLA-mPFC functional connectivity was increased in OCD mice. Together, these findings define a unique BLAGlu→mPFCGABA→Glu circuit that controls the checking symptoms of OCD.

Composition Engineering Growth of Cs3Bi2I9 Single Crystals with Low Defect Density for X-ray Detectors.

Cs3Bi2I9 (CBI) single crystal (SC) is a promising material for a higher-performance direct X-ray detector. However, the composition of CBI SC prepared by the solution method usually deviates from the ideal stoichiometric ratio, which limits the detector performance. In this paper, based on the finite element analysis method, the growth model of the top-seed solution method has been established, and then the influence of precursor ratio, temperature field, and other parameters on the composition of CBI SC has been simulated. The simulation results were used to guide the growth of the CBI SCs. Finally, a high-quality CBI SC with a stoichiometric ratio of Cs/Bi/I = 2.87:2:8.95 has been successfully grown, and the defect density is as low as 1.03 × 109 cm-3, the carrier lifetime is as high as 16.7 ns, and the resistivity is as high as 1.44 × 1012 Ω·cm. The X-ray detector based on this SC has a sensitivity of 29386.2 µC·Gyair-1 cm-2 at an electric field of 40 V·mm-1, and a low detection limit of 0.36 nGyair·s-1, creating a record for the all-inorganic perovskite materials.