Observation of plaid-like spin splitting in a noncoplanar antiferromagnet.

Spatial, momentum and energy separation of electronic spins in condensed-matter systems guides the development of new devices in which spin-polarized current is generated and manipulated1-3. Recent attention on a set of previously overlooked symmetry operations in magnetic materials4 leads to the emergence of a new type of spin splitting, enabling giant and momentum-dependent spin polarization of energy bands on selected antiferromagnets5-10. Despite the ever-growing theoretical predictions, the direct spectroscopic proof of such spin splitting is still lacking. Here we provide solid spectroscopic and computational evidence for the existence of such materials. In the noncoplanar antiferromagnet manganese ditelluride (MnTe2), the in-plane components of spin are found to be antisymmetric about the high-symmetry planes of the Brillouin zone, comprising a plaid-like spin texture in the antiferromagnetic (AFM) ground state. Such an unconventional spin pattern, further found to diminish at the high-temperature paramagnetic state, originates from the intrinsic AFM order instead of spin-orbit coupling (SOC). Our finding demonstrates a new type of quadratic spin texture induced by time-reversal breaking, placing AFM spintronics on a firm basis and paving the way for studying exotic quantum phenomena in related materials.

Improving resilience to high temperature in drought: water replenishment enhances sucrose and amino acid metabolisms in maize grain.

Heat stress poses a significant threat to maize, especially when combined with drought. Recent research highlights the potential of water replenishment to ameliorate grain weight loss. However, the mitigating mechanisms of heat in drought stress, especially during the crucial early grain-filling stage, remain poorly understood. We investigated the mechanism for mitigating heat in drought stress by water replenishment from the 12th to the 32nd days after silking in a controlled greenhouse experiment (Exp. I) and field trial (Exp. II). A significant reduction in grain weight was observed in heat stress compared to normal conditions. When water replenishment was applied to increase soil water content (SWC) under heat stress, the grain yield exhibited a notable increase ranging from 28.4 to 76.9%. XY335 variety was used for transcriptome sequencing to analyze starch biosynthesis and amino acid metabolisms in Exp. I. With water replenishment, the transcripts of genes responsible for trehalose 6-phosphate phosphates (TPP), alpha-trehalase (TRE), ADP-glcpyrophosphorylase, and starch synthase activity were stimulated. Additionally, the expression of genes encoding TPP and TRE contributed to an enhanced conversion of trehalose to glucose. This led to the conversion of sucrose from glucose-1-phosphate to ADP-glucose and ADP-glucose to amylopectin, ultimately increasing starch production by 45.1%. Water replenishment to boost SWC during heat stress also elevated the levels of essential amino acids in maize, including arginine, serine, tyrosine, leucine, glutamic acid, and methionine, providing valuable support to maize plants in adversity. Field trials further validated the positive impact of water replenishment on SWC, resulting in a notable increase in grain yield ranging from 7.1 to 9.2%. This study highlights the vital importance of adapting to abiotic stress and underscores the necessity of developing strategies to counteract its adverse effects on crop yield.

Adipose tissue browning and thermogenesis under physiologically energetic challenges: a remodelled thermogenic system.

Metabolic diseases such as obesity and diabetes are often thought to be caused by reduced energy expenditure, which poses a serious threat to human health. Cold exposure, exercise and caloric restriction have been shown to promote adipose tissue browning and thermogenesis. These physiological interventions increase energy expenditure and thus have emerged as promising strategies for mitigating metabolic disorders. However, that increased adipose tissue browning and thermogenesis elevate thermogenic consumption is not a reasonable explanation when humans and animals confront energetic challenges imposed by these interventions. In this review, we collected numerous results on adipose tissue browning and whitening and evaluated this bi-directional conversion of adipocytes from the perspective of energy homeostasis. Here, we propose a new interpretation of the role of adipose tissue browning under energetic challenges: increased adipose tissue browning and thermogenesis under energy challenge is not to enhance energy expenditure, but to reestablish a more economical thermogenic pattern to maintain the core body temperature. This can be achieved by enhancing the contribution of non-shivering thermogenesis (adipose tissue browning and thermogenesis) and lowering shivering thermogenesis and high intensity shivering. Consequently, the proportion of heat production in fat increases and that in skeletal muscle decreases, enabling skeletal muscle to devote more energy reserves to overcoming environmental stress.

