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Cd and Pb are common toxic contaminants prevailing in agricultural soils contaminated by mining activities. In this study, biochar was used to stabilize Cd and Pb contaminated soil for safe with three crops rotation condition within one year. Field experiments were carried out to investigate the effect of Litchi branch biochar (BC), pyrolyzed at 600 °C and applied at 4 rates [(0 t ha−1 (T0), 10 t ha−1 (T1), 20 t ha−1 (T2) and 30 t ha−1 (T3)]) on remediation of Cd and Pb in agricultural soil near Dabaoshan Mine in South China under a cucumber–sweet potato–rape rotation. The results showed that the application of BC can significantly increase the pH, cation exchange capacity and soil organic matter. After cultivation of crops, the pH values decrease gradually, with the biggest drop of 0.45 pH units in T3 treatment after rape cultivation. BC application increased the yield of three crops up to onefold to twofold in T3 treatment as compared to the control. The uptake of Cd and Pb in all three crops decreases with the increase in BC doses, which is mainly related to the decrease in bioavailable metals in their respective soil treatments. Under 1-year crops rotation, the remediation ability of BC still remains, while Cd and Pb can exhibit different risk to different crops. The data of this study can provide scientific suggestions for the selection of suitable crops and proper BC amount in remediation of heavy metal contaminated soil.

Lensless imaging eliminates the need for geometric isomorphism between a scene and an image while allowing the construction of compact, lightweight imaging systems. However, a challenging inverse problem remains due to the low reconstructed signal-to-noise ratio. Current implementations require multiple masks or multiple shots to denoise the reconstruction. We propose single-shot lensless imaging with a Fresnel zone aperture and incoherent illumination. By using the Fresnel zone aperture to encode the incoherent rays in wavefront-like form, the captured pattern has the same form as the inline hologram. Since conventional backpropagation reconstruction is troubled by the twin-image problem, we show that the compressive sensing algorithm is effective in removing this twin-image artifact due to the sparsity in natural scenes. The reconstruction with a significantly improved signal-to-noise ratio from a single-shot image promotes a camera architecture that is flat and reliable in its structure and free of the need for strict calibration., Lensless imaging: reduced noise Two simple innovations have been found to significantly improve the quality of lensless imaging. Jiachen Wu and coworkers from Tsinghua University in China and MIT in the US report that introducing a Fresnel optical element and applying a compressive sensing algorithm can significantly reduce the level of noise in reconstructed images. Their simple lensless system consists of a Fresnel Zone Aperture (FZA) placed 3 mm in front of a CMOS image sensor. The FZA is a mask of patterned chrome that consists of a series of concentric rings that alternatingly transmit and block light. Images on an LCD screen placed ~30 cm from the mask are used as a test object. The signal recorded by the CMOS sensor is then reconstructed by a compressive sensing algorithm with total variation denoising to generate an improved image of the object.

As the foundation of virtual content generation, cameras are crucial for augmented reality (AR) applications, yet their integration with transparent displays has remained a challenge. Prior efforts to develop see-through cameras have struggled to achieve high resolution and seamless integration with AR displays. In this work, we present LightguideCam, a compact and flexible see-through camera based on an AR lightguide. To address the overlapping artifacts in measurement, we present a compressive sensing algorithm based on an equivalent imaging model that minimizes computational consumption and calibration complexity. We validate our design using a commercial AR lightguide and demonstrate a field of view of 23.1° and an angular resolution of 0.1° in the prototype. Our LightguideCam has great potential as a plug-and-play extensional imaging component in AR head-mounted displays, with promising applications for eye-gaze tracking, eye-position perspective photography, and improved human–computer interaction devices, such as full-screen mobile phones.

A generalized explicit-restriction convolutional framework for lensless imaging is implemented by incorporating multiple hardware restrictions into the forward model. The proposed framework presents reconstructions with better perceptual quality and 4 × super-resolution.

