Grassland productivity in arid Central Asia depends on the greening rate rather than the growing season length.

Climate change has induced substantial impact on the gross primary productivity (GPP) of terrestrial ecosystems by affecting vegetation phenology. Nevertheless, it remains unclear which among the mean rates of grass greening (RG), yellowing (RY), and the length of growing season (LOS) exhibit stronger explanatory power for GPP variations, and how RG and RY affect GPP variations under warming scenarios. Here, we explored the relationship between RG, RY, LOS, and GPP in arid Central Asia (ACA) from 1982 to 2019, elucidating the response mechanisms of RG, RY, and GPP to the mean temperature (TMP), vapor pressure deficit (VPD), precipitation (PRE), and soil moisture (SM). The results showed that the multi-year average length of greening (LG) in ACA was 22.7 days shorter than that of yellowing (LY) and the multi-year average GPP during LG (GPPlg) was 38.28 g C m-2 d -1 more than that of during LY (GPPly). RG and RY were positively correlated with GPPlg and GPPly, although the degree of correlation between RG and GPPlg was higher than that between RY and GPPly. Increases in RG and RY contributed to an increase in GPPlg (55.44 % of annual GPP) and GPPly (35.44 % of annual GPP). The correlation between RG and GPPlg was the strongest (0.49), followed by RY and GPPly (0.33), and LOS and GPP was the weakest (0.21). TMP, VPD, PRE, and SM primarily affected GPP by influencing RG and RY, rather than direct effects. The positive effects of TMP during LG (TMPlg), PRE during LG (PRElg), and SM during LG (SMlg) facilitated increases in RG and GPPlg, and higher VPD during LY (VPDly) and lower PRE during LY (PREly) accelerated increases in RY. Our study elucidated the impact of vegetation growth rate on GPP, thus providing an alternate method of quantifying the relationship between vegetation phenology and GPP.

A Systematic Review and Meta-Analysis of Efficacy and Safety of Cilostazol Prescription in Patients With Femoropopliteal Peripheral Artery Disease After Endovascular Therapy.

PURPOSE: The purpose of this study is to assess the efficacy and safety of cilostazol prescription in patients with femoropopliteal peripheral artery disease (PAD) after endovascular therapy (EVT). MATERIALS AND METHODS: We conducted a systematic review and meta-analysis of all studies reporting the outcomes of cilostazol after femoropopliteal EVT of PAD up to September 2022. Clinical outcomes of interest included primary patency, in-stent restenosis (ISR), vessel re-occlusion, freedom from target lesion revascularization (TLR), repeat revascularization, all-cause mortality, amputation, major adverse cardiovascular events (MACEs) and major adverse limb events (MALEs), and bleeding complication. RESULTS: A total of 4 randomized controlled trials (RCTs) and 8 observational studies containing a total of 4898 patients met the inclusion criteria and were included in this systematic review and meta-analysis. We found that the use of cilostazol was associated with higher primary patency after femoropopliteal artery EVT (odds ratio [OR]=1.67, 95% confidence interval [CI]=1.50-1.87, p<0.001, I2=33.2%), a lower risk of ISR (OR=0.43, 95% CI=0.29-0.63, p<0.001, I2=37.6%), repeat revascularization (OR=0.43, 95% CI=0.24-0.76, p<0.005, I2=27.4%), and vessel re-occlusion (OR=0.59, 95% CI=0.38-0.93, p<0.05, I2=0%). There was an increase in freedom from TLR rate (OR=2.19, 95% CI=1.58-3.05, p<0.001, I2=0%), as well as a reduction in the occurrence of MALEs (OR=0.50, 95% CI=0.29-0.85, p<0.05, I2=0%). However, there was no significant difference in amputation, MACEs, all-cause mortality, and major bleeding complications. Subgroup analysis showed that cilostazol treatment in patients with femoropopliteal drug-eluting stents (DES) implantation remained associated with higher primary patency and a lower risk of ISR. CONCLUSIONS: After EVT of femoropopliteal artery lesions, additional oral cilostazol enhances primary patency, reduces the occurrences of ISR and vessel re-occlusion, diminishes the risks associated with MALEs, lowers the need for repeat revascularization, and increases freedom from TLR rates. However, it does not impact amputation, MACEs, all-cause mortality, or major bleeding complications. These findings suggest cilostazol as a potentially safe and effective adjunct therapy in patients with femoropopliteal PAD after EVT. CLINICAL IMPACT: After undergoing endovascular therapy (EVT) for femoropopliteal artery lesions, the addition of cilostazol to antiplatelet therapy can significantly improve primary patency, reducing the incidence of in-stent restenosis, repeat revascularization, vessel re-occlusion, and major adverse limb events while increasing freedom from target lesion revascularization rate. The simultaneous use of drug-eluting stents in the femoropopliteal artery lesions, combined with cilostazol, potentially results in a synergistic anti-stenotic effect. This therapeutic approach does not appear to be associated with an increased risk of major bleeding events or all-cause mortality. These findings provide additional evidence supporting the treatment of anti-stenosis in patients with femoropopliteal artery lesions after EVT.