Lateral GeSn p-i-n photodetectors on insulator prepared by the rapid melting growth method.

In this work, GeSn lateral p-i-n photodetectors (PDs) on insulator were fabricated with an active GeSn layer grown by the rapid melting growth (RMG) method. Taking advantages of the defect-free GeSn strips, GeSn PDs with 5.3% Sn content have low dark current and high responsivities, which are about 0.48, 0.47, and 0.24 A/W for wavelengths of 1550, 1630, and 2000 nm, respectively. The radio frequency of the lateral GeSn PDs was also studied and a 3 dB bandwidth of about 3.8 GHz was achieved. These results indicate that the GeSn grown by the rapid melting growth method is capable of fabricating high-performance Si-based optoelectronic devices.

Speckle visual cryptography for credential authentication.

Based on the high random distribution characteristic of the natural speckle image, a new method of speckle visual cryptography, to the best of our knowledge, is designed by combining the natural speckle image with the secret key in visual cryptography. Specifically, we designed an authentication system for user credentials by combining speckle visual cryptography and the QR code. By using the speckle visual cryptography method, the image of the QR code carrying user authentication information is hidden in the speckle image, and the speckle image is printed on the paper credentials. Through a simulation and analog experiments, we verify the possibility of applying speckle visual cryptography to a user credentials authentication system, compare the improved grayscale reordering algorithm and grayscale reordering algorithm, and prove that the improved grayscale reordering algorithm has more advantages in this system by comparing the PSNR and SSIM. Finally, the y-interference ability and the uniqueness of the virtual secret key in the system are analyzed to prove that the secret key has high anti-interference ability and security.

Optical information hiding for different surface images.

Optical hiding often requires the selection of specific artificial optical components as carriers, which results in poor versatility of the carriers and high costs for the hiding system. To conceal secret information on different surfaces such as metal, wood, and paper, we propose an optical information hiding method. In this method, we use images of surfaces, whose grayscale histograms have the characteristic of symmetric distribution. Based on this characteristic, we first scramble the surface image, and then adjust part of the gray value of the surface image to the complementary value to embed the secret information into a scrambled surface image to generate a key image. In the extraction process, a projector is used to reproduce the scrambled surface image and the key image, which are then incoherently superimposed to extract the secret information using the human visual system. The extraction process does not require complex optical knowledge and is simple and feasible. Simulation experiments and optical experiments indicate that this method is applicable in practice and possesses good security and imperceptibility. Furthermore, we prove the reliability of this method by embedding secret information in different surface images, demonstrating the potential application of more surface images in the field of optical information hiding. Finally, we discuss the applicability of surface information images and analyze the imperceptibility of key images.

Simulation-driven machine learning approach for high-speed correction of slope-dependent error in coherence scanning interferometry.

Slope-dependent error often occurs in the coherence scanning interferometry (CSI) measurement of functional engineering surfaces with complex geometries. Previous studies have shown that these errors can be corrected through the characterization and phase inversion of the instrument's three-dimensional (3D) surface transfer function. However, since CSI instrument is usually not completely shift-invariant, the 3D surface transfer function characterization and correction must be repeated for different regions of the full field of view, resulting in a long computational process and a reduction of measurement efficiency. In this work, we introduce a machine learning approach based on a deep neural network that is trainable for slope-dependent error correction in CSI. Our method leverages a deep neural network to directly learn errors characteristics from simulated surface measurements provided by a previously validated physics-based virtual CSI method. The experimental results demonstrate that the trained network is capable of correcting the surface height map with 1024 × 1024 sampling points within 0.1 seconds, covering a 178 µm field of view. The accuracy is comparable to the previous phase inversion approach while the new method is two orders of magnitude faster under the same computational condition.

Sn component gradient GeSn photodetector with 3†dB bandwidth over 50†GHz for extending L band telecommunication.

In this work, high-performance GeSn photodetectors with a Sn content gradient GeSn layer were fabricated on SOI substrate by CMOS-compatible process for C and L band telecommunication. The active GeSn layer has a Sn component increased from 9 to 10.7% with the controlled relaxation degree up to 84%. The responsivities of GeSn detectors at 1550 nm and 1630 nm are 0.47 A/W and 0.32 A/W under -4 V bias, respectively. Over 50 GHz 3 dB bandwidth with the eye pattern about 70 Gb/s was also evidenced at 1630 nm. These results indicate that the GeSn photodetectors have a promising application for extending the silicon photonics from C band to L band.

