Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.

A Wearable Electrochemical Biosensor Utilizing Functionalized Ti3C2Tx MXene for the Real-Time Monitoring of Uric Acid Metabolite.

Wearable, noninvasive sensors enable the continuous monitoring of metabolites in sweat and provide clinical information related to an individual's health and disease states. Uric acid (UA) is a key indicator highly associated with gout, hyperuricaemia, hypertension, kidney disease, and Lesch-Nyhan syndrome. However, the detection of UA levels typically relies on invasive blood tests. Therefore, developing a wearable device for noninvasive monitoring of UA concentrations in sweat could facilitate real-time personalized disease prevention. Here, we introduce 1,3,6,8-pyrene tetrasulfonic acid sodium salt (PyTS) as a bifunctional molecule functionalized with Ti3C2Tx via π-π conjugation to design nonenzymatic wearable sensors for sensitive and selective detection of UA concentration in human sweat. PyTS@Ti3C2Tx provides many oxidation-reduction active groups to enhance the electrocatalytic ability of the UA oxidation reaction. The PyTS@Ti3C2Tx-based electrochemical sensor demonstrates highly sensitive detection of UA in the concentration range of 5 µM-100 µM, exhibiting a lower detection limit of 0.48 µM compared to the uricase-based sensor (0.84 µM). In volunteers, the PyTS@Ti3C2Tx-based wearable sensor is integrated with flexible microfluidic sweat sampling and wireless electronics to enable real-time monitoring of UA levels during aerobic exercise. Simultaneously, it allows for comparison of blood UA levels via a commercial UA analyzer. Herein, this study provides a promising electrocatalyst strategy for nonenzymatic electrochemical UA sensor, enabling noninvasive real-time monitoring of UA levels in human sweat and personalized disease prevention.

Chronotype characteristics of professional athletes in China: a comprehensive descriptive study.

BACKGROUND: Chronotype has gained recognition as a significant factor in enhancing athletic performance. This study aimed to deepen our understanding of athletes' sleep chronotypes and provide a foundation for developing evidence-based training and competition programs. By comprehensively describing and analyzing the chronotype characteristics of Chinese professional athletes, considering individual and sports factors, sleep quality and habits, and mental energy, this research aimed to contribute valuable insights to the field. METHODS: A sample of 1069 professional athletes from sports teams in Shanghai completed the Athlete Sleep Screening Questionnaire and the Athlete Mental Energy Scale to assess chronotype, sleep quality, sleep-influencing habits, and mental energy. RESULTS: Among athletes, sleep typology fell within the intermediate range, slightly leaning toward morningness. Male athletes and those who engaged in static sports displayed a greater propensity for morningness. Age correlated with a preference for eveningness. High-level athletes exhibited a stronger inclination toward eveningness. Sleep quality issues were associated with an inclination toward eveningness. Daily caffeine intake and the habit of using electronic devices before bedtime are also linked to eveningness. Chronotype demonstrated the ability to predict various dimensions of athletes' mental energy. It was the strongest predictor of vigor, but the loadings were smaller than those of sleep quality. CONCLUSION: Chinese athletes' chronotypes primarily exhibit distinct characteristics related to individual factors such as gender, sports discipline, and ranking, as well as habits like caffeine consumption and electronic device use. Moreover, these sleep patterns demonstrate predictive capabilities across all dimensions of athletes' mental energy. This study sheds light on Chinese athletes' unique sleep chronotype attributes, enriching our understanding of sleep patterns among professional athletes under various systems. These insights offer an initial basis for enhancing the effectiveness of athlete scheduling and training management.

Adhesion Behavior in Fish: From Structures to Applications.

In nature, some fish can adhere tightly to the surface of stones, aquatic plants, and even other fish bodies. This adhesion behavior allows these fish to fix, eat, hide, and migrate in complex and variable aquatic environments. The adhesion function is realized by the special mouth and sucker tissue of fish. Inspired by adhesion fish, extensive research has recently been carried out. Therefore, this paper presents a brief overview to better explore underwater adhesion mechanisms and provide bionic applications. Firstly, the adhesion organs and structures of biological prototypes (e.g., clingfish, remora, Garra, suckermouth catfish, hill stream loach, and goby) are presented separately, and the underwater adhesion mechanisms are analyzed. Then, based on bionics, it is explained that the adhesion structures and components are designed and created for applications (e.g., flexible gripping adhesive discs and adhesive motion devices). Furthermore, we offer our perspectives on the limitations and future directions.

MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection.

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

Reliability and validity of the Chinese version of the Athens insomnia scale for non-clinical application in Chinese athletes.

Purpose: This study aimed to revise and examine the reliability and validity of the Chinese version of the Athens Insomnia Scale for Non-clinical Application (AIS-NCA) among Chinese athletes. Additionally, the study tested the scale in non-athlete individuals with similar sleep management practices to further analyze its cultural specificity among Chinese athletes and make preliminary inferences about its applicability in other Chinese populations. Methods: Four hundred twenty-six Chinese professional athletes and 779 high school students participated in this research. Both athletes and students were divided into two parallel groups for exploratory and confirmatory factor analyses. Additionally, three athlete samples and one student sample were established for reliability and validity assessments. Among athletes, the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Athlete Sleep Screening Questionnaire, and the Warwick-Edinburgh Mental Well-Being Scale were employed to evaluate convergent and discriminant validity. Re-test reliability was evaluated at intervals of 1 and 2 weeks. In the case of students, convergent and discriminant validity were tested using the Pittsburgh Sleep Quality Index and the General Self-Efficacy Scale, with re-test reliability assessed at two-week intervals. Results: The Chinese version of the AIS-NCA consists of six items, categorized into two dimensions: sleep problems and daytime functioning. This structure explained 65.08% (athletes) and 66.22% (students) of the variance. Confirmatory factor analysis revealed good model fit, with values of χ2/df = 2.217, CFI = 0.975, AGFI = 0.929, TLI = 0.953, and RMSEA = 0.076 among athletes, and χ2/df = 3.037, CFI = 0.979, AGFI = 0.947, TLI = 0.961, and RMSEA = 0.072 among students. The scale demonstrated a reasonable degree of measurement invariance. The overall scale and two subscales exhibited strong reliability and validity among athletes. Similar results in terms of reliability and validity were also observed within the student sample. Conclusion: The Chinese version of the AIS-NCA shows promise as an assessment tool for evaluating the sleep quality of Chinese athletes. It effectively captures both sleep-related concerns and daytime functionality within the athlete population. The scale demonstrates solid reliability and validity in professional athletes and holds potential for application across various other demographic groups in China.

An Electrochemical Sensor Based on Cu-MOF-199@MWCNTs Laden with CuNPs for the Sensitive Detection of Creatinine.

In this study, the synthesis of Cu-MOF-199@multiwalled carbon nanotubes (Cu-MOF-199@MWCNTs) composites was achieved and utilized to create an advanced electrochemical sensor for creatinine (Cre) detection. The composites were modified on a glassy carbon electrode surface through direct drip coating, followed by the deposition of copper nanoparticles (CuNPs) via constant potential deposition. Characterized by various techniques and electrochemical analyses, the Cu-MOF-199@MWCNTs composite increased the CuNPs load, improving the detection sensitivity for Cre. Under optimal conditions, the modified electrode exhibited good linearity across a broad range of Cre concentrations (0.05-40.0 µM) with a low detection limit of 11.3 nM. The developed sensor demonstrated remarkable stability, reproducibility, and selectivity, showing promise in sensitive and accurate Cre detection in serum samples.

The effect of short-term cranial electrotherapy stimulation on sleep quality in athletes: A pilot study.

