Efficient and Accurate pH Determination with pH Test Strips Based on Machine Learning.

The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.

High Confidence Single Particle Analysis with Machine Learning.

Single particle analysis can effectively determine the heterogeneity between particles based on the local information on a single particle, which is utilized extensively for monitoring chemical reactions and biological activities. However, the study of obtaining ensemble reaction information at the single particle level, which can obtain both the structural and functional heterogeneity of particles as well as the ensemble reaction information, is challenging because the selection of a single particle mainly depends on experience, which will lead to a certain randomness when analyzing the ensemble reaction with single particles. Using machine learning, it is demonstrated that the proposed intelligent single particle analysis strategy can provide single particle and ensemble analyses with high confidence. Convolutional neural network and Gaussian mixture model were utilized to develop a machine learning model for resonance scattering imaging analysis of plasmonic nanoparticles. It can identify the scattered light of single particles and select representative or diverse particles. When single particle scattering imaging is used to obtain ensemble information on the reaction, the error caused by the selection of individual particles can be significantly reduced by selecting representative particles. In addition, the real situation of the reaction can be better revealed by selecting diverse particles. These results indicate that the intelligent single particle analysis strategy has great potential for imaging analysis and biological sensing.

Effects of urea solution concentration on soil hydraulic properties and water infiltration capacity.

Elucidating the effect of fertigation on soil hydraulic parameters and water-solute transportation is fundamental to the design of farmland irrigation systems and their sustainable utilization. Few studies have focused on soil hydraulic parameters or water infiltration characteristics or how they are influenced by urea solution concentration. In this study, the clay loam and sandy loam in Yangling District of Shaanxi Province, China, were used as test soil, and experiments involving seven urea solution concentrations (0.2, 0.4, 0.6, 0.8, 1, 3, and 5 g/L) and a control treatment (0 g/L) were conducted to explore the influence of the various urea solution concentrations on soil hydraulic parameters and water infiltration characteristics. The results indicated that the cumulative infiltration and wetting front migration depth increased with urea solution concentration, as accurately estimated using the Kostiakov model and a power function, respectively. In addition, the coefficients of the Kostiakov model and the power function increased with urea solution concentration. Treatment with multiple concentrations of urea solution resulted in an increase in the volume of macro pores in the soil but a reduction in the volume of mesopores and micro pores in the soil, leading to increases in the saturated water content, saturated hydraulic conductivity, soil water diffusivity, and infiltration capacity and a reduction in the water-holding capacity of the soil. The effect of urea solute potential on the inhibition of soil water movement is small, and this inhibitory effect is far weaker than the improvement effect of the urea solution on soil structure, and hence enhance the soil water infiltration capacity. Our results increase the understanding of soil hydrological mechanisms and may be usefully applied for improving the management of fertigation.

Chromosome-level genome assembly reveals potential epigenetic mechanisms of the thermal tolerance in the oriental fruit fly, Bactrocera dorsalis.

The oriental fruit fly, Bactrocera dorsalis (Hendel), has very strong ecological adaptability and phenotypic plasticity. Here, the genome of B. dorsalis was assembled into 549.45 Mb sequences with a contig N50 length of 12.81 Mb. Among, 95.67 % assembled genome sequences were anchored on six chromosomes with an N50 length of 94.63 Mb. According to the basic characteristics of the sex chromosomes of Tephritidae, the X chromosome of B. dorsalis was identified. Significant gene expansions were detected in several important gene families related to adaptability. In particular, we annotated 50 histone modification enzymes (HMEs) in this genome. A comparative transcriptome analysis indicated that 12 HME genes were differentially expressed in two thermo-tolerant strains (heat and cold). Interestingly, four and seven of the 12 HME genes responded to heat shock or cold hardening, respectively. These evidences suggested that the histone modification as an epigenetic modification may be involved in the thermal tolerance of B. dorsalis, but with different regulation mechanisms in thermal acclimation and hardening. The high quality genome of B. dorsalis provides an invaluable resource for further functional genomic study. Moreover, comparative genomic analysis will shed insights on revealing the mechanisms of adaptive evolution in this fly.

