Attenuation of Polycyclic Aromatic Hydrocarbon (PAH)-Induced Carcinogenesis and Tumorigenesis by Omega-3 Fatty Acids in Mice In Vivo.

Lung cancer is the leading cause of cancer death worldwide. Polycyclic aromatic hydrocarbons (PAHs) are metabolized by the cytochrome P450 (CYP)1A and 1B1 to DNA-reactive metabolites, which could lead to mutations in critical genes, eventually resulting in cancer. Omega-3 fatty acids, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are beneficial against cancers. In this investigation, we elucidated the mechanisms by which omega-3 fatty acids EPA and DHA will attenuate PAH-DNA adducts and lung carcinogenesis and tumorigenesis mediated by the PAHs BP and MC. Adult wild-type (WT) (A/J) mice, Cyp1a1-null, Cyp1a2-null, or Cyp1b1-null mice were exposed to PAHs benzo[a]pyrene (BP) or 3-methylcholanthrene (MC), and the effects of omega-3 fatty acid on PAH-mediated lung carcinogenesis and tumorigenesis were studied. The major findings were as follows: (i) omega-3 fatty acids significantly decreased PAH-DNA adducts in the lungs of each of the genotypes studied; (ii) decreases in PAH-DNA adduct levels by EPA/DHA was in part due to inhibition of CYP1B1; (iii) inhibition of soluble epoxide hydrolase (sEH) enhanced the EPA/DHA-mediated prevention of pulmonary carcinogenesis; and (iv) EPA/DHA attenuated PAH-mediated carcinogenesis in part by epigenetic mechanisms. Taken together, our results suggest that omega-3 fatty acids have the potential to be developed as cancer chemo-preventive agents in people.

Pipelined biomedical event extraction rivaling joint learning.

Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or by machine learning. In this paper, we propose an n-ary relation extraction method based on the BERT pre-training model to construct Binding events, in order to capture the semantic information about an event's context and its participants. The experimental results show that our method achieves promising results on the GE11 and GE13 corpora of the BioNLP shared task with F1 scores of 63.14% and 59.40%, respectively. It demonstrates that by significantly improving the performance of Binding events, the overall performance of the pipelined event extraction approach or even exceeds those of current joint learning methods.

Magnetically Compatible Brain Electrode Arrays Based on Single-Walled Carbon Nanotubes for Long-Term Implantation.

Advancements in brain-machine interfaces and neurological treatments urgently require the development of improved brain electrodes applied for long-term implantation, where traditional and polymer options face challenges like size, tissue damage, and signal quality. Carbon nanotubes are emerging as a promising alternative, combining excellent electronic properties and biocompatibility, which ensure better neuron coupling and stable signal acquisition. In this study, a new flexible brain electrode array based on 99.99% purity of single-walled carbon nanotubes (SWCNTs) was developed, which has 30 um × 40 um size, about 5.1 kΩ impedance, and 14.01 dB signal-to-noise ratio (SNR). The long-term implantation experiment in vivo in mice shows the proposed brain electrode can maintain stable LFP signal acquisition over 12 weeks while still achieving an SNR of 3.52 dB. The histological analysis results show that SWCNT-based brain electrodes induced minimal tissue damage and showed significantly reduced glial cell responses compared to platinum wire electrodes. Long-term stability comes from SWCNT's biocompatibility and chemical inertness, the electrode's flexible and fine structure. Furthermore, the new brain electrode array can function effectively during 7-Tesla magnetic resonance imaging, enabling the collection of local field potential and even epileptic discharges during the magnetic scan. This study provides a comprehensive study of carbon nanotubes as invasive brain electrodes, providing a new path to address the challenge of long-term brain electrode implantation.

The disruption of LPS in Salmonella Typhimurium provides partial protection against Salmonella Choleraesuis as a live attenuated vaccine.