BACKGROUND: To verify the effect of a 5-day cranial electrotherapy stimulation (CES) intervention on sleep quality in professional athletes. METHODS: 25 professional athletes with poor sleep quality participated in the study. Athletes belonging to the CES group (12 athletes) received a 5-day CES intervention, and those in the control group did not receive any intervention. Objectively and subjected assessed sleep quality was measured 1 week before and after the intervention using an Actigraphy activity recorder, Pittsburgh sleep quality index (PSQI), and Insomnia Severity Index (ISI). RESULTS: Objectively measured sleep efficiency increased after CES intervention (P = .013), while the difference between the pretest and posttest of the control group was not significant. For total sleep time (TST), the main effects and interaction were not significant. However, the analysis on wake after sleep onset showed wake after sleep onset decreased after CES intervention (P = .015). No significant interaction was found in subjectively assessed sleep quality but only revealed an improvement in both groups. CONCLUSION: The CES intervention of 30 minutes per day for 5 consecutive days enhanced objective sleep quality in athletes with sleep quality problems. The intervention increased sleep efficiency by lowering awake time after falling asleep.

Heterologous expression of a deacetylase and its application in L-glufosinate preparation.

With potent herbicidal activity, biocatalysis synthesis of L-glufosinate has drawn attention. In present research, NAP-Das2.3, a deacetylase capable of stereoselectively resolving N-acetyl-L-glufosinate to L-glufosinate mined from Arenimonas malthae, was heterologously expressed and characterized. In Escherichia coli, NAP-Das2.3 activity only reached 0.25 U/L due to the formation of inclusive bodies. Efficient soluble expression of NAP-Das2.3 was achieved in Pichia pastoris. In shake flask and 5 L bioreactor fermentation, NAP-Das2.3 activity by recombinant P. pastoris reached 107.39 U/L and 1287.52 U/L, respectively. The optimum temperature and pH for N-acetyl-glufosinate hydrolysis by NAP-Das2.3 were 45 °C and pH 8.0, respectively. The Km and Vmax of NAP-Das2.3 towards N-acetyl-glufosinate were 25.32 mM and 19.23 µmol mg-1 min-1, respectively. Within 90 min, 92.71% of L-enantiomer in 100 mM racemic N-acetyl-glufosinate was converted by NAP-Das2.3. L-glufosinate with high optical purity (e.e.P above 99.9%) was obtained. Therefore, the recombinant NAP-Das2.3 might be an alternative for L-glufosinate biosynthesis.

[Microbial production of S-adenosyl-l-methionine: a review].

S-adenosyl-l-methionine (SAM) is ubiquitous in living organisms and plays important roles in transmethylation, transsulfuration and transamination in organisms. Due to its important physiological functions, production of SAM has attracted increasing attentions. Currently, researches on SAM production mainly focus on microbial fermentation, which is more cost-effective than that of the chemical synthesis and the enzyme catalysis, thus easier to achieve commercial production. With the rapid growth in SAM demand, interests in improving SAM production by developing SAM hyper-producing microorganisms aroused. The main strategies for improving SAM productivity of microorganisms include conventional breeding and metabolic engineering. This review summarizes the recent research progress in improving microbial SAM productivity to facilitate further improving SAM productivity. The bottlenecks in SAM biosynthesis and the solutions were also addressed.

Psychological changes in athletes infected with Omicron after return to training: fatigue, sleep, and mood.

Background: This study aims to analyze the changes of approximately 1 month in fatigue, sleep, and mood in athletes after returning to training following infection with the COVID-19 Omicron strain and provide recommendations for returning to training after infection. Methods: Two hundred and thirty professional athletes who had returned to training after being infected with COVID-19 in December 2022 were recruited to participate in three tests conducted from early January 2023. The second test was completed approximately 1 week after the first, and the third was completed about 2 weeks after the second. Each test consisted of completing scales and the exercise-induced fatigue measure. The scales included a visual analog scale, the Athens Insomnia Scale for non-clinical application, and the Depression-Anxiety-Stress scale. The exercise task was a six-minute stair climb test, and athletes evaluated subjective fatigue levels before and after exercise using another Visual Analog Scale and the Karolinska Sleepiness Scale. Results: After returning to training, athletes' physical fatigue decreased initially but increased as training progressed. Cognitive fatigue did not change significantly. The exercise task led to elevated levels of physical fatigue after a longer duration of training. Sleep quality problems decreased rapidly after the start of training but remained stable with prolonged training. Depression levels continued to decline, while anxiety levels only reduced after a longer duration of training. Stress levels decreased rapidly after the start of training but did not change with prolonged training. Conclusion: Athletes who return to training after recovering from COVID-19 experience positive effects on their fatigue, sleep, and mood. It is important to prioritize anxiety assessment and interventions during the short period after returning and to continue monitoring fatigue levels and implementing recovery interventions over a longer period of time.

