Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

Hydrothermal Hydrolyzation-Driven Topological Transformation of Ni-Co Bimetallic Compounds with Hollow Nanoflower Structure for Optimizing Hydrogen Evolution Catalysis.

Composition screening and structure optimization are two critical factors in improving the electrocatalytic performance of hybrid materials. Herein, we present a straightforward hydrothermal hydrolyzation-topological transformation strategy for the synthesis of a range of Ni-Co bimetallic compounds with a hollow nanoflower structure. Among these Ni-Co compounds, Ni2P/Co2P hollow nanoflowers (HNFs) exhibit the most impressive electrocatalytic activity for the hydrogen evolution reaction (HER), necessitating only an 153 mV overpotential to achieve a current density of 10 mA cm-2 under alkaline conditions. Importantly, this performance remains stable for over 48 h, indicating exceptional durability. The exceptional catalytic performance of Ni2P/Co2P HNFs arises from the synergy between the hybrid Ni2P/Co2P components and the hollow nanoflower structure. The former provides abundant catalytic sites, while electron rearrangement at the heterointerfaces enhances the adsorption/desorption of active species and facilitates electron transfer. The latter contributes to the exposure of catalytic sites, shortening mass and charge transfer routes, and bolstering structural stability during prolonged electrocatalysis. This research offers valuable insights into the screening and optimization of advanced hybrid electrocatalysts, holding significant promise for applications in the emerging field of new energy technologies.

CRISPR-Cas9-Mediated Cytosine Base Editing Screen for the Functional Assessment of CALR Intron Variants in Japanese Encephalitis Virus Replication.

The Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus that causes viral encephalitis in humans, pigs and other mammals across Asia and the Western Pacific. Genetic screening tools such as CRISPR screening, DNA sequencing and RNA interference have greatly improved our understanding of JEV replication and its potential antiviral approaches. However, information on exon and intron mutations associated with JEV replication is still scanty. CRISPR-Cas9-mediated cytosine base editing can efficiently generate C: G-to-T: A conversion in the genome of living cells. One intriguing application of base editing is to screen pivotal variants for gene function that is yet to be achieved in pigs. Here, we illustrate that CRISPR-Cas9-mediated cytosine base editor, known as AncBE4max, can be used for the functional analysis of calreticulin (CALR) variants. We conducted a CRISPR-Cas9-mediated cytosine base editing screen using 457 single guide RNAs (sgRNAs) against all exons and introns of CALR to identify loss-of-function variants involved in JEV replication. We unexpectedly uncovered that two enriched sgRNAs targeted the same site in intron-2 of the CALR gene. We found that mutating four consecutive G bases in the intron-2 of the CALR gene to four A bases significantly inhibited JEV replication. Thus, we established a CRISPR-Cas9-mediated cytosine-base-editing point mutation screening technique in pigs. Our results suggest that CRISPR-mediated base editing is a powerful tool for identifying the antiviral functions of variants in the coding and noncoding regions of the CALR gene.

Sensitive and Specific Exonuclease III-Assisted Recombinase-Aided Amplification Colorimetric Assay for Rapid Detection of Nucleic Acids.

The development of a contamination-free and on-site nucleic acid detection platform with high sensitivity and specificity but low-cost for the detection of pathogenic nucleic acids is critical for infectious disease diagnosis and surveillance. In this study, we combined the recombinase-aided amplification (RAA) with the exonuclease III (Exo III)-assisted signal amplification into a platform for sensitive and specific detection of nucleic acids of African swine fever virus (ASFV). We found that this platform enabled a naked eye visual detection of ASFV at a detection limit as low as 2 copies/µL in 30 min. As expected, no cross-reactivity was observed with other porcine viruses. In addition, to avoid aerosol contamination, a one-tube RAA-Exo III colorimetric assay was also established for the accurate detection of ASFV in clinical samples. Taken together, we developed a rapid, instrument-free, and low-cost Exo III-assisted RAA colorimetric-assay-based nucleic acid detection platform.

Development of a Naked Eye CRISPR-Cas12a and -Cas13a Multiplex Point-of-Care Detection of Genetically Modified Swine.

