Search

Targeted nanodrug delivery systems are highly anticipated for the treatment of malaria. It is known that Plasmodiumcan induce new permeability pathways (NPPs) on the membrane of infected red blood cells (iRBCs) for their nutrient uptake. The NPPs also enable the uptake of nanoparticles (NPs) smaller than 80 nm. Additionally, Plasmodiummaintains a stable, slightly acidic, and reductive internal environment with higher glutathione (GSH) levels. Based on this knowledge, methyl artelinate (MA, a prodrug-like derivative of dihydroartemisinin) nanoparticles (MA-PCL-NPs) were developed using poly(ethylene glycol)-b-poly(ε-caprolactone) (mPEG-PCL) by a thin-film dispersion method and were further coated with polydopamine (PDA) to obtain MA-PCL@PDA-NPs with a particle size of ∼30 nm. The biomaterial PDA can be degraded in slightly acidic and reductive environments, thereby serving as triggers for drug release. MA could generate reactive oxygen species and decrease GSH levels, consequently causing parasite damage. The in vitro release experiment results indicated that the cumulative release percentage of MA from MA-PCL@PDA-NPs was considerably higher in phosphate buffer with 10 mM GSH at pH 5.5 (88.10%) than in phosphate buffer without GSH at pH 7.4 (16.98%). The green fluorescence within iRBCs of coumarin 6, the probe of NPs (C6-PCL@PDA-NPs), could be reduced significantly after adding the NPP inhibitor furosemide (p< 0.001), which demonstrated that MA-PCL@PDA-NPs could be ingested into iRBCs through NPPs. In vivo antimalarial pharmacodynamics in Plasmodium bergheiK173-bearing mice showed that the inhibition ratio of MA-PCL@PDA-NPs (93.96%) was significantly higher than that of commercial artesunate injection (AS-Inj, 63.33%). The above results showed that the developed MA-PCL@PDA-NPs possessed pH-GSH dual-responsive drug release characteristics and targeting efficacy for iRBCs, leading to higher antimalarial efficacy against Plasmodium.

A system with light detection and ranging (LiDAR) and camera (named as LiDAR-camera system) plays the essential role in intelligent vehicle (IV), for it provides 3D spatial and 2D texture features for 3D scene understanding. To leverage LiDAR point cloud and image, extrinsic calibration is a crucial technique, for it can align 2D pixel and 3D point in the pixel-level accuracy. With the rapid development of IV, calibration demand is shifted from offline to online, from the specific scenes to the open scenes. It brings new challenge to the calibration task. Although numbers of approaches have been proposed in the last decade, there lacks an in-depth summary about this topic. Thus, we conduct a survey of extrinsic calibration. Theoretically, the key of calibration is to build correspondence from LiDAR point cloud and optical image. From the viewpoint of correspondence, we attempt to divide the mainstream approaches into explicit and implicit correspondence based methods. After that, we summarize both the strength and weakness of the current works, provide the methods comparison, and list the open-source implementations. Finally, we analyze the tendency of calibration approach, discuss the remained problems in this field. We believe that this survey benefits to the community of autonomous driving.

Deep learning-based methods have shown promising outcomes in many fields. However, the performance gain is always limited to a large extent in classifying hyperspectral image (HSI). We discover that the reason behind this phenomenon lies in the incomplete classification of HSI, i.e., existing works only focus on a certain stage that contributes to the classification, while ignoring other equally or even more significant phases. To address the above issue, we creatively put forward three elements needed for complete classification: the extensive exploration of available features, adequate reuse of representative features, and differential fusion of multidomain features. To the best of our knowledge, these three elements are being established for the first time, providing a fresh perspective on designing HSI-tailored models. On this basis, an HSI classification full model (HSIC-FM) is proposed to overcome the barrier of incompleteness. Specifically, a recurrent transformer corresponding to Element 1 is presented to comprehensively extract short-term details and long-term semantics for local-to-global geographical representation. Afterward, a feature reuse strategy matching Element 2 is designed to sufficiently recycle valuable information aimed at refined classification using few annotations. Eventually, a discriminant optimization is formulized in accordance with Element 3 to distinctly integrate multidomain features for the purpose of constraining the contribution of different domains. Numerous experiments on four datasets at small-, medium-, and large-scale demonstrate that the proposed method outperforms the state-of-the-art (SOTA) methods, such as convolutional neural network (CNN)-, fully convolutional network (FCN)-, recurrent neural network (RNN)-, graph convolutional network (GCN)-, and transformer-based models (e.g., accuracy improvement of more than 9% with only five training samples per class). The code will be available soon at https://github.com/jqyang22/ HSIC-FM.

