The knowledge of the critical periods of crop-weed competition is crucial for designing weed management strategies in cropping systems. In the Lower Yangtze Valley, China, field experiments were conducted in 2011 and 2012 to study the effect of interference from mixed natural weed populations on cotton growth and yield and determine the critical period for weed control (CPWC) in direct-seeded cotton. Two treatments were applied: allowing weeds to infest the crop or keeping plots weed-free for increasing periods (0, 1, 2, 4, 6, 8, 10, 12, 14, and 20 weeks) after crop emergence. The results showed that mixed natural weed infestations led to 35 to 55 cm shorter cotton plants with stem diameters 10 to 13 mm smaller throughout the season, fitting well with modified Gompertz and logistic models, respectively. Season-long competition with weeds reduced the number of fruit branches per plant by 65% to 82%, decreasing boll number per plant by 86% to 96% and single boll weight by approximately 24%. Weed-free seed cotton yields ranged from 2,900 to 3,130 kg ha-1, while yield loss increased with the duration of weed infestation, reaching up to 83% to 96% compared with permanent weed-free plots. Modified Gompertz and logistic models were used to analyze the impact of increasing weed control duration and weed interference on relative seed cotton yield (percentage of season-long weed-free cotton), respectively. Based on a 5% yield-loss threshold, the CPWC was found to be from 145 growing degree days (GDD) to 994 GDD, corresponding to 14 days after emergence (DAE) to 85 DAE. These findings emphasize the importance of implementing effective weed control measures from 14 DAE to 85 DAE in the Lower Yangtze Valley, China, to prevent crop losses exceeding a 5% yield-loss threshold.

The Northwest Tibet region is defined by several terranes, magmatic belts, basins and sutures, which were primarily shaped by the tectonic activities associated with Proto-, Palaeo- and Neo-Tethys Oceans. However, the basement nature and Precambrian tectonic evolution of the Northwest Tibet region, particularly within the Tashikuergan-Tianshuihai terrane, remain largely unknown. The Hongliutan area, located in the northeastern part of the Tashikuergan-Tianshuihai terrane, contains a critical sequence of Precambrian metamorphic rock strata. Detailed petrological, geochronological, and geochemical analyses of these metamorphic rocks – including plagioclase schist, quartz schist, amphibolite and nearby leucogranite – reveal the intricate processes of tectonic evolution within the Tianshuihai unit. Combining these findings with previous geochronological results is crucial for re-evaluating the nature of the Tashikuergan-Tianshuihai basement and its Precambrian tectonic evolution of the Tashikuergan-Tianshuihai basement. Our results reveal the following: (1) the leucogranite and amphibolite, identified as Cambrian igneous rocks, display distinct geochemical signatures indicative of a continental arc origin. These include calc-alkaline characteristics, enrichment in Th, U, Pb, Zr and Hf and depletion in Ba, Nb, Sr and Ti. Their εNd(t) values, close to zero, further support this tectonic setting, with the leucogranite and amphibolite formed at 506 and 522 Ma, respectively. (2) The plagioclase schist and quartz schist are interpreted to be Neoproterozoic volcaniclastic rocks that formed in a rifted (passive) continental margin setting. The quartz schist is particularly rich in detrital zircons, displaying a broad spectrum of 207Pb/206Pb ages, ranging from 901 to 3364 Ma. (3) A significant subset of detrital zircons within the quartz schist exhibits oscillatory zoning, high Th/U ratios and sharp-edged, anhedral-to-subhedral crystal forms, suggesting a derivation from proximal or deep-seated terranes. The concordant U–Pb zircon ages of 2468 and 974 Ma from the quartz schist, along with the 978 Ma age from the inherited zircons in the amphibolite, and the 1.2–2.1 Ga T2DM(Nd) from leucogranite and metamorphic rocks, collectively suggest that the Tianshuihai unit is likely underpinned by a Palaeoproterozoic basement that indicates Neoproterozoic reworking.

Therefore, our findings suggest the presence of a continuous, northwest-southeast trending Palaeoproterozoic basement underlying the entire Tashikuergan-Tianshuihai terrane. An alternative scenario posits that the ancient basement, currently beneath the Tashikuergan terrane, could extend into the Tianshuihai region, potentially indicating a Cambrian continental margin arc interspersed with remnants of older terranes.