Monitoring and influencing factors of grassland livestock overload in Xinjiang from 1982 to 2020.

It is crucial to estimate the theoretical carrying capacity of grasslands in Xinjiang to attain a harmonious balance between grassland and livestock, thereby fostering sustainable development in the livestock industry. However, there has been a lack of quantitative assessments that consider long-term, multi-scale grass-livestock balance and its impacts in the region. This study utilized remote sensing and empirical models to assess the theoretical livestock carrying capacity of grasslands. The multi-scale spatiotemporal variations of the theoretical carrying capacity in Xinjiang from 1982 to 2020 were analyzed using the Sen and Mann-Kendall tests, as well as the Hurst index. The study also examined the county-level grass-livestock balance and inter-annual trends. Additionally, the study employed the geographic detector method to explore the influencing factors. The results showed that: (1) The overall theoretical livestock carrying capacity showed an upward trend from 1982 to 2020; The spatial distribution gradually decreased from north to south and from east to west. In seasonal scale from large to small is: growing season > summer > spring > autumn > winter; at the monthly scale, the strongest livestock carrying capacity is in July. The different grassland types from largest to smallest are: meadow > alpine subalpine meadow > plain steppe > desert steppe > alpine subalpine steppe. In the future, the theoretical livestock carrying capacity of grassland will decrease. (2) From 1988 to 2020, the average grass-livestock balance index in Xinjiang was 2.61%, showing an overall increase. At the county level, the number of overloaded counties showed an overall increasing trend, rising from 46 in 1988 to 58 in 2020. (3) Both single and interaction factors of geographic detectors showed that annual precipitation, altitude and soil organic matter were the main drivers of spatiotemporal dynamics of grassland load in Xinjiang. The results of this study can provide scientific guidance and decision-making basis for achieving coordinated and sustainable development of grassland resources and animal husbandry in the region.

Identification of multiomics map and key biomarkers in uveal melanoma with chromosome 3 loss.

Purpose: Chromosome 3 loss is an independent risk factor for uveal melanoma (UM), but its exact molecular mechanisms remain unclear. This study was designed to investigate the relationship between chromosome 3 loss and molecular alterations at multiple levels to construct a prognostic model. Methods: Forty-four UM cases with chromosome 3 loss (chr3 del group) and 36 UM cases without copy number variation on chromosome 3 (chr3 wt group) were collected from the Cancer Genome Atlas (TCGA). The TCGA dataset was subjected to a univariate Cox regression analysis to identify different expressed genes, and a subsequent random forest algorithm analysis revealed significant changes in different expressed genes, which were used to develop key biomarkers for UM. Following that, the immune cell infiltration analysis and drug sensitivity analyses were carried out. The UM cell line was then utilized to investigate the potential functions of the key biomarker via cell apoptosis, proliferation, cycle assays, WB, and RT-qPCR. Results: By analyzing the 80 cases data in TCGA, the authors unveiled molecular changes relevant to loss of chromosome 3 in UM as well as their poor survival. In addition, machine learning analysis identified three hub genes (GRIN2A, ACAN, and MMP9) as potential therapeutic targets. The differentially enriched pathways between the two groups were mainly about immune-system activity, and hub genes expression was also highly correlated with immune infiltration levels. Conclusion: Chromosome 3 loss has considerable clinical significance for UM, and GRIN2A may be useful in diagnosing, treating, and prognosticating the condition.

Analysis of sustainable water resource management and driving mechanism in arid region: a case study of Xinjiang, China, from 2005 to 2020.