Waste polypropylene (PP) presents a significant environmental challenge, owing to its refractory nature and inert C–C backbone. In this study, we introduce a practical chemical recovery strategy from PP waste using a mild catalyst-free hydrothermal treatment (HT). The treatment converts 64.1% of the processed PP into dissolved organic products within 2 h in an air atmosphere at 160 °C. Higher temperatures increase the PP conversion efficiency. Distinct electron absorption and emission characteristics of the products are identified by spectral analysis. Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) reveals the oxidative cracking of PP into shorter-chain homologues (10–50 carbon atoms) containing carboxylic and carbonyl groups. Density functional theory (DFT) calculations support a reaction pathway involving thermal C–H oxidation at the tertiary carbon sites in the polymer chain. The addition of 1% H2O2further enhances the oxidation reaction to produce valuable short-chain acetic acids, enabling gram-scale recycling of both pure PP and disposable surgical masks from the real world. Techno-economic analysis (TEA) and environmental life cycle costing (E-LCC) analysis suggest that this hydrothermal oxidation recovery technology is financially viable, which shows significant potential in tackling the ongoing plastic pollution crisis and advancing plastic treatment methodologies toward a circular economy paradigm.

This paper presents a 3D registration method with maximal cliques (MAC) for 3D point cloud registration (PCR). The key insight is to loosen the previous maximum clique constraint and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each representing a consensus set. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. In addition, we present a variant of MAC if given overlap prior, called MAC-OP. Overlap prior further enhances MAC from many technical aspects, such as graph construction with re-weighted nodes, hypotheses generation from cliques with additional constraints, and hypothesis evaluation with overlap-aware weights. Extensive experiments demonstrate that both MAC and MAC-OP effectively increase registration recall, outperform various state-of-the-art methods, and boost the performance of deep-learned methods. For instance, MAC combined with GeoTransformer achieves a state-of-the-art registration recall of $\text{95.7}\% / \text{78.9}\%$95.7%/78.9% on 3DMatch / 3DLoMatch. We perform synthetic experiments on 3DMatch-LIR / 3DLoMatch-LIR, a dataset with extremely low inlier ratios for 3D registration in ultra-challenging cases.

Recent developments in semiconductor-based surface-enhanced Raman scattering (SERS) have achieved numerous advancements, primarily centered on the chemical mechanism. However, the role of the electromagnetic (electromagnetic mechanism) contribution in advancing semiconductor SERS substrates is still underexplored. In this study, we developed a SERS substrate based on densely aligned α-type MoO3(α-MoO3) semiconductor nanorods (NRs) with rectangular parallelepiped ribbon shapes with width measuring several hundred nanometers. These structural attributes strongly affect light transport in the visible range by multiple light scattering generated in narrow gaps between NRs, contributing to the improvement of SERS performance. Engineering the nanostructure and chemical composition of NRs realized high SERS sensitivity with an enhancement factor of 2 × 108and a low detection limit of 5 × 10–9M for rhodamine 6G (R6G) molecules, which was achieved by the stoichiometric NR sample with strong light scattering. Furthermore, it was observed that the scattering length becomes significantly shorter compared with the excitation wavelength in the visible regime, which indicates that light transport is strongly modified by mesoscopic interference related to Anderson localization. Additionally, high electric fields were found to be localized on the NR surfaces, depending on the excitation wavelength, similar to the SERS response. These optical phenomena indicate that electromagnetic excitation processes play an important role in plasmon-free SERS platforms based on α-MoO3NRs. We postulate that our study provides important guidance for designing effective EM-based SERS-active semiconductor substrates.

