By shedding light on the enduring social identities of rural settlement communities, often referred to as “natural villages” (自然村) by the current Chinese government, this article provides a new approach to the formation of China's rural collective land ownership system from the 1950s to the present. It reveals how a unique landholding structure, which I term “bounded collectivism,” was initially formed in southwest China as a result of the contestation and negotiation between the socialist state aiming to establish collective land ownership and rural settlement communities seeking exclusive control over land resources within their borders. Significant elements of that collective land ownership system would be perpetuated while accommodating “natural villages” in the three decades since the abolition of the communes and the creation of a system of household contracts.

This article examines how legacies of bounded collectivism in Southwest China play out in the form of land rights sharing involving the current administrative village and its constituent villagers’ groups, as well as in the form of political differentiation between administrative village cadres and leaders of villagers’ groups. It also documents the ways in which land markets change as the two levels of village administration compete to develop rural land.

Introduction: Late-life depression (LLD) is associated with cognitive deficit with risk of future dementia. By examining the entropy of the spontaneous brain activity, we aimed to understand the neural mechanism pertaining to cognitive decline in LLD.

Methods: We collected MRI scans in older adults with LLD (n = 32), mild cognitive impairment [MCI (n = 25)] and normal cognitive function [NC, (n = 47)]. Multiscale entropy analysis (MSE) was applied to resting-state fMRI data. Under the scale factor (tau) 1 and 2, reliable separation of fMRI data and noise was achieved. We calculated the brain entropy in 90 brain regions based on automated anatomical atlas (AAL). Due to exploratory nature of this study, we presented data of group-wise comparison in brain entropy between LLD vs. NC, MCI vs. NC, and LLD and MCD with a p-value below 0.001.

Results: The mean Mini-Mental State Examination (MMSE) score of LLD and MCI was 27.9 and 25.6. Under tau 2, we found higher brain entropy of LLD in left globus pallidus than MCI (p = 0.002) and NC (p = 0,009). Higher brain entropy of LLD than NC was also found in left frontal superior gyrus, left middle superior gyrus, left amygdala and left inferior parietal gyrus. The only brain region with higher brain entropy in MCI than control was left posterior cingulum (p-value = 0.015). Under tau 1, higher brain entropy was also found in LLD than in MCI in right orbital part of medial frontal gyrus and left globus pallidus (p-value = 0.007 and 0.005).

Conclusions: Our result is consistent with prior hypothesis where higher brain entropy was found during early aging process as compensation. We found such phenomenon particular in left globus pallidus in LLD, which could be served as a discriminative brain region. Being a key region in reward system, we hypothesis such region may be associated with apathy and with unique pathway of cognitive decline in LLD. We will undertake subsequent analysis longitudinally in this cohort

Key words: resting-state fMRI, Late-life depression, Brain entropy, globus pallidus, cognitive decline

The delay-shift of the pre-pulse may mislead the determination of its origination and cause problems for the temporal contrast improvement of high-peak-power lasers, especially when the corresponding post-pulse is beyond the time window of the measurement device. In this work, an empirical formula is proposed to predict the delay-shift of pre-pulses for the first time. The empirical formula shows that the delay-shift is proportional to the square of the post-pulse’s initial delay, and also the ratio of the third-order dispersion to the group delay dispersion’s square, which intuitively reveals the main cause for the delay-shift and may provide a convenient routing for identifying the real sources of pre-pulses in both chirped-pulse amplification (CPA) and optical parametric chirped-pulse amplification (OPCPA) systems. The empirical formula agrees well with the experimental results both in the CPA and the OPCPA systems. Besides, a numerical simulation is also carried out to further verify the empirical formula.

Choline and betaine are important in the body, from cell membrane components to methyl donors. We aimed to investigate trends in dietary intake and food sources of total choline, individual choline forms and betaine in Chinese adults using data from the China Health and Nutrition Survey (CHNS) 1991–2011, a prospective cohort with a multistage, random cluster design. Dietary intake was estimated using three consecutive 24-h dietary recalls in combination with a household food inventory. Linear mixed-effect models were constructed using R software. A total of 11 188 men and 12 279 women aged 18 years or older were included. Between 1991 and 2011, total choline intake increased from 219·3 (95 % CI 215·1, 223·4) mg/d to 269·0 (95 % CI 265·6, 272·5) mg/d in men and from 195·6 (95 % CI 191·8, 199·4) mg/d to 240·4 (95 % CI 237·4, 243·5) mg/d in women (both P-trends < 0·001). Phosphatidylcholine was the major form of dietary choline, and its contribution to total choline increased from 46·9 % in 1991 to 58·8 % in 2011. Cereals were the primary food source of total choline before 2000, while eggs had ranked at the top since 2004. Dietary betaine intake was relatively steady over time with a range of 134·0–151·5 mg/d in men (P-trend < 0·001) and 111·7–125·3 mg/d in women (P-trend > 0·05). Chinese adults experienced a significant increase in dietary intake of choline, particularly phosphatidylcholine during 1991–2011 and animal-derived foods have replaced plant-based foods as the main food sources of choline. Betaine intake remained relatively stable over time. Future efforts should address the health effects of these changes.

