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Excessive accumulation of adipose tissue may accelerate brain aging, but the underlying mechanisms are poorly understood. Several adiposity indices were proposed to assess obesity, while their linkage with brain health in older adults remained unclear. Here we aimed to examine the associations of adiposity indices with global and regional cerebral blood flow (CBF) in older adults, while considering insulin resistance. This was a cross-sectional population-based study that included older adults derived from the baseline participants in the ongoing Multimodal Interventions to Delay Dementia and Disability in rural China (MIND-China) study. The study included 103 Chinese rural-dwelling older adults (age≥60 years; 69.9% women) who underwent brain magnetic resonance imaging scans. We estimated eight adiposity indices based on anthropometric measures. We automatically quantified global and regional CBF using the arterial spin labeling scans. Insulin resistance was assessed using the triglyceride-glucose index and then dichotomized into high and low levels according to the median. Data were analyzed using general linear model and voxel-wise analysis. Of the eight examined adiposity indices, only higher waist-to-height ratio (WHtR) and body roundness index (BRI) were associated with reduced global CBF (multivariable-adjusted β-coefficients and 95%CI: −1.76; −3.25, −0.27 and −1.77; −3.25, −0.30, respectively) and hypoperfusion in bilateral middle temporal gyri, angular gyri and superior temporal gyri, left middle cingulum and precuneus (P<0.05). There were statistical interactions of WHtR and BRI with levels of insulin resistance on CBF, such that the significant associations of higher WHtR and BRI with lower global and regional CBF existed only in people with high insulin resistance (P<0.05). Higher WHtR and BRI are associated with cerebral hypoperfusion in older adults, especially in people with high insulin resistance. This may highlight the pathological role of visceral fat in vascular brain aging.

Modern practice for training classification deepnets involves a terminal phase of training (TPT), which begins at the epoch where training error first vanishes. During TPT, the training error stays effectively zero, while training loss is pushed toward zero. Direct measurements of TPT, for three prototypical deepnet architectures and across seven canonical classification datasets, expose a pervasive inductive bias we call neural collapse (NC), involving four deeply interconnected phenomena. (NC1) Cross-example within-class variability of last-layer training activations collapses to zero, as the individual activations themselves collapse to their class means. (NC2) The class means collapse to the vertices of a simplex equiangular tight frame (ETF). (NC3) Up to rescaling, the last-layer classifiers collapse to the class means or in other words, to the simplex ETF (i.e., to a self-dual configuration). (NC4) For a given activation, the classifier’s decision collapses to simply choosing whichever class has the closest train class mean (i.e., the nearest class center [NCC] decision rule). The symmetric and very simple geometry induced by the TPT confers important benefits, including better generalization performance, better robustness, and better interpretability.

The independent control of two magnetic electrodes and spin-coherent transport in magnetic tunnel junctions are strictly required for tunneling magnetoresistance, while junctions with only one ferromagnetic electrode exhibit tunneling anisotropic magnetoresistance dependent on the anisotropic density of states with no room temperature performance so far. Here, we report an alternative approach to obtaining tunneling anisotropic magnetoresistance in α′-FeRh-based junctions driven by the magnetic phase transition of α′-FeRh and resultantly large variation of the density of states in the vicinity of MgO tunneling barrier, referred to as phase transition tunneling anisotropic magnetoresistance. The junctions with only one α′-FeRh magnetic electrode show a magnetoresistance ratio up to 20% at room temperature. Both the polarity and magnitude of the phase transition tunneling anisotropic magnetoresistance can be modulated by interfacial engineering at the α′-FeRh/MgO interface. Besides the fundamental significance, our finding might add a different dimension to magnetic random access memory and antiferromagnet spintronics. Tunneling anisotropic magnetoresistance is promising for next generation memory devices but limited by the low efficiency and functioning temperature. Here the authors achieved 20% tunneling anisotropic magnetoresistance at room temperature in magnetic tunnel junctions with one α′-FeRh magnetic electrode.

Improving the strength of a metal alloy is hard to do without sacrificing the ductility. Yang et al. designed an iron-nickel-cobalt (Fe-Ni-Co) alloy laced with aluminum-titanium (Al-Ti) nanoparticles with both high strength and ductility. The key was getting the composition tuned correctly, because the Fe-Ni-Co matrix reacts with the Al-Ti nanoparticles. This was vital for avoiding environmental embrittlement, enhancing work hardening, and improving ductility. Science , this issue p. 933 Multicomponent nanoparticles enhance both the strength and ductility of an iron-nickel-cobalt alloy. Alloy design based on single–principal-element systems has approached its limit for performance enhancements. A substantial increase in strength up to gigapascal levels typically causes the premature failure of materials with reduced ductility. Here, we report a strategy to break this trade-off by controllably introducing high-density ductile multicomponent intermetallic nanoparticles (MCINPs) in complex alloy systems. Distinct from the intermetallic-induced embrittlement under conventional wisdom, such MCINP-strengthened alloys exhibit superior strengths of 1.5 gigapascals and ductility as high as 50% in tension at ambient temperature. The plastic instability, a major concern for high-strength materials, can be completely eliminated by generating a distinctive multistage work-hardening behavior, resulting from pronounced dislocation activities and deformation-induced microbands. This MCINP strategy offers a paradigm to develop next-generation materials for structural applications.