Quantitative phase imaging (QPI) is a label-free technique providing both morphology and quantitative biophysical information in biomedicine. However, applying such a powerful technique to in vivo pathological diagnosis remains challenging. Multi-core fiber bundles (MCFs) enable ultra-thin probes for in vivo imaging, but current MCF imaging techniques are limited to amplitude imaging modalities. We demonstrate a computational lensless microendoscope that uses an ultra-thin bare MCF to perform quantitative phase imaging of biomedical samples with up to 1 {\mu}m lateral resolution and nanoscale axial resolution. The incident complex light field at the measurement side is precisely reconstructed from a single-shot far-field speckle pattern at the detection side, enabling digital focusing and 3D volumetric reconstruction without any mechanical movement. The accuracy of the quantitative phase reconstruction is validated by imaging the phase target and hydrogel beads through the MCF. With the proposed imaging modality, 3D imaging of human cancer cells is achieved through the ultra-thin fiber endoscope, promising widespread clinical applications., Comment: 16pages, 6 figures

The developments of three-dimensional (3-D) imaging and display technology have created important demands for 3-D model with realistic texture. Due to texture misalignment and inconsistent illumination, texture reconstruction is subject to degradation like blurring, ghosting and color discontinuity. A composite weight texture blending approach is proposed to mitigate the artifacts. For inaccurate geometric model, bidirectional similarity function based multi-scale energy minimization method is proposed to correct texture misalignment. The average processing time of the proposed method is only 40% of existing method. The optical experiments demonstrate that photorealistic texture could be achieved for inaccurate geometric model and inconsistent illumination.

Learning-based computer-generated holography (CGH) provides a rapid hologram generation approach for holographic displays. Supervised training requires a large-scale dataset with target images and corresponding holograms. We propose an autoencoder-based neural network (holoencoder) for phase-only hologram generation. Physical diffraction propagation was incorporated into the autoencoder’s decoding part. The holoencoder can automatically learn the latent encodings of phase-only holograms in an unsupervised manner. The proposed holoencoder was able to generate high-fidelity 4K resolution holograms in 0.15 s. The reconstruction results validate the good generalizability of the holoencoder, and the experiments show fewer speckles in the reconstructed image compared with the existing CGH algorithms.

An autoencoder neural network is proposed for real-time phase-only CGH generation. As an unsupervised learning method, the input and output of the autoencoder are both the original images, which dispenses with calculating corresponding holograms. It could automatically learn the encoding of phase-only holograms during the training period. Once the training is completed, the phase-only hologram of any two-dimensional image can be quickly generated. The calculation time is 1-2 orders of magnitude faster than the traditional iterative algorithms and the reconstructed image quality is improved.