CT-based identification of pediatric non-Wilms tumors using convolutional neural networks at a single center.

BACKGROUND: Deep learning (DL) is more and more widely used in children's medical treatment. In this study, we have developed a computed tomography (CT)-based DL model for identifying undiagnosed non-Wilms tumors (nWTs) from pediatric renal tumors. METHODS: This study collected and analyzed the preoperative clinical data and CT images of pediatric renal tumor patients diagnosed by our center from 2008 to 2020, and established a DL model to identify nWTs noninvasively. RESULTS: A total of 364 children who had been confirmed by histopathology with renal tumors from our center were enrolled, including 269 Wilms tumors (WTs) and 95 nWTs. For DL model development, all cases were randomly allocated to training set (218 cases), validation set (73 cases), and test set (73 cases). In the test set, the DL model achieved area under the curve of 0.831 (95% CI: 0.712-0.951) in discriminating WTs from nWTs, with the accuracy, sensitivity, and specificity of 0.781, 0.563, and 0.842, respectively. The sensitivity of our model was higher than a radiologist with 15 years of experience. CONCLUSIONS: We presented a DL model for identifying undiagnosed nWTs from pediatric renal tumors, with the potential to improve the image-based diagnosis. IMPACT: Deep learning model was used for the first time to identify pediatric renal tumors in this study. Deep learning model can identify non-Wilms tumors from pediatric renal tumors. Deep learning model based on computed tomography images can improve tumor diagnosis rate.

Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study.

OBJECTIVES: To evaluate the image quality and diagnostic performance of AI-assisted compressed sensing (ACS) accelerated two-dimensional fast spin-echo MRI compared with standard parallel imaging (PI) in clinical 3.0T rapid knee scans. METHODS: This prospective study enrolled 130 consecutive participants between March and September 2022. The MRI scan procedure included one 8.0-min PI protocol and two ACS protocols (3.5 min and 2.0 min). Quantitative image quality assessments were performed by evaluating edge rise distance (ERD) and signal-to-noise ratio (SNR). Shapiro-Wilk tests were performed and investigated by the Friedman test and post hoc analyses. Three radiologists independently evaluated structural disorders for each participant. Fleiss κ analysis was used to compare inter-reader and inter-protocol agreements. The diagnostic performance of each protocol was investigated and compared by DeLong's test. The threshold for statistical significance was set at p  < 0.05. RESULTS: A total of 150 knee MRI examinations constituted the study cohort. For the quantitative assessment of four conventional sequences with ACS protocols, SNR improved significantly (p < 0.001), and ERD was significantly reduced or equivalent to the PI protocol. For the abnormality evaluated, the intraclass correlation coefficient ranged from moderate to substantial between readers (κ = 0.75-0.98) and between protocols (κ = 0.73-0.98). For meniscal tears, cruciate ligament tears, and cartilage defects, the diagnostic performance of ACS protocols was considered equivalent to PI protocol (Delong test, p > 0.05). CONCLUSIONS: Compared with the conventional PI acquisition, the novel ACS protocol demonstrated superior image quality and was feasible for achieving equivalent detection of structural abnormalities while reducing acquisition time by half. CLINICAL RELEVANCE STATEMENT: Artificial intelligence-assisted compressed sensing (ACS) providing excellent quality and a 75% reduction in scanning time presents significant clinical advantages in improving the efficiency and accessibility of knee MRI for more patients. KEY POINTS: • The prospective multi-reader study showed no difference in diagnostic performance between parallel imaging and AI-assisted compression sensing (ACS) was found. • Reduced scan time, sharper delineation, and less noise with ACS reconstruction. • Improved efficiency of the clinical knee MRI examination by the ACS acceleration.

Self-Assembly of Intelligent Nanoplatform for Endogenous H2S-Triggered Multimodal Cascade Therapy of Colon Cancer.

