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The Cucurbitaceae family hosts many economically important fruit vegetables (cucurbits) such as cucumber, melon, watermelon, pumpkin/squash, and various gourds. The cucurbits are probably best known for the diverse fruit sizes and shapes, but little is known about their genetic basis and molecular regulation. Here, we reviewed the literature on fruit size (FS), shape (FSI), and fruit weight (FW) QTL identified in cucumber, melon, and watermelon, from which 150 consensus QTL for these traits were inferred. Genome-wide survey of the three cucurbit genomes identified 253 homologs of eight classes of fruit or grain size/weight-related genes cloned in Arabidopsis, tomato, and rice that encode proteins containing the characteristic CNR (cell number regulator), CSR (cell size regulator), CYP78A (cytochrome P450), SUN, OVATE, TRM (TONNEAU1 Recruiting Motif), YABBY, and WOX domains. Alignment of the consensus QTL with candidate gene homologs revealed widespread structure and function conservation of fruit size/shape gene homologs in cucurbits, which was exemplified with the fruit size/shape candidate genes CsSUN25-26-27a and CsTRM5 in cucumber, CmOFP1a in melon, and ClSUN25-26-27a in watermelon. In cucurbits, the andromonoecy (for 1-aminocyclopropane-1-carboxylate synthase) and the carpel number (for CLAVATA3) loci are known to have pleiotropic effects on fruit shape, which may complicate identification of fruit size/shape candidate genes in these regions. The present work illustrates the power of comparative analysis in understanding the genetic architecture of fruit size/shape variation, which may facilitate QTL mapping and cloning for fruit size-related traits in cucurbits. The limitations and perspectives of this approach are also discussed.

Summary Domestication and improvement are two important stages in crop evolution. Melon (Cucumis melo L.) is an important vegetable crop with wide phenotypic diversity in many horticultural traits, especially fruit size, flesh thickness and aroma, which are likely the results of long‐term extensive selection during its evolution. However, selective signals in domestication and improvement stages for these remarkable variations remain unclear. We resequenced 297 wild, landrace and improved melon accessions and obtained 2 045 412 high‐quality SNPs. Population structure and genetic diversity analyses revealed independent and two‐step selections in two subspecies of melon: ssp. melo and ssp. agrestis during melon breeding. We detected 233 (~18.35 Mbp) and 159 (~17.71 Mbp) novel potential selective signals during the improvement stage in ssp. agrestis and spp. melo, respectively. Two alcohol acyltransferase genes (CmAATs) unique to the melon genome compared with other cucurbit crops may have undergone stronger selection in ssp. agrestis for the characteristic aroma as compared with other cucurbits. Genome‐wide association analysis identified eight fruit size and seven flesh thickness signals overlapping with selective sweeps. Compared with thin‐skinned ssp. agrestis, thick‐skinned ssp. melo has undergone a stronger selection for thicker flesh. In most melon accessions, CmCLV3 has pleiotropic effects on carpel number and fruit shape. Findings from this study provide novel insights into melon crop evolution, and new tools to advance melon breeding.

Background S. pyogenes , is a primary pathogen that leads to pharyngitis and can also trigger severe conditions like necrotizing fasciitis and streptococcal toxic shock syndrome (STSS), often resulting in high mortality rates. Therefore, prompt identification and appropriate treatment of S. pyogenes infections are crucial in preventing the worsening of symptoms and alleviating the disease's impact. Results In this study, a newly developed technique called multiple cross displacement amplification (MCDA) was employed to detect S. pyogenes ,specifically targeting the speB gene, at a temperature of 63°C within 30 min. Then, an easily portable and user-friendly nanoparticles-based lateral flow biosensor (LFB) assay was introduced for the rapid analysis of MCDA products in just 2 min. The results indicated that the LFB offers greater objectivity compared to Malachite Green and is simpler than electrophoresis. The MCDA-LFB assay boasts a low detection limit of 200 fg and exhibits no cross-reaction with non- S. pyogenes strains. Among 230 clinical swab throat samples, the MCDA-LFB method identified 27 specimens as positive, demonstrating higher sensitivity compared to 23 samples detected positive by qPCR assay and 18 samples by culture. The only equipment needed for this assay is a portable dry block heater. Moreover, each MCDA-LFB test is cost-effective, priced at approximately $US 5.5. Conclusion The MCDA-LFB assay emerges as a straightforward, specific, sensitive, portable, and user-friendly method for the rapid diagnosis of S. pyogenes in clinical samples.

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning.

Cucurbits (Cucurbitaceae family) include many economically important fruit vegetable crops such as watermelon, pumpkin/squash, cucumber, and melon. Seed size (SS) is an important trait in cucurbits breeding, which is controlled by quantitative trait loci (QTL). Recent advances have deciphered several signaling pathways underlying seed size variation in model plants such as Arabidopsis and rice, but little is known on the genetic basis of SS variation in cucurbits. Here we conducted literature review on seed size QTL identified in watermelon, pumpkin/squash, cucumber and melon, and inferred 14, 9 and 13 consensus SS QTL based on their physical positions in respective draft genomes. Among them, four from watermelon ( , , , and ), two from cucumber ( and ), and one from melon ( ) were major-effect, stable QTL for seed size and weight. Whole genome sequence alignment revealed that these major-effect QTL were located in syntenic regions across different genomes suggesting possible structural and functional conservation of some important genes for seed size control in cucurbit crops. Annotation of genes in the four watermelon consensus SS QTL regions identified genes that are known to play important roles in seed size control including members of the zinc finger protein and the E3 ubiquitin-protein ligase families. The present work highlights the utility of comparative analysis in understanding the genetic basis of seed size variation, which may help future mapping and cloning of seed size QTL in cucurbits.

