B‒N covalent bond-involved π-extension of multiple resonance emitters enables high-performance narrowband electroluminescence.

Multi-boron-embedded multiple resonance thermally activated delayed fluorescence (MR-TADF) emitters show promise for achieving both high color-purity emission and high exciton utilization efficiency. However, their development is often impeded by a limited synthetic scope and excessive molecular weights, which challenge material acquisition and organic light-emitting diode (OLED) fabrication by vacuum deposition. Herein, we put forward a B‒N covalent bond-involved π-extension strategy via post-functionalization of MR frameworks, leading to the generation of high-order B/N-based motifs. The structurally and electronically extended π-system not only enhances molecular rigidity to narrow emission linewidth but also promotes reverse intersystem crossing to mitigate efficiency roll-off. As illustrated examples, ultra-narrowband sky-blue emitters (full-width at half-maximum as small as 8 nm in n-hexane) have been developed with multi-dimensional improvement in photophysical properties compared to their precursor emitters, which enables narrowband OLEDs with external quantum efficiencies (EQEmax) of up to 42.6%, in company with alleviated efficiency decline at high brightness, representing the best efficiency reported for single-host OLEDs. The success of these emitters highlights the effectiveness of our molecular design strategy for advanced MR-TADF emitters and confirms their extensive potential in high-performance optoelectronic devices.

Impact of LDLR polymorphisms on lipid levels and atorvastatin's efficacy in a northern Chinese adult Han cohort with dyslipidemia.

BACKGROUND: Dyslipidemia, a significant risk factor for atherosclerotic cardiovascular disease (ASCVD), is influenced by genetic variations, particularly those in the low-density lipoprotein receptor (LDLR) gene. This study aimed to elucidate the effects of LDLR polymorphisms on baseline serum lipid levels and the therapeutic efficacy of atorvastatin in an adult Han population in northern China with dyslipidemia. METHODS: In this study, 255 Han Chinese adults receiving atorvastatin therapy were examined and followed up. The 3' untranslated region (UTR) of the LDLR gene was sequenced to identify polymorphisms. The associations between gene polymorphisms and serum lipid levels, as well as changes in lipid levels after intervention, were evaluated using the Wilcoxon rank sum test, with a P < 0.05 indicating statistical significance. Assessment of linkage disequilibrium patterns and haplotype structures was conducted utilizing Haploview. RESULTS: Eleven distinct polymorphisms at LDLR 3' UTR were identified. Seven polymorphisms (rs1433099, rs14158, rs2738466, rs5742911, rs17249057, rs55971831, and rs568219285) were correlated with the baseline serum lipid levels (P < 0.05). In particular, four polymorphisms (rs14158, rs2738466, rs5742911, and rs17249057) were in strong linkage disequilibrium (r2 = 1), and patients with the AGGC haplotype had higher TC and LDL-C levels at baseline. Three polymorphisms (rs1433099, rs2738467, and rs7254521) were correlated with the therapeutic efficacy of atorvastatin (P < 0.05). Furthermore, carriers of the rs2738467 T allele demonstrated a significantly greater reduction in low-density lipoprotein cholesterol (LDL-C) levels post-atorvastatin treatment (P = 0.03), indicating a potentially crucial genetic influence on therapeutic outcomes. Two polymorphisms (rs751672818 and rs566918949) were neither correlated with the baseline serum lipid levels nor atorvastatin's efficacy. CONCLUSIONS: This research outlined the complex genetic architecture surrounding LDLR 3' UTR polymorphisms and their role in lipid metabolism and the response to atorvastatin treatment in adult Han Chinese patients with dyslipidemia, highlighting the importance of genetic profiling in enhancing tailored therapeutic strategies. Furthermore, this investigation advocates for the integration of genetic testing into the management of dyslipidemia, paving the way for customized therapeutic approaches that could significantly improve patient care. TRIAL REGISTRATION: This multicenter study was approved by the Ethics Committee of Xiangya Hospital Central South University (ethics number K22144). It was a general ethic. In addition, this study was approved by The First Hospital of Hebei Medical University (ethics number 20220418).

PMONN: an optical neural network for photonic integrated circuits based on micro-resonator.

We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.

A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy.