Interference with the normal synthesis of LPS was shown to enhance immune responses to conserved outer membrane proteins. In the present study, we have constructed three vaccine candidates by deleting four genes (rfaL, rfbB, rffG and wzy) associated with LPS synthesis in the wild-type strain UK-1. Virulence assessment showed that after oral immunization of BALB/c mice, all mutant strains were attenuated and had significantly reduced ability to colonize host tissues compared to the wild-type strain. In addition, all three vaccine candidates induced elevated humoral, mucosal and cellular immune responses against S. Typhimurium and S. Choleraesuis OMPs compared to the PBS-treated group. Finally, immunization of mice with the rSC0136 vaccine candidate strain provided 100 % and 40 % protection against S. Typhimurium and S. Choleraesuis challenge, respectively. These results suggest that the deletion of LPS synthesis-related genes may be an effective strategy against homologous serotypes, but provides only partial protection against heterologous serotypes.

Derivation and calibration of spectral response for a channeled spectropolarimeter.

The channeled spectropolarimeter (CSP) measures the spectrally-resolved Stokes vector from a snapshot by employing spectral modulation. The spectral modulation transfer function (SMTF) of the spectrometer preferentially suppresses the high-frequency channel amplitude in CSP, resulting in reduced measurement accuracy. This paper rigorously derives the SMTF theory and proposes an efficient calibration method for SMTF via channel shifting in a CSP. The SMTF value, obtained by channel shifting, is used to correct the high-frequency channel amplitude. Moreover, alignment and phase errors, as well as nonlinear dispersion, are compensated in situ. Other than rotating the retarder twice, no additional instruments or algorithms are required in the proposed method. In simulations and experiments, the proposed method shows high accuracy, with a maximum root-mean-square error (RMSE) of the reconstructed Stokes spectrum below 0.01, demonstrating its potential for enhancing the simplicity and practicability of Stokes CSP.

Recombinant-attenuated Salmonella enterica serovar Choleraesuis vector expressing the PlpE protein of Pasteurella multocida protects mice from lethal challenge.

BACKGROUND: Bacterial surface proteins play key roles in pathogenicity and often contribute to microbial adhesion and invasion. Pasteurella lipoprotein E (PlpE), a Pasteurella multocida (P. multocida) surface protein, has recently been identified as a potential vaccine candidate. Live attenuated Salmonella strains have a number of potential advantages as vaccine vectors, including immunization with live vector can mimic natural infections by organisms, lead to the induction of mucosal, humoral, and cellular immune responses. In this study, a previously constructed recombinant attenuated Salmonella Choleraesuis (S. Choleraesuis) vector rSC0016 was used to synthesize and secrete the surface protein PlpE of P. multocida to form the vaccine candidate rSC0016(pS-PlpE). Subsequently, the immunogenicity of S. Choleraesuis rSC0016(pS-PlpE) as an oral vaccine to induce protective immunity against P. multocida in mice was evaluated. RESULTS: After immunization, the recombinant attenuated S. Choleraesuis vector can efficiently delivered P. multocida PlpE protein in vivo and induced a specific immune response against this heterologous antigen in mice. In addition, compared with the inactivated vaccine, empty vector (rSC0016(pYA3493)) and PBS immunized groups, the rSC0016(pS-PlpE) vaccine candidate group induced higher antigen-specific mucosal, humoral and mixed Th1/Th2 cellular immune responses. After intraperitoneal challenge, the rSC0016(pS-PlpE) immunized group had a markedly enhanced survival rate (80%), a better protection efficiency than 60% of the inactivated vaccine group, and significantly reduced tissue damage. CONCLUSIONS: In conclusion, our study found that the rSC0016(pS-PlpE) vaccine candidate provided good protection against challenge with wild-type P. multocida serotype A in a mouse infection model, and may potentially be considered for use as a universal vaccine against multiple serotypes of P. multocida in livestock, including pigs.

CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones.