The specific in situ generation and activation of therapeutic agents with high spatiotemporal precision is expected to revolutionize cancer treatment. Here, we develop an intelligent nanoplatform (termed as NP-Cu), which is constructed by assembling photosensitizer chlorin e6 (Ce6), hypoxia-responsive prodrug banoxantrone (AQ4N) with clickable dibenzocyclooctyne (DIBO) functionalized lysine (D-K), and cyclen-Cu2+ complex, for improving combination anticancer therapy. Cyclen-Cu2+ complex-induced photodynamic therapy (PDT) quenching in NP-Cu can be effectively and selectively activated by tumor-overproduced hydrogen sulfide (H2S). More importantly, the reaction of endogenous H2S with Cu2+ can generate photothermal agent copper sulfide (CuS) for photothermal therapy (PTT). Furthermore, with the activation of PTT and PDT, intracellular hypoxic stress is amplified to trigger AQ4N-associated chemodynamic therapy (CDT), leading to light-enhanced cascade therapy of PDT, PTT and CDT. Therefore, we present a simple and practical strategy for developing pathological stimuli responsive combination therapy, which has the potential of advancing precision cancer medicine.

Coherent modulation imaging using a physics-driven neural network.

Coherent modulation imaging (CMI) is a lessness diffraction imaging technique, which uses an iterative algorithm to reconstruct a complex field from a single intensity diffraction pattern. Deep learning as a powerful optimization method can be used to solve highly ill-conditioned problems, including complex field phase retrieval. In this study, a physics-driven neural network for CMI is developed, termed CMINet, to reconstruct the complex-valued object from a single diffraction pattern. The developed approach optimizes the network's weights by a customized physical-model-based loss function, instead of using any ground truth of the reconstructed object for training beforehand. Simulation experiment results show that the developed CMINet has a high reconstruction quality with less noise and robustness to physical parameters. Besides, a trained CMINet can be used to reconstruct a dynamic process with a fast speed instead of iterations frame-by-frame. The biological experiment results show that CMINet can reconstruct high-quality amplitude and phase images with more sharp details, which is practical for biological imaging applications.

Modeling, analysis, and demonstration of a carrier-injection electro-absorption modulator at 2 µm on Ge-on-Si platform.

In this paper, a carrier-injection electro-absorption modulator (EAM) at 2 µm is demonstrated on Ge-on-Si platform. The EAM shows a compact size and high modulation efficiency due to the strong free-carrier electroabsorption (FCEA) effect in Ge. A modulation depth of 40 dB can be obtained under the injection current of only 420 mA. Small-signal frequency response measurement is performed and a small-signal equivalent circuit model is proposed. Based on reflection coefficients and equivalent circuit, the frequency response of carrier-injection EAM is discussed in detail. The 500 Mbps open eye diagram verifies the data-processing capacity of our EAM at 2 µm wavelength for its application in biological, chemical molecular detection, and infrared imaging systems.

High-speed and high-power germanium photodetector based on a trapezoidal absorber.

A compact high-power germanium photodetector (Ge PD) is experimentally demonstrated by re-engineering light distribution in the absorber. Compared with a conventional Ge PD, the proposed structure shows a DC saturation photocurrent improved by 28.9% and 3 dB bandwidth as high as 49.5 GHz at 0.1 mA. Under the same photocurrent of 10.5 mA, the proposed Ge PD shows a 3 dB bandwidth of 11.1 GHz, which is almost double the conventional Ge PD (5.6 GHz). The 25 Gb/s eye-diagram measurement verifies the improved power handling capability. The compact size and manufacturing simplicity of this structure will enable new applications for integrated silicon photonics.

GeSn resonance cavity enhanced photodetector with gold bottom reflector for the L band optical communication.

In this work, GeSn resonant cavity enhanced (RCE) p-i-n photodetectors (PDs) with 3.7% Sn content in a GeSn layer were fabricated on a silicon on insulator (SOI) substrate. The gold (Au) layer and the deposited SiO2 layer constitute the bottom reflector and top reflector of the RCE detectors, respectively. The GeSn RCE PD has three resonant peaks and its responsivity is improved about 4.5 times at 1630 nm, compared with GeSn PDs without a gold bottom mirror. The cutoff wavelength of GeSn RCE PDs is up to 1820 nm, while it is only 1730 nm for GeSn PDs without a gold reflector. The responsivity of RCE PDs at 1630 nm reaches 0.126 A/W and 3-dB bandwidth at about 36 GHz is achieved. These results indicate that the RCE structure is an effective approach for enhancing the GeSn PD performance operated at the L band.

High-performance waveguide-coupled lateral Ge/Si avalanche photodetector.