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning.

The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the limitation of the optical diffraction limit, it is still a challenging task to accurately distinguish two or more nanoparticles whose distance is less than the diffraction limit. Here, we propose a computational strategy based on a deep learning framework (NanoNet), which will realize the effective segmentation of the scattered light spots in diffraction-limited DFM images and obtain high-resolution plasmonic light scattering imaging. A small data set of DFM and the corresponding scanning electron microscopy (SEM) image pairs are used to learn for obtaining a highly resolved semantic imaging model using NanoNet, and thus highly resolved DFM images matching the resolution of those acquired using SEM can be obtained. Our method has the ability to transform diffraction-limited DFM images to highly resolved ones without adding a complex optical system. As a proof of concept, a highly resolved DFM image of living cells through the NanoNet technique is successfully made, opening up a new avenue for high-resolution optical nanoscopic imaging.

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning.

Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10-10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.

Identification and Expression Characterization of ATP-Binding Cassette (ABC) Transporter Genes in Melon Fly.

The ATP-binding cassette (ABC) transporter is a protein superfamily that transports specific substrate molecules across lipid membranes in all living species. In insects, ABC transporter is one of the major transmembrane protein families involved in the development of xenobiotic resistance. Here, we report 49 ABC transporter genes divided into eight subfamilies (ABCA-ABCH), including seven ABCAs, seven ABCBs, 10 ABCCs, two ABCDs, one ABCE, three ABCFs, 16 ABCGs, and three ABCHs according to phylogenetic analysis in Zeugodacus cucurbitae, a highly destructive insect pest of cucurbitaceous and other related crops. The expressions level of 49 ABC transporters throughout various developmental stages and within different tissues were evaluated by quantitative transcriptomic analysis, and their expressions in response to three different insecticides were evaluated by quantitative real-time polymerase chain reaction (qRT-PCR). These ABC transporter genes were widely expressed at developmental stages but most highly expressed in tissues of the midgut, fat body and Malpighian tube. When challenged by exposure to three insecticides, abamectin, ß-cypermethrin, and dinotefuran, the expressions of ZcABCB7 and ZcABCC2 were significantly up-regulated. ZcABCB1, ZcABCB6, ZcABCB7, ZcABCC2, ZcABCC3, ZcABCC4, ZcABCC5, and ZcABCC7 were significantly up-regulated in the fat body at 24 h after ß-cypermethrin exposure. These data suggest that ZcABCB7 and ZcABCC2 might play key roles in xenobiotic metabolism in Z. cucurbitae. Collectively, these data provide a foundation for further analysis of ABCs in Z. cucurbitae.

Genome-wide and expression-profiling analyses of the cytochrome P450 genes in Bactrocera dorsalis (Hendel) and screening of candidate P450 genes associated with malathion resistance.

BACKGROUND: Bactrocera dorsalis (Hendel) is a notorious agricultural pest worldwide, and its resistance to insecticides is a major obstacle in successful control. Cytochrome P450s (P450s) are major metabolic enzymes associated with insecticide resistance. The genome of B. dorsalis was sequenced recently, allowing an integrated genome-wide analysis of P450 genes (P450s) and the analysis of correlations between these genes and insecticide resistance in this pest. RESULTS: Totally, 101 P450s were identified in the B. dorsalis genome and classified into four clans, 25 families and 57 subfamilies. Quantitative reverse transcription polymerase chain reaction results showed that most of these genes were highly expressed in adults (46) and in metabolic tissues, including the fatbody (63), midgut (61) and Malphagian tubules (66). In a malathion-resistant strain, 13 and 9 genes were significantly upregulated and downregulated, respectively, compared with a susceptible strain, and these genes were screened as candidate genes associated with malathion resistance. CONCLUSION: This study provides useful information for understanding the evolution and potential functions of P450s in B. dorsalis, and the results lay the foundation for further studies on the correlations between P450s and malathion resistance in B. dorsalis. © 2020 Society of Chemical Industry.

