Physiological and biochemical mechanisms underlying the role of anthocyanin in acquired tolerance to salt stress in peanut (Arachis hypogaea L.).

Anthocyanin is an important pigment that prevents oxidative stress and mediates adaptation of plants to salt stress. Peanuts with dark red and black testa are rich in anthocyanin. However, correlation between salt tolerance and anthocyanin content in black and dark red testa peanuts is unknown. In this study, three peanut cultivars namely YZ9102 (pink testa), JHR1 (red testa) and JHB1 (black testa) were subjected to sodium chloride (NaCl) stress. The plant growth, ion uptake, anthocyanin accumulation, oxidation resistance and photosynthetic traits were comparatively analyzed. We observed that the plant height, leaf area and biomass under salt stress was highly inhibited in pink color testa (YZ9102) as compare to black color testa (JHB1). JHB1, a black testa colored peanut was identified as the most salt-tolerance cultivar, followed by red (JHR1) and pink(YZ9102). During salt stress, JHB1 exhibited significantly higher levels of anthocyanin and flavonoid accumulation compared to JHR1 and YZ9102, along with increased relative activities of antioxidant protection and photosynthetic efficiency. However, the K+/Na+ and Ca2+/Na+ were consistently decreased among three cultivars under salt stress, suggesting that the salt tolerance of black testa peanut may not be related to ion absorption. Therefore, we predicted that salt tolerance of JHB1 may be attributed to the accumulation of the anthocyanin and flavonoids, which activated antioxidant protection against the oxidative damage to maintain the higher photosynthetic efficiency and plant growth. These findings will be useful for improving salt tolerance of peanuts.

High-density bin-based genetic map reveals a 530-kb chromosome segment derived from wild peanut contributing to late leaf spot resistance.

KEY MESSAGE: Twenty-eight QTLs for LLS disease resistance were identified using an amphidiploid constructed mapping population, a favorable 530-kb chromosome segment derived from wild species contributes to the LLS resistance. Late leaf spot (LLS) is one of the major foliar diseases of peanut, causing serious yield loss and affecting the quality of kernel and forage. Some wild Arachis species possess higher resistance to LLS as compared with cultivated peanut; however, ploidy level differences restrict utilization of wild species. In this study, a synthetic amphidiploid (Ipadur) of wild peanuts with high LLS resistance was used to cross with Tifrunner to construct TI population. In total, 200 recombinant inbred lines were collected for whole-genome resequencing. A high-density bin-based genetic linkage map was constructed, which includes 4,809 bin markers with an average inter-bin distance of 0.43 cM. The recombination across cultivated and wild species was unevenly distributed, providing a novel recombination landscape for cultivated-wild Arachis species. Using phenotyping data collected across three environments, 28 QTLs for LLS disease resistance were identified, explaining 4.35-20.42% of phenotypic variation. The major QTL located on chromosome 14, qLLS14.1, could be consistently detected in 2021 Jiyang and 2022 Henan with 20.42% and 12.12% PVE, respectively. A favorable 530-kb chromosome segment derived from Ipadur was identified in the region of qLLS14.1, in which 23 disease resistance proteins were located and six of them showed significant sequence variations between Tifrunner and Ipadur. Allelic variation analysis indicating the 530-kb segment of wild species might contribute to the disease resistance of LLS. These associate genomic regions and candidate resistance genes are of great significance for peanut breeding programs for bringing durable resistance through pyramiding such multiple LLS resistance loci into peanut cultivars.

Black Arsenic Phosphorus Mid-Wave Infrared Barrier Detector with High Detectivity at Room Temperature.