As one of the most consumed stable foods around the world, wheat plays a crucial role in ensuring global food security. The ability to quantify key yield components under complex field conditions can help breeders and researchers assess wheat's yield performance effectively. Nevertheless, it is still challenging to conduct large-scale phenotyping to analyse canopy-level wheat spikes and relevant performance traits, in the field and in an automated manner. Here, we present CropQuant-Air, an AI-powered software system that combines state-of-the-art deep learning (DL) models and image processing algorithms to enable the detection of wheat spikes and phenotypic analysis using wheat canopy images acquired by low-cost drones. The system includes the YOLACT-Plot model for plot segmentation, an optimised YOLOv7 model for quantifying the spike number per m2 (SNpM2) trait, and performance-related trait analysis using spectral and texture features at the canopy level. Besides using our labelled dataset for model training, we also employed the Global Wheat Head Detection dataset to incorporate varietal features into the DL models, facilitating us to perform reliable yield-based analysis from hundreds of varieties selected from main wheat production regions in China. Finally, we employed the SNpM2 and performance traits to develop a yield classification model using the Extreme Gradient Boosting (XGBoost) ensemble and obtained significant positive correlations between the computational analysis results and manual scoring, indicating the reliability of CropQuant-Air. To ensure that our work could reach wider researchers, we created a graphical user interface for CropQuant-Air, so that non-expert users could readily use our work. We believe that our work represents valuable advances in yield-based field phenotyping and phenotypic analysis, providing useful and reliable toolkits to enable breeders, researchers, growers, and farmers to assess crop-yield performance in a cost-effective approach.

Improving Room-Temperature Li-Metal Battery Performance by In Situ Creation of Fast Li+ Transport Pathways in a Polymer-Ceramic Electrolyte.

Composite polymer-ceramic electrolytes have shown considerable potential for high-energy-density Li-metal batteries as they combine the benefits of both polymers and ceramics. However, low ionic conductivity and poor contact with electrodes limit their practical usage. In this study, a highly conductive and stable composite electrolyte with a high ceramic loading is developed for high-energy-density Li-metal batteries. The electrolyte, produced through in situ polymerization and composed of a polymer called poly-1,3-dioxolane in a poly(vinylidene fluoride)/ceramic matrix, exhibits excellent room-temperature ionic conductivity of 1.2 mS cm-1 and high stability with Li metal over 1500 h. When tested in a Li|electrolyte|LiFePO4 battery, the electrolyte delivers excellent cycling performance and rate capability at room temperature, with a discharge capacity of 137 mAh g-1 over 500 cycles at 1 C. Furthermore, the electrolyte not only exhibits a high Li+ transference number of 0.76 but also significantly lowers contact resistance (from 157.8 to 2.1 Ω) relative to electrodes. When used in a battery with a high-voltage LiNi0.8 Mn0.1 Co0.1 O2 cathode, a discharge capacity of 140 mAh g-1 is achieved. These results show the potential of composite polymer-ceramic electrolytes in room-temperature solid-state Li-metal batteries and provide a strategy for designing highly conductive polymer-in-ceramic electrolytes with electrode-compatible interfaces.

Mueller matrix imaging polarimeter at the wavelength of 265 nm.

Mueller matrix imaging polarimeters (MMIPs) have been developed in the wavelength region of >400n m with great potential in many fields yet leaving a void of instrumentation and application in the ultraviolet (UV) region. For the first time to our knowledge, an UV-MMIP is developed for high resolution, sensitivity, and accuracy at the wavelength of 265 nm. A modified polarization state analyzer is designed and applied to suppress stray light for nice polarization images, and the errors of the measured Mueller matrices are calibrated to lower than 0.007 in pixel level. The finer performance of the UV-MMIP is demonstrated by the measurements of unstained cervical intraepithelial neoplasia (CIN) specimens. The contrasts of depolarization images obtained by the UV-MMIP are dramatically improved over those obtained by our previous VIS-MMIP at the wavelength of 650 nm. A distinct evolution of depolarization in normal cervical epithelium tissue, CIN-I, CIN-II, and CIN-III specimens can be observed by the UV-MMIP with mean depolarization promotion by up to 20 times. This evolution could provide important evidence for CIN staging but can hardly be distinguished by the VIS-MMIP. The results prove that the UV-MMIP could be an effective tool in polarimetric applications with higher sensitivity.

Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.

Manipulation of host immune defenses by effector proteins delivered from multiple secretion systems of Salmonella and its application in vaccine research.

