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The emergence and rapid advancement of large language models (LLMs), represented by OpenAI’s Generative Pre-trained Transformer (GPT), has brought up new opportunities across various industries and disciplines. These cutting-edge technologies are transforming the way we interact with information, communicate, and solve complex problems. We conducted a pilot study exploring making maps with ChatGPT, a popular artificial intelligence (AI) chatbot. Specifically, we tested designing thematic maps using given or public geospatial data, as well as creating mental maps purely using textual descriptions of geographic space. We conclude that ChatGPT provides a useful alternative solution for mapping given its unique advantages, such as lowering the barrier to producing maps, boosting the efficiency of massive map production, and understanding geographical space with its spatial thinking capability. However, mapping with ChatGPT still has limitations at the current stage, such as its unequal benefits for different users and dependence on user intervention for quality control.

Limited by the size of microelectronics, as well as the space of electrical vehicles, there are tremendous demands for lithium-ion batteries with high volumetric energy densities. Current lithium-ion batteries, however, adopt graphite-based anodes with low tap density and gravimetric capacity, resulting in poor volumetric performance metric. Here, by encapsulating nanoparticles of metallic tin in mechanically robust graphene tubes, we show tin anodes with high volumetric and gravimetric capacities, high rate performance, and long cycling life. Pairing with a commercial cathode material LiNi 0.6 Mn 0.2 Co 0.2 O 2 , full cells exhibit a gravimetric and volumetric energy density of 590 W h Kg −1 and 1,252 W h L −1 , respectively, the latter of which doubles that of the cell based on graphite anodes. This work provides an effective route towards lithium-ion batteries with high energy density for a broad range of applications. Here the authors report a tin anode design by encapsulating tin nanoparticles in graphene tubes. The design exhibits high capacity, good rate performance and cycling stability. Pairing with NMC, the full cell delivers a volumetric energy density twice as high as that for the commercial cell.

This essay draws on an original cross-sectional survey of 1,010 children and their guardians in highly migratory regions of Anhui and Jiangxi provinces located in China's interior. It uses propensity score matching, a technique that mitigates endogenity, to examine the impact of parental migration and post-migration guardianship arrangements on the children's educational performance as measured by test scores for Chinese and mathematics. One core finding is that the educational performance of children is adversely affected by parental migration only when both parents migrate or when a non-parent guardian is the principal carer. Additionally, longer durations of parental absence are associated with poorer educational performance. The migration of two parents only significantly adversely affects the educational performance of boys. There is no significant effect on the educational performance of girls. On the basis of our findings we argue that rather than support left-behind children within the countryside, the long-term policy response should be to remove the institutional obstacles that prevent family resettlement in the cities.

Genetically programmed circuits allowing bifunctional dynamic regulation of enzyme expression have far-reaching significances for various bio-manufactural purposes. However, building a bio-switch with a post log-phase response and reversibility during scale-up bioprocesses is still a challenge in metabolic engineering due to the lack of robustness. Here, we report a robust thermosensitive bio-switch that enables stringent bidirectional control of gene expression over time and levels in living cells. Based on the bio-switch, we obtain tree ring-like colonies with spatially distributed patterns and transformer cells shifting among spherical-, rod- and fiber-shapes of the engineered Escherichia coli . Moreover, fed-batch fermentations of recombinant E. coli are conducted to obtain ordered assembly of tailor-made biopolymers polyhydroxyalkanoates including diblock- and random-copolymer, composed of 3-hydroxybutyrate and 4-hydroxybutyrate with controllable monomer molar fraction. This study demonstrates the possibility of well-organized, chemosynthesis-like block polymerization on a molecular scale by reprogrammed microbes, exemplifying the versatility of thermo-response control for various practical uses. Genetic circuits can be built with bifunctional dynamic regulation of gene expression. Here the authors design a thermosensitive switch for spatial and temporal control of colony pattern, cell shape and polymer production.

Effect size indices are useful tools in study design and reporting because they are unitless measures of association strength that do not depend on sample size. Existing effect size indices are developed for particular parametric models or population parameters. Here, we propose a robust effect size index based on M-estimators. This approach yields an index that is very generalizable because it is unitless across a wide range of models. We demonstrate that the new index is a function of Cohen’s d , R 2 , and standardized log odds ratio when each of the parametric models is correctly specified. We show that existing effect size estimators are biased when the parametric models are incorrect (e.g., under unknown heteroskedasticity). We provide simple formulas to compute power and sample size and use simulations to assess the bias and standard error of the effect size estimator in finite samples. Because the new index is invariant across models, it has the potential to make communication and comprehension of effect size uniform across the behavioral sciences.