The Rapid Visual CRISPR (RAVI-CRISPR) assay employs Cas12a and Cas13a enzymes for precise gene detection in a sample. However, RAVI-CRISPR is limited in single-tube multiplex detection applications due to the lack of specific single-strand (ss) DNA-fluorescently quenched (ssDNA-FQ) and RNA-fluorescently quenched (ssRNA-FQ) reporter cleavage mechanisms. We report the development of a sensitive and specific dual-gene Cas12a and Cas13a diagnostic system. To optimize the application for field testing, we designed a portable multiplex fluorescence imaging assay that could distinguish test results with the naked eye. Herein, dual gene amplified products from multiplex recombinase polymerase amplification (RPA) were simultaneously detected in a single tube using Cas12a and Cas13a enzymes. The resulting orthogonal DNA and RNA collateral cleavage specifically distinguishes individual and mixed ssDNA-FQ and ssRNA-FQ reporters using the green-red-yellow, fluorescent signal conversion reaction system, detectable with portable blue and ultraviolet (UV) light transilluminators. As a proof-of-concept, reliable multiplex RAVI-CRISPR detection of genome-edited pigs was demonstrated, exhibiting 100% sensitivity and specificity for the analysis of CD163 knockout, lactoferrin (LF) knock-in, and wild-type pig samples. This portable naked-eye multiplex RAVI-CRISPR detection platform can provide accurate point-of-care screening of genetically modified animals and infectious diseases in resource-limited settings.

Detecting Melanocortin 1 Receptor Gene's SNPs by CRISPR/enAsCas12a.

Beyond its powerful genome-editing capabilities, the CRISPR/Cas system has opened up a new era of molecular diagnostics due to its highly specific base recognition and trans-cleavage activity. However, most CRISPR/Cas detection systems are mainly used to detect nucleic acids of bacteria or viruses, while the application of single nucleotide polymorphism (SNP) detection is limited. The MC1R SNPs were investigated by CRISPR/enAsCas12a and are not limited to the protospacer adjacent motif (PAM) sequence in vitro. Specifically, we optimized the reaction conditions, which proved that the enAsCas12a has a preference for divalent magnesium ion (Mg2+) and can effectively distinguish the genes with a single base difference in the presence of Mg2+, and the Melanocortin l receptor (MC1R) gene with three kinds of SNP sites (T305C, T363C, and G727A) was quantitatively detected. Since the enAsCas12a is not limited by PAM sequence in vitro, the method shown here can extend this extraordinary CRISPR/enAsCas12a detection system to other SNP targets, thus providing a general SNP detection toolbox.

A Simple, Affordable, and Rapid Visual CRISPR-Based Field Test for Sex Determination of Earlier Porcine Embryos and Pork Products.

Sex selection technologies have immensely impacted swine production globally. Conventional earlier embryo sex identification methods require professional technicians and sophisticated laboratory instruments. Rapid on-site gender identification of porcine embryos and pork products remains challenging. In this study, we developed a CRISPR/Cas12a-based fluorescence visualization point-of-care sex determination test that is rapid, accurate and easy to implement on-site. The CRISPR/Cas12a assay coupled with either the polymerase chain reaction (PCR) or loop-mediated isothermal amplification (LAMP) employs precisely designed primers and single-guide RNAs targeting the sex-determining region Y (SRY) and the zinc finger protein X-linked (ZFX) genes. PCR and LAMP amplicons were cleaved with the subsequent generation of fluorescing products detectable with portable blue and ultraviolet light transilluminators. Approximately two copies per microliter of the ZFX and SRY genes were detected using the RApid VIsual CRISPR (RAVI-CRISPR) assay. This method is a sensitive, inexpensive, versatile, and point-of-care test. The technology has other potential applications like determining the sex of diverse livestock species, detecting livestock disease-causing pathogens and evaluating the quality of meat products.

A one-tube rapid visual CRISPR assay for the field detection of Japanese encephalitis virus.

Early and rapid detection of Japanese encephalitis virus (JEV) is necessary for timely preventive and control measures. However, JEV RNA detection remains challenging due to the low level of viremia. In this study, a RApid VIsual CRISPR (RAVI-CRISPR) assay based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) and CRISPR/Cas12a targeting was developed for easy detection of JEV in the field. We showed successful detection of 8.97 or more copies of the C gene sequence of JEV RNA within approximately 60 min. This assay also displayed no cross-reactivity with other porcine pathogens. We applied our one-tube RAVI-CRISPR assay to 18 brain tissue sample for JE diagnosis. The results from both fluorescence intensity measurements and directly naked-eye visualization were consistent with those from real-time PCR analysis. Taken together, our results showed that one-tube RAVI-CRISPR assay is robust, convenient, sensitive, specific, affordable, and potentially adaptable to on-site detection or surveillance of JEV in clinical and vector samples.

Sensitive and Specific Detection of Lumpy Skin Disease Virus in Cattle by CRISPR-Cas12a Fluorescent Assay Coupled with Recombinase Polymerase Amplification.