Salmonella is an important zoonotic bacterial species and hazardous for the health of human beings and livestock globally. Depending on the host, Salmonella can cause diseases ranging from gastroenteritis to life-threatening systemic infection. In this review, we discuss the effector proteins used by Salmonella to evade or manipulate four different levels of host immune defenses: commensal flora, intestinal epithelial-mucosal barrier, innate and adaptive immunity. At present, Salmonella has evolved a variety of strategies against host defense mechanisms, among which various effector proteins delivered by the secretory systems play a key role. During its passage through the digestive system, Salmonella has to face the intact intestinal epithelial barrier as well as competition with commensal flora. After invasion of host cells, Salmonella manipulates inflammatory pathways, ubiquitination and autophagy processes with the help of effector proteins. Finally, Salmonella evades the adaptive immune system by interfering the migration of dendritic cells and interacting with T and B lymphocytes. In conclusion, Salmonella can manipulate multiple aspects of host defense to promote its replication in the host.

An aerosol deposition based MEMS piezoelectric accelerometer for low noise measurement.

Potentially applied in low-noise applications such as structural health monitoring (SHM), a 1-axis piezoelectric MEMS accelerometer based on aerosol deposition is designed, fabricated, simulated, and measured in this study. It is a cantilever beam structure with a tip proof mass and PZT sensing layer. To figure out whether the design is suitable for SHM, working bandwidth and noise level are obtained via simulation. For the first time, we use aerosol deposition method to deposit thick PZT film during the fabrication process to achieve high sensitivity. In performance measurement, we obtain the charge sensitivity, natural frequency, working bandwidth and noise equivalent acceleration of 22.74 pC/g, 867.4 Hz, 10-200 Hz (within ±5% deviation) and 5.6 µ g / Hz (at 20 Hz). To demonstrate its feasibility for real applications, vibrations of a fan are measured by our designed sensor and a commercial piezoelectric accelerometer, and the results match well with each other. Moreover, shaker vibration measurement with ADXL1001 indicates that the fabricated sensor has a much lower noise level. In the end, we show that our designed accelerometer has good performance compared to piezoelectric MEMS accelerometers in relevant studies and great potential for low-noise applications compared to low-noise capacitive MEMS accelerometers.

Engineered Bacterial Outer Membrane Vesicles with Lipidated Heterologous Antigen as an Adjuvant-Free Vaccine Platform for Streptococcus suis.

Bacterial outer membrane vesicles (OMVs) are considered a promising vaccine platform for their high built-in adjuvanticity and ability to efficiently induce immune responses. OMVs can be engineered with heterologous antigens based on genetic engineering strategies. However, several critical issues should still be validated, including optimal exposure to the OMV surface, increased production of foreign antigens, nontoxicity, and induction of powerful immune protection. In this study, engineered OMVs with the lipoprotein transport machinery (Lpp) were designed to present SaoA antigen as a vaccine platform against Streptococcus suis. The results suggest that Lpp-SaoA fusions can be delivered on the OMV surface and do not have significant toxicity. Moreover, they can be engineered as lipoprotein and significantly accumulated in OMVs at high levels, thus accounting for nearly 10% of total OMV proteins. Immunization with OMVs containing Lpp-SaoA fusion antigen induced strong specific antibody responses and high levels of cytokines, as well as a balanced Th1/Th2 immune response. Furthermore, the decorated OMV vaccination significantly enhanced microbial clearance in a mouse infection model. It was found that antiserum against lipidated OMVs significantly promoted the opsonophagocytic uptake of S. suis in RAW246.7 macrophages. Lastly, OMVs engineered with Lpp-SaoA induced 100% protection against a challenge with 8× the 50% lethal dose (LD50) of S. suis serotype 2 and 80% protection against a challenge with 16× the LD50 in mice. Altogether, the results of this study provide a promising versatile strategy for the engineering of OMVs and suggest that Lpp-based OMVs may be a universal adjuvant-free vaccine platform for important pathogens. IMPORTANCE Bacterial outer membrane vesicles (OMVs) have become a promising vaccine platform due to their excellent built-in adjuvanticity properties. However, the location and amount of the expression of the heterologous antigen in the OMVs delivered by the genetic engineering strategies should be optimized. In this study, we exploited the lipoprotein transport pathway to engineer OMVs with heterologous antigen. Not only did lapidated heterologous antigen accumulate in the engineered OMV compartment at high levels, but also it was engineered to be delivered on the OMV surface, thus leading to the optimal activation of antigen-specific B cells and T cells. Immunization with engineered OMVs induced a strong antigen-specific antibodies in mice and conferred 100% protection against S. suis challenge. In general, the data of this study provide a versatile strategy for the engineering of OMVs and suggest that OMVs engineered with lipidated heterologous antigens may be a vaccine platform for significant pathogens.