Apolipoprotein A-I(ApoA-I) is a member of blood apolipoproteins, it is the main component of High density lipoprotein(HDL). ApoA-I undergoes a series of complex processes from its generation to its composition as spherical HDL. It not only has a cholesterol reversal transport function, but also has a function in modulating the inflammatory response. ApoA-I exerts its anti-inflammatory effects mainly by regulating the functions of immune cells, such as monocytes/macrophages, dendritic cells, neutrophils, and T lymphocytes. It also modulates the function of vascular endothelial cells and adipocytes. Additionally, ApoA-I directly exerts anti-inflammatory effects against pathogenic microorganisms or their products. Intensive research on ApoA-I will hopefully lead to better diagnosis and treatment of inflammatory diseases.

Loss of the E3 ubiquitin ligase Parkin causes early onset Parkinson's disease, a neurodegenerative disorder of unknown etiology. Parkin has been linked to multiple cellular processes including protein degradation, mitochondrial homeostasis, and autophagy; however, its precise role in pathogenesis is unclear. Recent evidence suggests that Parkin is recruited to damaged mitochondria, possibly affecting mitochondrial fission and/or fusion, to mediate their autophagic turnover. The precise mechanism of recruitment and the ubiquitination target are unclear. Here we show in Drosophila cells that PINK1 is required to recruit Parkin to dysfunctional mitochondria and promote their degradation. Furthermore, PINK1 and Parkin mediate the ubiquitination of the profusion factor Mfn on the outer surface of mitochondria. Loss of Drosophila PINK1 or parkin causes an increase in Mfn abundance in vivo and concomitant elongation of mitochondria. These findings provide a molecular mechanism by which the PINK1/Parkin pathway affects mitochondrial fission/fusion as suggested by previous genetic interaction studies. We hypothesize that Mfn ubiquitination may provide a mechanism by which terminally damaged mitochondria are labeled and sequestered for degradation by autophagy.

The application of graphene for electrochemical energy storage has received tremendous attention; however, challenges remain in synthesis and other aspects. Here we report the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles through chemical vapor deposition with magnesium-oxide particles as the catalyst and template. Such particles possess excellent structural and electrochemical stability, electronic and ionic conductivity, enabling their use as high-performance anodes with high reversible capacity, outstanding rate performance (e.g., 1,138 mA h g −1 at 0.2 C or 440 mA h g −1 at 60 C with a mass loading of 1 mg cm −2 ), and excellent cycling stability (e.g., >99% capacity retention for 500 cycles at 2 C with a mass loading of 1 mg cm −2 ). Interestingly, thick electrodes could be fabricated with high areal capacity and current density (e.g., 6.1 mA h cm −2 at 0.9 mA cm −2 ), providing an intriguing class of materials for lithium-ion batteries with high energy and power performance. Here, Lu and co-workers show the synthesis of high-quality, nitrogen-doped, mesoporous graphene particles using CVD with MgO as the catalyst and template. When used as the anode for a lithium ion battery, their unique architecture allows for excellent rate performance and cycling stability.

This article draws on a survey conducted in six provinces in summer 2008 to investigate the determinants of son preference in rural China. The analysis confirms the conventional wisdom that son preference is embedded within patrilineal family structures and practices. We extend our analysis by exploring specific aspects of variation within patrilineal family culture. We find that the patrilineal group (clan) composition of villages and family participation in practices such as building ancestral halls and updating genealogies significantly influence son preference. Yet even though son preference is embedded within patrilineal family culture, our analysis suggests that over time the attenuation of son preference is likely. This is because determinants associated with socioeconomic change—for instance, higher levels of education, direct exposure to official policy education materials, higher income (a proxy for rural industrialization), and agricultural mechanization—all attenuate son preference. Being younger and female are also associated with weaker son preference, and both characteristics are likely to interact with education and industrialization to further dilute son preference in the longer term. Nevertheless, our findings suggest that concerted efforts are needed to ameliorate institutional discrimination against rural people in welfare provisioning and in labor markets, and to promote multiple dimensions of gender equality, including in land rights, wage rates, and education.

In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection due to extremely limited available labeled samples that are insufficient to train deep networks. In this paper, a novel target detection framework for deep learning is proposed, denoted as HTD-Net. To overcome the few-training-sample issue, the proposed framework utilizes an improved autoencoder (AE) to generate target signatures, and then finds background samples which differ significantly from target samples based on a linear prediction (LP) strategy. Then, the obtained target and background samples are used to enlarge the training set by generating pixel-pairs, which is viewed as the input of a pre-designed network architecture to learn discriminative similarity. During testing, pixel-pairs of a pixel to be labeled are constructed with both available target samples and background samples. Spectral difference between these pixel-pairs is classified by the well-trained network with results of similarity measurement. The outputs from a two-branch averaged similarity scores are combined to generate the final detection. Experimental results with several real hyperspectral data demonstrate the superiority of the proposed algorithm compared to some traditional target detectors.