The barrier structure is designed to enhance the operating temperature of the infrared detector, thereby improving the efficiency of collecting photogenerated carriers and reducing dark current generation, without suppressing the photocurrent. However, the development of barrier detectors using conventional materials is limited due to the strict requirements for lattice and band matching. In this study, a high-performance unipolar barrier detector is designed utilizing a black arsenic phosphorus/molybdenum disulfide/black phosphorus van der Waals heterojunction. The device exhibits a broad response bandwidth ranging from visible light to mid-wave infrared (520 nm to 4.6 µm), with a blackbody detectivity of 2.7 × 1010 cmHz-1/2 W-1 in the mid-wave infrared range at room temperature. Moreover, the optical absorption anisotropy of black arsenic phosphorus enables polarization resolution detection, achieving a polarization extinction ratio of 35.5 at 4.6 µm. Mid-wave infrared imaging of the device is successfully demonstrated at room temperature, highlighting the significant potential of barrier devices based on van der Waals heterojunctions in mid-wave infrared detection.

Versatile photonic molecule switch in multimode microresonators.

Harnessing optical supermode interaction to construct artificial photonic molecules has uncovered a series of fundamental optical phenomena analogous to atomic physics. Previously, the distinct energy levels and interactions in such two-level systems were provided by coupled microresonators. The reconfigurability is limited, as they often require delicate external field stimuli or mechanically altering the geometric factors. These highly specific approaches also limit potential applications. Here, we propose a versatile on-chip photonic molecule in a multimode microring, utilizing a flexible regulation methodology to dynamically control the existence and interaction strength of spatial modes. The transition between single/multi-mode states enables the "switched-off/on" functionality of the photonic molecule, supporting wider generalized applications scenarios. In particular, "switched-on" state shows flexible and multidimensional mode splitting control in aspects of both coupling strength and phase difference, equivalent to the a.c. and d.c. Stark effect. "Switched-off" state allows for perfect low-loss single-mode transition (Qi ~ 10 million) under an ultra-compact bend size (FSR ~ 115 GHz) in a foundry-based silicon microring. It breaks the stereotyped image of the FSR-Q factor trade-off, enabling ultra-wideband and high-resolution millimeter-wave photonic operations. Our demonstration provides a flexible and portable solution for the integrated photonic molecule system, extending its research scope from fundamental physics to real-world applications such as nonlinear optical signal processing and sixth-generation wireless communication.

Nitrogen application in pod zone improves yield and quality of two peanut cultivars by modulating nitrogen accumulation and metabolism.

Cultivated peanut (Arachis hypogaea L.) represents one of the most important oil and cash crops world-widely. Unlike many other legumes, peanuts absorb nitrogen through their underground pods. Despite this unique feature, the relationship between yield and nitrogen uptake within the pod zone remains poorly understood. In our pot experiment, we divided the underground peanut part into two zones-pod and root-and investigated the physiological and agronomic traits of two peanut cultivars, SH11 (large seeds, LS) and HY23 (small seeds, SS), at 10 (S1), 20 (S2), and 30 (S3) days after gynophores penetrated the soil, with nitrogen application in the pod zone. Results indicated that nitrogen application increased pod yield, kernel protein content, and nitrogen accumulation in plants. For both LS and SS peanut cultivars, optimal nitrogen content was 60 kg·hm- 2, leading to maximum yield. LS cultivar exhibited higher yield and nitrogen accumulation increases than SS cultivar. Nitrogen application up-regulated the expression of nitrogen metabolism-related genes in the pod, including nitrate reductase (NR), nitrite reductase (NIR), glutamine synthetase (GS), glutamate synthase (NADH-GOGAT), ATP binding cassette (ABC), and nitrate transporter (NRT2). Additionally, nitrogen application increased enzyme activity in the pod, including NR, GS, and GOGAT, consistent with gene expression levels. These nitrogen metabolism traits exhibited higher up-regulations in the large-seeded cultivar than in the small-seeded one and showed a significant correlation with yield in the large-seeded cultivar at S2 and S3. Our findings offer a scientific basis for the judicious application and efficient utilization of nitrogen fertilization in peanuts, laying the groundwork for further elucidating the molecular mechanisms of peanut nitrogen utilization.

Associations Between Sleep Spindle Metrics, Age, Education and Executive Function in Young Adult and Middle-Aged Patients with Obstructive Sleep Apnea.

