Ribosome stalling during c-myc translation presents actionable cancer cell vulnerability.

Myc is a major driver of tumor initiation, progression, and maintenance. Up-regulation of Myc protein level rather than acquisition of neomorphic properties appears to underlie most Myc-driven cancers. Cellular mechanisms governing Myc expression remain incompletely defined. In this study, we show that ribosome-associated quality control (RQC) plays a critical role in maintaining Myc protein level. Ribosomes stall during the synthesis of the N-terminal portion of cMyc, generating aberrant cMyc species and necessitating deployment of the early RQC factor ZNF598 to handle translational stress and restore cMyc translation. ZNF598 expression is up-regulated in human glioblastoma (GBM), and its expression positively correlates with that of cMyc. ZNF598 knockdown inhibits human GBM neurosphere formation in cell culture and Myc-dependent tumor growth in vivo in Drosophila. Intriguingly, the SARS-COV-2-encoded translational regulator Nsp1 impinges on ZNF598 to restrain cMyc translation and consequently cMyc-dependent cancer growth. Remarkably, Nsp1 exhibits synthetic toxicity with the translation and RQC-related factor ATP-binding cassette subfamily E member 1, which, despite its normally positive correlation with cMyc in cancer cells, is co-opted by Nsp1 to down-regulate cMyc and inhibit tumor growth. Ribosome stalling during c-myc translation thus offers actionable cancer cell vulnerability.

Risk factors associated with intraoperative persistent hypotension in pancreaticoduodenectomy.

BACKGROUND: Intraoperative persistent hypotension (IPH) during pancreaticoduodenectomy (PD) is linked to adverse postoperative outcomes, yet its risk factors remain unclear. AIM: To clarify the risk factors associated with IPH during PD, ensuring patient safety in the perioperative period. METHODS: A retrospective analysis of patient records from January 2018 to December 2022 at the First Affiliated Hospital of Nanjing Medical University identified factors associated with IPH in PD. These factors included age, gender, body mass index, American Society of Anesthesiologists classification, comorbidities, medication history, operation duration, fluid balance, blood loss, urine output, and blood gas parameters. IPH was defined as sustained mean arterial pressure < 65 mmHg, requiring prolonged deoxyepinephrine infusion for > 30 min despite additional deoxyepinephrine and fluid treatments. RESULTS: Among 1596 PD patients, 661 (41.42%) experienced IPH. Multivariate logistic regression identified key risk factors: increased age [odds ratio (OR): 1.20 per decade, 95% confidence interval (CI): 1.08-1.33] (P < 0.001), longer surgery duration (OR: 1.15 per additional hour, 95%CI: 1.05-1.26) (P < 0.01), and greater blood loss (OR: 1.18 per 250-mL increment, 95%CI: 1.06-1.32) (P < 0.01). A novel finding was the association of arterial blood Ca2+ < 1.05 mmol/L with IPH (OR: 2.03, 95%CI: 1.65-2.50) (P < 0.001). CONCLUSION: IPH during PD is independently associated with older age, prolonged surgery, increased blood loss, and lower plasma Ca2+.

Comparison of automated deep neural network against manual sleep stage scoring in clinical data.

OBJECTIVE: To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines. METHODS: Sleep recordings from 104 participants were analyzed by a convolutional neural network (CNN), the Somnolyzer and skillful technicians. Evaluation metrics were derived for different combinations of sleep stages. A further comparison between the Somnolyzer and the CNN model using a single-channel signal as input was also performed. Sleep recordings from 263 participants with a lower prevalence of OSA served as a cross-validation dataset to validate the generalizability of the CNN model. RESULTS: The overall agreement between automated and manual scoring for sleep staging in 104 participants outperformed that of the Somnolyzer according to various metrics (accuracy: 81.81 % vs. 77.07 %; F1: 76.36 % vs. 73.80 %; Cohen's kappa: 0.7403 vs. 0.6848). The results showed that the left electrooculography (EOG) single-channel model had minor advantages over the Somnolyzer. In terms of consistency with manual sleep staging, the CNN model demonstrated superior performance in identifying more pronounced sleep transitions, particularly in the N2 stage and sleep latency metrics. Conversely, the Somnolyzer showed enhanced proficiency in the analysis of REM stages, notably in measuring REM latency. The accuracy in the cross-validation set of 263 participants was also above 80 %. CONCLUSIONS: The CNN-based automated deep neural network outperformed the Somnolyzer and is sufficiently accurate for sleep study analyses using the AASM classification criteria.

Waveguide-integrated twisted bilayer graphene photodetectors.

Graphene photodetectors have exhibited high bandwidth and capability of being integrated with silicon photonics (SiPh), holding promise for future optical communication devices. However, they usually suffer from a low photoresponsivity due to weak optical absorption. In this work, we have implemented SiPh-integrated twisted bilayer graphene (tBLG) detectors and reported a responsivity of 0.65 A W-1 for telecom wavelength 1,550 nm. The high responsivity enables a 3-dB bandwidth of >65 GHz and a high data stream rate of 50 Gbit s-1. Such high responsivity is attributed to the enhanced optical absorption, which is facilitated by van Hove singularities in the band structure of high-mobility tBLG with 4.1o twist angle. The uniform performance of the fabricated photodetector arrays demonstrates a fascinating prospect of large-area tBLG as a material candidate for heterogeneous integration with SiPh.

The uS10c-BPG2 module mediates ribosomal RNA processing in chloroplast nucleoids.

In plant chloroplasts, certain ribosomal proteins (RPs) and ribosome biogenesis factors (RBFs) are present in nucleoids, implying an association between nucleoids and ribosome biogenesis. In Arabidopsis, the YqeH-type GTPase Brassinazole-Insensitive Pale Green2 (BPG2) is a chloroplast nucleoid-associated RBF. Here, we investigated the relationship between nucleoids and BPG2-involved ribosome biogenesis steps by exploring how BPG2 targets ribosomes. Our findings demonstrate that BPG2 interacts with an essential plastid RP, uS10c, in chloroplast nucleoids in a ribosomal RNA (rRNA)-independent manner. We also discovered that uS10c is a haploinsufficient gene, as the heterozygous deletion of this gene leads to variegated shoots and chlorophyll aggregation. uS10c is integrated into 30S ribosomal particles when rRNA is relatively exposed and also exists in polysome fractions. In contrast, BPG2 exclusively associates with 30S ribosomal particles. Notably, the interaction between BPG2 and 30S particles is influenced by the absence of uS10c, resulting in BPG2 diffusing in chloroplasts instead of targeting nucleoids. Further, our results reveal that the loss of BPG2 function and the heterozygous deletion of uS10c impair the processing of 16S and 23S-4.5S rRNAs, reduce plastid protein accumulation, and trigger the plastid signaling response. Together, these findings indicate that the uS10c-BPG2 module mediates ribosome biogenesis in chloroplast nucleoids.

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.

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.

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.

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.

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.

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.

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.

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.

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.