Porcine deltacoronavirus nucleocapsid protein antagonizes JAK-STAT signaling pathway by targeting STAT1 through KPNA2 degradation.

Porcine deltacoronavirus (PDCoV) is an enteric pathogenic coronavirus that causes acute and severe watery diarrhea in piglets and has the ability of cross-species transmission, posing a great threat to swine production and public health. The interferon (IFN)-mediated signal transduction represents an important component of virus-host interactions and plays an essential role in regulating viral infection. Previous studies have suggested that multifunctional viral proteins encoded by coronaviruses antagonize the production of IFN via various means. However, the function of these viral proteins in regulating IFN-mediated signaling pathways is largely unknown. In this study, we demonstrated that PDCoV and its encoded nucleocapsid (N) protein antagonize type I IFN-mediated JAK-STAT signaling pathway. We identified that PDCoV infection stimulated but delayed the production of IFN-stimulated genes (ISGs). In addition, PDCoV inhibited JAK-STAT signal transduction by targeting the nuclear translocation of STAT1 and ISGF3 formation. Further evidence showed that PDCoV N is the essential protein involved in the inhibition of type I IFN signaling by targeting STAT1 nuclear translocation via its C-terminal domain. Mechanistically, PDCoV N targets STAT1 by interacting with it and subsequently inhibiting its nuclear translocation. Furthermore, PDCoV N inhibits STAT1 nuclear translocation by specifically targeting KPNA2 degradation through the lysosomal pathway, thereby inhibiting the activation of downstream sensors in the JAK-STAT signaling pathway. Taken together, our results reveal a novel mechanism by which PDCoV N interferes with the host antiviral response.IMPORTANCEPorcine deltacoronavirus (PDCoV) is a novel enteropathogenic coronavirus that receives increased attention and seriously threatens the pig industry and public health. Understanding the underlying mechanism of PDCoV evading the host defense during infection is essential for developing targeted drugs and effective vaccines against PDCoV. This study demonstrated that PDCoV and its encoded nucleocapsid (N) protein antagonize type I interferon signaling by targeting STAT1, which is a crucial signal sensor in the JAK-STAT signaling pathway. Further experiments suggested that PDCoV N-mediated inhibition of the STAT1 nuclear translocation involves the degradation of KPNA2, and the lysosome plays a role in KPNA2 degradation. This study provides new insights into the regulation of PDCoV N in the JAK-STAT signaling pathway and reveals a novel mechanism by which PDCoV evades the host antiviral response. The novel findings may guide us to discover new therapeutic targets and develop live attenuated vaccines for PDCoV infection.

Application of Surface Wettability to Control Spreading of an Impacting Droplet.

While the simplest outcome of a normal impact on a flat stationary solid surface is radially symmetric spreading, it is important to note that asymmetric spreading can intrinsically occur with a tangential velocity along the surface. However, no previous attempt has been made to restore the symmetry of a lamella that intrinsically spreads asymmetrically. Adjusting the lamella's asymmetric shape to a symmetric one is achieved in this work by varying wettability to affect the receding velocity of the contact line, according to the Taylor-Culick theory. Here we theoretically and practically show how restoring the symmetry can be achieved. Theoretically we built a framework to map the needed receding velocity at every given point of the contact line to allow for symmetry to be restored, and then this framework was applied to generate a wetting map that shows how at each local the wettability of the surface needs to be defined. Simulated results confirmed the effectiveness of our framework and identified the envelope of its applicability. Next, to apply the idea experimentally, the wetting map was transformed to a single wettability contrast area dubbed the "patch". Experimental results showed the effectiveness of the patch design in correcting the asymmetric spreading lamella for water droplets impacting a surface for the following Weber number conditions: Wen ≤ 300, Wet ≤ 300, and 0.51 ≤ Wen/Wet ≤ 2.04.

Engineering carboxylic acid reductase for selective synthesis of medium-chain fatty alcohols in yeast.