Although numerous studies have separately investigated the associations of changes in weight or waist circumference with mortality risk, few studies have examined the associations of concurrent changes in these 2 anthropometric parameters with all-cause mortality.To assess the associations of changes in body weight, waist circumference, or both, combined with all-cause mortality.This cohort study used data from 2 longitudinal cohort studies in Dongfeng-Tongji and Kailuan, China. Participants included 58 132 adults (aged 40 years and older) with measures of weight and waist circumference at baseline and follow-up visit. Statistical analysis was performed from June 2020 to September 2021.Changes in weight and waist circumference between 2 visits (2008-2010 to 2013 in the Dongfeng-Tongji cohort, and 2006-2007 to 2010-2011 in the Kailuan study). Stable weight was defined as change in weight within 2.5 kg between the 2 visits and stable waist circumference was defined as changes within 3.0 cm. Changes were categorized as loss, stable, or gain for weight and waist circumference separately, and created a 9-category variable to represent the joint changes.All-cause mortality from follow-up visit (2013 in Dongfeng-Tongji cohort and 2010-2011 in Kailuan study) until December 31, 2018. Cox proportional hazard regression models were used to estimate the associations with adjustment for potential confounders. Results were obtained in the 2 cohorts separately and pooled via fixed-effect meta-analysis.A total of 10 951 participants in the Dongfeng-Tongji cohort (median [IQR] age, 62 [56-66] years; 4203 [38.4%] men) and 47 181 participants in the Kailuan study (median [IQR] age, 51 [46-58] years; 36 663 [77.7%] men) were included in the analysis. During 426 072 person-years of follow-up, 4028 deaths (523 in the Dongfeng-Tongji cohort and 3505 in the Kailuan study) were documented. When changes in weight and waist circumference were examined separately, U-shape associations were found: both gain and loss in weight (weight loss: pooled hazard ratio [HR], 1.33; 95% CI, 1.23-1.43; weight gain: HR, 1.10; 95% CI, 1.02-1.19) or waist circumference (waist circumference loss: HR, 1.14; 95% CI, 1.05-1.24; waist circumference gain: HR, 1.11; 95% CI, 1.03-1.21) were associated with higher mortality risk compared with stable weight or waist group. When changes in weight and waist circumference were jointly assessed, compared with participants with stable weight and waist circumference (16.9% of the total population [9828 of 58 132] with 508 deaths), participants with different combinations of weight and waist circumference change all had higher mortality risks except for those with stable weight but significant loss in waist. Notably, those who lost weight but gained waist circumference (6.4% of the total population [3698 of 58 132] with 308 deaths) had the highest risk of all-cause mortality (HR, 1.69; 95% CI, 1.46-1.96; absolute rate difference per 100 000 person-years in the Dongfeng-Tongji cohort: 414; 95% CI, 116-819; and in the Kailuan study: 333; 95% CI, 195-492) among the joint subgroups.In this cohort study, weight loss with concurrent waist circumference gain was associated with a higher mortality risk in middle-aged and older Chinese adults. This study's findings suggest the importance of evaluating the changes in both body weight and waist circumference when assessing their associations with mortality.

In mask-based lensless imaging, iterative reconstruction methods based on the geometric optics model produce artifacts and are computationally expensive. We present a prototype of a lensless camera that uses a deep neural network (DNN) to realize rapid reconstruction for Fresnel zone aperture (FZA) imaging. A deep back-projection network (DBPN) is connected behind a U-Net providing an error feedback mechanism, which realizes the self-correction of features to recover the image detail. A diffraction model generates the training data under conditions of broadband incoherent imaging. In the reconstructed results, blur caused by diffraction is shown to have been ameliorated, while the computing time is 2 orders of magnitude faster than the traditional iterative image reconstruction algorithms. This strategy could drastically reduce the design and assembly costs of cameras, paving the way for integration of portable sensors and systems.

FZA imaging is one of the most promising lensless techniques because of its simple structure without calibration. The conventional image reconstruction methods are based on geometric optics model, then the error brought by diffraction degrades the imaging quality and limits the resolution. Here we quantitatively analyze the relationship between the width of FZA zone and the imaging resolution. The effects of illumination wavelength and the degree of coherence on image quality are also discussed. To improve the imaging quality, an image reconstruction method is proposed to minimize the influence of diffraction effect.

Mask-based lensless cameras break the constraints of traditional lens-based cameras, introducing highly flexible imaging systems. However, the inherent restrictions of imaging devices lead to low reconstruction quality. To overcome this challenge, we propose an explicit-restriction convolutional framework for lensless imaging, whose forward model effectively incorporates multiple restrictions by introducing the linear and noise-like nonlinear terms. As examples, numerical and experimental reconstructions based on the limitation of sensor size, pixel pitch, and bit depth are analyzed. By tailoring our framework for specific factors, better perceptual image quality or reconstructions with 4× pixel density can be achieved. This proposed framework can be extended to lensless imaging systems with different masks or structures.