Plasmonic surface lattice resonance (plasmonic SLR) is a remarkable platform for chemical-specific detection based on surface-enhanced infrared absorption (SEIRA). In this study, we investigated polarized reflectance spectra of plasmonic SLR comprising Ga-doped ZnO (ZnO:Ga) two-dimensional microdot arrays to understand the relationship between light polarization and SEIRA. The periodicity of the microdot arrays was varied to elucidate the optical processes involved in plasmonic SLRs depending on the light polarization. In particular, the out-of-plane polarization induced optical interactions between the propagated surface plasmon polariton (SPP) and diffractive waves scattered from the microdot arrays, resulting in a higher electric field on the microdot surfaces than that under in-plane polarization, which was attributed to the coupling between localized SPR and diffractive waves. This difference in the optical excitation significantly affected the optical properties of the ZnO-based SPR and its SEIRA performance. Finally, we demonstrated high sensitivity SEIRA detection at the monolayer scale using self-assembled layers. We found that the light polarization control is effective in improving the ZnO-based SPR and SEIRA effects. This study demonstrates the importance of light polarization in enhancing SEIRA.

Objectives:Sepsis is defined as a life-threatening disease associated with a dysfunctional host immune response. Stratified identification of critically ill patients might significantly improve the survival rate. The present study sought to probe molecular markers associated with cuproptosis in septic patients to aid in stratification and improve prognosis. Methods:We studied expression of cuproptosis-related genes (CRGs) using peripheral blood samples from septic patients. Further classification was made by examining levels of expression of these potential CRGs in patients. Coexpression networks were constructed using the Weighted Gene Coexpression Network Analysis (WGCNA) method to identify crucial prognostic CRGs. Additionally, we utilized immune cell infiltration analysis to further examine the immune status of septic patients with different subtypes and its association with the CRGs. scRNA-seq data were also analyzed to verify expression of key CRGs among specific immune cells. Finally, immunoblotting, flow cytometry, immunofluorescence, and CFSE analysis were used to investigate possible regulatory mechanisms. Results:We classified septic patients based on CRG expression levels and found significant differences in prognosis and gene expression patterns. Three key CRGs that may influence the prognosis of septic patients were identified. A decrease in GLS expression was subsequently verified in Jurkat cells, accompanied by a reduction in O-GlcNAc levels, and chelation of copper by tetrathiomolybdate could not rescue the reduction in GLS and O-GLcNAc levels. Moreover, immoderate chelation of copper was detrimental to mitochondrial function, cell viability, and cell proliferation, as well as the immune status of the host. Conclusion:We have identified novel molecular markers associated with cuproptosis, which could potentially function as diagnostic indicators for septic patients. The reversible nature of the observed alterations in FDX1 and LIAS was demonstrated through copper chelation, whereas the correlation between copper and the observed changes in GLS requires further investigation.

A brief, practical catalytic process for the production of optically active γ-functionalized alcohols from relevant alkenes has been developed by using a robust Mn(III)/air/(Me2SiH)2O catalytic system combined with lipase-catalyzed kinetic resolution. This approach demonstrates exceptional tolerance toward proximal functional groups present on alkenes, enabling the achievement of high yields and exclusive enantioselectivity. Under this sequential catalytic system, the chiral alkene precursors can also be converted into γ-functionalized alcohols and related acetates as separable single enantiomers.

Research has consistently demonstrated the positive influence of preschool on students' development, with a longer duration of attendance generally correlating with improved academic achievements. However, according to the marginal utility theory, it is conceivable that there might exist an optimal duration of preschool attendance to maximize academic benefits. Currently, the optimal duration remains uncertain, and it may vary depending on the distinctive preschool characteristics of different countries. Therefore, the aim of this study was to examine the effects of different durations of preschool attendance on academic achievements in mathematics, reading, and science and explore the optimal duration in B-S-J-Z (China), Italy, New Zealand, Denmark, and Sweden, employing the generalized propensity score (GPS) method to control for confounding factors based on the Programme for International Student Assessment (PISA) 2018. The findings revealed a generally inverted U-shaped relationship between the duration of preschool attendance and academic achievements. Moreover, the optimal duration varied among the five countries: students in New Zealand benefited the most from attending preschool for 2 years, 3 years in Italy, and 4 years in B-S-J-Z (China), Denmark, and Sweden. The findings offer practical guidance for parents and educators in these countries to tailor their children's duration of preschool attendance for the enhancement of academic achievements.