Maternal health during pregnancy plays a major role in shaping health and disease risks in the offspring. The maternal immune activation hypothesis proposes that inflammatory perturbations in utero can affect fetal neurodevelopment, and evidence from human epidemiological studies supports an association between maternal inflammation during pregnancy and offspring neurodevelopmental disorders (NDDs). Diverse maternal inflammatory factors, including obesity, asthma, autoimmune disease, infection and psychosocial stress, are associated with an increased risk of NDDs in the offspring. In addition to inflammation, epigenetic factors are increasingly recognized to operate at the gene–environment interface during NDD pathogenesis. For example, integrated brain transcriptome and epigenetic analyses of individuals with NDDs demonstrate convergent dysregulated immune pathways. In this Review, we focus on the emerging human evidence for an association between maternal immune activation and childhood NDDs, including autism spectrum disorder, attention-deficit/hyperactivity disorder and Tourette syndrome. We refer to established pathophysiological concepts in animal models, including immune signalling across the placenta, epigenetic ‘priming’ of offspring microglia and postnatal immune–brain crosstalk. The increasing incidence of NDDs has created an urgent need to mitigate the risk and severity of these conditions through both preventive strategies in pregnancy and novel postnatal therapies targeting disease mechanisms.The maternal immune activation (MIA) hypothesis proposes that inflammatory perturbations in utero can affect fetal neurodevelopment. This Review examines the emerging human evidence for an association between MIA and childhood neurodevelopmental disorders, including autism spectrum disorder, attention-deficit/hyperactivity disorder and Tourette syndrome.

Genetic algorithm (GA) is used for the topological optimization of phononic crystal thin plate composed of aluminum and epoxy resin. Plane wave expansion (PWE) method is used for calculations of band gaps. Fourier displacement property is used to calculate the structure function in PWE. The crossover rate and the mutation rate are calculated according to the adaptive GA method. Results indicate that filling rates, symmetry, polymerization degree and material parameters are key factors for design of topological configurations. The relations between the key factors and different topologies are studied in detail.

Traditional structural uncertainty analysis is mainly based on probability models and requires the establishment of accurate parametric probability distribution functions using large numbers of experimental samples. In many actual engineering problems, the probability distributions of some parameters can be established due to sufficient samples available, whereas for some parameters, due to the lack or poor quality of samples, only their variation intervals can be obtained, or their probability distribution types can be determined based on the existing data while some of the distribution parameters such as mean and standard deviation can only be given interval estimations. This thus will constitute an important type of probability-interval hybrid uncertain problem, in which the aleatory and epistemic uncertainties both exist. The probability-interval hybrid uncertainty analysis provides an important mean for reliability analysis and design of many complex structures, and has become one of the research focuses in the field of structural uncertainty analysis over the past decades. This paper reviews the four main research directions in this area, i.e., uncertainty modeling, uncertainty propagation analysis, structural reliability analysis, and reliability-based design optimization. It summarizes the main scientific problems, technical difficulties, and current research status of each direction. Based on the review, this paper also provides an outlook for future research in probability-interval hybrid uncertainty analysis.

Soil acidification is a major problem in soils of intensive Chinese agricultural systems. We used two nationwide surveys, paired comparisons in numerous individual sites, and several long-term monitoring-field data sets to evaluate changes in soil acidity. Soil pH declined significantly (P < 0.001) from the 1980s to the 2000s in the major Chinese crop-production areas. Processes related to nitrogen cycling released 20 to 221 kilomoles of hydrogen ion (H⁺) per hectare per year, and base cations uptake contributed a further 15 to 20 kilomoles of H⁺ per hectare per year to soil acidification in four widespread cropping systems. In comparison, acid deposition (0.4 to 2.0 kilomoles of H⁺ per hectare per year) made a small contribution to the acidification of agricultural soils across China.

Dissipative solitons, which result from the intricate balance between dispersion and nonlinearity as well as gain and loss, are of the fundamental scientific interest and numerous important applications. Here, we report a fiber laser that generates bisoliton – two consecutive dissipative solitons that preserve a fixed separation between them. Deviations from this separation result in its restoration. It is also found that these bisolitons have multiple discrete equilibrium distances with the quantized separations, as is confirmed by the theoretical analysis and the experimental observations. The main feature of our laser is the anomalous dispersion that is increased by an order of magnitude in comparison to previous studies. Then the spectral filtering effect plays a significant role in pulse-shaping. The proposed laser has the potential applications in optical communications and high-resolution optics for coding and transmission of information in higher-level modulation formats.

The absence of mirror symmetry, or chirality, is behind striking natural phenomena found in systems as diverse as DNA and crystalline solids. A remarkable example occurs when chiral semimetals with topologically protected band degeneracies are illuminated with circularly polarized light. Under the right conditions, the part of the generated photocurrent that switches sign upon reversal of the light’s polarization, known as the circular photo-galvanic effect, is predicted to depend only on fundamental constants. The conditions to observe quantization are non-universal, and depend on material parameters and the incident frequency. In this work, we perform terahertz emission spectroscopy with tunable photon energy from 0.2 –1.1 eV in the chiral topological semimetal CoSi. We identify a large longitudinal photocurrent peaked at 0.4 eV reaching  ~550 μ A/V 2 , which is much larger than the photocurrent in any chiral crystal reported in the literature. Using first-principles calculations we establish that the peak originates only from topological band crossings, reaching 3.3 ± 0.3 in units of the quantization constant. Our calculations indicate that the quantized circular photo-galvanic effect is within reach in CoSi upon doping and increase of the hot-carrier lifetime. The large photo-conductivity suggests that topological semimetals could potentially be used as novel mid-infrared detectors. Quantized circular photogalvanic effect (CPGE) is predicted in chiral topological semimetals, but the experimental observation remains challenging. Here, Ni et al. observe a large topological longitudinal photocurrent in CoSi, which is much larger than the photocurrent in any other chiral crystals, indicating quantized CPGE within reach upon doping and increase of the hot-carrier lifetime.