A high-performance waveguide-coupled lateral avalanche photodetector (APD) is experimentally demonstrated without silicon epitaxy and charge layer ion implantation. At the wavelength of 1550 nm, it shows a high responsivity of 48 A/W and a gain-bandwidth product (GBP) of 360 GHz. Wide-open eye diagrams at 25 Gbps can be observed at various avalanche gains. These outstanding performances indicate the proposed APD has great potential in high-speed optical transceivers for optical links.

Resolution-enhanced ptychography framework with an equivalent upsampling and precise position.

As a lensless imaging technique, ptychography provides a new way to resolve the conflict between the spatial resolution and the field of view. However, due to the pixel size limit of the sensor, a compromise has to be reached between the spatial resolution and the signal-to-noise ratio. Here, we propose a resolution-enhanced ptychography framework with equivalent upsampling and subpixel accuracy in position to further improve the resolution of ptychography. According to the theory of pixel superresolved techniques, the inherent shift illumination scheme in ptychography can additionally enhance the resolution with the redundant data. An additional layer of pooling is used to simulate the downsampling of a digital record, and the pixel superresolved problem is transformed into an automatic optimization problem. The proposed framework is verified by optical experiments, both in biological samples and the resolution targets. Compared to the traditional algorithm, the spatial lateral resolution is twice as large using the same data set.

Exercise-Induced Adipose Tissue Thermogenesis and Browning: How to Explain the Conflicting Findings?

Brown adipose tissue (BAT) has been widely studied in targeting against metabolic diseases such as obesity, type 2 diabetes and insulin resistance due to its role in nutrient metabolism and energy regulation. Whether exercise promotes adipose tissue thermogenesis and browning remains controversial. The results from human and rodent studies contradict each other. In our opinion, fat thermogenesis or browning promoted by exercise should not be a biomarker of health benefits, but an adaptation under the stress between body temperature regulation and energy supply and expenditure of multiple organs. In this review, we discuss some factors that may contribute to conflicting experimental results, such as different thermoneutral zones, gender, training experience and the heterogeneity of fat depots. In addition, we explain that a redox state in cells potentially causes thermogenesis heterogeneity and different oxidation states of UCP1, which has led to the discrepancies noted in previous studies. We describe a network by which exercise orchestrates the browning and thermogenesis of adipose tissue with total energy expenditure through multiple organs (muscle, brain, liver and adipose tissue) and multiple pathways (nerve, endocrine and metabolic products), providing a possible interpretation for the conflicting findings.

Modified phase-generated carrier demodulation of fiber-optic interferometric ultrasound sensors.

We propose and demonstrate a modified phase-generated carrier (PGC) demodulation scheme optimized for detection of ultrasound using interferometric sensors with sinusoidal fringes. The sensor used in demonstration is made from a pair of weak fiber Bragg-gratings at the ends of a coiled fiber that form a low-finesse Fabry-Perot interferometer. The phase of the laser source is modulated using an electro-optic phase modulator to generate the carrier signal and obtain 2 quadrature (the sine and cosine) terms at the first and the second order carrier frequencies. The signal of interest (ultrasound) has much higher frequency than the environmental perturbation but a very small amplitude that causes only small phase shift. Using small-signal approximation, for each of the 2 quadrature terms, we separate the contributions from the environmental perturbations (quasi-DC component) and from the ultrasound (AC component). The AC components that contain the information of the ultrasound signal are then further amplified with a large gain. The signal of interest is constructed by simple algebraic operations on the 2 quasi-DC components and the 2 amplified AC components involving multiplying and summing. This work provides a simple and robust demodulation method with potentially high sensitivity for fiber-optic interferometric ultrasound sensors.

Spatiotemporal coherent modulation imaging for dynamic quantitative phase and amplitude microscopy.

The single-shot capability of coherent modulation imaging (CMI) makes it have great potential in the investigation of dynamic processes. Its main disadvantage is the relatively low signal-to-noise ratio (SNR) which affects the spatial resolution and reconstruction accuracy. Here, we propose the improvement of a general spatiotemporal CMI method for imaging of dynamic processes. By making use of the redundant information in time-series reconstructions, the spatiotemporal CMI can achieve robust and fast reconstruction with higher SNR and spatial resolution. The method is validated by numerical simulations and optical experiments. We combine the CMI module with an optical microscope to achieve quantitative phase and amplitude reconstruction of dynamic biological processes. With the reconstructed complex field, we also demonstrate the 3D digital refocusing ability of the CMI microscope. With further development, we expect the spatiotemporal CMI method can be applied to study a range of dynamic phenomena.