High extinction coefficient material combined with multi-line lateral flow immunoassay strip for ultrasensitive detection of bacteria.

Lateral flow immunoassay strips (LFIAs) are a reliable and point-of-care detection method for rapid monitoring of bacteria, but their sensitivity was limited by the low extinction coefficient of colloidal gold nanoparticles (Au NPs) and low capture efficiency of test-line. In this study, polydopamine nanoparticles (PDA NPs) were employed to replace Au NPs, due to their high extinction coefficient. And the amount of test-line was increased to 5 for further improving the efficiency of bacteria capture. Thus, under visual observation, the detection limits of PDA-based LFIAs (102 CFU/mL) were about 2 orders of magnitude lower than Au-based LFIAs (104 CFU/mL). Furthermore, the invisible signal could be collected by Image J and the detection limit can reach 10 CFU/mL. The proposed test strips were successfully applied for the quantitative, accurate, and rapid screening of E. coli in food samples. This study provided a universal approach to enhance the sensitivity of bacteria LFIAs.

Clinicopathologic features, genomic profiles and outcomes of younger vs. older Chinese hormone receptor-positive (HR+)/HER2-negative (HER2-) metastatic breast cancer patients.

Background: Poor outcomes have been widely reported for younger vs. older breast cancer patients, but whether this is due to age itself or the enrichment of aggressive clinical features remains controversial. We have evaluated the clinicopathologic characteristics and genomic profiles of real-world hormone receptor-positive (HR+)/HER2-negative (HER2-) metastatic breast cancer (MBC) patients to examine the determinants of outcome for younger vs. older patients in a single clinical subtype undergoing treatment in the same clinic. Patients and methods: This study included patients presenting at the Peking University Cancer Hospital with primary stage IV or first-line metastatic HR+/HER2- breast cancer who consented to an additional blood draw for genomic profiling prior to treatment. Plasma samples were analyzed with a targeted 152-gene NGS panel to assess somatic circulating tumor DNA (ctDNA) alterations. Genomic DNA (gDNA) extracted from peripheral blood mononuclear cells was analyzed for germline variants using a targeted 600-gene NGS panel. Kaplan-Meier survival analysis was performed to analyze disease free survival (DFS), progression free survival (PFS) and overall survival (OS) in association with clinicopathologic and genomic variables. Results: Sixty-three patients presenting with HR+/HER2- MBC were enrolled in this study. Fourteen patients were < 40 years, 19 were 40-50 years, and 30 were > 50 years at the time of primary cancer diagnosis. No significant associations were observed between age and DFS, PFS or OS. Shorter OS was associated with de novo Stage IV disease (p = 0.002), Luminal B subtype (p = 0.006), high Ki67 index (p = 0.036), resistance to adjuvant endocrine therapy (p = 0.0001) and clinical stage (p = 0.015). Reduced OS was also observed in association with somatic alterations in FGFR1 (p = 0.008), CCND2 (p = 0.012), RB1 (p = 0.029) or TP53 (p = 0.029) genes, but not in association with germline variants. Conclusion: In this group of real-world HR+/HER2- MBC breast cancer patients younger age was not associated with poor outcomes. While current guidelines recommend treatment decisions based on tumor biology rather than age, young HR+ breast cancer patients are more likely to receive chemotherapy. Our findings support the development of biomarker-driven treatment strategies for these patients.

The effect of prolonged closed-loop management on athletes' sleep and mood during COVID-19 pandemic: Evidence from the 2022 Shanghai Omicron Wave.