Purpose: This study aimed to investigate the association between sleep spindle metrics and executive function in individuals with obstructive sleep apnea (OSA). Furthermore, we examined the association of age and education on executive function. Patients and Methods: A total of 230 (40.90 ± 8.83 years, F/M = 45/185) participants were enrolled. Overnight electroencephalogram (C3-M2) recording detected sleep spindles by a novel U-Net-type neural network that integrates temporal information with time-frequency images. Sleep spindle metrics, including frequency (Hz), overall density (events/min), fast density (events/min), slow density (events/min), duration (sec) and amplitude (µV), were measured. Executive function was assessed using standardized neuropsychological tests. Associations between sleep spindle metrics, executive function, and demographic factors were analyzed using multivariate linear regression. Results: In fully adjusted linear regression models, higher overall sleep spindle density (TMT-A, B=-1.279, p=0.009; TMT-B, B=-1.813, p=0.008), fast sleep spindle density (TMT-A, B=-1.542, p=0.048; TMT-B, B=-2.187, p=0.036) and slow sleep spindle density (TMT-A, B=-1.731, p=0.037; TMT-B, B=-2.449, p=0.034) were associated with better executive function. And the sleep spindle duration both during N2 sleep time (TMT-A, B=-13.932, p=0.027; TMT-B, B=-19.001, p=0.034) and N3 sleep time (TMT-B, B=-29.916, p=0.009; Stroop-incongruous, B=-21.303, p=0.035) was independently associated with better executive function in this population. Additionally, age and education were found to be highly associated with executive function. Conclusion: Specific sleep spindle metrics, higher overall density, fast density and slow density during N2 sleep time, and longer duration during N2 and N3 sleep time, are independent and sensitive indicators of better executive function in young adult and middle-aged patients with OSA. Further research is needed to explore the underlying mechanisms and clinical implications of these findings.

Genome-wide identification and expression analysis of GA20ox and GA3ox genes during pod development in peanut.

Background: Gibberellins (GAs) play important roles in regulating peanut growth and development. GA20ox and GA3ox are key enzymes involved in GA biosynthesis. These enzymes encoded by a multigene family belong to the 2OG-Fe (II) oxygenase superfamily. To date, no genome-wide comparative analysis of peanut AhGA20ox and AhGA3ox-encoding genes has been performed, and the roles of these genes in peanut pod development are not clear. Methods: A whole-genome analysis of AhGA20ox and AhGA3ox gene families in peanut was carried out using bioinformatic tools. The expression of these genes at different stage of pod development was analyzed using qRT-PCR. Results: In this study, a total of 15 AhGA20ox and five AhGA3ox genes were identified in peanut genome, which were distributed on 14 chromosomes. Phylogenetic analysis divided the GA20oxs and GA3oxs into three groups, but AhGA20oxs and AhGA3oxs in two groups. The conserved pattern of gene structure, cis-elements, and protein motifs further confirmed their evolutionary relationship in peanut. AhGA20ox and AhGA3ox genes were differential expressed at different stages of pod development. The strong expression of AhGA20ox1/AhGA20ox4, AhGA20ox12/AhGA20ox15, AhGA3ox1 and AhGA3ox4/AhGA3ox5 in S1-stage indicated that these genes could have a key role in controlling peg elongation. Furthermore, AhGA20ox and AhGA3ox also showed diverse expression patterns in different peanut tissues including leaves, main stems, flowers and inflorescences. Noticeably, AhGA20ox9/AhGA20ox11 and AhGA3o4/AhGA3ox5 were highly expressed in the main stem, whereas the AhGA3ox1 and AhGA20ox10 were strongly expressed in the inflorescence. The expression levels of AhGA20ox2/AhGA20ox3, AhGA20ox5/AhGA20ox6, AhGA20ox7/AhGA20ox8, AhGA20ox13/AhGA20ox14 and AhGA3ox2/AhGA3ox3 were high in the flowers, suggesting their involvement in flower development. These results provide a basis for deciphering the roles of AhGA20ox and AhGA3ox in peanut growth and development, especially in pod development.