Purple nutsedge (Cyperus rotundus L.) is one of the world’s resilient upland weeds, primarily spreading through its tubers. Its emergence in rice (Oryza sativa L.) fields has been increasing, likely due to changing paddy-farming practices. This study aimed to investigate how C. rotundus, an upland weed, can withstand soil flooding and become a problematic weed in rice fields. The first comparative analysis focused on the survival and recovery characteristics of growing and mature tubers of C. rotundus exposed to soil-flooding conditions. Notably, mature tubers exhibited significant survival and recovery abilities in these environments. Based on this observation, further investigation was carried out to explore the morphological structure, nonstructural carbohydrates, and respiratory mechanisms of mature tubers in response to prolonged soil flooding. Over time, the mature tubers did not form aerenchyma but instead gradually accumulated lignified sclerenchymal fibers, with lignin content also increasing. After 90 d, the lignified sclerenchymal fibers and lignin contents were 4.0 and 1.1 times higher than those in the no soil-flooding treatment. Concurrently, soluble sugar content decreased while starch content increased, providing energy storage, and alcohol dehydrogenase activity rose to support anaerobic respiration via alcohol fermentation. These results indicated that mature tubers survived in soil-flooding conditions by adopting a low-oxygen quiescence strategy, which involves morphological adaptations through the development of lignified sclerenchymal fibers, increased starch reserves for energy storage, and enhanced anaerobic respiration. This mechanism likely underpins the flooding tolerance of mature C. rotundus tubers, allowing them to endure unfavorable conditions and subsequently germinate and grow once flooding subsides. This study provides a preliminary explanation of the mechanism by which mature tubers of C. rotundus from the upland areas confer flooding tolerance, shedding light on the reasons behind this weed’s increasing presence in rice fields.

To address the issues of low positioning accuracy and weak robustness of prior visual simultaneous localization and mapping (VSLAM) systems in dynamic environments, a semantic VSLAM (Sem-VSLAM) approach based on deep learning is proposed in this article. The proposed Sem-VSLAM algorithm adds semantic segmentation threads in parallel based on the open-source ORB-SLAM2’s visual odometry. First, while extracting the ORB features from an RGB-D image, the frame image is semantically segmented, and the segmented results are detected and repaired. Then, the feature points of dynamic objects are eliminated by using semantic information and motion consistency detection, and the poses are estimated by using the remaining feature points after the dynamic feature elimination. Finally, a 3D point cloud map is constructed by using tracking information and semantic information. The experiment uses Technical University of Munich public data to show the usefulness of the Sem-VSLAM algorithm. The experimental results show that the Sem-VSLAM algorithm can reduce the absolute trajectory error and relative attitude error of attitude estimation by about 95% compared to the ORB-SLAM2 algorithm and by about 14% compared to the VO-YOLOv5s in a highly dynamic environment and the average time consumption of tracking each frame image reaches 61 ms. It is verified that the Sem-VSLAM algorithm effectively improves the robustness and positioning accuracy in high dynamic environment and owning a satisfying real-time performance. Therefore, the Sem-VSLAM has a better mapping effect in a highly dynamic environment.

MicroRNAs (miRNAs) are endogenous, non-coding RNAs, which are functional in a variety of biological processes through post-transcriptional regulation of gene expression. However, the role of miRNAs in the interaction between Bacillus thuringiensis and insects remains unclear. In this study, small RNA libraries were constructed for B. thuringiensis-infected (Bt) and uninfected (CK) Spodoptera exigua larvae (treated with double-distilled water) using Illumina sequencing. Utilising the miRDeep2 and Randfold, a total of 233 known and 726 novel miRNAs were identified, among which 16 up-regulated and 34 down-regulated differentially expressed (DE) miRNAs were identified compared to the CK. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that potential target genes of DE miRNAs were associated with ABC transporters, fatty acid metabolism and MAPK signalling pathway which are related to the development, reproduction and immunity. Moreover, two miRNA core genes, SeDicer1 and SeAgo1 were identified. The phylogenetic tree showed that lepidopteran Dicer1 clustered into one branch, with SeDicer1 in the position closest to Spodoptera litura Dicer1. A similar phylogenetic relationship was observed in the Ago1 protein. Expression of SeDicer1 increased at 72 h post infection (hpi) with B. thuringiensis; however, expression of SeDicer1 and SeAgo1 decreased at 96 hpi. The RNAi results showed that the knockdown of SeDicer1 directly caused the down-regulation of miRNAs and promoted the mortality of S. exigua infected by B. thuringiensis GS57. In conclusion, our study is crucial to understand the relationship between miRNAs and various biological processes caused by B. thuringiensis infection, and develop an integrated pest management strategy for S. exigua via miRNAs.