Medium-chain fatty alcohols (MCFOHs, C6 to C12) are potential substitutes for fossil fuels, such as diesel and jet fuels, and have wide applications in various manufacturing processes. While today MCFOHs are mainly sourced from petrochemicals or plant oils, microbial biosynthesis represents a scalable, reliable, and sustainable alternative. Here, we aim to establish a Saccharomyces cerevisiae platform capable of selectively producing MCFOHs. This was enabled by tailoring the properties of a bacterial carboxylic acid reductase from Mycobacterium marinum (MmCAR). Extensive protein engineering, including directed evolution, structure-guided semirational design, and rational design, was implemented. MmCAR variants with enhanced activity were identified using a growth-coupled high-throughput screening assay relying on the detoxification of the enzyme's substrate, medium-chain fatty acids (MCFAs). Detailed characterization demonstrated that both the specificity and catalytic activity of MmCAR was successfully improved and a yeast strain harboring the best MmCAR variant generated 2.8-fold more MCFOHs than the strain expressing the unmodified enzyme. Through deletion of the native MCFA exporter gene TPO1, MCFOH production was further improved, resulting in a titer of 252 mg/L for the final strain, which represents a significant improvement in MCFOH production in minimal medium by S. cerevisiae.

Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms.

Mildew of maize seeds may affect their germination rates and reduce crop quality. It is crucial to classify maize seeds efficiently and without destroying their original structure. This study aimed to establish hyperspectral datasets using hyperspectral imaging (HSI) of maize seeds with different degrees of mildew and then classify them using spectral characteristics and machine learning algorithms. Initially, the images were processed with Otus and morphological operations. Each seed's spectral features were extracted based on its coding, its edge, region of interest (ROI), and original pixel coding. Random forest (RF) models were optimized using the sparrow search algorithm (SSA), which is incapable of escaping the local optimum; hence, it was optimized using a modified reverse sparrow search algorithm (JYSSA) strategy. This reverse strategy selects the top 10% as the elite group, allowing us to escape from local optima while simultaneously expanding the range of the sparrow search algorithm's optimal solution. Finally, the JYSSA-RF algorithm was applied to the validation set, with 96% classification accuracy, 100% precision, and a 93% recall rate. This study provides novel ideas for future nondestructive detection of seeds and moldy seed selection by combining hyperspectral imaging and JYSSA algorithms based on optimized RF.

Simulation of Acute Pulmonary Hypertension in Beagle Dogs.

Acoustic cardiography (AC) combined with heart sound (HS) recording and electrocardiogram (ECG) provides a noninvasive and inexpensive way to understand the electrical mechanical activity of the heart. Pulmonary artery stenosis can cause hemodynamic abnormalities that might lead to pulmonary hypertension (PH). In this paper, we examined the relationships between the acoustic characteristics of the AC and hemodynamic changes in a beagle dog model of PH.Four healthy beagle dogs were injected with the prostaglandin endoperoxide receptor agonist U-44069 to induce acute PH states. AC was employed to analyze the process of pre-PH, intra-PH, and post-PH. Right ventricular blood pressure (RVBP) was measured via right cardiac catheterization, an invasive method performed in parallel for comparative hemodynamic evaluation. As RVBP increased or decreased, the HS features changed accordingly during acute PH occurrence and development. Right ventricular systolic blood pressure (RVSBP) significantly correlated with the minimum of the first HS (S1) amplitude (correlation coefficient (CC) = -0.82), energy of the S1 (CC = 0.86), energy of the second HS (S2) (CC = 0.67), entropy of the S1 (CC = -0.94), and ratio of electromechanical systolic time (EMST) to the cardiac cycle time (CC = 0.81). The two techniques (AC [HSs and ECG] versus right cardiac catheterization [RVBP]) were significantly correlated. Especially, the diastolic filling time (DFT) had a significant relationship with the right ventricular diastolic time (RVDT) (CC = 0.97), perfusion time (PT) (CC = 0.96), and cardiac cycle time (RR) (CC = 0.96). The CCs between the RVDT and the max dp/dt to min dp/dt, the EMST and the Q to min dp/dt, and the electromechanical activation time and the Q to max dp/dt were 0.95, 0.99, and 0.86, respectively. Furthermore, the logistic regression model with different combinations was used to identify the effective features for monitoring hemodynamic and pathophysiologic conditions.AC provided significant insight into mechanical dysfunction in a rapid and noninvasive way that could be used for early screening of PH.