The widespread prevalence of coronavirus disease-2019 (COVID-19) which is caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has resulted in a severe global public health emergency. However, there are no sensitive biomarkers to predict the disease prognosis of COVID-19 patients. Here, we have identified interleukin-8 (IL-8) as a biomarker candidate to predict different disease severity and prognosis of COVID-19 patients. While serum IL-6 become obviously elevated in severe COVID-19 patients, serum IL-8 was easily detectible in COVID-19 patients with mild syndromes. Furthermore, lL-8 levels correlated better than IL-6 levels with the overall clinical disease scores at different stages of the same COVID-19 patients. Thus, our studies suggest that IL-6 and IL-8 can be respectively used as biomarkers for severe COVID-19 patients and for COVID-19 disease prognosis.

Background The strong genotype dependence of embryogenic callus (EC) induction limits the high-frequency regeneration of Korean pine ( Pinus koraiensis ) via somatic embryogenesis (SE). Methods We examined the morphological and histocytological features of various callus types using external morphology, microstructure, and ultrastructure analyses. To assess their embryogenic potential, we conducted somatic embryogenesis (SE) experiments. Additionally, we investigated programmed cell death (PCD) events associated with genotype-dependent embryogenic callus (EC) formation in Korean pine by performing TUNEL assays, detecting caspase-3 and vacuolar processing enzyme (VPE) activity, and measuring H 2 O 2 and O 2 •− levels. Results Obvious cell death was observed in the cell ultrastructure of LEC and NEC induced by recalcitrant genotypes. In LEC cells, we found typical characteristics of autolytic PCD, such as vacuole fusion, vacuolation of cells, invagination and rupture of the plasma membrane, few cytoplasm and organelles. In the NEC, degrading nucleus, withered cytoplasm and invagination and rupture of the plasma membrane were detected in early stage, but vacuole fusion was not observed. In addition, we observed DNA fragmentation into small fragments in LEC and NEC. Significant changes in the activities of caspase-3 and VPE, reactive oxygen species (ROS) levels, especially for H 2 O 2 , have been identified in LEC. However, in NEC, only the significant changes in the O 2 •− level and the caspase-3 activity were observed. Conclusion PCD was present in NEC and LEC cells in recalcitrant genotypes of Korean pine, which may be responsible for the loss or reduction of embryonic capacity of induced Korean pine callus. Our novel findings on PCD help elucidate the underlying causes of the difficulty in inducing EC from recalcitrant genotypes of Korean pine from a novel perspective.

Accurate estimation of hourly near-surface air temperature (NSAT) is critical for climate analysis, environmental monitoring, and urban thermal studies. However, existing temperature datasets remain constrained by coarse spatial resolution and limited hourly accuracy. This study systematically evaluates four widely used land surface temperature (LST) datasets—MODIS, ERA5-Land, FY-2F, and CGLS—and five machine learning models (RF, MDN, DNN, XGBoost, and GTNNWR) for NSAT estimation across two contrasting regions in Shaanxi, China: a complex-terrain region in southwestern Shaanxi and the urban area of Xi’an. Results demonstrate that single-source LST inputs outperform multi-source LST stacking, largely due to compounded systematic biases across heterogeneous datasets. MODIS provides the best performance in the mountainous region, while CGLS excels in the urban environment. Among all models, GTNNWR—which explicitly captures spatiotemporal non-stationarity—consistently achieves the highest accuracy, reducing RMSE by 44.8% and 44.2% relative to the second-best model in the two study areas, respectively, whereas the remaining four models exhibit broadly comparable performance. This work identifies effective data–model configurations for generating high-resolution hourly NSAT products and provides methodological insights for climate and environmental applications in regions with complex terrain or strong urban heterogeneity.

Understanding the effects of natural and human disturbance factors on fractional vegetation coverage (FVC) is significant in the promotion of ecological and environmental protection. However, most of the relevant studies neglect to consider differences in the effect of driving factors on areas with different vegetation change characteristics. In this paper, we have combined Theil-Sen median trend analysis and Mann-Kendall testing to identify degraded and restored areas. Differences in the impact of various factors on FVC in terms of degradation, restoration, and the whole region were distinguished quantitatively using the geodetector model. Additionally, the constraint line approach was used to detect the influence thresholds of factors on FVC. The results are shown as below: (1) FVC showed an overall improving trend, and vegetation restoration and degradation areas accounted for 69.2% and 22.0%, respectively. (2) The two dominant factors affecting FVC were Digital Elevation Model (DEM) and temperature for both degraded and restored regions. However, the explanatory power of precipitation was noticeably different between regions. (3) Most natural factors had a “convex” constraint effect on FVC, which gradually weakened with an increase in the variable below the threshold and vice versa. Human disturbance factors negatively constrained FVC, and the constraint effect increased with increased human activity. This study can help decision-makers optimize specific implementation policies relating to ecological restoration and sustainable development.