Joint learning-based causal relation extraction from biomedical literature.

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 57.0% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.

High-Entropy Perovskites for Energy Conversion and Storage: Design, Synthesis, and Potential Applications.

Perovskites have shown tremendous promise as functional materials for several energy conversion and storage technologies, including rechargeable batteries, (electro)catalysts, fuel cells, and solar cells. Due to their excellent operational stability and performance, high-entropy perovskites (HEPs) have emerged as a new type of perovskite framework. Herein, this work reviews the recent progress in the development of HEPs, including synthesis methods and applications. Effective strategies for the design of HEPs through atomistic computations are also surveyed. Finally, an outlook of this field provides guidance for the development of new and improved HEPs.

LitCovid ensemble learning for COVID-19 multi-label classification.

The Coronavirus Disease 2019 (COVID-19) pandemic has shifted the focus of research worldwide, and more than 10 000 new articles per month have concentrated on COVID-19-related topics. Considering this rapidly growing literature, the efficient and precise extraction of the main topics of COVID-19-relevant articles is of great importance. The manual curation of this information for biomedical literature is labor-intensive and time-consuming, and as such the procedure is insufficient and difficult to maintain. In response to these complications, the BioCreative VII community has proposed a challenging task, LitCovid Track, calling for a global effort to automatically extract semantic topics for COVID-19 literature. This article describes our work on the BioCreative VII LitCovid Track. We proposed the LitCovid Ensemble Learning (LCEL) method for the tasks and integrated multiple biomedical pretrained models to address the COVID-19 multi-label classification problem. Specifically, seven different transformer-based pretrained models were ensembled for the initialization and fine-tuning processes independently. To enhance the representation abilities of the deep neural models, diverse additional biomedical knowledge was utilized to facilitate the fruitfulness of the semantic expressions. Simple yet effective data augmentation was also leveraged to address the learning deficiency during the training phase. In addition, given the imbalanced label distribution of the challenging task, a novel asymmetric loss function was applied to the LCEL model, which explicitly adjusted the negative-positive importance by assigning different exponential decay factors and helped the model focus on the positive samples. After the training phase, an ensemble bagging strategy was adopted to merge the outputs from each model for final predictions. The experimental results show the effectiveness of our proposed approach, as LCEL obtains the state-of-the-art performance on the LitCovid dataset. Database URL: https://github.com/JHnlp/LCEL.

Oral Immunization with Attenuated Salmonella Choleraesuis Expressing the P42 and P97 Antigens Protects Mice against Mycoplasma hyopneumoniae Challenge.