Recently, we developed a bivalent prostate-specific membrane antigen (PSMA) radioligand ([18F]AlF-Bi-PSMA), which showed higher tumor uptake and retention in PSMA-positive mouse models than the clinically used radioligands, [68Ga]Ga-PSMA-11 and [18F]PSMA-1007. Here, we developed two 177Lu-labeled bivalent PSMA ligands with (DOTA-Alb-Bi-PSMA) or without an albumin-binding motif (DOTA-Bi-PSMA) to enhance radiotherapeutic efficacy with minimal toxicity. The results demonstrated that both 177Lu-labeled bivalent radioligands showed good stability, high binding affinity, and PSMA-targeting specificity in vitro. Compared with [177Lu]Lu-PSMA-617, both [177Lu]Lu-Bi-PSMA and [177Lu]Lu-Alb-Bi-PSMA showed a higher area under the curve (AUC) of tumor accumulation and superior therapeutic efficacy. However, [177Lu]Lu-Alb-Bi-PSMA exhibited a dose-dependent increase in acute damage to kidneys. In terms of the radionuclide therapy efficacy and side effects, [177Lu]Lu-Bi-PSMA exhibited well-balanced action with high tumor-to-organs AUC ratios, resulting in remarkable therapeutic efficacy and negligible side effects. These promising results warrant further investigations to achieve the clinical translation of [177Lu]Lu-Bi-PSMA.

Emerging applications utilize numerous Deep Neural Networks (DNNs) to address multiple tasks simultaneously. As these applications continue to expand, there is a growing need for off-chip memory access optimization and innovative architectures that can adapt to diverse computation, memory, and communication requirements of various DNN models. To address these challenges, we propose Versa-DNN, a versatile DNN accelerator that can provide efficient computation, memory, and communication support for the simultaneous execution of multiple DNNs. Versa-DNN features three unique designs: a flexible off-chip memory access optimization strategy, adaptable communication fabrics, and a communication and computational aware scheduling algorithm. The proposed off-chip memory optimization strategy can improve performance and energy efficiency by increasing hardware utilization, eliminating excess data duplication, and reducing off-chip memory accesses. The adaptable communication fabrics consist of distributed buffers, processing elements, and a flexible Network-on-Chip (NoC), which can dynamically morph and fission to support distinct communication and computation needs for simultaneously running DNN models. Furthermore, the proposed scheduling policy manages the simultaneous execution of multiple DNN models with improved performance and energy efficiency. Simulation results using several DNN models, show that the proposed Versa-DNN architecture achieves 41%, 238%, 392% throughput speedup and 30%, 59%, 63% energy reduction on average for different workloads when compared to state-of-the-art accelerators such as Planaria, Herald, and AI-MT, respectively.

Consistent correspondences between point clouds are vital to 3D vision tasks such as registration and recognition. In this paper, we present a mutual voting method for ranking 3D correspondences. The key insight is to achieve reliable scoring results for correspondences by refining both voters and candidates in a mutual voting scheme. First, a graph is constructed for the initial correspondence set with the pairwise compatibility constraint. Second, nodal clustering coefficients are introduced to preliminarily remove a portion of outliers and speed up the following voting process. Third, we model nodes and edges in the graph as candidates and voters, respectively. Mutual voting is then performed in the graph to score correspondences. Finally, the correspondences are ranked based on the voting scores and top-ranked ones are identified as inliers. Feature matching, 3D point cloud registration, and 3D object recognition experiments on various datasets with different nuisances and modalities verify that MV is robust to heavy outliers under different challenging settings, and can significantly boost 3D point cloud registration and 3D object recognition performance.

The original Leray’s problem concerns the well-posedness of weak solutions to the steady incompressible Navier-Stokes equations in a distorted pipe, which approach the Poiseuille flow subject to the no-slip boundary condition at spatial infinity. In this paper, the same problem with the Navier-slip boundary condition instead of the no-slip boundary condition, is addressed. Due to the complexity of the boundary condition, some new ideas, presented as follows, are introduced to handle the extra difficulties caused by boundary terms. First, the Poiseuille flow in the semi-infinite straight pipe with the Navier-slip boundary condition will be introduced, which will serve as the asymptotic profile of the solution to the generalized Leray’s problem at spatial infinity. Second, a solenoidal vector function defined in the whole pipe, satisfying the Navier-slip boundary condition, having the designated flux and equalling the Poiseuille flow at a large distance, will be carefully constructed. This plays an important role in reformulating our problem. Third, the energy estimates depend on a combined L2-estimate of the gradient and the stress tensor of the velocity.

