Genome-Wide Identification and Expression Profiling of the ABF Transcription Factor Family in Wheat (Triticum aestivum L.).

Members of the abscisic acid (ABA)-responsive element (ABRE) binding factor (ABF) and ABA-responsive element binding protein (AREB) families play essential roles in the regulation of ABA signaling pathway activity and shape the ability of plants to adapt to a range of stressful environmental conditions. To date, however, systematic genome-wide analyses focused on the ABF/AREB gene family in wheat are lacking. Here, we identified 35 ABF/AREB genes in the wheat genome, designated TaABF1-TaABF35 according to their chromosomal distribution. These genes were further classified, based on their phylogenetic relationships, into three groups (A-C), with the TaABF genes in a given group exhibiting similar motifs and similar numbers of introns/exons. Cis-element analyses of the promoter regions upstream of these TaABFs revealed large numbers of ABREs, with the other predominant elements that were identified differing across these three groups. Patterns of TaABF gene expansion were primarily characterized by allopolyploidization and fragment duplication, with purifying selection having played a significant role in the evolution of this gene family. Further expression profiling indicated that the majority of the TaABF genes from groups A and B were highly expressed in various tissues and upregulated following abiotic stress exposure such as drought, low temperature, low nitrogen, etc., while some of the TaABF genes in group C were specifically expressed in grain tissues. Regulatory network analyses revealed that four of the group A TaABFs (TaABF2, TaABF7, TaABF13, and TaABF19) were centrally located in protein-protein interaction networks, with 13 of these TaABF genes being regulated by 11 known miRNAs, which play important roles in abiotic stress resistance such as drought and salt stress. The two primary upstream transcription factor types found to regulate TaABF gene expression were BBR/BPC and ERF, which have previously been reported to be important in the context of plant abiotic stress responses. Together, these results offer insight into the role that the ABF/AREB genes play in the responses of wheat to abiotic stressors, providing a robust foundation for future functional studies of these genes.

Map conformational landscapes of intrinsically disordered proteins with polymer physics quantities.

Disordered proteins are conformationally flexible proteins that are biologically important and have been implicated in devastating diseases such as Alzheimer's disease and cancer. Unlike stably folded structured proteins, disordered proteins sample a range of different conformations that needs to be accounted for. Here, we treat disordered proteins as polymer chains, and compute a dimensionless quantity called instantaneous shape ratio (Rs), as Rs = Ree2/Rg2, where Ree is end-to-end distance and Rg is radius of gyration. Extended protein conformations tend to have high Ree compared with Rg, and thus have high Rs values, whereas compact conformations have smaller Rs values. We use a scatter plot of Rs (representing shape) against Rg (representing size) as a simple map of conformational landscapes. We first examine the conformational landscape of simple polymer models such as Random Walk, Self-Avoiding Walk, and Gaussian Walk (GW), and we notice that all protein/polymer maps lie within the boundaries of the GW map. We thus use the GW map as a reference and, to assess conformational diversity, we compute the fraction of the GW conformations (fC) covered by each protein/polymer. Disordered proteins all have high fC scores, consistent with their disordered nature. Each disordered protein accesses a different region of the reference map, revealing differences in their conformational ensembles. We additionally examine the conformational maps of the nonviral gene delivery vector polyethyleneimine at various protonation states, and find that they resemble disordered proteins, with coverage of the reference map decreasing with increasing protonation state, indicating decreasing conformational diversity. We propose that our method of combining Rs and Rg in a scatter plot generates a simple, meaningful map of the conformational landscape of a disordered protein, which in turn can be used to assess conformational diversity of disordered proteins.

Plasmonic couplings in Ag-Au heterodimers.