Mycoplasma hyopneumoniae (M. hyopneumoniae, Mhp) is the etiological agent of swine enzootic pneumonia (EP), which has been associated with considerable economic losses due to reduced daily weight gain and feed efficiency. Adhesion to the cilia is important for Mhp to colonize the respiratory epithelium. Therefore, a successful vaccine must induce broad Mhp-specific immune responses at the mucosal surface. Recombinant attenuated Salmonella strains are believed to act as powerful live vaccine vectors that are able to elicit mucosal immune responses against various pathogens. To develop efficacious and inexpensive vaccines against Mhp, the immune responses and protection induced by recombinant attenuated Salmonella vaccines based on the P42 and P97 antigens of Mhp were evaluated. In general, the oral inoculation of recombinant rSC0016(pS-P42) or rSC0016(pS-P97) resulted in strong mucosal immunity, cell-mediated immunity, and humoral immunity, which was a mixed Th1/Th2-type response. In addition, the levels of specific IL-4 and IFN-γ in the immunized mice were increased, and the proliferation of lymphocytes was also enhanced, confirming the production of a good cellular immune response. Finally, both vaccine candidate strains were able to improve the weight loss of mice after a challenge and reduce clinical symptoms, lung pathological damage, and the inflammatory cell infiltration. These results suggest that the delivery of protective antigens with recombinant attenuated Salmonella vectors may be an effective means by which to combat Mhp infection. IMPORTANCE Mhp is the main pathogen of porcine enzootic pneumonia, a highly infectious and economically significant respiratory disease that affects pigs of all ages. As the target tissue of Mhp infections are the mucosal sites of the respiratory tract, the induction of protective immunity at the mucosal tissues is the most efficient strategy by which to block disease transmission. Because the stimulation of mucosal immune responses is efficient, Salmonella-vector oral vaccines are expected to be especially useful against mucosal-invading pathogens. In this study, we expressed the immunogenic proteins of P42 and P97 with the attenuated Salmonella Choleraesuis vector rSC0016, thereby generating a low-cost and more effective vaccine candidate against Mhp by inducing significant mucosal, humoral and cellular immunity. Furthermore, rSC0016(pS-P42) effectively prevents Mhp-induced weight loss and the pulmonary inflammation of mice. Because of the effectiveness of rSC0016(pS-P42) against Mhp infection in mice, this novel vaccine candidate strain shows great potential for its use in the pig breeding industry.

Optimal design for a broadband Stokes polarimeter of liquid crystal variable retarders.

Liquid crystal variable retarders (LCVRs) are the core component for rapid and high-precision broadband polarization detection. Additionally, the ability to suppress noise greatly affects the results of polarization measurements. In this work, a solving optimal design approach is proposed for building a high-performance broadband Stokes polarimeter based on LCVRs, which greatly reduces the influences of data fluctuation from liquid crystals and dispersion on the experimental results. This method relies on evaluation criteria of the condition number (CN) to build a gradual optimization that includes the following three steps: fixing the fast axis angles, meeting the requirements of a wideband, and ensuring a minimum CN. Additionally, with the method of increasing the measurement analysis vector, we ensure the whole band in the low CN and offer a solution to the problem of the difficulty in optimizing the LCVRs caused by the large change of retardance at 490-700 nm. Finally, the rapid and high-precision Stokes measurement of 490-700 nm wavelengths is achieved. We test the performance of the polarimeter after optimization in our simulation and experiment, which shows that the total RMS error is less than 0.032 and the single point error is small. This work not only reduces the influence of LCVR error on the experimental results but also makes it possible to apply LCVRs to 490-700 nm detection.

Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives.

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.

Multi-probe attention neural network for COVID-19 semantic indexing.

BACKGROUND: The COVID-19 pandemic has increasingly accelerated the publication pace of scientific literature. How to efficiently curate and index this large amount of biomedical literature under the current crisis is of great importance. Previous literature indexing is mainly performed by human experts using Medical Subject Headings (MeSH), which is labor-intensive and time-consuming. Therefore, to alleviate the expensive time consumption and monetary cost, there is an urgent need for automatic semantic indexing technologies for the emerging COVID-19 domain. RESULTS: In this research, to investigate the semantic indexing problem for COVID-19, we first construct the new COVID-19 Semantic Indexing dataset, which consists of more than 80 thousand biomedical articles. We then propose a novel semantic indexing framework based on the multi-probe attention neural network (MPANN) to address the COVID-19 semantic indexing problem. Specifically, we employ a k-nearest neighbour based MeSH masking approach to generate candidate topic terms for each input article. We encode and feed the selected candidate terms as well as other contextual information as probes into the downstream attention-based neural network. Each semantic probe carries specific aspects of biomedical knowledge and provides informatively discriminative features for the input article. After extracting the semantic features at both term-level and document-level through the attention-based neural network, MPANN adopts a linear multi-view classifier to conduct the final topic prediction for COVID-19 semantic indexing. CONCLUSION: The experimental results suggest that MPANN promises to represent the semantic features of biomedical texts and is effective in predicting semantic topics for COVID-19 related biomedical articles.