The honeysuckle genome provides insight into the molecular mechanism of carotenoid metabolism underlying dynamic flower coloration.

Lonicera japonica is a widespread member of the Caprifoliaceae (honeysuckle) family utilized in traditional medical practices. This twining vine honeysuckle also is a much-sought ornamental, in part due to its dynamic flower coloration, which changes from white to gold during development. The molecular mechanism underlying dynamic flower coloration in L. japonica was elucidated by integrating whole genome sequencing, transcriptomic analysis and biochemical assays. Here, we report a chromosome-level genome assembly of L. japonica, comprising nine pseudochromosomes with a total size of 843.2 Mb. We also provide evidence for a whole-genome duplication event in the lineage leading to L. japonica, which occurred after its divergence from Dipsacales and Asterales. Moreover, gene expression analysis not only revealed correlated expression of the relevant biosynthetic genes with carotenoid accumulation, but also suggested a role for carotenoid degradation in L. japonica's dynamic flower coloration. The variation of flower color is consistent with not only the observed carotenoid accumulation pattern, but also with the release of volatile apocarotenoids that presumably serve as pollinator attractants. Beyond novel insights into the evolution and dynamics of flower coloration, the high-quality L. japonica genome sequence also provides a foundation for molecular breeding to improve desired characteristics.

Zika virus NS5 protein inhibits cell growth and invasion of glioma.

Glioma is the most common primary brain tumor with high mortality. Given the poor outcomes with standard-of-care treatments, novel treatment strategies are needed. Oncolytic viral therapy for glioma has developed as an exciting therapeutic method in recent years. Zika virus, a member of flavivirus family, has oncolytic activity against glioma cells but the mechanism is unknown. Here, we aimed to determine which viral protein might play a critical role in mitigating glioma cell growth. We examined the tumor suppressor function of four nonstructural proteins NS1, NS3, NS4B and NS5 in human glioma cell line U87. As a result, we found that only NS5 significantly inhibited proliferation, migration and invasion of U87 cells. Moreover, expression of NS5 suppressed tumorigenicity of mouse GL261 glioma cell in vivo. Our findings provide some clues for further exploration of oncolytic Zika virus in the treatment of glioma.

Strong Charge Transfer at 2H-1T Phase Boundary of MoS2 for Superb High-Performance Energy Storage.

Transition metal dichalcogenides exhibit several different phases (e.g., semiconducting 2H, metallic 1T, 1T') arising from the collective and sluggish atomic displacements rooted in the charge-lattice interaction. The coexistence of multiphase in a single sheet enables ubiquitous heterophase and inhomogeneous charge distribution. Herein, by combining the first-principles calculations and experimental investigations, a strong charge transfer ability at the heterophase boundary of molybdenum disulfide (MoS2 ) assembled together with graphene is reported. By modulating the phase composition in MoS2 , the performance of the nanohybrid for energy storage can be modulated, whereby remarkable gravimetric and volumetric capacitances of 272 F g-1 and 685 F cm-3 are demonstrated. As a proof of concept for energy application, a flexible solid-state asymmetric supercapacitor is constructed with the MoS2 -graphene heterolayers, which shows superb energy and power densities (46.3 mWh cm-3 and 3.013 W cm-3 , respectively). The present work demonstrates a new pathway for efficient charge flow and application in energy storage by engineering the phase boundary and interface in 2D materials of transition metal dichalcogenides.