The plasmonic coupling between silver (Ag) and gold (Au) nanoparticles (NPs) under four polarization modes was examined: a longitudinal mode (L-mode), where the electric field of a linearly polarized incident light parallels the dimer axis, and three transverse modes (T-modes), where the electric field of the light is perpendicular to the dimer axis. The coupling was studied using the discrete dipole approximation followed by an in-house postprocessing code that determines the extinction (Qext), absorption (Qabs), and near-field (Qnf) spectra from the individual NPs as well as the whole system. In agreement with the literature results, the extinction/absorption spectra of the whole dimer have two peaks, one near the Ag localized surface plasmon resonance (LSPR) region and the other at the Au LSPR region, with the peak at Ag LSPR being reduced in all modes and the peak at Au LSPR being red-shifted and increased in the L-mode but not in the T-modes. It is further shown that the scattering at the Ag LSPR region is reduced and becomes less than the isolated Ag NPs, but the absorption at the Ag LSPR is increased and becomes greater than the isolated Ag NPs for the 50 nm Ag-Au heterodimer. This suggests that the scattering from Ag NPs is being reabsorbed by the neighboring Au NPs due to the interband electronic transition in Au at that wavelength range. The Qext from the individual NP in the heterodimer shows the presence of the Fano profile on the Au NP but not on the Ag NP. This phenomenon was further investigated by using a dielectric particle (DP) placed near the Ag or Au NPs. The Fano profile appears in the absorbing DP spectra placed near either Ag or Au NPs. However, the Fano profile is masked upon further increases in the refractive index value of the DP particle. This explains the absence of a Fano profile on the Ag NPs in the Ag-Au heterodimer. The large near-field enhancement on both Ag and Au NPs at the Au plasmonic wavelength in the L-mode for large NPs was investigated through a DP-Au system. The large enhancement was shown to arise from a large imaginary component of the DP refractive index and a small real component. Through examination of both the near- and far-field properties of the individual NPs as well as the whole system and examinations of DP-Ag and DP-Au systems, our study provides a new understanding of the couplings between Ag and Au NPs.

DNA Packaging and Polycation Length Determine DNA Susceptibility to Free Radical Damage in Condensed DNA.

In nature, DNA exists primarily in a highly compacted form. The compaction of DNA in vivo is mediated by cationic proteins: histones in somatic nuclei and protamines in sperm chromatin. The extreme, nearly crystalline packaging of DNA by protamines in spermatozoa is thought to be essential for both efficient genetic delivery as well as DNA protection against damage by mutagens and oxidative species. The protective role of protamines is required in sperm, as they are sensitive to ROS damage due to the progressive loss of DNA repair mechanisms during maturation. The degree to which DNA packaging directly relates to DNA protection in the condensed state, however, is poorly understood. Here, we utilized different polycation condensing agents to achieve varying DNA packaging densities and quantify DNA damage by free radical oxidation within the condensates. Although we see that tighter DNA packaging generally leads to better protection, the length of the polycation also plays a significant role. Molecular dynamics simulations suggest that longer polyarginine chains offer increased protection by occupying more space on the DNA surface and forming more stable interactions. Taken together, our results suggest a complex interplay among polycation properties, DNA packaging density, and DNA protection against free radical damage within condensed states.

Comparative Transcriptome Analysis Reveals the Genes and Pathways Related to Wheat Root Hair Length.

Tube-like outgrowths from root epidermal cells, known as root hairs, enhance water and nutrient absorption, facilitate microbial interactions, and contribute to plant anchorage by expanding the root surface area. Genetically regulated and strongly influenced by environmental conditions, longer root hairs generally enhance water and nutrient absorption, correlating with increased stress resistance. Wheat, a globally predominant crop pivotal for human nutrition, necessitates the identification of long root hair genotypes and their regulatory genes to enhance nutrient capture and yield potential. This study focused on 261 wheat samples of diverse genotypes during germination, revealing noticeable disparities in the length of the root hair among the genotypes. Notably, two long root hair genotypes (W106 and W136) and two short root hair genotypes (W90 and W100) were identified. Transcriptome sequencing resulted in the development of 12 root cDNA libraries, unveiling 1180 shared differentially expressed genes (DEGs). Further analyses, including GO function annotation, KEGG enrichment, MapMan metabolic pathway analysis, and protein-protein interaction (PPI) network prediction, underscored the upregulation of root hair length regulatory genes in the long root hair genotypes. These included genes are associated with GA and BA hormone signaling pathways, FRS/FRF and bHLH transcription factors, phenylpropanoid, lignin, lignan secondary metabolic pathways, the peroxidase gene for maintaining ROS steady state, and the ankyrin gene with diverse biological functions. This study contributes valuable insights into modulating the length of wheat root hair and identifies candidate genes for the genetic improvement of wheat root traits.

Multiplexed Surface Protein Detection and Cancer Classification Using Gap-Enhanced Magnetic-Plasmonic Core-Shell Raman Nanotags and Machine Learning Algorithm.