The long-term dynamic comprehensive evaluation of the water resource carrying capacity (WRCC) and the analysis of its potential driving mechanism in arid areas are contemporary research issues and technical means of mitigating and coordinating the conflict between severe resource shortages and human needs. The purpose of this study was to explore the distribution of the WRCC and the spatiotemporal heterogeneity of drivers in arid areas based on an improved two-dimensional spatiotemporal dynamic evaluation model. The results show that (1) the spatial distribution of the WRCC in Xinjiang, China, is high in the north, low in the south, high in the west, and low in the east. (2) From 2005 to 2020, the centers of gravity of the WRCC in northern and southern Xinjiang moved to the southeast and west, respectively, and the spatial distribution exhibited slight diffusion. (3) The factors influencing the WRCC exhibit more obvious spatial and temporal heterogeneity. The domestic waste disposal rate and ecological water use rate were the main factors influencing the WRCC in the early stage, while the GDP per capita gradually played a dominant role in the later stage. (4) In the next 30 years, the WRCC in Xinjiang will increase. The results provide a theoretical reference for the sustainable development of water resources in arid areas.

Impacts of climate change and human activity on the potential distribution of Aconitum leucostomum in China.

Aconitum leucostomum is a poisonous grass that disturbs grassland populations and livestock development, and its spread is influenced by climate change and human activities. Therefore, exploring its potential distribution area under such conditions is crucial to maintain grassland ecological security and livestock development. The present study initially selected 39 variables that may influence the spatial distribution of A. leucostomum, including bioclimate, soil, topography, solar radiation, and human footprint data; the variables were screened by Spearman's correlation coefficient and the jackknife method. Twenty variables were finally identified, and three types of models based on the maximum entropy (MaxEnt) model were constructed to predict the distribution of A. leucostomum within China under three shared economy pathways (SSP126, SSP245, and SSP585): A: prediction of environmental variables under the current climate model; B: prediction of environmental variables + human footprint under the current climate model; and C: prediction of environmental variables under the future climate model (including the 2030s, 2050s, and 2070s). The effects of human activities and climate change on the potential geographic distribution of A. leucostomum were explored separately. The results show that precipitation seasonality, human footprint, solar radiation and mean diurnal range are the main factors affecting the distribution of A. leucostomum. Human activities inhibit the spread of A. leucostomum, and climate change promotes its growth, with areas of high suitability and area variation mainly in northern Xinjiang and northern Yunnan. With climate change, in the future, the distribution center of A. leucostomum shows a tendency to migrate to the southeast on the horizontal gradient and to move to higher altitudes on the vertical gradient. This study provides a positive reference value for the control of A. leucostomum and the maintenance of grassland ecological security.

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms.

The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

Grassland cover dynamics and their relationship with climatic factors in China from 1982 to 2021.

China possesses abundant grassland resources, making it imperative to comprehend the influence of climate change on Chinese grassland ecosystems. Nonetheless, the impact pathways and lag effects of climate factors on various grassland types in this region at multiple temporal scales are still to be investigated in long-term sequences. This study investigated the dynamics of grassland FVC (fractional vegetation cover), temperature, precipitation, and drought from 1982 to 2021 using trend analysis, multiple linear regression, path analysis, and correlation analysis and explored the dominant, direct, indirect, and time-lag effects of climate factors on different grassland types at multiple time scales. Precipitation-grassland correlation pathways dominated the annual-scale grassland FVC. The correlation path of temperature to grassland FVC and the direct path of temperature dominated spring grassland FVC. The correlation path of drought to grassland FVC and the direct path of drought dominated summer grassland FVC. The correlation path of temperature to grassland FVC and the direct path of temperature dominated autumn and winter grassland FVC. The effects of temperature and precipitation on alpine and subalpine meadows, desert grasslands, and alpine and subalpine plains grasslands had a 1-month lag. The response to drought exhibited a 1-month lag in desert grasslands, a 2-month lag in alpine and subalpine meadows, plains grasslands, meadows, and alpine and subalpine plains grasslands, and a 3-month lag in sloped grasslands. This study seeks to provide a scientific reference to reveal the impact of climate change on grasslands and to protect grassland ecosystems.

Cumulative effects of drought have an impact on net primary productivity stability in Central Asian grasslands.