A novel method, combining an asymmetric four-grating compressor (AFGC) with pulse post-compression, is numerically demonstrated to improve the spatial uniformity of laser beams and hence to suppress small-scale self-focusing (SSSF) during the beam propagation in nonlinear materials of high peak power lasers. The spatial uniformity of laser beams is an important factor in performing post-compression, due to the spatial intensity modulation, or hot spots will be aggravated during the nonlinear propagation and then seriously damage the subsequent optical components. Three-dimensional numerical simulations of post-compression are implemented based on a femtosecond laser with a standard compressor and an AFGC, respectively. The simulated results indicate that post-compression with the AFGC can efficiently suppress the SSSF and also shorten the laser pulses from 30 fs to sub-10 fs. This work can provide a promising route to overcome the challenge of SSSF and will be meaningful to promote the practical application of the post-compression technique in high peak power lasers.

Neuro-therapeutic intervention joins play for older adults with and without cognitive decline in Asia. During the Covid-19 outbreak, older adults report an increased isolation and increased risk for cognitive decline and medical complications than younger populations. It is therefore reasonable to implement creative nonpharmacological interventions to satisfy older adults psycho-social needs while maintaining their cognitive functioning without being burdened with healthcare costs. In this interventional study, a group of 60 older adults aged 60 years old and older participated in sand tray activity for six weeks. Participants were interviewed for their experiences with six sand tray activities under social distance restrictions in Taiwan. Thematic analysis techniques are applied to examine the interviewing data to identify comment themes across participants. The qualitative results show that social connection is the need of older adults during the pandemic, that neuro-therapeutic play satisfies older adults’ emotional needs, and that sand tray activity offers an opportunity for older adults to exercise their brain. Implications are discussed.

A comprehensive direct numerical simulation of electroconvection (EC) turbulence caused by strong unipolar charge injection in a two-dimensional cavity is performed. The EC turbulence has strong fluctuations and intermittency in the closed cavity. Several dominant large-scale structures are found, including two vertical main rolls and a single primary roll. The flow mode significantly influences the charge transport efficiency. A nearly $Ne \sim T^{1/2}$ scaling stage is observed, and the optimal $Ne$ increment is related to the mode with two vertical rolls, while the single roll mode decreases the charge transport efficiency. As the flow strength increases, EC turbulence transitions from an electric force-dominated mode to an inertia-dominated mode. The former utilizes the Coulomb force more effectively and allocates more energy to convection. The vertical mean profiles of charge, electric field and energy budget provide intuitive information on the spatial energy distribution. With the aid of the energy-box technique, a detailed energy transport evolution is illustrated with changing electric Rayleigh numbers. This exploration of EC turbulence can help explain more complicated electrokinetic turbulence mechanisms and the successful utilization of Fourier mode decomposition and energy-box techniques is expected to benefit future EC studies.

Very metal-poor (VMP, [Fe/H]<-2.0) stars serve as invaluable repositories of insights into the nature and evolution of the first-generation stars formed in the early galaxy. The upcoming China Space Station Telescope (CSST) will provide us with a large amount of spectral data that may contain plenty of VMP stars, and thus it is crucial to determine the stellar atmospheric parameters ($T_{\textrm{eff}}$, $\log$ g, and [Fe/H]) for low-resolution spectra similar to the CSST spectra ($R\sim 200$). This study introduces a novel two-dimensional Convolutional Neural Network (CNN) model, comprised of three convolutional layers and two fully connected layers. The model’s proficiency is assessed in estimating stellar parameters, particularly metallicity, from low-resolution spectra ($R \sim 200$), with a specific focus on enhancing the search for VMP stars within the CSST spectral data. We mainly use 10 008 spectra of VMP stars from LAMOST DR3, and 16 638 spectra of non-VMP stars ([Fe/H]>-2.0) from LAMOST DR8 for the experiments and apply random forest and support vector machine methods to make comparisons. The resolution of all spectra is reduced to $R\sim200$ to match the resolution of the CSST, followed by pre-processing and transformation into two-dimensional spectra for input into the CNN model. The validation and practicality of this model are also tested on the MARCS synthetic spectra. The results show that using the CNN model constructed in this paper, we obtain Mean Absolute Error (MAE) values of 99.40 K for $T_{\textrm{eff}}$, 0.22 dex for $\log$ g, 0.14 dex for [Fe/H], and 0.26 dex for [C/Fe] on the test set. Besides, the CNN model can efficiently identify VMP stars with a precision rate of 94.77%, a recall rate of 93.73%, and an accuracy of 95.70%. This paper powerfully demonstrates the effectiveness of the proposed CNN model in estimating stellar parameters for low-resolution spectra ($R\sim200$) and recognizing VMP stars that are of interest for stellar population and galactic evolution work.