Survey instruments and assessments are frequently used in many domains of social science. When the constructs that these assessments try to measure become multifaceted, multidimensional item response theory (MIRT) provides a unified framework and convenient statistical tool for item analysis, calibration, and scoring. However, the computational challenge of estimating MIRT models prohibits its wide use because many of the extant methods can hardly provide results in a realistic time frame when the number of dimensions, sample size, and test length are large. Instead, variational estimation methods, such as Gaussian variational expectation–maximization (GVEM) algorithm, have been recently proposed to solve the estimation challenge by providing a fast and accurate solution. However, results have shown that variational estimation methods may produce some bias on discrimination parameters during confirmatory model estimation, and this note proposes an importance-weighted version of GVEM (i.e., IW-GVEM) to correct for such bias under MIRT models. We also use the adaptive moment estimation method to update the learning rate for gradient descent automatically. Our simulations show that IW-GVEM can effectively correct bias with modest increase of computation time, compared with GVEM. The proposed method may also shed light on improving the variational estimation for other psychometrics models.

A number of parametric and nonparametric methods for estimating cognitive diagnosis models (CDMs) have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. In this paper, we propose a unified estimation framework to bridge the divide between parametric and nonparametric methods in cognitive diagnosis to better understand their relationship. We also develop iterative joint estimation algorithms and establish consistency properties within the proposed framework. Lastly, we present comprehensive simulation results to compare different methods and provide practical recommendations on the appropriate use of the proposed framework in various CDM contexts.

Estimation of the large Q-matrix in cognitive diagnosis models (CDMs) with many items and latent attributes from observational data has been a huge challenge due to its high computational cost. Borrowing ideas from deep learning literature, we propose to learn the large Q-matrix by restricted Boltzmann machines (RBMs) to overcome the computational difficulties. In this paper, key relationships between RBMs and CDMs are identified. Consistent and robust learning of the Q-matrix in various CDMs is shown to be valid under certain conditions. Our simulation studies under different CDM settings show that RBMs not only outperform the existing methods in terms of learning speed, but also maintain good recovery accuracy of the Q-matrix. In the end, we illustrate the applicability and effectiveness of our method through a TIMSS mathematics data set.

Cognitive Diagnosis Models (CDMs) are a special family of discrete latent variable models that are widely used in educational and psychological measurement. A key component of CDMs is the Q-matrix characterizing the dependence structure between the items and the latent attributes. Additionally, researchers also assume in many applications certain hierarchical structures among the latent attributes to characterize their dependence. In most CDM applications, the attribute–attribute hierarchical structures, the item-attribute Q-matrix, the item-level diagnostic models, as well as the number of latent attributes, need to be fully or partially pre-specified, which however may be subjective and misspecified as noted by many recent studies. This paper considers the problem of jointly learning these latent and hierarchical structures in CDMs from observed data with minimal model assumptions. Specifically, a penalized likelihood approach is proposed to select the number of attributes and estimate the latent and hierarchical structures simultaneously. An expectation-maximization (EM) algorithm is developed for efficient computation, and statistical consistency theory is also established under mild conditions. The good performance of the proposed method is illustrated by simulation studies and real data applications in educational assessment.

Sjögren's syndrome (SS) is a chronic autoimmune disease caused by immune system disorders. The main clinical manifestations of SS are dry mouth and eyes caused by the destruction of exocrine glands, such as the salivary and lacrimal glands, and systemic manifestations, such as interstitial pneumonia, interstitial nephritis and vasculitis. The pathogenesis of this condition is complex. However, this has not been fully elucidated. Treatment mainly consists of glucocorticoids, disease-modifying antirheumatic drugs and biological agents, which can only control inflammation but not repair the tissue. Therefore, identifying methods to regulate immune disorders and repair damaged tissues is imperative. Cell therapy involves the transplantation of autologous or allogeneic normal or bioengineered cells into the body of a patient to replace damaged cells or achieve a stronger immunomodulatory capacity to cure diseases, mainly including stem cell therapy and immune cell therapy. Cell therapy can reduce inflammation, relieve symptoms and promote tissue repair and regeneration of exocrine glands such as the salivary glands. It has broad application prospects and may become a new treatment strategy for patients with SS. However, there are various challenges in cell preparation, culture, storage and transportation. This article reviews the research status and prospects of cell therapies for SS.