Cancer is the second leading cause of death attributed to disease worldwide. Current standard detection methods often rely on a single cancer marker, which can lead to inaccurate results, including false negatives, and an inability to detect multiple cancers simultaneously. Here, we developed a multiplex method that can effectively detect and classify surface proteins associated with three distinct types of breast cancer by utilizing gap-enhanced Raman scattering nanotags and machine learning algorithm. We synthesized anisotropic magnetic core-gold shell gap-enhanced Raman nanotags incorporating three different Raman reporters. These multicolor Raman nanotags were employed to distinguish specific surface protein markers in breast cancer cells. The acquired signals were deconvoluted and analyzed using classical least-squares regression to generate a surface protein profile and characterize the breast cancer cells. Furthermore, computational data obtained via finite-difference time-domain and discrete dipole approximation showed the amplification of the electric fields within the gap region due to plasmonic coupling between the two gold layers. Finally, a random forest classifier achieved an impressive classification and profiling accuracy of 93.9%, enabling effective distinguishing between the three different types of breast cancer cell lines in a mixed solution. With the combination of immunomagnetic multiplex target specificity and separation, gap-enhancement Raman nanotags, and machine learning, our method provides an accurate and integrated platform to profile and classify different cancer cells, giving implications for identification of the origin of circulating tumor cells in the blood system.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity.

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Effect of different antibacterial agents doping in PET-based electrospun nanofibrous membranes on air filtration and antibacterial performance.

In order to reduce the particulate matter pollution to human health in producing environments, series of polyethylene terephthalate/polyvinyl alcohol (PET/PVA) based nanofibrous membranes were fabricated and investigated the dust collection and antibacterial activity. Silver nanoparticles (AgNPs), berberine (Ber) and titanium oxide nanoparticles (TiO2NPs) were selected as antibacterial agents. These novel membranes were well-characterized using SEM, FTIR, TG, etc. techniques. Results of the dust filtration showed that PET/PVA/Ag membrane had the best filtration efficiency of 99.87% for sodium chloride (NaCl) and 99.89% for dioctyl sebacate (DEHS), held low pressure drop of 160.1 Pa for NaCl and 165.3 Pa for DEHS, and posed a high tensile strength of 4.91 MPa. The bacteriostasis studies exhibited that PET/PVA/TiO2 and PET/PVA/Ag membrane showed the highest bacteriological effect on Escherichia coli (98.7%) and Staphylococcus aureus (95.9%), respectively. Meanwhile, in vitro cytotoxicity test indicated no potential cytotoxicity existed in the cell culture process of these two antibacterial membranes. Moreover, the charge distribution in the nanofibers was increased by these antibacterial agents to improve the filtration performance. The dust filtration process synergistically promoted with the antibacterial process in the antibacterial membranes. It was expected that these membranes could be efficient filter medias with broad application prospects in the field of individual protection.

Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.

RATIONALE AND OBJECTIVES: To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. MATERIALS AND METHODS: We enrolled 327 BC patients with axillary lymph node (ALN) metastases receiving axillary operations after NAC. The deep learning features were extracted by ResNet34, which was pretrained by a large, well-annotated dataset from ImageNet. Then we identified deep learning radiomics on magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI) in predicting axillary response after NAC in BC patients. RESULTS: The extraction of 128 deep learning radiomics (DLR) features relied on the DCE-MRI for each patient. After the least absolute shrinkage and selection operator regression analysis, 13, 8, and 21 features remained from the pre-treatment, post-treatment, and combined DCE-MRI, respectively. The DLR signature established based on the combined DCE-MRI achieved good capacity in ALN response after NAC. The support vector machine achieved the best performance with an 0.99 area under the curve (AUC) of (95% confidence interval (CI), 0.98-1.00) and 0.83 (95% CI, 0.73-0.92) in the training and test sets, respectively. The LR model established with clinical parameters represented the best performance with 0.73 AUC (95% CI, 0.62-0.84), 0.73 sensitivity, 0.73 specificity, 0.63 PPV, and 0.81 NPV in the test set, respectively. Finally, the integration of radiomic signature and clinical signature resulted in establishing a predictive radiomic nomogram, with an AUC of 0.99 (95%CI, 0.99-1.00). CONCLUSION: In conclusion, our current study constructed a predictive nomogram through the deep learning method, demonstrating favorable performance in the training and test cohort. The present prognostic model furnishes a precise and objective foundation for directing the surgical strategy toward ALN management in BC patients receiving NAC.

An Investigation on the Use of Au@SiO2@Au Nanomatryoshkas as Gap-Enhanced Raman Tags.