Moiré superlattices have emerged as a new platform for studying strongly correlated quantum phenomena, but these systems have been largely limited to van der Waals layer two-dimensional materials. Here we introduce moiré superlattices leveraging ultrathin, ligand-free halide perovskites, facilitated by ionic interactions. Square moiré superlattices with varying periodic lengths are clearly visualized through high-resolution transmission electron microscopy. Twist-angle-dependent transient photoluminescence microscopy and electrical characterizations indicate the emergence of localized bright excitons and trapped charge carriers near a twist angle of ~10°. The localized excitons are accompanied by enhanced exciton emission, attributed to an increased oscillator strength by a theoretically predicted flat band. This research showcases the promise of two-dimensional perovskites as unique room-temperature moiré materials.

Reducing cumulative registration error is critical to accurate 3D multi-view registration. Meta-shape based methods optimize rigid transformations of point clouds by iteratively registering each point cloud with a meta-shape, which remain popular solutions to 3D multi-view registration. However, the merits and demerits of existing meta-shape based methods remain unclear. Moreover, we argue that simpler meta-shape based solutions can achieve even better performance. To this end, we evaluate seven representative meta-shape based methods in this work, including four existing ones and three modified ones, in order to investigate the problem of defining a good meta-shape. In particular, we first abstract the main steps of considered methods. Then, experiments on both object and scene datasets with real and synthetic cumulative registration errors are deployed for an in-depth evaluation. Finally, based on the experimental outcomes, we give a discussion on the advantages and limitations of meta-shape based methods. We demonstrate prior works have used unnecessarily complicated techniques for cumulative error elimination and our slightly modified simpler solutions can achieve competitive performance on experimental datasets.

Touch pad based on triboelectric nanogenerator are attracting attention in the field of wearable electronics and human-machine interaction. However, it is still a challenge to realize touch pads combining large-area metamorphic stretchability, high power triboelectric sensing, intelligent free-sliding recognition and availability in extreme environments. Here, we fabricate an ultra-stretchable triboelectric touch pad by integrating liquid metal and high performance hydrogel-based triboelectric sensor arrays, which achieves Transformer-assisted gesture recognition for drone flight direction control. The internal hydrogel electrode has great stretchability, electrical conductivity and anti-freeze properties. Encapsulated in Ecoflex with sandpaper micro-surfaces, it endows high triboelectric power density and pressure sensitivity. With screen-printed liquid metal, the integrated touch pad is fully ultra-stretchable and large-area strain stability. Combining Internet of Things, we build a Transformer-assisted human machine interface. Notably, slide gesture signal peak deviation is reduced by integral processing. The random free-sliding gesture signals are parsed by the Transformer algorithm with 96.83 % accuracy. Multi-threaded signal processing improves the response speed and throughput of the system. The accurate real-time gesture recognition for drone flight direction control in extreme environment is realized. This human machine interface can facilitate development of next generation intelligent remote interactive platforms.

Three-dimensional nanoparticle (NP) assemblies show interesting optical responses that differ from naturally occurring materials, such as metals, oxides, and semiconductors. In this study, we investigate the optical response of thin films comprising Sn:In2O3NPs (ITO NP films) based on the correlation between complex permittivity and infrared (IR) reflectance for solar-thermal shielding applications. IR ellipsometry measurements are conducted to clarify the presence of Lorentz resonances in plasmonic metamaterials. The Lorentz resonances are correlated to the electric field strength at interparticle gaps by varying the Sn dopant concentration, as confirmed using finite-difference time-domain (FDTD) simulations. High solar-thermal shielding performance was obtained owing to selective near-IR reflection based on strong Lorentz resonances as the ITO NP films were electrically polarizable but magnetically inactive. Thermal shielding efficiency was demonstrated via a comparison of the air temperature change in a simulated box used as a model house. Additionally, we demonstrate the significance of NP packing density on the enhancement of the near-IR reflectance. The role of interparticle spacing for high near-IR reflectance was revealed by comparing effective medium approximation analyses and FDTD simulations. This relationship was also demonstrated by the reduction of solar-thermal shielding performance when using aggregated ITO NPs. Our work confirmed that the control of complex permittivity in plasmonic metamaterials must be considered in the structural design of transparent and reflective materials for solar-thermal shielding applications.