2D Metal-Organic Frameworks Derived Nanocarbon Arrays for Substrate Enhancement in Flexible Supercapacitors.

Direct assembling of active materials on carbon cloth (CC) is a promising way to achieve flexible electrodes for energy storage. However, the overall surface area and electrical conductivity of such electrodes are usually limited. Herein, 2D metal-organic framework derived nanocarbon nanowall (MOFC) arrays are successfully developed on carbon cloth by a facile solution + carbonization process. Upon growth of the MOFC arrays, the sites for growth of the active materials are greatly increased, and the equivalent series resistance is decreased, which contribute to the enhancement of the bare CC substrate. After decorating ultrathin flakes of MnO2 and Bi2 O3 on the flexible CC/MOFC substrate, the hierarchical electrode materials show an abrupt improvement of areal capacitances by around 50% and 100%, respectively, compared to those of the active materials on pristine carbon cloth. A flexible supercapacitor can be further assembled using two hierarchical electrodes, which demonstrates an energy density of 124.8 µWh cm-2 at the power density of 2.55 mW cm-2 .

Heterologous transporter expression for improved fatty alcohol secretion in yeast.

The yeast Saccharomyces cerevisiae is an attractive host for industrial scale production of biofuels including fatty alcohols due to its robustness and tolerance towards harsh fermentation conditions. Many metabolic engineering strategies have been applied to generate high fatty alcohol production strains. However, impaired growth caused by fatty alcohol accumulation and high cost of extraction are factors limiting large-scale production. Here, we demonstrate that the use of heterologous transporters is a promising strategy to increase fatty alcohol production. Among several plant and mammalian transporters tested, human FATP1 was shown to mediate fatty alcohol export in a high fatty alcohol production yeast strain. An approximately five-fold increase of fatty alcohol secretion was achieved. The results indicate that the overall cell fitness benefited from fatty alcohol secretion and that the acyl-CoA synthase activity of FATP1 contributed to increased cell growth as well. This is the first study that enabled an increased cell fitness for fatty alcohol production by heterologous transporter expression in yeast, and this investigation indicates a new potential function of FATP1, which has been known as a free fatty acid importer to date. We furthermore successfully identified the functional domain of FATP1 involved in fatty alcohol export through domain exchange between FATP1 and another transporter, FATP4. This study may facilitate a successful commercialization of fatty alcohol production in yeast and inspire the design of novel cell factories.

Metabolic engineering of Saccharomyces cerevisiae for production of germacrene A, a precursor of beta-elemene.

Beta-elemene, a sesquiterpene and the major component of the medicinal herb Curcuma wenyujin, has antitumor activity against various types of cancer and could potentially serve as a potent antineoplastic drug. However, its current mode of production through extraction from plants has been inefficient and suffers from limited natural resources. Here, we engineered a yeast cell factory for the sustainable production of germacrene A, which can be transformed to beta-elemene by a one-step chemical reaction in vitro. Two heterologous germacrene A synthases (GASs) converting farnesyl pyrophosphate (FPP) to germacrene A were evaluated in yeast for their ability to produce germacrene A. Thereafter, several metabolic engineering strategies were used to improve the production level. Overexpression of truncated 3-hydroxyl-3-methylglutaryl-CoA reductase and fusion of FPP synthase with GAS, led to a sixfold increase in germacrene A production in shake-flask culture. Finally, 190.7 mg/l of germacrene A was achieved. The results reported in this study represent the highest titer of germacrene A reported to date. These results provide a basis for creating an efficient route for further industrial application re-placing the traditional extraction of beta-elemene from plant sources.

RNA interference-mediated repression of SmCPS (copalyldiphosphate synthase) expression in hairy roots of Salvia miltiorrhiza causes a decrease of tanshinones and sheds light on the functional role of SmCPS.