Gap-enhanced Raman tags are a new type of optical probe that have wide applications in sensing and detection. A gap-enhanced Raman tag is prepared by embedding Raman molecules inside a gap between two plasmonic metals such as an Au core and Au shell. Even though placing Raman molecules beneath an Au shell seems counter-intuitive, it has been shown that such systems produce a stronger surface-enhanced Raman scattering response due to the strong electric field inside the gap. While the theoretical support of the stronger electric field inside the gap was provided in the literature, a comprehensive understanding of how the electric field inside the gap compares with that of the outer surface of the particle was not readily available. We investigated Au@SiO2@Au nanoparticles with diameters ranging from 35 nm to 70 nm with varying shell (2.5-10 nm) and gap (2.5-15 nm) thicknesses and obtained both far-field and near-field spectra. The extinction spectra from these particles always have two peaks. The low-energy peak redshifts with the decreasing shell thickness. However, when the gap thickness decreases, the low-energy peaks first blueshift and then redshift, producing a C-shape in the peak position. For every system we investigated, the near-field enhancement spectra were stronger inside the gap than on the outer surface of the nanoparticle. We find that a thin shell combined with a thin gap will produce the greatest near-field enhancement inside the gap. Our work fills the knowledge gap between the exciting potential applications of gap-enhanced Raman tags and the fundamental knowledge of enhancement provided by the gap.

Pre-Launch Spectral Calibration of the Absorbed Aerosol Sensor.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument's spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the SRF is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which has a distribution similar to the flat-topped multi-Gaussian function, is employed to fit the measured data in a spectral calibration. According to the results, the SRF's shapes resembling a Gaussian curve with a flat top could be derived, which contains a full width at half maximum (FWHM) of 1.78-1.82 nm for the AAS. The results show that the correlation is about 0.99, which indicates the usefulness of the fitting function that could better characterize the SRF of the instrument.

Relationship between serum vitamin D and thyroid hormone profiles in male patients with alcohol dependence.

BACKGROUND: Alcohol dependence (AD) results in several medical problems including vitamin D deficiency and thyroid dysfunction. However, the relationship between these two complications remains unclear. The aim of the present study was to explore the relationship between serum vitamin D and thyroid hormone profiles in male patients with AD. METHODS: A total of 117 male patients with AD were enrolled. Vitamin D deficiency was defined as serum concentrations of the main circulating vitamin D, 25-hydroxy vitamin D [25(OH)D], below 50 nmol/L. The AD patients were divided into two groups accordingly: 46 patients with normal vitamin D levels (normal group) and 71 patients with vitamin D deficiency (deficiency group). The levels of thyroid hormone profiles including total triiodothyronine 3 (TT3), total thyroxine 4 (TT4), thyroid stimulating hormone (TSH), free triiodothyronine (fT3), and free thyroxine (fT4) between the two groups were compared. Correlation between the serum levels of 25(OH)D and thyroid hormone profiles was evaluated using simple correlation (Pearson's correlation) and multivariable analysis using linear regression models. RESULTS: The prevalence of vitamin D deficiency in male patients with AD is 60.7% (71/117; 95% confidence interval: 51.6-69.1%). Moreover, the serum levels of TT3 (t = -2.682, p = 0.009), TT4 (t = -2.033, p = 0.044), fT3 (t = -2.986, p = 0.003), and fT4 (t = -2.558, p = 0.012) in deficiency group were significantly higher than those in normal group. Post hoc power analyses showed that the power for fT3 was sufficient (power > 0.80). Furthermore, univariate analysis showed that the serum vitamin D levels were negatively correlated with the TT3 (r = -0.189, p = 0.044), fT3 (r = -0.350, p < 0.001), and fT4 (r = -0.198, p = 0.033) levels, while multivariate analysis indicated that only fT3 was independently related to the serum levels of vitamin D in male patients with AD. CONCLUSIONS: These results suggested that the serum vitamin D levels may be associated with fT3 in male patients with AD.

Knocking out Selenium Binding Protein 1 Induces Depressive-Like Behavior in Mice.