Slow-light silicon modulator with 110-GHz bandwidth.

Silicon modulators are key components to support the dense integration of electro-optic functional elements for various applications. Despite numerous advances in promoting the modulation speed, a bandwidth ceiling emerges in practices and becomes an obstacle toward Tbps-level throughput on a single chip. Here, we demonstrate a compact pure silicon modulator that shatters present bandwidth ceiling to 110 gigahertz. The proposed modulator is built on a cascade corrugated waveguide architecture, which gives rise to a slow-light effect. By comprehensively balancing a series of merits, the modulators can benefit from the slow light for better efficiency and compact size while remaining sufficiently high bandwidth. Consequently, we realize a 110-gigahertz modulator with 124-micrometer length, enabling 112 gigabits per second on-off keying operation. Our work proves that silicon modulators with 110 gigahertz are feasible, thus shedding light on its potentials in ultrahigh bandwidth applications such as optical interconnection and photonic machine learning.

Harnessing microcomb-based parallel chaos for random number generation and optical decision making.

Optical chaos is vital for various applications such as private communication, encryption, anti-interference sensing, and reinforcement learning. Chaotic microcombs have emerged as promising sources for generating massive optical chaos. However, their inter-channel correlation behavior remains elusive, limiting their potential for on-chip parallel chaotic systems with high throughput. In this study, we present massively parallel chaos based on chaotic microcombs and high-nonlinearity AlGaAsOI platforms. We demonstrate the feasibility of generating parallel chaotic signals with inter-channel correlation <0.04 and a high random number generation rate of 3.84 Tbps. We further show the application of our approach by demonstrating a 15-channel integrated random bit generator with a 20 Gbps channel rate using silicon photonic chips. Additionally, we achieved a scalable decision-making accelerator for up to 256-armed bandit problems. Our work opens new possibilities for chaos-based information processing systems using integrated photonics, and potentially can revolutionize the current architecture of communication, sensing and computations.

Improving performance of human action intent recognition: Analysis of gait recognition machine learning algorithms and optimal combination with inertial measurement units.

Human action intent recognition has become increasingly dependent on computational accuracy, real-time responsiveness, and model lightness. Model selection, data filtering, and experimental design are three critical factors for the recognition of human intention in research. However, the performance of machine learning algorithms can vary depending on factors such as sensor location, the number of sensors used, channel selection, and dimensional combinations. Moreover, the collection of adequate and balanced data in such scenarios can be challenging. To address this issue, we present a comparative analysis of 12 commonly used machine learning algorithms for human action intention recognition. The synthetic minority oversampling technique is applied to fill in missing data. Traversing all possible combinations would require conducting 686 experiments, which is a daunting task in terms of both cost and efficiency. To tackle this challenge, we employ an orthogonal experiment design based on the Quasi-horizontal method. Our analysis indicates that lightGBM outperforms other algorithms in recognizing eight human daily activities. Furthermore, we conduct a polar difference and variance analysis based on a comprehensive balanced multi-metric orthogonal experiment for lightGBM using various sensor combinations and dimensions. The optimal combinations of different sensor numbers in terms of position, channel, and dimension are derived using this approach. Notably, our experimental design reduces the number of experiments required to only 49 times.

Quantum decoherence of dark pulses in optical microresonators.