Closed-loop management of athletes at the training base is a compromise approach that balanced epidemic prevention and sports training during the COVID-19 pandemic. This study investigated the impact of prolonged closed-loop management on athletes' sleep and mood during the 2022 Shanghai Omicron wave. The Pittsburgh Sleep Quality Index and the Profile of Mood States were used to assess the sleep and mood states of 110 professional athletes in "closed-loop management" at the training base after 1 and 2 months of closed-loop management, respectively, to characterize changes in sleep and mood with prolonged closed-loop management. After two months of control, the sleep and mood of 69 athletes and students of the same age were measured using the Pittsburgh Sleep Quality Index and Perceptual Stress Scale, as well as the Warwick-Edinburgh Mental Well-being Scale, to compare the differences in sleep and mood between athletes undergoing closed-loop management and the general population who were managed in the community. Paired sample t-tests and independent sample t-tests were used for comparisons across different time intervals and different management approaches. Results showed that with the time of closed-loop management increased, athletes woke up earlier (p = 0.002), slept less (p = 0.024), and became angrier (p = 0.014); athletes had poorer overall sleep quality (p < 0.001) but lower stress level (p = 0.004) than those who were outside the base. In closed-loop management, the athletes were able to maintain a stable sleep and mood state. Sports team administrators must be aware of the need to improve athletes' sleep quality and help athletes to agree with this approach of management.

Text Sentiment Classification Based on BERT Embedding and Sliced Multi-Head Self-Attention Bi-GRU.

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model.

An electrochemical sensor for purine base detection with ZIF-8-derived hollow N-doped carbon dodecahedron and AuNPs as electrocatalysts.

In this paper, a novel electrochemical sensor was constructed for the detection of purine bases. Ultrafine carbide nanocrystals confined within porous nitrogen-doped carbon dodecahedrons (PNCD) were synthesized by adding molybdate to ZIF-8 followed by annealing. With MoC-based PNCDs (MC-PNCDs) as the carrier, gold nanoparticles (AuNPs) were deposited on the electrode surface via potentiostatic deposition as the promoter of electron transfer, forming a AuNPs/MC-PNCDs/activated glassy carbon electrode (AGCE) sensor. MC-PNCDs had a large specific surface area, which combined with the excellent electrocatalytic activity of AuNPs, synergistically improved the electrocatalytic activity. The morphology and structure of the electrode surface modifier were characterized by scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray photoelectron spectroscopy, infrared spectroscopy, X-ray diffraction, nitrogen adsorption-desorption analysis, and electrochemical characterization. Under the optimal conditions, the linear detection range of guanine (G) and adenine (A) was 0.5-160.0 µM, and the detection limits (S/N=3) were 72.1 and 69.6 nM, respectively. AuNPs/MC-PNCDs/AGCE was successfully constructed, and was used to simultaneously detect G and A with high sensitivity and selectivity. Moreover, the sensor was successfully used to detect G and A in herring sperm DNA samples.

Differential impact of heat and hypoxia on dynamic oxygen uptake and deoxyhemoglobin parameters during incremental exhaustive exercise.

Purpose: This study aims to explore the relationship between the dynamic changes in oxygen uptake (V˙O2) and deoxyhemoglobin (HHb) and peripheral fatigue in athletes during incremental exhaustive exercise under different environmental conditions, including high temperature and humidity environment, hypoxic environment, and normal conditions. Methods: 12 male modern pentathlon athletes were recruited and performed incremental exhaustive exercise in three different environments: normal condition (23°C, 45%RH, FiO2 = 21.0%, CON), high temperature and humidity environment (35°C, 70%RH, FiO2 = 21.0%, HOT), and hypoxic environment (23°C, 45%RH, FiO2 = 15.6%, HYP). Gas metabolism data of the athletes were collected, and muscle oxygen saturation (SmO2) and total hemoglobin content in the vastus lateralis muscles (VL) were measured to calculate the deoxyhemoglobin content. Linear and nonlinear function models were used to fit the characteristic parameters of V˙O2 and HHb changes. Results: The results showed that compared to the CON, V˙O2, V˙CO2, and exercise time were decreased in the HOT and HYP (p < 0.05). ΔEV˙O2 and OUES were reduced in the HOT and HYP compared to the CON (p < 0.05). The Gas exchange threshold in the CON corresponded to higher V˙O2 than in the HYP and HOT (p < 0.05). ΔEV˙O2-1 was reduced in the HOT compared to the HYP (p < 0.05). ΔEHHb was higher in the HOT compared to the CON (p < 0.05). ΔEHHb-1 was increased in the HYP compared to the CON (p < 0.05). There was a negative correlation between ΔEHHb and corresponding V˙O2⁡max in the HOT (r = -0.655, p < 0.05), and a negative correlation between ΔEHHb-1 and corresponding V˙O2⁡max in the HYP (r = -0.606, p < 0.05). Conclusion: Incremental exhaustive exercise in hypoxic environment and high temperature and humidity environments inhibits gas exchange and oxygen supply to skeletal muscle tissue in athletes. For athletes, the accelerated deoxygenation response of skeletal muscles during incremental exhaustive exercise in high temperature and humidity environments, as well as the excessive deoxygenation response before BP of deoxyhemoglobin in hypoxic environment, may be contributing factors to peripheral fatigue under different environmental conditions.

