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.

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.

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.

Broken-fat pad sign: a characteristic radiographic finding to distinguish between knee rheumatoid arthritis and osteoarthritis.

OBJECTIVES: Diagnostic imaging plays an important role in the pre-treatment workup of knee osteoarthritis (OA) and rheumatoid arthritis (RA). Herein, we identified a useful MRI sign of infrapatellar fat pad (IPFP) to improve diagnosis. METHODS: Eighty-one age- and sex-matched RA and OA patients each, with pathological diagnosis and pre-treatment MRI were retrospectively evaluated. All randomized MR images were blinded and independently reviewed by two radiologists. The assessment process included initial diagnosis, sign evaluation, and final diagnosis, with a 3-week interval between each assessment. Broken-fat pad (BFP) sign was assessed on sagittal T2-weighted-imaging in routine MRI. The area under the curve and Cohen's kappa (κ) were used to assess the classification performance. Two shape features were extracted from IPFP for quantitative interpretation. RESULTS: The median age of the study population was 57.6 years (range: 31.0-78.0 years). The BFP sign was detected more frequently in patients with RA (72.8%) than those with OA (21.0%). Both radiologists achieved better performance by referring to the BFP sign, with accuracies increasing from 58.0 to 75.9% and 72.8 to 79.6%, respectively. The inter-reader correlation coefficient showed an increase from fair (κ = 0.30) to substantial (κ = 0.75) upon the consideration of the BFP sign. For quantitative analysis, the IPFP of RA had significantly lower sphericity (0.54 ± 0.04 vs. 0.59 ± 0.03, p < 0.01). Despite larger surface-volume-ratio of RA (0.38 ± 0.05 vs. 0.37 ± 0.04, p = 0.25) than that of OA, there was no statistical difference. CONCLUSIONS: The BFP sign is a potentially important diagnostic clue for differentiating RA from OA with routine MRI and reducing misdiagnosis. CRITICAL RELEVANCE STATEMENT: With the simple and feasible broken-fat pad sign, clinicians can help more patients with early accurate diagnosis and proper treatment, which may be a valuable addition to the diagnostic workup of knee MRI assessment. KEY POINTS: • Detailed identification of infrapatellar fat pad alterations of patients may be currently ignored in routine evaluation. • Broken-fat pad sign is helpful for differentiating rheumatoid arthritis and osteoarthritis. • The quantitative shape features of the infrapatellar fat pad may provide a possible explanation of the signs. • This sign has good inter-reader agreements and is feasible for clinical application.

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.

Can high-yielding maize system decrease greenhouse gas emissions largely while simultaneously enhancing economic and ecosystem benefits through the "Rhizobiont" concept? Evidence from field.

Ensuring high grain yields while minimizing environmental costs is a pressing imperative aligned with the Sustainable Development Goals (SDGs). In this study, we sought to establish a high-yielding maize system (HYMS) by implementing the innovative "Rhizobiont" concept for nutrient management, while substantially reducing greenhouse gas emissions. A 2-yr field study was conducted in a station of China Agriculture University (Wuqiao) with six treatments. The HYMS was established to achieve a harmonious equilibrium among genetic factors, environmental conditions, and management practices. HYMS demonstrated a significant boost in grain yield, averaging 12,706.6 kg ha-1 in 2021 and 13,676.4 kg ha-1 in 2022. These represented substantial increases of 25.6 % and 25.5 %, respectively, when compared to the current farmers practices (CP). More importantly, the N rate in HYMS was optimized to 148.2 kg ha-1 in 2021 and 138.0 kg ha-1 in 2022 with the implementation of the "Rhizobiont" concept. This represented a remarkable reduction of 35.5 % to 39.9 % in N application compared to CP. As a direct consequence, the measured cumulative emissions of greenhouse gases such as CO2, N2O, and CH4 in HYMS were notably decreased, showing reductions of 24.1 %, 36.0 %, and 7.0 %, respectively, compared to CP. Furthermore, the carbon intensity in HYMS was significantly reduced by 43.7 %. These considerable reductions in fertilizer use translated into tangible economic benefits (EB) and ecosystem economic benefit (EEB) in HYMS. EB was found to be 90.9 % higher, while EEB was 117.9 % higher than CP. These findings underscore the vast potential of HYMS and the "Rhizobiont" concept in promoting sustainable agriculture, with far-reaching implications for global food security and the well-being of smallholder farmers.