Quantum fluctuations disrupt the cyclic motions of dissipative Kerr solitons (DKSs) in nonlinear optical microresonators and consequently cause timing jitter of the emitted pulse trains. This problem is translated to the performance of several applications that employ DKSs as compact frequency comb sources. Recently, device manufacturing and noise reduction technologies have advanced to unveil the quantum properties of DKSs. Here we investigate the quantum decoherence of DKSs existing in normal-dispersion microresonators known as dark pulses. By virtue of the very large material nonlinearity, we directly observe the quantum decoherence of dark pulses in an AlGaAs-on-insulator microresonator, and the underlying dynamical processes are resolved by injecting stochastic photons into the microresonators. Moreover, phase correlation measurements show that the uniformity of comb spacing of quantum-limited dark pulses is better than 1.2 × 10-16 and 2.5 × 10-13 when normalized to the optical carrier frequencies and repetition frequencies, respectively. Comparing DKSs generated in different material platforms explicitly confirms the advantages of dark pulses over bright solitons in terms of quantum-limited coherence. Our work establishes a critical performance assessment of DKSs, providing guidelines for coherence engineering of chip-scale optical frequency combs.

High-Sensitivity 2D MoS2/1D MWCNT Hybrid Dimensional Heterostructure Photodetector.

A photodetector based on a hybrid dimensional heterostructure of laterally aligned multiwall carbon nanotubes (MWCNTs) and multilayered MoS2 was prepared using the micro-nano fixed-point transfer technique. Thanks to the high mobility of carbon nanotubes and the efficient interband absorption of MoS2, broadband detection from visible to near-infrared (520-1060 nm) was achieved. The test results demonstrate that the MWCNT-MoS2 heterostructure-based photodetector device exhibits an exceptional responsivity, detectivity, and external quantum efficiency. Specifically, the device demonstrated a responsivity of 3.67 × 103 A/W (λ = 520 nm, Vds = 1 V) and 718 A/W (λ = 1060 nm, Vds = 1 V). Moreover, the detectivity (D*) of the device was found to be 1.2 × 1010 Jones (λ = 520 nm) and 1.5 × 109 Jones (λ = 1060 nm), respectively. The device also demonstrated external quantum efficiency (EQE) values of approximately 8.77 × 105% (λ = 520 nm) and 8.41 × 104% (λ = 1060 nm). This work achieves visible and infrared detection based on mixed-dimensional heterostructures and provides a new option for optoelectronic devices based on low-dimensional materials.

Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction.

Background: Obstructive sleep apnea (OSA) is a common sleep disorder. However, current diagnostic methods are labor-intensive and require professionally trained personnel. We aimed to develop a deep learning model using upper airway computed tomography (CT) to predict OSA and to warn the medical technician if a patient has OSA while the patient is undergoing any head and neck CT scan, even for other diseases. Methods: A total of 219 patients with OSA [apnea-hypopnea index (AHI) ≥10/h] and 81 controls (AHI <10/h) were enrolled. We reconstructed each patient's CT into 3 types (skeletal structures, external skin structures, and airway structures) and captured reconstructed models in 6 directions (front, back, top, bottom, left profile, and right profile). The 6 images from each patient were imported into the ResNet-18 network to extract features and output the probability of OSA using two fusion methods: Add and Concat. Five-fold cross-validation was used to reduce bias. Finally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Results: All 18 views with Add as the feature fusion performed better than did the other reconstruction and fusion methods. This gave the best performance for this prediction method with an AUC of 0.882. Conclusions: We present a model for predicting OSA using upper airway CT and deep learning. The model has satisfactory performance and enables CT to accurately identify patients with moderate to severe OSA.

Uniaxial Strain-Induced Tunable Mid-infrared Light Emission from Thin Film Black Phosphorus.

Strain engineering is a powerful tool that can modulate semiconductor device performance. Here, we demonstrate that the bandgap of thin film (∼40 nm) black phosphorus (bP) can be continuously tuned from 2.9 to 3.9 µm by applying an in-plane uniaxial strain, as evidenced by mid-infrared photoluminescence (PL) spectroscopy. The deduced bandgap strain coefficients are ∼103 meV %-1, which coincide with those obtained in few-layer bP. On the basis of first-principles calculations, the origin of the uniaxial tensile strain-induced PL enhancement is suggested to be due to the increase in both the effective mass ratio (me*/mh*) and the bandgap, leading to the increment of the radiative efficiency. Moreover, the mid-infrared PL emission remains perfectly linear-polarized along the armchair direction regardless of tensile or compressive strain. The highly tunable bandgap of bP in the mid-infrared regime opens up opportunities for the realization of mid-infrared light-emitting diodes and lasers using layered materials.