Design of All-Dielectric Metasurface-Based Subtractive Color Filter by Artificial Neural Network.

Structural colors produced by light manipulating at subwavelength dimensions have been widely studied. In this work, a metasurface-based subtractive color filter (SCF) is demonstrated. The color display of the SCF is confirmed by finding the complementary color of colors filtered by SCF within the color wheel. In addition, two artificial neural network (ANN) models are utilized to accelerate the metasurface forward prediction, and the long short-term memory (LSTM) shows much better performance than traditional multilayer perceptron (MLP). Meanwhile, we train an inverse ANN model established with LSTM to recover the optimal geometric parameter combinations of the meta-atoms. With the variation of the geometric parameters of meta-atoms, versatile color displays of structural colors are realized. The metasurface we propose exhibits good performance of transmissive-type structural color in visible range. The work provides a method for high-efficiency geometric parameter prediction, and paves the way to nanostructure-based color design for display and anticounterfeiting applications.

Rice ß-Glucosidase 4 (Os1ßGlu4) Regulates the Hull Pigmentation via Accumulation of Salicylic Acid.

Salicylic acid (SA) is a stress hormone synthesized in phenylalanine ammonia-lyase (PAL) and the branching acid pathway. SA has two interconvertible forms in plants: SAG (SA O-ß-glucoside) and SA (free form). The molecular mechanism of conversion of SA to SAG had been reported previously. However, which genes regulate SAG to SA remained unknown. Here, we report a cytoplasmic ß-glucosidase (ß-Glu) which participates in the SA pathway and is involved in the brown hull pigmentation in rice grain. In the current study, an EMS-generated mutant brown hull 1 (bh1) displayed decreased contents of SA in hulls, a lower photosynthesis rate, and high-temperature sensitivity compared to the wild type (WT). A plaque-like phenotype (brown pigmentation) was present on the hulls of bh1, which causes a significant decrease in the seed setting rate. Genetic analysis revealed a mutation in LOC_Os01g67220, which encodes a cytoplasmic Os1ßGlu4. The knock-out lines displayed the phenotype of brown pigmentation on hulls and decreased seed setting rate comparable with bh1. Overexpression and complementation lines of Os1ßGlu4 restored the phenotype of hulls and normal seed setting rate comparable with WT. Subcellular localization revealed that the protein of Os1ßGlu4 was localized in the cytoplasm. In contrast to WT, bh1 could not hydrolyze SAG into SA in vivo. Together, our results revealed the novel role of Os1ßGlu4 in the accumulation of flavonoids in hulls by regulating the level of free SA in the cellular pool.

Long short-term memory neural network for directly inverse design of nanofin metasurface.

In this Letter, the neural network long short-term memory (LSTM) is used to quickly and accurately predict the polarization sensitivity of a nanofin metasurface. In the forward prediction, we construct a deep neural network (DNN) with the same structure for comparison with LSTM. The test results show that LSTM has a higher accuracy and better robustness than DNN in similar cases. In the inverse design, we directly build an LSTM to reverse the design similar to the forward prediction network. By inputting the extinction ratio value in 8-12 µm, the inverse network can directly provide the unit cell geometry of the nanofin metasurface. Compared with other methods used to inverse design photonic structures using deep learning, our method is more direct because no other networks are introduced.