Graphical abstract:

For semiconductor gas sensors, surface gas chemical reactions cause resistance changes in bulk semiconductors, and this tight connection between surface chemical reactions and changes in semiconductors could be a challenging opportunity to develop a reasonable theory. In this work, WO3nanoplates were synthesized through a hydrothermal approach and WO3nanoplate sensors also show high sensitivities to trimethylamine (TMA) gas. Both IR and NMR spectra show that this orthorhombic WO3structure has water molecules acting as the coordination solvent. The IR spectra of the samples also demonstrate the existence of W═O bonds. The loss of coordinated water molecules in WO3lattices caused the formation of oxygen vacancies, and the electrons trapped by oxygen vacancies can be effectively stabilized by the W═O conjugate bonds. The coordinated water molecules in WO3lattices and the W═O bonds should be responsible for the high sensing performance of WO3nanoplates. Our observation connected the surface gas chemical affair and the bulk physical one.

Topologically associating domain (TAD) reorganization commonly occurs in the cell nucleus and contributes to gene activation and inhibition through the separation or fusion of adjacent TADs. However, functional genes impacted by TAD alteration and the underlying mechanism of TAD reorganization regulating gene transcription remain to be fully elucidated. Here, we first developed a novel approach termed Inter3D to specifically identify genes regulated by TAD reorganization. Our study revealed that the segregation of TADs led to the disruption of intrachromosomal looping at the myosin light chain 12B (MYL12B) locus, via the meticulous reorganization of TADs mediating epigenomic landscapes within tumor cells, thereby exhibiting a significant correlation with the down-regulation of its transcriptional activity. Conversely, the fusion of TADs facilitated intrachromosomal interactions, suggesting a potential association with the activation of cytochrome P450 family 27 subfamily B member 1 (CYP27B1). Our study provides comprehensive insight into the capture of TAD rearrangement-mediated gene loci and moves toward understanding the functional role of TAD reorganization in gene transcription. The Inter3D pipeline developed in this study is freely available at https://github.com/bm2-lab/inter3Dand https://ngdc.cncb.ac.cn/biocode/tool/BT7399.

Dammann gratings (DGs) can generate a spot array in a particular arrangement. In recent years, DGs have been used in many fields such as laser beam splitting and optical coupling. Nanograting encoding technology can achieve a high signal-to-noise ratio and high-efficiency diffraction distribution; it also provides new design ideas for realizing the miniaturization and deviceization of DGs. In this work, we have comprehensively studied the DG based on an all-dielectric metasurface, which can produce a 5×5 diffraction spot array with a diffraction angle of 20^∘×20^∘. In an operation waveband from 650 to 690 nm, the DG has superior performance with high efficiency ≥60%; meanwhile, it achieves a relative low contrast ratio ≤0.33. Owing to high efficiency, wide waveband performance, and polarization insensitive property, the all-dielectric metasurface DG can provide possibilities for various application, including laser technology and optical information processing.

In fringe projection profilometry, phase shifting (PS) is the most used technique for phase retrieval. However, it suffers from periodicity of sine wave in PS; the result is wrapped into [−π,π]; and additional phase unwrapping (PU) is necessary to retrieve the absolute phase. In this paper, a more general technique termed frequency shifting is proposed, based on which the behavior of periodicity is eliminated and absolute phase can be retrieved pixelwisely without any phase unwrapping. The effectiveness of the proposed technique was verified by extensive experimental results, and they demonstrate comparable performance with those of the traditional technique combining PS and PU even in only one step and less projection.

Previous evidence varied on the relationship of dietary sodium, potassium, sodium/potassium, and salt with cognitive function. This study aimed to explore the associations of sodium, potassium, sodium/potassium, and salt intakes with cognitive function among the elderly in China.

Estimating an accurate six-degree-of-freedom (6-DoF) pose from correspondences with outliers remains a critical issue to 3D rigid registration. Random sample consensus (RANSAC) and its variants are popular solutions to this problem. Although there have been a number of RANSAC-fashion estimators, two issues remain unsolved. First, it is unclear which estimator is more appropriate to a particular application. Second, the impacts of different sampling strategies, hypothesis generation methods, hypothesis evaluation metrics, and stop criteria on the overall estimators remain ambiguous. This work fills these gaps by first considering six existing RANSAC-fashion methods and then proposing eight variants for a comprehensive evaluation. The objective is to thoroughly compare estimators in the RANSAC family, and evaluate the effects of each key stage on the eventual 6-DoF pose estimation performance. Experiments have been carried out on four standard datasets with different application scenarios, data modalities, and nuisances. They provide us with input correspondence sets with a variety of inlier ratios, spatial distributions, and scales. Based on the experimental results, we summarize remarkable outcomes and valuable findings, so as to give practical instructions to real-world applications, and highlight current bottlenecks and potential solutions in this research realm.