In the fight against oral cancer, innovative methods like Raman spectroscopy and deep learning have become powerful tools, particularly in integral tasks encompassing tumor staging, lymph node staging, and histological grading. These aspects are essential for the development of effective treatment strategies and prognostic assessment. However, it is important to note that most research so far has focused on solutions to one of these problems and has not taken full advantage of the potential wealth of information in the data. To compensate for this shortfall, we conceived a method that combines Raman spectroscopy with deep learning for simultaneous processing of multiple classification tasks, including tumor staging, lymph node staging, and histological grading. To achieve this innovative approach, we collected 1750 Raman spectra from 70 tissue samples, including normal and cancerous tissue samples from 35 patients with oral cancer. In addition, we used a deep neural network architecture to design four distinct multi-task network (MTN) models for intelligent oral cancer diagnosis, named MTN-Alexnet, MTN-Googlenet, MTN-Resnet50, and MTN-Transformer. To determine their effectiveness, we compared these multitask models to each other and to single-task models and traditional machine learning methods. The preliminary experimental results show that our multi-task network model has good performance, among which MTN-Transformer performs best. Specifically, MTN-Transformer has an accuracy of 81.5%, a precision of 82.1%, a sensitivity of 80.2%, and an F1_score of 81.1% in terms of tumor staging. In the field of lymph node staging, the accuracy, precision, sensitivity, and F1_score of MTN-Transformer are 81.3%, 83.0%, 80.1%, and 81.5% respectively. Similarly, for the histological grading classification tasks, the accuracy was 83.0%, the precision 84.3%, the sensitivity 76.7%, and the F1_score 80.2%. This code is available at https://github.com/ISCLab-Bistu/MultiTask-OralRamanSystem.

Picea wilsonii NAC31 and DREB2A Cooperatively Activate ERD1 to Modulate Drought Resistance in Transgenic Arabidopsis.

The NAC family of transcription factors (TFs) regulate plant development and abiotic stress. However, the specific function and response mechanism of NAC TFs that increase drought resistance in Picea wilsonii remain largely unknown. In this study, we functionally characterized a member of the PwNAC family known as PwNAC31. PwNAC31 is a nuclear-localized protein with transcriptional activation activity and contains an NAC domain that shows extensive homology with ANAC072 in Arabidopsis. The expression level of PwNAC31 is significantly upregulated under drought and ABA treatments. The heterologous expression of PwNAC31 in atnac072 Arabidopsis mutants enhances the seed vigor and germination rates and restores the hypersensitive phenotype of atnac072 under drought stress, accompanied by the up-regulated expression of drought-responsive genes such as DREB2A (DEHYDRATION-RESPONSIVE ELEMENT BINDING PROTEIN 2A) and ERD1 (EARLY RESPONSIVE TO DEHYDRATION STRESS 1). Yeast two-hybrid and bimolecular fluorescence complementation assays confirmed that PwNAC31 interacts with DREB2A and ABF3 (ABSCISIC ACID-RESPONSIVE ELEMENT-BINDING FACTOR 3). Yeast one-hybrid and dual-luciferase assays showed that PwNAC31, together with its interaction protein DREB2A, directly regulated the expression of ERD1 by binding to the DRE element of the ERD1 promoter. Collectively, our study provides evidence that PwNAC31 activates ERD1 by interacting with DREB2A to enhance drought tolerance in transgenic Arabidopsis.

Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms.

Introduction: Oral cancer, a predominant malignancy in developing nations, represents a global health challenge with a five-year survival rate below 50%. Nonetheless, substantial reductions in both its incidence and mortality rates can be achieved through early detection and appropriate treatment. Crucial to these treatment plans and prognosis predictions is the identification of the pathological type of oral cancer. Methods: Toward this end, fiber-optic Raman spectroscopy emerges as an effective tool. This study combines Raman spectroscopy technology with deep learning algorithms to develop a portable intelligent prototype for oral case analysis. We propose, for the first time, a multi-task network (MTN) Raman spectroscopy classification model that utilizes a shared backbone network to simultaneously achieve different clinical staging and histological grading diagnoses. Results: The developed model demonstrated accuracy rates of 94.88%, 94.57%, and 94.34% for tumor staging, lymph node staging, and histological grading, respectively. Its sensitivity, specificity, and accuracy compare closely with the gold standard: routine histopathological examination. Discussion: Thus, this prototype proposed in this study has great potential for rapid, non-invasive, and label-free pathological diagnosis of oral cancer.

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder.

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Dynamic bond interactions fine-tune the properties of multiple resonance emitters towards highly efficient narrowband green OLEDs.

