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The purpose of this study was to look at the relationship between the Systemic Immune Inflammatory Index (SII) and bone mineral density (BMD) in the pelvis, left upper and lower limbs, lumbar spine, thoracic spine, and trunk in a chronic kidney disease (CKD) population in the United States. The National Health and Nutrition Examination Survey (2011-2016) yielded 2302 people with CKD aged >18 years. CKD was defined as eGFR less than 90 ml/min/1.73 m2 or eGFR greater than 90 ml/min/1.73 m2 with urine ACR greater than 30 mg/L.SII was calculated as PC * (NC / LC) from platelet count (PC), neutrophil count (NC), and lymphocyte count (LC). Multiple logistic regression was used to examine the relationship between BMD and SII at different sites in CKD patients, smoothed curve-fitting and generalized weighting models were used to investigate non-linear relationships, and a two-tailed linear regression model was used to find potential inflection points in the model. We discovered a negative correlation between SII and pelvic BMD among 2302 participants after controlling for gender, age, and race [β = -0.008; 95% confidence value -0.008; 95% confidence interval (CI) -0.014, -0.002]. Lower PEBMD was related to increasing SII (trend p = 0.01125). After additional correction, only pelvic BMD remained adversely linked with SII [value -0.006; 95% CI -0.012, -0.000, p = 0.03368]. Smoothed curve fitting revealed a consistent inverse relationship between SII and pelvic BMD. Further stratified analyses revealed a substantial positive negative connection between SII and pelvic BMD in individuals who did not have hypertension, diabetes, a BMI of more than 30 kg/m2, or stage 2 CKD. The connection between SII and PEBMD in people without diabetes revealed a strong inverted U-shaped curve. In individuals with CKD in the United States, there was a negative connection between the systemic immunoinflammatory index (SII) and pelvic BMD. The SII might be a low-cost and simple test for CKD-related BMD loss.

The widespread use of Bt crops reduces the outbreaks of certain targeted pests and the need for insecticide use, leading to enhanced biocontrol of other potential pest species in the Bt crops; neighbouring non-Bt crops may also benefit. GM cotton protects insect predators Transgenic crops producing insecticidal proteins derived from Bacillus thuringiensis (Bt) have proved effective in controlling bollworm and reducing the need for pesticides in cotton crops in China. This study of Bt crop performance at sites across northern China identifies a decrease in aphid pests and a marked increase in the numbers of ladybirds, lacewings and spiders — natural enemies of insect pests — compared with conventional crops. There is also evidence that these predators thrive in neighbouring non-transgenic maize, soyabean and peanut crops. These results suggest that Bt cotton can promote biological control in agricultural ecosystems by decreasing insecticide use and increasing predator populations. Over the past 16 years, vast plantings of transgenic crops producing insecticidal proteins from the bacterium Bacillus thuringiensis (Bt) have helped to control several major insect pests 1 , 2 , 3 , 4 , 5 and reduce the need for insecticide sprays 1 , 5 , 6 . Because broad-spectrum insecticides kill arthropod natural enemies that provide biological control of pests, the decrease in use of insecticide sprays associated with Bt crops could enhance biocontrol services 7 , 8 , 9 , 10 , 11 , 12 . However, this hypothesis has not been tested in terms of long-term landscape-level impacts 10 . On the basis of data from 1990 to 2010 at 36 sites in six provinces of northern China, we show here a marked increase in abundance of three types of generalist arthropod predators (ladybirds, lacewings and spiders) and a decreased abundance of aphid pests associated with widespread adoption of Bt cotton and reduced insecticide sprays in this crop. We also found evidence that the predators might provide additional biocontrol services spilling over from Bt cotton fields onto neighbouring crops (maize, peanut and soybean). Our work extends results from general studies evaluating ecological effects of Bt crops 1 , 2 , 3 , 4 , 6 , 12 , 13 by demonstrating that such crops can promote biocontrol services in agricultural landscapes.