Pi-d2, which encodes a potential serine-threonine receptor-like kinase (RLK) membrane-spanning protein consisting of 825 amino acids, confers resistance to Magnaporthe oryzae strain ZB15 via an unidentified recognition mechanism. In this study, the Pid2 alleles of 303 rice (O. sativa) varieties from China's Yunnan region were amplified and sequenced in order to produce 24 haplotypes and 16 translation variants. Six of twenty-four alleles possessing the resistant site at the 441st amino acid were chosen for evaluating blast resistance by transforming into the blast-vulnerable rice variety Nipponbare. After being infected with 11 strains of M. oryzae, all transgenic lines exhibited resistance to ZB-15, whereas resistance to other strains varied. Notably, Pi-d2_H23 and Pi-d2_H24 exhibited resistance to all M. oryzae strains tested, indicating that these two alleles may have a broader resistance spectrum to M. oryzae. Alignment of these alleles’ amino acid sequences revealed that the differences in blast resistance spectra were primarily related to the amino acids present in the PAN domain at position 363 (valine/alanine). These findings suggested that the two extracellular signal recognition domains of PI-D2, B-lectin and PAN, may play a role in the identification of M. oryzae effectors. The present results provide insight into the mechanism of interaction between RLKs and M. oryzae.

To meet the demands of laser-ion acceleration at a high repetition rate, we have developed a comprehensive diagnostic system for real-time and in situ monitoring of liquid sheet targets (LSTs). The spatially resolved rapid characterizations of an LST’s thickness, flatness, tilt angle and position are fulfilled by different subsystems with high accuracy. With the help of the diagnostic system, we reveal the dependence of thickness distribution on collision parameters and report the 238-nm liquid sheet generated by the collision of two liquid jets. Control methods for the flatness and tilt angle of LSTs have also been provided, which are essential for applications of laser-driven ion acceleration and others.

The Righi–Leduc heat flux generated by the self-generated magnetic field in the ablative Rayleigh–Taylor instability driven by a laser irradiating thin targets is studied through two-dimensional extended-magnetohydrodynamic simulations. The perturbation structure gets into a low magnetization state though the peak strength of the self-generated magnetic field could reach hundreds of teslas. The Righi–Leduc effect plays an essential impact both in the linear and nonlinear stages, and it deflects the total heat flux towards the spike base. Compared to the case without the self-generated magnetic field included, less heat flux is concentrated at the spike tip, finally mitigating the ablative stabilization and leading to an increase in the velocity of the spike tip. It is shown that the linear growth rate is increased by about 10% and the amplitude during the nonlinear stage is increased by even more than 10% due to the feedback of the magnetic field, respectively. Our results reveal the importance of Righi–Leduc heat flux to the growth of the instability and promote deep understanding of the instability evolution together with the self-generated magnetic field, especially during the acceleration stage in inertial confinement fusion.

Zygotic genome activation (ZGA) is a critical event in early embryonic development, and thousands of genes are involved in this delicate and sophisticated biological process. To date, however, only a handful of these genes have revealed their core functions in this special process, and therefore the roles of other genes still remain unclear. In the present study, we used previously published transcriptome profiling to identify potential key genes (candidate genes) in minor ZGA and major ZGA in both human and mouse specimens, and further identified the conserved genes across species. Our results showed that 887 and 760 genes, respectively, were thought to be specific to human and mouse in major ZGA, and the other 135 genes were considered to be orthologous genes. Moreover, the conserved genes were most enriched in rRNA processing in the nucleus and cytosol, ribonucleoprotein complex biogenesis, ribonucleoprotein complex assembly and ribosome large subunit biogenesis. The findings of this first comprehensive identification and characterization of candidate genes in minor and major ZGA provide relevant insights for future studies on ZGA.

Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

Childhood maltreatment is an established risk factor for psychopathology. However, it remains unclear how childhood traumatic events relate to mental health problems and how the brain is involved. This study examined the serial mediation effect of brain morphological alterations and emotion-/reward-related functions on linking the relationship from maltreatment to depression. We recruited 156 healthy adolescents and young adults and an additional sample of 31 adolescents with major depressive disorder for assessment of childhood maltreatment, depressive symptoms, cognitive reappraisal and anticipatory/consummatory pleasure. Structural MRI data were acquired to identify maltreatment-related cortical and subcortical morphological differences. The mediation models suggested that emotional maltreatment of abuse and neglect, was respectively associated with increased gray matter volume in the ventral striatum and greater thickness in the middle cingulate cortex. These structural alterations were further related to reduced anticipatory pleasure and disrupted cognitive reappraisal, which contributed to more severe depressive symptoms among healthy individuals. The above mediating effects were not replicated in our clinical group partly due to the small sample size. Preventative interventions can target emotional and reward systems to foster resilience and reduce the likelihood of future psychiatric disorders among individuals with a history of maltreatment.

Creating an environmentally friendly precursor to form a kaolinite intercalation compound is important for promoting the applications of nanohybrid kaolinite in electrochemical sensors, low- or zero-toxicity drug carriers, and clay-polymer nanocompounds. In the present study, a stable hydrated kaolinite pre-cursor with d001= 0.84 nm was prepared successfully by heating the transition phase, the as-prepared kaolinite-hydrazine intercalate, at temperatures between 40 and 70ºC. The structure of the hydrated kaolinite was characterized by X-ray diffraction and infrared spectroscopy. The morphology was examined using scanning electron microscopy. The results showed that the hydrated hydrazine of the transition phase was easy to decompose to hydrazines and water molecules in the interlayer at 40-70ºC. Hydrazine molecules de-intercalated gradually, and water molecules remained in the ditrigonal holes of the silicate layer with sufficient stability, finally forming the stable 0.84 nm hydrated kaolinite in the system with a success rate of 80–90%. The 0.84 nm hydrated kaolinite may become an excellent precursor for the preparation of other kaolinite intercalates. A degree of intercalation of ~100% was obtained for the kaolinite-ethylene glycol intercalate, and a degree of intercalation of ~80% was obtained for the kaolinite-glycine intercalate from the 0.84 nm hydrated kaolinite precursor.

Here, we report the generation of MeV alpha-particles from H-11B fusion initiated by laser-accelerated boron ions. Boron ions with maximum energy of 6 MeV and fluence of 109/MeV/sr@5 MeV were generated from 60 nm-thick self-supporting boron nanofoils irradiated by 1 J femtosecond pulses at an intensity of 1019 W/cm2. By bombarding secondary hydrogenous targets with the boron ions, 3 × 105/sr alpha-particles from H-11B fusion were registered, which is consistent with the theoretical yield calculated from the measured boron energy spectra. Our results demonstrated an alternative way toward ultrashort MeV alpha-particle sources employing compact femtosecond lasers. The ion acceleration and product measurement scheme are referential for the studies on the ion stopping power and cross section of the H-11B reaction in solid or plasma.

The laboratory generation and diagnosis of uniform near-critical-density (NCD) plasmas play critical roles in various studies and applications, such as fusion science, high energy density physics, astrophysics as well as relativistic electron beam generation. Here we successfully generated the quasistatic NCD plasma sample by heating a low-density tri-cellulose acetate (TCA) foam with the high-power-laser-driven hohlraum radiation. The temperature of the hohlraum is determined to be 20 eV by analyzing the spectra obtained with the transmission grating spectrometer. The single-order diffraction grating was employed to eliminate the high-order disturbance. The temperature of the heated foam is determined to be T = 16.8 ± 1.1 eV by analyzing the high-resolution spectra obtained with a flat-field grating spectrometer. The electron density of the heated foam is about $N_e=4.0\pm 0.3\times {10}^{20}{\text{cm}}^{-3}$ under the reasonable assumption of constant mass density.

In preparation for an experiment with a laser-generated intense proton beam at the Laser Fusion Research Center at Mianyang to investigate the 11B(p,α)2α reaction, we performed a measurement at very low proton energy between 140 keV and 172 keV using the high-voltage platform at the Institute of Modern Physics, Lanzhou. The aim of the experiment was to test the ability to use CR-39 track detectors for cross-section measurements and to remeasure the cross-section of this reaction close to the first resonance using the thick target approach. We obtained the cross-section σ = 45.6 ± 12.5 mb near 156 keV. Our result confirms the feasibility of CR-39 type track detector for nuclear reaction measurement also in low-energy regions.