Multiple resonance (MR) molecules based on a B/N polycyclic aromatic framework are the cutting-edge materials in the field of organic light-emitting diodes (OLEDs) owing to their superb photophysical properties. Tailoring the MR molecular framework with various functional groups toward ideal properties has become an emerging topic in the field of materials chemistry. Dynamic bond interactions are versatile and powerful tools in regulating the properties of materials. Herein, the pyridine moiety, which presents high affinity to form dynamic bond interactions such as hydrogen bonds and N→B dative bonds, was introduced into the MR framework for the first time, and the designed emitters are synthesized in a feasible way. The introduction of the pyridine moiety not only maintained the conventional MR properties of the emitters, but also endowed the emitters with tunable emission spectra, narrowed emission, enhanced photoluminescence quantum yield (PLQY), and intriguing supramolecular assembly in the solid state. Thanks to the overall superior properties brought by the hydrogen-bond promoted molecular rigidity, green OLEDs based on the emitter exhibit excellent device performance with external quantum efficiency (EQE) up to 38% and a small FWHM of 26 nm, together with good roll-off performance.

Metal-Perturbed Multiresonance TADF Emitter Enables High-Efficiency and Ultralow Efficiency Roll-Off Nonsensitized OLEDs with Pure Green Gamut.

Multiresonance (MR)-induced thermally activated delayed fluorescence (TADF) emitters based on B- and N-embedded polycyclic aromatics are desirable for ultrahigh-definition organic light-emitting diodes (OLEDs) due to their high photoluminescence quantum yield (PLQY) and narrow bandwidth. But the reverse intersystem crossing (RISC) rates of MR-TADF emitters are usually small, resulting in severe device efficiency roll-off at high brightness. To solve this issue, a sensitizer for the MR-TADF emitter has been required. Herein, a new MR-TADF emitter is developed through coordination of Au with B/N-embedded polycyclic ligand. Benefitting from the Au perturbation, the RISC rate is dramatically accelerated to 2.3 × 107 s-1 , leading to delayed fluorescence lifetime as short as 4.3 µs. Meanwhile, the PLQY of 95% and full width at half maximum of 39 nm (0.18 eV) are essentially unchanged after metal coordination. Therefore, a high PLQY, short delayed fluorescence lifetime, and high color purity are concurrently realized in a single TADF emitter. Accordingly, vacuum-deposited OLEDs exhibit high-performance electroluminescence with a maximum external quantum efficiency (EQE) of 35.8% without sensitization. The EQE is maintained as high as 32.3% at 10 000 cd m-2 . Furthermore, solution-processed OLED based on the emitter also achieves excellent performance with a maximum EQE of 25.7% and a small efficiency roll-off.

The association between experience-based risky choice and mathematical ability.

Mathematical ability has always been considered an important influencing factor in description-based risky choices. Experience-based risky choices, which occur frequently in daily life, are very different from description-based risky choices. The association between experience-based risky choice and mathematical ability remains unknown. This study adopts the feedback paradigm for experience-based risky choice to explore the association between multiple mathematical abilities and experience-based risky choice. The results show that, in experience-based risky choice, mathematical ability did not influence the decision to pursue higher expected value, but it did influence preference for risky. Thus, our study contributes to a more comprehensive view of mathematical ability and risky choice.

Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression.

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls.

Integrative Physiological and Transcriptomic Analysis Reveals the Transition Mechanism of Sugar Phloem Unloading Route in Camellia oleifera Fruit.

Sucrose phloem unloading plays a vital role in photoassimilate distribution and storage in sink organs such as fruits and seeds. In most plants, the phloem unloading route was reported to shift between an apoplasmic and a symplasmic pattern with fruit development. However, the molecular transition mechanisms of the phloem unloading pathway still remain largely unknown. In this study, we applied RNA sequencing to profile the specific gene expression patterns for sucrose unloading in C. oleifera fruits in the apo- and symplasmic pathways that were discerned by CF fluoresce labelling. Several key structural genes were identified that participate in phloem unloading, such as PDBG11, PDBG14, SUT8, CWIN4, and CALS10. In particular, the key genes controlling the process were involved in callose metabolism, which was confirmed by callose staining. Based on the co-expression network analysis with key structural genes, a number of transcription factors belonging to the MYB, C2C2, NAC, WRKY, and AP2/ERF families were identified to be candidate regulators for the operation and transition of phloem unloading. KEGG enrichment analysis showed that some important metabolism pathways such as plant hormone metabolism, starch, and sucrose metabolism altered with the change of the sugar unloading pattern. Our study provides innovative insights into the different mechanisms responsible for apo- and symplasmic phloem unloading in oil tea fruit and represents an important step towards the omics delineation of sucrose phloem unloading transition in crops.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer.

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

High-Performance Narrowband Pure-Red OLEDs with External Quantum Efficiencies up to 36.1% and Ultralow Efficiency Roll-Off.