Tanshinones are a group of bioactive abietane-type norditerpenoid quinone compounds in Salvia miltiorrhiza. Copalyldiphosphate synthase of S. miltiorrhiza (SmCPS) is the first key enzyme in tanshinone biosynthesis from the universal diterpene precursor geranylgeranyl diphosphate. Hairy roots of S. miltiorrhiza were transformed with Agrobacterium rhizogenes carrying an RNA interference (RNAi) construct designed to silence SmCPS, and we examined the resulting SmCPS expression and tanshinone accumulation. In SmCPS­RNAi hairy roots, the transcript level of SmCPS was reduced to 26 % while the dihydrotanshinone I and cryptotanshinone levels were decreased by 53 and 38 % compared to those of the vector control hairy roots; tanshinone IIA was not detected. Therefore, the decreased expression of SmCPS caused a decrease in tanshinone levels which verifies that SmCPS is a key enzyme for tanshinone biosynthesis in S. miltiorrhiza.

[Cloning and bioinformatics analysis of ent-kaurene oxidase synthase gene in Salvia miltiorrhiza].

Based on the transcriptome database of Salvia miltiorrhiza, specific primers were designed to clone a full-length cDNA of ent-kaurene oxidase synthase (SmKOL) using the RACE strategy. ORF Finder was used to find the open reading frame of SmKOL cDNA, and ClustalW has been performed to analysis the multiple amino acid sequence alignment. Phylogenetic tree has been constructed using MEGA 5.1. The transcription level of SmKOL from the hairy roots induced by elicitor methyl jasmonate (MeJA) was qualifiedby real-time quantitative PCR. The full length of SmKOL cDNA was of 1 884 bp nucleotides encoding 519 amino acids. The molecular weight of the SmKOL protein was about 58.88 kDa with isoelectric point (pI) of 7.62. Results of real-time quantitative PCR analyses indicated that the level of SmKOL mRNA expression in hairy roots was increased by elicitor oMeJA, and reached maximum in 36 h. The full-length cDNA of SmKOL was cloned from S. miltiorrhiza hairy root, which provides a target gene for further studies of its function, gibberellin biosynthesis and regulation of secondary metabolites.

SALL4 promotes cancer stem-like cell phenotype and radioresistance in oral squamous cell carcinomas via methyltransferase-like 3-mediated m6A modification.

Radioresistance imposes a great challenge in reducing tumor recurrence and improving the clinical prognosis of individuals having oral squamous cell carcinoma (OSCC). OSCC harbors a subpopulation of CD44(+) cells that exhibit cancer stem-like cell (CSC) characteristics are involved in malignant tumor phenotype and radioresistance. Nevertheless, the underlying molecular mechanisms in CD44( + )-OSCC remain unclear. The current investigation demonstrated that methyltransferase-like 3 (METTL3) is highly expressed in CD44(+) cells and promotes CSCs phenotype. Using RNA-sequencing analysis, we further showed that Spalt-like transcription factor 4 (SALL4) is involved in the maintenance of CSCs properties. Furthermore, the overexpression of SALL4 in CD44( + )-OSCC cells caused radioresistance in vitro and in vivo. In contrast, silencing SALL4 sensitized OSCC cells to radiation therapy (RT). Mechanistically, we illustrated that SALL4 is a direct downstream transcriptional regulation target of METTL3, the transcription activation of SALL4 promotes the nuclear transport of ß-catenin and the expression of downstream target genes after radiation therapy, there by activates the Wnt/ß-catenin pathway, effectively enhancing the CSCs phenotype and causing radioresistance. Herein, this study indicates that the METTL3/SALL4 axis promotes the CSCs phenotype and resistance to radiation in OSCC via the Wnt/ß-catenin signaling pathway, and provides a potential therapeutic target to eliminate radioresistant OSCC.

Extraction of eutrophic and green ponds from segmentation of high-resolution imagery based on the EAF-Unet algorithm.