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.

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.

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.

Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model.

BACKGROUND: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints. METHODS: This retrospective study included 109 patients diagnosed with non-active axSpA (n = 68) and active axSpA (n = 41); patients were divided into training and testing cohorts at a ratio of 8:2. Radiomics features were extracted from 3.0 T sacroiliac MRI using two different heterogeneous regions of interest (ROIs, Circle and Facet). Various methods were used to select relevant and robust features, and different classifiers were used to build Circle-based, Facet-based, and a fusion prediction model. Their performance was compared using various statistical parameters. p < 0.05 is considered statistically significant. RESULTS: For both Circle- and Facet-based models, 2284 radiomics features were extracted. The combined fusion ROI model accurately differentiated between active and non-active axSpA, with high accuracy (0.90 vs.0.81), sensitivity (0.90 vs. 0.75), and specificity (0.90 vs. 0.85) in both training and testing cohorts. CONCLUSION: The multi-ROI fusion radiomics model developed in this study differentiated between active and non-active axSpA using sacroiliac FS T2w-MRI. The results suggest MRI-based radiomics of the SIJ can distinguish axSpA activity, which can improve the therapeutic result and patient prognosis. To our knowledge, this is the only study in the literature that used a radiomics approach to determine axSpA activity.

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.

Spectroscopic signature of obstructed surface states in SrIn2P2.

The century-long development of surface sciences has witnessed the discoveries of a variety of quantum states. In the recently proposed "obstructed atomic insulators", symmetric charges are pinned at virtual sites where no real atoms reside. The cleavage through these sites could lead to a set of obstructed surface states with partial electronic occupation. Here, utilizing scanning tunneling microscopy, angle-resolved photoemission spectroscopy and first-principles calculations, we observe spectroscopic signature of obstructed surface states in SrIn2P2. We find that a pair of surface states that are originated from the pristine obstructed surface states split in energy by a unique surface reconstruction. The upper branch is marked with a striking differential conductance peak followed by negative differential conductance, signaling its localized nature, while the lower branch is found to be highly dispersive. This pair of surface states is in consistency with our calculational results. Our finding not only demonstrates a surface quantum state induced by a new type of bulk-boundary correspondence, but also provides a platform for exploring efficient catalysts and related surface engineering.

Integrated contra-directionally coupled chirped Bragg grating waveguide with a linear group delay spectrum.

Due to the advantages of low propagation loss, wide operation bandwidth, continuous delay tuning, fast tuning speed, and compact footprints, chirped Bragg grating waveguide has great application potential in wideband phased array beamforming systems. However, the disadvantage of large group delay error hinders their practical applications. The nonlinear group delay spectrum is one of the main factors causing large group delay errors. To solve this problem, waveguides with nonlinear gradient widths are adopted in this study to compensate for the nonlinear effect of the grating apodization on the mode effective index. As a result, a linear group delay spectrum is obtained in the experiment, and the group delay error is halved.

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.

Dibenzocyclooctyne linked lysine-cyclodextrin for efficient intranucleus delivery of proteins.

Intranuclear protein delivery shows great prospects in broadening the application scope of protein therapy and revolutionizing medicine, however, effective delivery of native proteins into the nucleus of cells remains a great challenge. Herein, we report the supramolecular nanoparticles based on the self-assembly of dibenzocyclooctyne (DIBO) linked lysine-cyclodextrin (DLC) for efficient intranucleus delivery of proteins. Coordination-driven self-assembly of DLCs in aqueous solution enables efficient encapsulation of proteins just by simple mixing, so as to maintain their biological activity in a reliable way. DLC nanoparticles ensure effective intranuclear protein delivery for therapeutic applications and gene regulation. This rationally designed DIBO containing amino acid-cyclodextrin derivative allows the development of a convenient and universal nanoplatform for intranuclear delivery of native proteins.

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.

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.

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.