High-color-purity blue and green organic light-emitting diodes (OLEDs) have been resolved thanks to the development of B/N-based polycyclic multiple resonance (MR) emitters. However, due to the derivatization limit of B/N polycyclic structures, the design of red MR emitters remains challenging. Herein, a series of novel red MR emitters is reported by para-positioning N-π-N, O-π-O, B-π-B pairs onto a benzene ring to construct an MR central core. These emitters can be facilely and modularly synthesized, allowing for easy fine-tuning of emission spectra by peripheral groups. Moreover, these red MR emitters display excellent photophysical properties such as near-unity photoluminescence quantum yield (PLQY), fast radiative decay rate (kr ) up to 7.4 × 107 s-1 , and most importantly, narrowband emission with full-width at half-maximum (FWHM) of 32 nm. Incorporating these MR emitters, pure red OLEDs sensitized by phosphor realize state-of-the-art device performances with external quantum efficiency (EQE) exceeding 36%, ultralow efficiency roll-off (EQE remains as high as 25.1% at the brightness of 50 000 cd m-2 ), ultrahigh brightness over 130 000 cd m-2 , together with good device lifetime.

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer.

Background: As one of the main causes leading to female cancer deaths, cervical cancer shows malignant features of local infiltration and invasion into adjacent organs and tissues. This study was designed to categorize novel molecular subtypes according to cervical cancer invasion and screen reliable prognostic markers. Methods: Invasion-related gene sets and expression profiles of invasion-related genes were collected from the CancerSEA database and The Cancer Genome Atlas (TCGA), respectively. Samples were clustered by nonnegative matrix factorization (NMF) to obtain different molecular subgroups, immune microenvironment characteristics of which were further systematically compared. Limma was employed to screen differentially expressed gene sets in different subtypes, followed by Lasso analysis for dimension reduction. Multivariate and univariate Cox regression analysis was performed to determine prognostic characteristics. The Kaplan-Meier test showed the prognostic differences of patients with different risks. Additionally, receiver operating characteristic (ROC) curves were applied to validate the prognostic model performance. A nomogram model was developed using clinical and prognostic characteristics of cervical cancer, and its prediction accuracy was reflected by calibration curve. Results: This study filtered 19 invasion-related genes with prognosis significance in cervical cancer and 2 molecular subtypes (C1, C2). Specifically, the C1 subtype had an unfavorable prognosis, which was associated with the activation of the TGF-beta signaling pathway, focal adhesion, and PI3K-Akt signaling pathway. 875 differentially expressed genes were screened, and 8 key genes were finally retained by the dimension reduction analysis. An 8-gene signature was established as an independent factor predictive of the prognosis of cervical cancer. The signature performance was even stronger when combined with N stage. Conclusion: Based on invasion-related genes, the present study categorized two cervical cancer subtypes with distinct TME characteristics and established an 8-gene marker that can accurately and independently predict the prognosis of cervical cancer.

Efficacy evaluation of vaginal-assisted laparoscopic radical hysterectomy and laparoscopic radical hysterectomy for cervical cancer: a single-center retrospective case series study.

Background: To investigate the survival outcomes of abdominal radical hysterectomy (ARH), laparoscopic radical hysterectomy (LRH), and vaginal-assisted laparoscopic radical hysterectomy (VALRH) in the treatment of cervical cancer patients. Methods: This was a retrospective study. We collected the clinical data of 654 patients with cervical cancer (406 ARH, 172 LRH, and 76 VALRH), then compared the effects of different surgical methods on recurrence and survival. Results: Total overall survival (OS) were no significant differences in three groups (P>0.05). Total disease-free survival (DFS) was significantly higher in ARH group than in LRH group [hazard ratio (HR) =2.8, 95% confidence interval (CI): 1.199-3.607, P=0.004]; however, there were no significant differences between the VALRH (94.7%) and ARH (93.3%) groups. Subgroup stratification analysis showed that the overall recurrence rate in LRH group was significantly higher than that of the ARH groups for patients with a tumor size from ≥2 to <4 cm, negative postoperative lymph nodes, and no postoperative adjuvant therapy (all P<0.05). However, in the subgroup with tumor sizes of ≥2, <4, and ≥4 cm, no matter whether the lymph nodes were positive or not, and those with no postoperative supplementary adjuvant therapy, LRH was associated with a significantly higher local pelvic recurrence rate than ARH (all P<0.05). No significant differences between VALRH and ARH in any of the subgroup analyses (all P>0.05). A Cox analysis indicated that LRH increased the risk of overall and local pelvic recurrence after surgery compared with ARH (HR =2.338, 95% CI: 1.186-4.661, P=0.014; HR =10.313, 95% CI: 2.839-37.460, P<0.001); however, no significant difference between VALRH and ARH (all P>0.05). Sensitivity analysis of surgeons did not change the conclusions. Conclusions: Our analyses showed that the local pelvic recurrence rates and overall recurrence rates of LRH were significantly higher than ARH. VALRH could avoid tumor intraperitoneal exposure and achieve the same tumor prognosis as open surgery. By improving the standardization of minimally invasive surgery for early cervical cancer and paying close attention to the tumor-free concept, minimally invasive radical hysterectomy may achieve the same tumor outcome as open surgery.