Microcomb-based integrated photonic processing unit.

The emergence of parallel convolution-operation technology has substantially powered the complexity and functionality of optical neural networks (ONN) by harnessing the dimension of optical wavelength. However, this advanced architecture faces remarkable challenges in high-level integration and on-chip operation. In this work, convolution based on time-wavelength plane stretching approach is implemented on a microcomb-driven chip-based photonic processing unit (PPU). To support the operation of this processing unit, we develop a dedicated control and operation protocol, leading to a record high weight precision of 9 bits. Moreover, the compact architecture and high data loading speed enable a preeminent photonic-core compute density of over 1 trillion of operations per second per square millimeter (TOPS mm-2). Two proof-of-concept experiments are demonstrated, including image edge detection and handwritten digit recognition, showing comparable processing capability compared to that of a digital computer. Due to the advanced performance and the great scalability, this parallel photonic processing unit can potentially revolutionize sophisticated artificial intelligence tasks including autonomous driving, video action recognition and image reconstruction.

Transcriptional networks orchestrating red and pink testa color in peanut.

BACKGROUND: Testa color is an important trait of peanut (Arachis hypogaea L.) which is closely related with the nutritional and commercial value. Pink and red are main color of peanut testa. However, the genetic mechanism of testa color regulation in peanut is not fully understood. To elucidate a clear picture of peanut testa regulatory model, samples of pink cultivar (Y9102), red cultivar (ZH12), and two RNA pools (bulk red and bulk pink) constructed from F4 lines of Y9102 x ZH12 were compared through a bulk RNA-seq approach. RESULTS: A total of 2992 differential expressed genes (DEGs) were identified among which 317 and 1334 were up-regulated and 225 and 1116 were down-regulated in the bulk red-vs-bulk pink RNA pools and Y9102-vs-ZH12, respectively. KEGG analysis indicates that these genes were divided into significantly enriched metabolic pathways including phenylpropanoid, flavonoid/anthocyanin, isoflavonoid and lignin biosynthetic pathways. Notably, the expression of the anthocyanin upstream regulatory genes PAL, CHS, and CHI was upregulated in pink and red testa peanuts, indicating that their regulation may occur before to the advent of testa pigmentation. However, the differential expression of down-stream regulatory genes including F3H, DFR, and ANS revealed that deepening of testa color not only depends on their gene expression bias, but also linked with FLS inhibition. In addition, the down-regulation of HCT, IFS, HID, 7-IOMT, and I2'H genes provided an alternative mechanism for promoting anthocyanin accumulation via perturbation of lignin and isoflavone pathways. Furthermore, the co-expression module of MYB, bHLH, and WRKY transcription factors also suggested a fascinating transcriptional activation complex, where MYB-bHLH could utilize WRKY as a co-option during the testa color regulation by augmenting anthocyanin biosynthesis in peanut. CONCLUSIONS: These findings reveal candidate functional genes and potential strategies for the manipulation of anthocyanin biosynthesis to improve peanut varieties with desirable testa color.

Chromosome-length genome assemblies of six legume species provide insights into genome organization, evolution, and agronomic traits for crop improvement.