We performed density functional theory calculations to investigate the adsorption of cathinone (CA) drug on a pristine magnesium oxide (MgO) nanocage to find a proper sensor. All of the calculations were performed at M06-2X level of theory. The CA drug adsorbs on the MgO nanocage via a strong donor-acceptor interaction, releasing an energy of about − 36.21 kcal. mol-1. By the CA adsorption, the HOMO-LUMO energy gap of MgO nanocage significantly decreases which increases the electrical conductivity. The change of electrical conductivity of MgO can be converted to an electrical signal which helps to detect the drug presence. Also, in the presence of a CA molecule, the work function of MgO nanocage decreases significantly. We conclude that the MgO nanocage is both a work function type and an electronic sensor for CA drug detection. We found that the recovery time of CA desorption from the MgO surface is short (∼ 29.63 ms). Our results pay the way for application of MgO nanostructures in drug recognition.

Three-dimensional (3D) model with high-quality texture is a powerful way to interact between virtual and reality 3D scene. It has significant applications in digital preservation of cultural heritage, virtual reality / augmented reality (VR/AR), medical imaging and other domains. High-quality texture reconstruction is one of the essential elements for 3D model. Due to the camera pose error and inaccurate model, current texture reconstruction methods could not eliminate the artifacts like blurring, ghosting and discontinuity. In this work, multi-view high-definition images are captured for texture mapping. Normal-weight, depth-weight and edge-weight parameter are introduced to evaluate texture color confidence, respectively. The normalized weight factors are multiplied to generate a comprehensive weight parameter. By taking a weighted average of projected texture images, discontinuity of texture can be smoothed to a large extent. For large misalignment, bidirectional similarity (BDS) function, which represent the structural similarity between two images, are utilized to improve the texture image. The energy function is composed of two terms. One is Euclidean distance between target texture image and merged texture image. The other is BDS between target texture image and original texture image. By minimizing the energy function, the texture image could generate small local displacement while retaining the original structural information. The target and merged images are optimized alternately, the target image is calculated by patch-match algorithm, and the merged image is derived from weighted average of target images. The method we proposed could produce seamless texture comparing with Markov random field (MRF) algorithm. The definition could be higher than the camera parameter optimization algorithm. The experimental results show that structural similarity (SSIM) between the reconstructed images and the ground truth is higher than traditional algorithms. When dealing with inaccurate models with less than 10,000 facets, the SSIM value could be doubled compared with current algorithms.

Digital holographic imaging is able to reconstruct phase and three-dimensional (3D) information of an object from a one-shot two-dimensional (2D) lensless hologram. A dense encoder–decoder network, called Dense-U-net, is proposed to realize the reconstruction of a 3D particle field. The radii and 3D coordinates of the particles are encoded into a 2D grayscale image as the ground truth. The 2D hologram was served as the input of the network. A Dense_Block is designed and added to the encoder and decoder of the traditional U-net model. Each layer takes the inputs from all previous layers and passes its own feature map to all subsequent layers. Thus, as the diffraction is distributed throughout the entire hologram, both the features with high signal-to-noise ratio at the center of the particle and the diffraction features with low signal-to-noise ratio around the particle can be learned, thereby facilitating the full characterization of the particles. Using the specially designed dense connection module, network training can be accomplished with fewer parameters and faster speeds.

The traditional phase-shifting interferometry (PSI)-based cryptosystem is one of the most classical optical cryptosystems. It employs the Mach–Zahnder interferometer to record the intensity distributions to partly overcome the inconvenience while storing the complex-valued ciphertext in some other optical cryptosystems (e.g., double random phase encoding technique). However, it has been proven to be vulnerable to chosen-plaintext attack and known-plaintext attack. In this manuscript, we propose an alternative method to enhance the security strength of the traditional PSI-based cryptosystem. By substituting the fixed random phase mask (RPM) and the phase retarder in the reference arm with four independent and different RPMs (served as secret keys) in four exposures, we can correspondingly capture four intensity-only patterns (regarded as ciphertexts). Theoretical analysis, especially with respect to security characteristics, as well as the numerical simulations are presented to verify the feasibility and reliability of the proposed cryptosystem.