Modeling and simulation of all-optical diffractive neural network based on nonlinear optical materials.

In this Letter, we propose an all-optical diffractive deep neural network modeling method based on nonlinear optical materials. First, the nonlinear optical properties of graphene and zinc selenide (ZnSe) are analyzed. Then the optical limiting effect function corresponding to the saturation absorption coefficient of the nonlinear optical materials is fitted. The optical limiting effect function is taken as the nonlinear activation function of the neural network. Finally, the all-optical diffractive neural network model based on nonlinear materials is established. The numerical simulation results show that the model can effectively improve the nonlinear representation ability of the all-optical diffractive neural network. It provides a theoretical support for the further realization of a photonic artificial intelligence chip based on nonlinear optical materials.

NAC Transcription Factor PwNAC11 Activates ERD1 by Interaction with ABF3 and DREB2A to Enhance Drought Tolerance in Transgenic Arabidopsis.

NAC (NAM, ATAF1/2, and CUC2) transcription factors are ubiquitously distributed in eukaryotes and play significant roles in stress response. However, the functional verifications of NACs in Picea (P.) wilsonii remain largely uncharacterized. Here, we identified the NAC transcription factor PwNAC11 as a mediator of drought stress, which was significantly upregulated in P. wilsonii under drought and abscisic acid (ABA) treatments. Yeast two-hybrid assays showed that both the full length and C-terminal of PwNAC11 had transcriptional activation activity and PwNAC11 protein cannot form a homodimer by itself. Subcellular observation demonstrated that PwNAC11 protein was located in nucleus. The overexpression of PwNAC11 in Arabidopsis obviously improved the tolerance to drought stress but delayed flowering time under nonstress conditions. The steady-state level of antioxidant enzymes' activities and light energy conversion efficiency were significantly increased in PwNAC11 transgenic lines under dehydration compared to wild plants. PwNAC11 transgenic lines showed hypersensitivity to ABA and PwNAC11 activated the expression of the downstream gene ERD1 by binding to ABA-responsive elements (ABREs) instead of drought-responsive elements (DREs). Genetic evidence demonstrated that PwNAC11 physically interacted with an ABA-induced protein-ABRE Binding Factor3 (ABF3)-and promoted the activation of ERD1 promoter, which implied an ABA-dependent signaling cascade controlled by PwNAC11. In addition, qRT-PCR and yeast assays showed that an ABA-independent gene-DREB2A-was also probably involved in PwNAC11-mediated drought stress response. Taken together, our results provide the evidence that PwNAC11 plays a dominant role in plants positively responding to early drought stress and ABF3 and DREB2A synergistically regulate the expression of ERD1.

A Method for Estimating 24-Hour Urinary Sodium Excretion by Casual Urine Specimen in Chinese Hypertensive Patients.

BACKGROUND: High salt intake is a known risk factor of hypertension, which in turn increases the risk of stroke and cardiovascular diseases. The aim of this study was to develop and evaluate a method for predicting 24-hour urinary sodium excretion (UNa24h) using casual urine specimens in Chinese hypertensive patients. METHODS: A total of 966 patients with hypertension were included from 8 provinces across China. A UNa24h prediction model (Sun_C method) was developed for males and females using linear regression based on age, weight, sodium concentration in the spot urine (UNaspot), and creatinine concentration in the spot urine (UCrspot). The data were split into the training (70%) and testing (30%) sets to, respectively, develop and evaluate the Sun_C method. RESULTS: Compared with the Kawasaki, INTERSALT, and Tanaka methods, Sun_C method achieved a low and consistent mean bias (1.1 mmol/d) within the range from 106 to 212 mmol/d of UNa24h (equivalent to NaCl intake of 6-12 g/d). In addition, the Sun_C method showed no significant difference between the measured and estimated UNa24h in a paired t-test (P = 0.689). At individual level, Sun_C method had 79.8% of individuals at the cutoff under ±30% level. CONCLUSIONS: Sun_C method may prove a reasonable method to estimate the daily dietary sodium intakes (particularly in the range of 6-12 g/d of NaCl) in Chinese hypertensive patients using spot urine measurements. As the amount of data increases in the future, the performance of our formulae will be further improved.