Lumpy skin disease (LSD) is a severe and highly infectious pox disease of cattle caused by the lumpy skin disease virus (LSDV). To facilitate early control of LSD, this study aimed to develop a new rapid on-site LSDV detection method using an orf068 gene-based recombinase polymerase amplification assay (RPA) coupled with a CRISPR-Cas12a-based fluorescence assay (RPA-Cas12a-fluorescence assay). The results showed that the sensitivity of our RPA-Cas12a-fluorescence assay for detecting LSDV orf068 gene reached 5 copies/µL with plasmid as a template, and 102 TCID50/mL with viral genomic DNA as a template. No cross-reaction with other common bovine viruses was observed. Further, an on-site RPA-Cas12a-fluorescence assay of 40 clinical samples from cattle with or without LSD showed a diagnostic sensitivity of 96.3% (95% CI: 81.0-99.9%) and specificity of 92.31% (95% CI: 62.1-99.6%), which was close to those of the quantitative PCR assay. Therefore, our RPA-Cas12a-fluorescence assay has promising prospects in on-site rapid LSDV detection.

An Inexpensive CRISPR-Based Point-of-Care Test for the Identification of Meat Species and Meat Products.

The growing demand for and supply of meat and meat products has led to a proportional increase in cases of meat adulteration. Adulterated meat poses serious economic and health consequences globally. Current laboratory methods for meat species identification require specialized equipment with limited field applications. This study developed an inexpensive, point-of-care Loop-Mediated Isothermal Amplification (LAMP)-CRISPR/Cas12a colorimetric assay to detect meat species using a Texas Red-labelled single-strand (ssDNA) reporter. As low as 1.0 pg/µL of the porcine NADH4, the chicken NADH dehydrogenase subunit 2 (ND2) and the duck D-loop genes was detectable under white, blue and ultraviolet light. The test turnaround time from DNA extraction to visualization was approximately 40 min. The assay accurately detected pure and mixed-meat products in the laboratory (n = 15) and during a pilot point-of-care test (n = 8) in a food processing factory. The results are 100% reproducible using lateral flow detection strips and the real-time PCR detection instrument. This technology is fully deployable and usable in any standard room. Thus, our study demonstrates that this method is a straightforward, specific, sensitive, point-of-care test (POCT) adaptable to various outlets such as customs, quarantine units and meat import/export departments.

Detection of Four Porcine Enteric Coronaviruses Using CRISPR-Cas12a Combined with Multiplex Reverse Transcriptase Loop-Mediated Isothermal Amplification Assay.

Porcine enteric coronaviruses have caused immense economic losses to the global pig industry, and pose a potential risk for cross-species transmission. The clinical symptoms of the porcine enteric coronaviruses (CoVs) are similar, making it difficult to distinguish between the specific pathogens by symptoms alone. Here, a multiplex nucleic acid detection platform based on CRISPR/Cas12a and multiplex reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) was developed for the detection of four diarrhea CoVs: porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV), and swine acute diarrhea syndrome coronavirus (SADS-CoV). With this strategy, we realized a visual colorimetric readout visible to the naked eye without specialized instrumentation by using a ROX-labeled single-stranded DNA-fluorescence-quenched (ssDNA-FQ) reporter. Our method achieved single-copy sensitivity with no cross-reactivity in the identification and detection of the target viruses. In addition, we successfully detected these four enteric CoVs from RNA of clinical samples. Thus, we established a rapid, sensitive, and on-site multiplex molecular differential diagnosis technology for porcine enteric CoVs.

Few-Shot Domain Adaptation via Mixup Optimal Transport.

Unsupervised domain adaptation aims to learn a classification model for the target domain without any labeled samples by transferring the knowledge from the source domain with sufficient labeled samples. The source and the target domains usually share the same label space but are with different data distributions. In this paper, we consider a more difficult but insufficient-explored problem named as few-shot domain adaptation, where a classifier should generalize well to the target domain given only a small number of examples in the source domain. In such a problem, we recast the link between the source and target samples by a mixup optimal transport model. The mixup mechanism is integrated into optimal transport to perform the few-shot adaptation by learning the cross-domain alignment matrix and domain-invariant classifier simultaneously to augment the source distribution and align the two probability distributions. Moreover, spectral shrinkage regularization is deployed to improve the transferability and discriminability of the mixup optimal transport model by utilizing all singular eigenvectors. Experiments conducted on several domain adaptation tasks demonstrate the effectiveness of our proposed model dealing with the few-shot domain adaptation problem compared with state-of-the-art methods.

Generative Mixup Networks for Zero-Shot Learning.