INTRODUCTION: Legume crops are an important source of protein and oil for human health and in fixing atmospheric N2 for soil enrichment. With an objective to accelerate much-needed genetic analyses and breeding applications, draft genome assemblies were generated in several legume crops; many of them are not high quality because they are mainly based on short reads. However, the superior quality of genome assembly is crucial for a detailed understanding of genomic architecture, genome evolution, and crop improvement. OBJECTIVES: Present study was undertaken with an objective of developing improved chromosome-length genome assemblies in six different legumes followed by their systematic investigation to unravel different aspects of genome organization and legume evolution. METHODS: We employed in situ Hi-C data to improve the existing draft genomes and performed different evolutionary and comparative analyses using improved genome assemblies. RESULTS: We have developed chromosome-length genome assemblies in chickpea, pigeonpea, soybean, subterranean clover, and two wild progenitor species of cultivated groundnut (A. duranensis and A. ipaensis). A comprehensive comparative analysis of these genome assemblies offered improved insights into various evolutionary events that shaped the present-day legume species. We highlighted the expansion of gene families contributing to unique traits such as nodulation in legumes, gravitropism in groundnut, and oil biosynthesis in oilseed legume crops such as groundnut and soybean. As examples, we have demonstrated the utility of improved genome assemblies for enhancing the resolution of "QTL-hotspot" identification for drought tolerance in chickpea and marker-trait associations for agronomic traits in pigeonpea through genome-wide association study. Genomic resources developed in this study are publicly available through an online repository, 'Legumepedia'. CONCLUSION: This study reports chromosome-length genome assemblies of six legume species and demonstrates the utility of these assemblies in crop improvement. The genomic resources developed here will have significant role in accelerating genetic improvement applications of legume crops.

Effect of Salt Stress on Growth and Physiological Properties of Asparagus Seedlings.

Salt stress could inhibit the growth and development of crops and negatively affect yield and quality. The objective of this study was to investigate the physiological responses of different asparagus cultivars to salt stress. Twenty days old seedlings ofasalt-tolerant Apollo andasalt-sensitive cultivar JL1 were subjected to 0 (CK) and120 mM NaCl stress for 20 d. Their changes in growth, ion contents, antioxidant enzyme activities and gene expression were analyzed. Salt stress significantly inhibited the growth of both cultivars, and JL1 showed a greater decrease than Apollo. The root development of Apollo was promoted by 120 mM NaCl treatment. The Na+ content in roots, stems, and leaves of both cultivars was increased under salt stress, while K+ content and K+/Na+ decreased. The salt-tolerant cultivar Apollo showed less extent of increase in Na+ and decrease in K+ content and kept a relatively high K+/Na+ ratio to compare with JL1. The contents of proline, soluble sugar and protein increased in Apollo, while thesesubstances changed differently in JL1 under salt stress. Activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were gradually increased under salt stress in Apollo, while the corresponding enzyme activities in JL1 were decreased at the late stage of salt stress. The expression of SOD, POD, and CAT genes of both cultivars changed in a similar way to the enzyme activities. Malondialdehyde (MDA) content was increased slightly in Apollo, while increased significantly in JL1. At the late stage of salt stress, Apollomaintained a relatively high K+/Na+, osmotic adjustment ability and antioxidant defense capability, and therefore exhibited higher tolerance to salt stress than that of JL1.

Experimental Study on OH*, CH*, and CO2* Chemiluminescence Diagnosis of CH4/O2 Diffusion Flame with CO2-Diluted Fuel.

OH* and CH* chemiluminescence in hydrocarbon flames are often applied to characterize flame structure, equivalence ratio, strain rate, heat release rate, etc. In this study, chemiluminescence images of OH*, CH*, and CO2* in the CH4/O2 diffusion flame were obtained using a CCD camera imaging system. The effect of CO2 dilution on the flame structure, strain rate, and other flame characteristics of CH4/O2 diffusion flame was discussed. The results show that CO2 dilution greatly affects flame morphology and chemiluminescence intensity. There are quantitative functions between the chemiluminescence peak intensity of OH* and CH* and the CO2 dilution level. The CO2* average intensity in the flame zone is better suited to characterize the dilution level than the CO2* peak intensity. Moreover, the strain rate of CO2-diluted laminar flame is defined. It is found that there is a linear relationship between the thickness of the OH* reaction zone and the square root of the strain rate.

Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning.

Purpose: This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder. Methods: We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard. Results: A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%. Conclusion: Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.