Selenium binding protein 1 (SELENBP1) is involved in neurologic disorders, such as multiple sclerosis, spinal cord injury, Parkinson's disease, epilepsy, and schizophrenia. However, the role of SELENBP1 in the neurogenesis of depression, which is a neurologic disorder, and the underlying mechanisms of oxidative stress and inflammation in depression remain unknown. In this study, we evaluated the changes in the expression levels of SELENBP1 in the hippocampus of a mouse model of depression and in the serum of human patients with depression using the Gene Expression Omnibus database. These changes were validated using blood samples from human patients with depression and mouse models with chronic unpredictable mild stress (CUMS)-induced depressive-like behavior. We also investigated the effects of SELENBP1 knockout (KO) on inflammation, oxidative stress, and hippocampal neurogenesis in mice with CUMS-induced depression. Our results revealed that SELENBP1 levels was decreased in the blood of human patients with depression and in the hippocampus of mice with CUMS-induced depression. SELENBP1 KO increased CUMS-induced depressive behavior in mice and caused dysregulation of inflammatory cytokines and oxidative stress. This led to a decrease in the numbers of doublecortin- and Ki67-positive cells, which might aggravate CUMS-induced depressive symptoms. These findings suggest that SELENBP1 might be involved in the regulation of neurogenesis in mice with depression and could be served as a potential target for diagnosing and treating depression.

Organic Matrix Derived from Host-Microbe Interplay Contributes to Pathological Renal Biomineralization.

Matrix stones are a rare form of kidney stones. They feature a high percentage of hydrogel-like organic matter, and their formation is closely associated with urinary tract infections. Herein, comprehensive materials and biochemical approaches were taken to map the organic-inorganic interface and gather insights into the host-microbe interplay in pathological renal biomineralization. Surgically extracted soft and slimy matrix stones were examined using micro-X-ray computed tomography and various microspectroscopy techniques. Higher-mineral-density laminae were positive for calcium-bound Alizarin red. Lower-mineral-density laminae revealed periodic acid-Schiff-positive organic filamentous networks of varied thickness. These organic filamentous networks, which featured a high polysaccharide content, were enriched with zinc, carbon, and sulfur elements. Neutrophil extracellular traps (NETs) along with immune response-related proteins, including calprotectin, myeloperoxidase, CD63, and CD86, also were identified in the filamentous networks. Expressions of NETs and upregulation of polysaccharide-rich mucin secretion are proposed as a part of the host immune defense to "trap" pathogens. These host-microbe derived organic matrices can facilitate heterogeneous nucleation and precipitation of inorganic particulates, resulting in macroscale aggregates known as "matrix stones". These insights into the plausible aggregation of constituents through host-microbe interplay underscore the unique "double-edged sword" effect of the host immune response to pathogens and the resulting renal biominerals.

Clinical characteristics of severe influenza virus-associated pneumonia complicated with bacterial infection in children: a retrospective analysis.

BACKGROUND: We aimed to investigate the clinical characteristics of severe influenza virus-associated pneumonia complicated with bacterial infection in children. METHODS: We retrospectively analysed data concerning 64 paediatric patients with severe influenza virus-associated pneumonia who had been treated at our hospital. The patients were divided into observation (44 patients) and control (20 patients) groups, based on the presence or absence of concomitant bacterial infection, and clinical data were compared between the groups. RESULTS: The mean age in the observation group was 2.71 ± 1.44 years, 42 (95.45%) were aged ≤ 5 years, and 18 (40.9%) had underlying diseases. The mean age in the control group was 4.05 ± 2.21 years, 13 (65%) were aged ≤ 5 years, and 3 (15%) had underlying diseases. There was a statistically significant difference in patient age and the proportion of patients with underlying diseases (P < 0.05). The observation group had higher duration of fever values, a higher number of patients with duration of fever ≥ 7 days, a higher incidence of gasping, and a higher incidence of seizures/consciousness disturbance, and the differences were statistically significant (P < 0.05). Secondary bacterial infections in the observation group were mainly due to gram-negative bacteria, with Haemophilus influenzae and Moraxella catarrhalis being the most common pathogens. The observation group had a higher proportion of patients treated in the paediatric intensive care unit and a longer hospital stay, and the differences were statistically significant (P < 0.05). CONCLUSION: Severe influenza virus-associated pneumonia complicated with bacterial infection was more common in children aged ≤ 5 years. Younger patients with underlying diseases were more susceptible to bacterial infection (mainly due to gram-negative bacteria). The timely administration of neuraminidase inhibitors and antibiotics against susceptible bacteria is likely to help improve cure rates.

Return to Work Experience of Young and Middle-Aged Patients With Acute Myocardial Infarction: A Longitudinal Qualitative Study.