Zero-shot learning casts light on lacking unseen class data by transferring knowledge from seen classes via a joint semantic space. However, the distributions of samples from seen and unseen classes are usually imbalanced. Many zero-shot learning methods fail to obtain satisfactory results in the generalized zero-shot learning task, where seen and unseen classes are all used for the test. Also, irregular structures of some classes may result in inappropriate mapping from visual features space to semantic attribute space. A novel generative mixup networks with semantic graph alignment is proposed in this article to mitigate such problems. To be specific, our model first attempts to synthesize samples conditioned with class-level semantic information as the prototype to recover the class-based feature distribution from the given semantic description. Second, the proposed model explores a mixup mechanism to augment training samples and improve the generalization ability of the model. Third, triplet gradient matching loss is developed to guarantee the class invariance to be more continuous in the latent space, and it can help the discriminator distinguish the real and fake samples. Finally, a similarity graph is constructed from semantic attributes to capture the intrinsic correlations and guides the feature generation process. Extensive experiments conducted on several zero-shot learning benchmarks from different tasks prove that the proposed model can achieve superior performance over the state-of-the-art generalized zero-shot learning.

Rapid Visual CRISPR Assay: A Naked-Eye Colorimetric Detection Method for Nucleic Acids Based on CRISPR/Cas12a and a Convolutional Neural Network.

Rapid diagnosis based on naked-eye colorimetric detection remains challenging, but it could build new capacities for molecular point-of-care testing (POCT). In this study, we evaluated the performance of 16 types of single-stranded DNA-fluorophore-quencher (ssDNA-FQ) reporters for use with clusters of regularly spaced short palindrome repeats (CRISPR)/Cas12a-based visual colorimetric assays. Among them, nine ssDNA-FQ reporters were found to be suitable for direct visual colorimetric detection, with especially very strong performance using ROX-labeled reporters. We optimized the reaction concentrations of these ssDNA-FQ reporters for a naked-eye read-out of assay results (no transducing component required for visualization). In particular, we developed a convolutional neural network algorithm to standardize and automate the analytical colorimetric assessment of images and integrated this into the MagicEye mobile phone software. A field-deployable assay platform named RApid VIsual CRISPR (RAVI-CRISPR) based on a ROX-labeled reporter with isothermal amplification and CRISPR/Cas12a targeting was established. We deployed RAVI-CRISPR in a single tube toward an instrument-less colorimetric POCT format that required only a portable rechargeable hand warmer for incubation. The RAVI-CRISPR was successfully used for the high-sensitivity detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and African swine fever virus (ASFV). Our study demonstrates this RAVI-CRISPR/MagicEye system to be suitable for distinguishing different pathogenic nucleic acid targets with high specificity and sensitivity as the simplest-to-date platform for rapid pen- or bed-side testing.

Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation.

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly focus on practical knowledge transfer through domain alignment. Unfortunately, they ignore the fairness issue when the auxiliary source is extremely imbalanced across different categories, which results in severe under-presented knowledge adaptation of minority source set. To this end, we propose a Towards Fair Knowledge Transfer (TFKT) framework to handle the fairness challenge in imbalanced cross-domain learning. Specifically, a novel cross-domain knowledge propagation technique is proposed with the guidance of within-source and cross-domain structure graphs to smooth the manifold of the minority source set. Besides, a cross-domain fulfillment augmentation strategy is exploited achieve domain adaptation. Moreover, hybrid distinct classifiers and cross-domain prototype alignment are adopted to seek a more robust classifier boundary and mitigate the domain shift. Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge. Extensive experiments over two popular benchmarks have verified the effectiveness of our proposed model by comparing to existing state-of-the-art DA models, and especially our model significantly improves over 20% on two benchmarks in terms of the overall accuracy.

Bacterial strain for bast fiber crops degumming and its bio-degumming technique.

The research and development of bio-degumming technology is under a slow progress due to the shortage of proper efficient bacterial strains and processes. A degumming bacterial strain-Pectobacterium wasabiae (PW)-with broad-spectrum degumming abilities was screened out in this study. After the fermentation for 12 h, the residual gum contents of kenaf bast, ramie bast, hemp bast, flax bast, and Apocynum venetum bast were all lower than 15%. This bacterial strain could realize the simultaneous extracellular secretion of pectinase, mannase, and xylanase with the maximum enzyme activity levels of 130.25, 157.58, and 115.24 U/mL, respectively. The optimal degumming conditions of this bacterial strain were as follows: degumming time of 12 h, bath ratio of 1:10, temperature of 33 °C, and inoculum size of 2%. After the bio-degumming through this bacterial strain, the COD in wastewater was below 4000 mg/L, which was over 60% lower than that in boiling-off wastewater generated by chemical degumming. This technology achieves higher efficiency, higher quality, and lower pollution.

Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning.

Zero-shot learning has received great interest in visual recognition community. It aims to classify new unobserved classes based on the model learned from observed classes. Most zero-shot learning methods require pre-provided semantic attributes as the mid-level information to discover the intrinsic relationship between observed and unobserved categories. However, it is impractical to annotate the enriched label information of the observed objects in real-world applications, which would extremely hurt the performance of zero-shot learning with limited labeled seen data. To overcome this obstacle, we develop a Low-rank Semantics Grouping (LSG) method for zero-shot learning in a semi-supervised fashion, which attempts to jointly uncover the intrinsic relationship across visual and semantic information and recover the missing label information from seen classes. Specifically, the visual-semantic encoder is utilized as projection model, low-rank semantic grouping scheme is explored to capture the intrinsic attributes correlations and a Laplacian graph is constructed from the visual features to guide the label propagation from labeled instances to unlabeled ones. Experiments have been conducted on several standard zero-shot learning benchmarks, which demonstrate the efficiency of the proposed method by comparing with state-of-the-art methods. Our model is robust to different levels of missing label settings. Also visualized results prove that the LSG can distinguish the test unseen classes more discriminative.

Application of CRISPR-Cas12a Enhanced Fluorescence Assay Coupled with Nucleic Acid Amplification for the Sensitive Detection of African Swine Fever Virus.

African swine fever (ASF) is one of the most severe diseases of pigs. In this study, a CRISPR-Cas12a (also known as Cpf1) system coupled with nucleic acid amplification was optimized for the detection of ASF virus (ASFV). Two novel single-stranded DNA-fluorophore-quencher (ssDNA-FQ) reporters were developed to increase the brightness of the fluorescent signal for the visualization of nucleic acid detection. The CRISPR-Cas12a system was used to simultaneously cleave the polymerase chain reaction (PCR) or loop-mediated isothermal amplification (LAMP) amplicons and the newly developed ssDNA-FQ reporter, resulting in fluorescence that could be easily detected in multiple platforms, especially on cheap and portable blue or UV light transilluminators. This specific cleavage with fluorescence reveals the presence of the amplicon and confirms its identity, thereby preventing false-positive test results from nonspecific amplicons. This method is also uninterfered by the presence of large amounts of irrelevant background DNA and displays no cross-reactivity with other porcine DNA or RNA viruses. When coupled with LAMP, the Cas12a platform can detect a plasmid containing p72 with as few as 2 copies/µL reaction. Our results indicate that the CRISPR-Cas12a enhanced fluorescence assay coupled with nucleic acid amplification is robust, convenient, specific, confirmatory, affordable, and potentially adaptable for ASF diagnosis.

A Discrete-Time Projection Neural Network for Sparse Signal Reconstruction With Application to Face Recognition.

This paper deals with sparse signal reconstruction by designing a discrete-time projection neural network. Sparse signal reconstruction can be converted into an L1 -minimization problem, which can also be changed into the unconstrained basis pursuit denoising problem. To solve the L1 -minimization problem, an iterative algorithm is proposed based on the discrete-time projection neural network, and the global convergence of the algorithm is analyzed by using Lyapunov method. Experiments on sparse signal reconstruction and several popular face data sets are organized to illustrate the effectiveness and performance of the proposed algorithm. The experimental results show that the proposed algorithm is not only robust to different levels of sparsity and amplitude of signals and the noise pixels but also insensitive to the diverse values of scalar weight. Moreover, the value of the step size of the proposed algorithm is close to 1/2, thus a fast convergence rate is potentially possible. Furthermore, the proposed algorithm achieves better classification performance compared with some other algorithms for face recognition.

3-(2,5-Di-methyl-phen-yl)-8-meth-oxy-2-oxo-1-aza-spiro-[4.5]dec-3-en-4-yl 3-(2-bromo-4-fluoro-phen-yl)acrylate.

In the title compound, C27H27BrFNO4, which is an inhibitor of acetyl-CoA carboxyl-ase, the cyclo-hexane ring displays a chair comformation with the spiro-C and meth-oxy-bearing C atoms deviating by 0.681 (7) and -0.655 (1) Å, resppectively, from the mean plane formed by the other four C atoms of the spiro-C6 ring. The mean planes of the cyclo-hexane and 2-bromo-4-fluoro-phenyl rings are nearly perpendicular to that of the pyrrolidine ring, making dihedral angles 89.75 (6) and 87.60 (9)°, respectively. In the crystal, mol-ecules are linked via pairs of N-H⋯O hydrogen bonds, forming inversion dimers.