An electronic skin (ES) is developed by embedding a liquid-core poly(vinylidene fluoride) fiber into a silicone rubber. The experimental results show that the ES can detect the waveform, frequency, amplitude, and other parameters of the surface vibration pressure. The ES can sense the surface pressure amplitude over a range of 1.5–2.5 kPa and exhibits a sensitivity of 0.0472 fC/Pa when the pressure is less than 60 Pa. The resonant frequency of the ES is 0.4 Hz. The ES can also detect the elongation strain, and its sensitivity is 0.0058 fC/μe. The ES has the characteristics of flexibility, high sensitivity, and a wide measuring range. Therefore, the ES can be used as a robot finger skin, which enables the robot to have touch perception capabilities.

A imaging method using single Fresnel zone aperture for coding the incoherent rays as wavefront is proposed. Digital holography and compressive sensing techniques are utilized to reconstruct image with an improved signal to noise ratio.

Background: The associations of age-related changes in body weight and waist circumferences with cardiovascular disease (CVD) and mortality remain unclear. Methods: We assessed changes in weight and waist circumference from baseline survey and resurvey among 27 964 participants in the China Kadoorie Biobank (CKB, 2004-2008) and Dongfeng-Tongji Cohort (DF-TJ, 2008-2013). We used Cox proportional hazards models to evaluate the associations between adiposity changes and the subsequent risks of incident CVD, CVD mortality and all-cause mortality. Findings: On average, during a median of 3·4 years from baseline to resurvey, participants experienced a weight loss of 0·6 kg, accompanied by a 2·1 cm increase in waist circumference. We identified 3589 incident CVD cases, 485 CVD deaths, and 1283 all-cause deaths during the follow-up in the CKB (2008-2016) and DF-TJ (2013-2016). Weight loss and waist gain were associated with increased risks of those outcomes in the two cohorts. In particular, compared with participants who had stable waist (waist change -2·0 to 7·0 cm) and weight (weight change -2·5 to 2·5 kg), those who gained waist but lost weight (waist circumference change >7·0 cm, weight change ≤ -2·5 kg) had the highest risk of incident CVD, CVD mortality and all-cause mortality, with the pooled multivariate hazard ratios (HRs) of 1·41 (95% CI, 1·13 to 1·75), 2·48 (1·40 to 4·37), and 1·71 (1·16 to 2·51), respectively. Among the weight-stable participants, waist circumference was linearly associated with CVD risk, with 17% higher risk (HR 1·17, 1·04 to 1·32) for those gained waist while 13% lower risk (HR 0·87, 0·77 to 0·99) for those lost waist compared to waist-stable participants. Interpretation: Increased waist circumference with concurrent weight loss is significantly associated with increased risks of CVD morbidity, CVD mortality, and all-cause mortality among Chinese middle-aged and elderly adults. Funding Statement: This work was partly supported by grants from the Foundation of National Key Programs of Research and Development of China (2016YFC0900800, 2016YFC0900500, 2017YFC0907501 and 2017YFC0907504), the National Natural Science Foundation of China (91643202 and 81390540), the 111 Project and the Program for Changjiang Scholars and Innovative Research Team in University. The China Kadoorie Biobank baseline survey and the first resurvey were supported by the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up has been supported by the UK Wellcome Trust (grants 088158/Z/09/Z, 104085/Z/14/Z), and the Chinese Ministry of Science and Technology (2011BAI09B01). The British Heart Foundation, UK Medical Research Council, and Cancer Research provide core funding to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University for the project. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The CKB study was approved by the ethics committees or institutional review boards at the University of Oxford, the Chinese Center for Disease Control and Prevention (China CDC), the Chinese Academy of Medical Sciences. The DF-TJ cohort was approved by the ethics committees of Tongji Medical College and the Sinopharm Dongfeng General Hospital. All participants provided written informed consents.