Traditional Chinese customs practicing in postpartum yield mixed results on maternal health. The objectives of this study are 1) to depict the postpartum customs in Chinese lactating women in varied areas and 2) to explore its effects on dietary intake.

Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder that often occurs in children and seriously affects daily life. In recent years, many studies have shown that intestinal microbial imbalance and intestinal-brain dysfunction may be the critical mechanism for the formation of ASD. This article reviews the changes in the gut microbiota of patients and their impact mechanisms, and the current mechanisms of probiotic and traditional Chinese medicine therapies. Methods: A review of contemporary peer-reviewed studies. Pubmed and Cnki were the databases used to identify the studies. Results: The majority of the reviewed studies demonstrated that changes in the gut microbiota can directly or indirectly induce ASD by affecting the immune system, nervous system, and endocrine system. Probiotics can improve brain function by affecting the vagus nerve, and improve metabolism by regulating the expression of neuroendocrine hormones. According to the Chinese medicine theory, there are three leading causes of ASD such as deficiency of kidney essence, stagnation of liver qi, and malnutrition of heart spirit. Conclusions: The fermentation of Chinese herbal medicine and probiotics can be further studied and may become a new type of treatment for ASD in the future.

ABSTRACTAlthough members of the Mycobacterium tuberculosis complex (MTBC) exhibit high similarity, they are characterized by differences with respect to virulence, immune response, and transmissibility. To understand the virulence of these bacteria and identify potential novel therapeutic targets, we systemically investigated the total cell protein contents of virulent H37Rv, attenuated H37Ra, and avirulent M. bovis BCG vaccine strains at the log and stationary phases, based on tandem mass tag (TMT) quantitative proteomics. Data analysis revealed that we obtained deep-coverage protein identification and high quantification. Although 272 genetic variations were reported in H37Ra and H37Rv, they showed very little expression difference in log and stationary phase. Quantitative comparison revealed H37Ra and H37Rv had significantly dysregulation in log phase (227) compared with stationary phase (61). While BCG and H37Rv, and BCG and H37Ra showed notable differences in stationary phase (1171 and 1124) with respect to log phase (381 and 414). In the log phase, similar patterns of protein abundance were observed between H37Ra and BCG, whereas a more similar expression pattern was observed between H37Rv and H37Ra in the stationary phase. Bioinformatic analysis revealed that the upregulated proteins detected for H37Rv and H37Ra in log phase were virulence-related factors. In both log and stationary phases, the dysregulated proteins detected for BCG, which have also been identified as M. tuberculosis response proteins under dormancy conditions. We accordingly describe the proteomic profiles of H37Rv, H37Ra, and BCG, which we believe will potentially provide a better understanding of H37Rv pathogenesis, H37Ra attenuation, and BCG immuno protection.

A basis of fundamental knowledge of different basic income approaches (BI) is essential to be elaborated for enhancing the prevalent debates on BI as an important solution on social transformation. Aiming to contribute to the current knowledge gaps and future research agendas on BI, this paper elaborates on BI theories and BI empirical cases, linkage, and gaps between these two parts. A total of 152 pieces of literature on BI theories and BI empirical cases were intensively reviewed, which includes BI definitions, positive and negative conceptual impacts of BI argued to bring to human society from social, economic, and policy-and-governance perspectives, as well as the implementation and the outcomes of 15 selected BI empirical cases. Our findings indicate that BI definitions remain imprecise, and the BI empirical cases are implemented in diverse ways. We also identified that many conceptual impacts of BI were not achieved in the reviewed BI empirical cases. We further argue that the policy environment for implementing BI has not been entirely created at present, and a key issue  is that the ambiguity between the BI theories and BI empirical cases causes difficulties to generate widely agreed principles guiding the implementation of BI. This paper suggests that future studies on BI should focus more on revising existing BI definitions which need to be simplified, detailed, and unified, generating universal principles guiding the implementation of BI, and developing indicators measuring BI's consequences.