[Treatment and prevention of serious perioperative complications of obstructive sleep apnea hypopnea syndrome].

OBJECTIVE: To summarize experiences of serious perioperative complications management of obstructive sleep apnea hypopnea syndrome (OSAHS), and evaluate the effect of intervention in decreasing the incidence of serious complications. METHODS: Retrospective analysis of clinical data in Shenyang General Hospital of PLA and Liaoning Province Jinqiu Hospital of OSAHS surgery cases from January 1995 to December 2009 were included in this study, patients were divided into two groups according to with or without intervention. Experience and lessons were analyzed. RESULTS: Patients without and with intervention were 402 and 521 respectively, and uvulopalatopharyngoplasty (UPPP) cases in each group were 387 and 390. Five patients in the first group who accepted UPPP had breathing difficulty and were all successfully rescued, while no one in the second group had breathing difficulty. The difference was significant (P < 0.05). Sixteen patients in the first group had severe bleeding after UPPP, while only 5 patients had the severe bleeding in the second group. The difference was significant, too P < 0.05. No breathing difficulty cases in the second group, and serious bleeding cases in each group was 5 and in 1, there was no significant difference (P > 0.05). CONCLUSIONS: Breath difficulty and serious bleeding are serious perioperative complications of OSAHS surgery, and with systemic intervention the incidence of the complications can be decreased.

[Down-regulation of Twist1 increases the sensitivity of nasopharyngeal carcinoma cell lines HNE1 to taxol].

OBJECTIVE: To investigate whether down-regulation of Twist1 could change sensitivity of nasopharyngeal carcinoma cell line HNE1 to taxol. METHODS: HNE1 cells were transfected with the small interfering RNA (siRNA) expression vector pSuppressor-Retro-Si-Twist, containing the short hairpin RNA (shRNA) sequence targeting the Twist gene-coding region by Fugene 6. Positive clones were then selected in Neomycin (400 microg/ml) for 21 days. The low expressions of Twist1 were examined by real-time reverse-transcription polymerase chain reaction (RT-PCR) and Western blot. Drug sensitivity of si-Twist1 HNE1 to taxol was determined by Annexin V-fluorescein isothiocyanate( FITC)/propidium lodide (PI) double-labeled flow cytometry and detection of DNA ladder. The Effect of Twist1 inactivation on HNE1 cell proliferation was observed by MTT assay and flow cytometry. RESULTS: Annexin V- FITC-PI assay showed that apoptosis ratio was 40.2% in si-Twist HNE1 after treated with 10 ng/ml taxol, significantly higher than that in the control siRNA group 24.3%. The deference had statistic meaning. After the re-expression of HNE1, apoptosis ratio was 44.80% +/- 4.80% (x +/- s) in low Twist1 protein expression group and that was 27.00% +/- 2.91% in high expression group. The deference had statistic meaning (t = 4.374, P = 0.049). Real time PCR test revealed apoptosis protein bcl-2 expression in si-Twist HNE1 was 0.28 +/- 0.05, significantly lower than that in the control siRNA HNE1 (0.57 +/- 0.08, t = 6.710, P = 0.021), nevertheless, significant bax and bcl-XL changes were not observed (t = 2.000, P = 0.184 and t = 1.502, P = 0.272). MTT and FCM showed that down-regulation of Twist1 did not alter cell proliferation rate (P>0.05). CONCLUSIONS: Down-regulation of Twist1 could increase drug sensitivity of nasopharyngeal carcinoma cell line HNE1 to taxol by inducing apoptosis. These results suggested that Twist1 may be a promising treatment target for nasopharyngeal carcinoma therapy.