The decrease of neurotransmitter dopamine (DA) levels in the intestine is closely related to the development of inflammatory bowel disease (IBD). However, the functional relevance and underlying mechanistic basis of the effects of DA signaling on IBD remains unclear. Here, we observed that the DRD5 receptor is highly expressed in colonic macrophages, and the deficiency of DA-DRD5 signaling exacerbated experimental colitis. Moreover, DA-DRD5 signaling can inhibit M1 by negatively regulating NF-κB signaling but promote M2 macrophage polarization through activation of the CREB pathway, respectively. The deficiency of DRD5 signaling increased colonic M1 macrophages but reduced M2 cells during colitis. Additionally, the administration of a D1-like agonist that has a higher affinity to DRD5 can attenuate the colitogenic phenotype of mice. Collectively, these findings provide the first demonstration of DA-DRD5 signaling in colonic macrophages controlling the development of colitis by regulating M1/M2 macrophage polarization.

Terahertz time domain spectroscopy imaging systems suffer from the problems of long image acquisition time and massive data processing. Reducing the sampling rate will lead to the degradation of the imaging reconstruction quality. To solve this issue, a novel terahertz imaging model, named the dual sparsity constraints terahertz image reconstruction model (DSC-THz), is proposed in this paper. DSC-THz fuses the sparsity constraints of the terahertz image in wavelet and gradient domains into the terahertz image reconstruction model. Differing from the conventional wavelet transform, we introduce a non-linear exponentiation transform into the shift invariant wavelet coefficients, which can amplify the significant coefficients and suppress the small ones. Simultaneously, the sparsity of the terahertz image in gradient domain is used to enhance the sparsity of the image, which has the advantage of edge preserving property. The split Bregman iteration scheme is utilized to tackle the optimization problem. By using the idea of separation of variables, the optimization problem is decomposed into subproblems to solve. Compared with the conventional single sparsity constraint terahertz image reconstruction model, the experiments verified that the proposed approach can achieve higher terahertz image reconstruction quality at low sampling rates.

Terahertz (THz)-detection technology has been proven to be an effective and rapid non-destructive detection approach in biomedicine, quality control, and safety inspection, among other applications. However, the sensitivity of such a detection method is limited due to the insufficient power of the terahertz source and the low content, or ambiguous characteristics, of the analytes to be measured. Metamaterial (MM) is an artificial structure in which periodic sub-wavelength units are arranged in a regular manner, resulting in extraordinary characteristics beyond those possessed by natural materials. It is an effective method to improve the ability of terahertz spectroscopy detection by utilizing the metamaterial as a sensor. In this paper, a dual-band, high-sensitivity THz MM sensor based on the split metal stacking ring resonator (SMSRR) is proposed. The appliance exhibited two resonances at 0.97 and 2.88 THz in the range of 0.1 to 3 THz, realizing multi-point matching between the resonance frequency and the characteristic frequency of the analytes, which was able to improve the reliability and detection sensitivity of the system. The proposed sensor has good sensing performance at both resonant frequencies and can achieve highest sensitivities of 304 GHz/RIU and 912 GHz/RIU with an appropriate thickness of the analyte. Meanwhile, the advantage of multi-point matching of the proposed sensor has been validated by distinguishing four edible oils based on their different refractive indices and demonstrating that the characteristics obtained in different resonant frequency bands are consistent. This work serves as a foundation for future research on band extension and multi-point feature matching in terahertz detection, potentially paving the way for the development of high-sensitivity THz MM sensors.

Background Complex chromosomal rearrangement (CCR) refers to a structural rearrangement involving at least two chromosomes or a minimum of three breakpoints. CCR may lead to intellectual disability, structural anomalies, infertility, and recurrent miscarriages. Chromosome karyotyping and chromosomal microarray analysis (CMA) are unable to detect complex chromosomal rearrangements. As multiple diagnostic approaches are available in clinical practice for detecting chromosomal structural abnormalities and copy number variations—each with its own advantages and limitations—selecting the appropriate testing method is crucial for effective clinical management. Optical genome mapping (OGM) is an advanced genomic technology that utilizes ultra-long single-molecule analysis to comprehensively detect chromosomal aberrations and structural variants at high resolution. Material and methods Amniocentesis was performed for a 36-year-old multipara (advanced maternal age), with subsequent comprehensive fetal genetic analysis including chromosome karyotyping, CMA, and OGM. Family members underwent peripheral blood karyotyping and OGM. Results The fetal karyotype derived from amniotic fluid was 46,XN,?ins(18)(q21.2;p11.31p11.2). CMA demonstrated duplications of four segments and a deletion of one segment on chromosome 18. Therefore, OGM was performed on the fetal and family members to further elucidate the chromosomal structure. The fetus has derived CCRs on chromosome 18 of maternal origin. In contrast, both the mother and the second daughter, who carried the identical CCRs, were phenotypically normal. Conclusion OGM is of significant importance in the diagnosis and characterization of CCRs. OGM plays a critical role in diagnosing complex chromosomal rearrangements and has proven to be invaluable in clinical utility.

Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consumer safety, this study innovatively proposes a YOLOv-MA-based foreign object detection algorithm for rice, leveraging deep learning techniques. The proposed algorithm adaptively enhances multi-scale feature representation across small, medium, and large object detection layers by incorporating the multi-scale dilated attention (MSDA) mechanism. Additionally, the adaptive spatial feature fusion (ASFF) module is employed to improve multi-scale feature fusion in rice foreign object detection, significantly boosting YOLOv8’s object detection capability in complex scenarios. Compared to the original YOLOv8 model, the improved YOLOv-MA model achieves performance gains of 3%, 3.5%, 2%, 3.9%, and 4.2% in mean Average Precision (mAP@[0.5:0:95]) for clods, corn, screws, stones, and wheat, respectively. The overall mAP@[0.5:0:95] reaches 90.8%, reflecting an improvement of 3.3%. Furthermore, the proposed model outperforms SSD, FCOS, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv11, and YOLOv12 in overall performance. Thus, this model not only reduces the burden of manual inspection but also provides an efficient and high-precision solution for rice foreign object detection.

Long-term ecological effects of transgenic Bacillus thuringiensis (Bt) crops on nontarget pests have received limited attention, more so in diverse small holder-based cropping systems of the developing world. Field trials conducted over 10 years in northern China show that mirid bugs (Heteroptera: Miridae) have progressively increased population sizes and acquired pest status in cotton and multiple other crops, in association with a regional increase in Bt cotton adoption. More specifically, our analyses show that Bt cotton has become a source of mirid bugs and that their population increases are related to drops in insecticide use in this crop. Hence, alterations of pest management regimes in Bt cotton could be responsible for the appearance and subsequent spread of nontarget pests at an agro-landscape level.

Global warming has pronounced impacts on the physiology, development and behavior of multiple organisms and affects the geographical distribution of arthropods. Yet, little is known about how a heightened ambient temperature influences migratory insects that are globally relevant to agriculture. Here, we quantified the extent to which increases in surface temperature, over the span of 3 decades, impact long-distance migration processes of the globally important cutworm Agrotis ipsilon (Lepidoptera: Noctuidae). Multi-year monitoring in China indicated how high-altitude migrants of A. ipsilon annually attain two distinct seasonal peaks, i.e., during spring and summer. Stable isotope analyses of field-caught individuals further showed a gradually shifting origin of migrant populations, with ground-level A. ipsilon abundance partially reflecting population levels of high-altitude migrants. Over a 27-year time period, respective population peaks for overwintering and the first-generation A. ipsilon adults were reached 17 and 8 days earlier in the year. Also, the northern border of the A. ipsilon potential overwintering area progressed northward by 58–232 km, and its migration range expanded by 708 km. Our work reveals how a progressively increasing surface temperature affects long-distance migration of a cosmopolitan agricultural pest and alters the population phenology and geographical distribution of A. ipsilon in eastern Asia. Global warming thus influences ecological dynamics in temperate agro-ecosystems and can magnify the economic impacts of long-distance migratory pests.

In order to improve the detection accuracy for the quality of wheat, a recognition method for wheat quality using the terahertz (THz) spectrum and multi-source information fusion technology is proposed. Through a combination of the absorption and the refractive index spectra of samples of normal, germinated, moldy, and worm-eaten wheat, support vector machine (SVM) and Dempster-Shafer (DS) evidence theory with different kernel functions were used to establish a classification fusion model for the multiple optical indexes of wheat. The results showed that the recognition rate of the fusion model for wheat samples can be as high as 96%. Furthermore, this approach was compared to the regression model based on single-spectrum analysis. The results indicate that the average recognition rates of fusion models for wheat can reach 90%, and the recognition rate of the SVM radial basis function (SVM-RBF) fusion model can reach 97.5%. The preliminary results indicated that THz-TDS combined with DS evidence theory analysis was suitable for the determination of the wheat quality with better detection accuracy.