Inland ponds exhibit remarkable ubiquity across the globe, playing a vital role in the sustainability of global continental freshwater resources and contributing significantly to their biodiversity. Numerous ponds are eutrophic and experience recurrent seasonal or year-round algal blooms or persistent duckweed cover, conferring a characteristic green hue. Here, we denote these eutrophic and green ponds as EGPs. The excessive proliferation of algal blooms and duckweed within these EGPs poses a significant threat to the ecological functioning of these aquatic systems, which can lead to hypoxia or the release of microcystins. To identify these EGPs automatically, we constructed an Efficient Attention Fusion Unet (EAF-Unet) algorithm using Gaofen-2 (GF2) panchromatic and multispectral imagery. The attention mechanism was incorporated in Unet to help better detect EGPs. Using the first EGP labeled dataset, we determined the best input feature combination (RGB, NIR, NDVI, and Bright) and the most effective encoding (Rasnet50) for EAF-Unet for distinguishing EGPs from other ground cover types. The evaluation indices - Precision (0.81), Recall (0.79), F1-Score (0.80), and Intersection over Union (IoU, 0.67) - indicate that EAF-Unet can accurately and robustly extract EGPs from GF2 images without relying on pond water masks. Remote-sensing EGP products can assist in identifying ponds with severe eutrophication. Moreover, these products can serve as references for identifying high-risk areas prone to improper sewage discharge or inadequate sewer construction.

Numerical simulation investigation on optimal air volume for dilution dust-gas by forced ventilation in fully mechanized driving face.

In order to ensure the best dust removal on the basis of the optimal gas emission, and to determine the best dust and gas exhaust air volume, numerical simulation research was carried out on the airflow-dust-gas field of the fully mechanized driving face. The results indicate that under different air volumes, with an increase in the distance from the head-on, the airflow velocity of the fully mechanized driving face first increased and then decreased, and gradually tended to be stable. When Q = 800-900 m3/min, the head-on gas dilution ability is strong and the range of high gas content was the minimum. When Q > 900 m3/min, the gas dilution efficiency was reduced and easy to cause secondary dust. In the height of the respiratory zone, the relationship between the dust concentration distribution and air volume is [Formula: see text], and that between the gas content and air volume is [Formula: see text]. Finally, the optimal air volume range was determined to be Q = 800-900 m3/min. By comparing the measured and simulated airflow velocity, dust concentration, and gas content, the average errors were 6.77%, 6.83%, and 7.73%, respectively, which proves the reliability of the numerical simulation results.

Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model.

BACKGROUND AND OBJECTIVES: Atrial fibrillation (AF) is one of the most common arrhythmias clinically. Aging tends to increase the risk of AF, which also increases the burden of other comorbidities, including coronary artery disease (CAD), and even heart failure (HF). The precise detection of AF is a challenge due to its intermittence and unpredictability. A method for the accurate detection of AF is still needed. METHODS: A deep learning model was used to detect atrial fibrillation. Here, a distinction was not made between AF and atrial flutter (AFL), both of which manifest as a similar pattern on an electrocardiogram (ECG). This method not only discriminated AF from normal rhythm of the heart, but also detected its onset and offset. The proposed model involved residual blocks and a Transformer encoder. RESULTS AND CONCLUSIONS: The data used for training were obtained from the CPSC2021 Challenge, and were collected using dynamic ECG devices. Tests on four public datasets validated the availability of the proposed method. The best performance for AF rhythm testing attained an accuracy of 98.67%, a sensitivity of 87.69%, and a specificity of 98.56%. In onset and offset detection, it obtained a sensitivity of 95.90% and 87.70%, respectively. The algorithm with a low FPR of 0.46% was able to reduce troubling false alarms. The model had a great capability to discriminate AF from normal rhythm and to detect its onset and offset. Noise stress tests were conducted after mixing three types of noise. We visualized the model's features using a heatmap and illustrated its interpretability. The model focused directly on the crucial ECG waveform where showed obvious characteristics of AF.

Detecting different pesticide residues on Hami melon surface using hyperspectral imaging combined with 1D-CNN and information fusion.

Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging.

In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000-2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.