BACKGROUND: Return to work (RTW) is a critical component of rehabilitation for most young and middle-aged patients after an acute myocardial infarction (AMI). Its success is related to the quality of life and social psychological function of patients, and their social economic growth. However, healthcare professionals often do not deeply understand the patients' experience and their difficulties and coping methods during this process, which limits their ability to institute effective management and support. OBJECTIVE: In this study, we aimed to explore the lived experiences and change processes of young and middle-aged patients with AMI at the different stages of RTW. METHODS: A descriptive qualitative approach was used. Patients aged 20 to 59 years with AMI were recruited from the Department of Cardiology of 3 general hospitals. Data were collected via semistructured interviews. Data analysis was performed by conventional content analysis methods. RESULTS: In total, 18 participants were included. Five main themes emerged: (1) "chaos," (2) "rebuilding," (3) "conflict," (4) "coping," and (5) "benefits." Patients may be more concerned about physical recovery during the initial clinical event. They then begin to plan and adjust for an RTW. Patients in the maintenance phase need strategies to prevent, identify, and respond to conflicts and challenges to maintain long-term stable work. CONCLUSION: We identified several post-AMI stages spanning from the initial illness event to the maintenance of stable work. We described their perceived barriers, coping strategies, and support needs at these various stages. These data are crucial for healthcare professionals to develop improved vocational rehabilitation strategies for patients with AMI.

Planar hot-electron photodetection with polarity-switchable photocurrents controlled by the working wavelength.

Hot-electron photodetection is attracting increasing interests. Based on internal photoemission mechanism, hot-electron photodetectors (HE PDs) convert incident photon energy into measurable photocurrent. To obtain polarity-switchable photocurrent, one often applies electric bias to reverse the hot-electron flow. However, the employment of bias reduces the device flexibility and increasing the bias voltage degrades the detectivity of the device. Herein, we design a planar HE PD with the polarity-switchable photocurrent controlled by the working wavelength. Optical simulations show that the device exhibits two absorption peaks due to the resonances of two Tamm plasmons (TPs). Electrical calculations predict two corresponding TP-assisted responsivity peaks, but with opposite photocurrent polarities, which are determined by the hot-electron flows with opposite directions. We find that the hot-electron flows are closely related with the population differences of TP-induced hot electrons in two electrodes. We further demonstrate that the photocurrent polarity of the HE PD can be switched by altering working wavelength from one TP wavelength to the other. We believe that this approach paves a route to achieve flexible hot-electron photodetection for extensive applications.

TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and their related relationships when searching for papers. To address these issues, a database on tea risk substances and their research relationships was constructed. These data were correlated by knowledge mapping techniques, and a Neo4j graph database centered on tea risk substance research was constructed, containing 4189 nodes and 9400 correlations (e.g. research category-PMID, risk substance category-PMID, and risk substance-PMID). This is the first knowledge-based graph database that is specifically designed for integrating and analyzing risk substances in tea and related research, containing nine main types of tea risk substances (including a comprehensive discussion of inclusion pollutants, heavy metals, pesticides, environmental pollutants, mycotoxins, microorganisms, radioactive isotopes, plant growth regulators, and others) and six types of tea research papers (including reviews, safety evaluations/risk assessments, prevention and control measures, detection methods, residual/pollution situations, and data analysis/data measurement). It is an essential reference for exploring the causes of the formation of risk substances in tea and the safety standards of tea in the future. Database URL http://trsrd.wpengxs.cn.

Intrinsic piezoelectricity of 2D violet phosphorene.

Violet phosphorene (VP) has an extensive range of potential uses in piezoelectric gadgets due to its excellent electron transport capabilities. Nevertheless, VP materials are devoid of the piezoelectric effect because of the absence of ionic polarity. In this study, the piezoelectric capacity of the VP nano-sheet was investigated. According to the findings, the VP exhibited precise in-plane and out-of-plane piezoelectricity. For both in-plane as well as out-of-plane applications, the monolayer of VP had a characteristic direct piezoelectric reaction, and piezoelectric hysteresis loops were established at an electric field excitation of -18 V to +18 V. According to calculations using the density functional theory, VP has inside inherent dipoles. Outcomes from piezoelectric force microscopy, in particular, for VP with a thickness of 17.6 nm revealed that d33 is up to 12.65 pm V-1 and d31 is up to 1.03 pm V-1. The thickness-relied piezoelectric results demonstrated that VP's piezoelectric capabilities degraded as width increased.