Global warming has exacerbated the threat of drought in Central Asia, amplifying its ecological implications within the region's grassland ecosystems. This has become an increasingly prominent issue that requires attention and action. The temporal link between grassland development and drought is asymmetric. However, a quantitative assessment of the temporal effects of multiscale drought on Central Asian grasslands has yet to be explored. Based on correlation analysis and the coefficient of variation method, this study analysed the cumulative and lag effects of multitimescale drought on grassland NPP (net primary productivity) under different climatic zones, altitudes and water availabilities in Central Asia from 1982 to 2018, and discussed the impact of temporal effects on grassland NPP stability. Our results on the cumulative effects of drought on grasslands indicate the 6.72 months preceding NPP measurement was the duration for which, on average, drought was most strongly correlated with NPP. Additionally, we found a mean lagged effect of 5.36 months, meaning that the monthly drought 5.36 months prior to NPP measurement was, on average, most strongly correlated with NPP. The degree to which grassland NPP was affected by cumulative drought at a given level of water availability was inversely proportional to the number of cumulative drought months. Under different water availabilities, the lagged effect of grassland NPP was stronger in dry areas than in wet areas, and the number of lag months tended to decrease and then increase as the water availability increased. The percentage of areas where grassland NPP was dominated by the cumulative and lagging effects of drought was 30.02% and 69.98%, respectively. The stability of grassland NPP was adversely affected by the drought accumulation effect. The findings of this study contribute to a deeper understanding of the long-term effects of drought on grassland ecosystems. Additionally, it will aid in the development of strategies for mitigating and adapting to drought events, thereby minimizing their negative impacts on agriculture, livestock, and ecosystems.

Probabilistic assessment of drought stress vulnerability in grasslands of Xinjiang, China.

In the process of climate warming, drought has increased the vulnerability of ecosystems. Due to the extreme sensitivity of grasslands to drought, grassland drought stress vulnerability assessment has become a current issue to be addressed. First, correlation analysis was used to determine the characteristics of the normalized precipitation evapotranspiration index (SPEI) response of the grassland normalized difference vegetation index (NDVI) to multiscale drought stress (SPEI-1 ~ SPEI-24) in the study area. Then, the response of grassland vegetation to drought stress at different growth periods was modeled using conjugate function analysis. Conditional probabilities were used to explore the probability of NDVI decline to the lower percentile in grasslands under different levels of drought stress (moderate, severe and extreme drought) and to further analyze the differences in drought vulnerability across climate zones and grassland types. Finally, the main influencing factors of drought stress in grassland at different periods were identified. The results of the study showed that the spatial pattern of drought response time of grassland in Xinjiang had obvious seasonality, with an increasing trend from January to March and November to December in the nongrowing season and a decreasing trend from June to October in the growing season. August was the most vulnerable period for grassland drought stress, with the highest probability of grassland loss. When the grasslands experience a certain degree of loss, they develop strategies to mitigate the effects of drought stress, thereby decreasing the probability of falling into the lower percentile. Among them, the highest probability of drought vulnerability was found in semiarid grasslands, as well as in plains grasslands and alpine subalpine grasslands. In addition, the primary drivers of April and August were temperature, whereas for September, the most significant influencing factor was evapotranspiration. The results of the study will not only deepen our understanding of the dynamics of drought stress in grasslands under climate change but also provide a scientific basis for the management of grassland ecosystems in response to drought and the allocation of water in the future.

Important role of precipitation in controlling a more uniform spring phenology in the Qinba Mountains, China.

Under global warming, the gradual pattern of spring phenology along elevation gradients (EG) has significantly changed. However, current knowledge on the phenomenon of a more uniform spring phenology is mainly focused on the effect of temperature and neglected precipitation. This study aimed to determine whether a more uniform spring phenology occurs along EG in the Qinba Mountains (QB) and explore the effect of precipitation on this pattern. We used Savitzky-Golay (S-G) filtering to extract the start of season (SOS) of the forest from the MODIS Enhanced Vegetation Index (EVI) during 2001-2018 and determined the main drivers of the SOS patterns along EG by partial correlation analyses. The SOS showed a more uniform trend along EG in the QB with a rate of 0.26 ± 0.01 days 100 m-1 per decade during 2001-2018, but there were differences around 2011. A delayed SOS at low elevations was possibly due to the reduced spring precipitation (SP) and spring temperature (ST) between 2001 and 2011. Additionally, an advanced SOS at high elevations may have been caused by the increased SP and reduced winter temperature (WT). These divergent trends contributed to a significant uniform trend of SOS with a rate of 0.85 ± 0.02 days 100 m-1 per decade. Since 2011, significantly higher SP (especially at low elevations) and rising ST advanced the SOS, and the SOS at lower altitudes was more advanced than at higher altitudes, resulting in greater SOS differences along EG (0.54 ± 0.02 days 100 m-1 per decade). The SP determined the direction of the uniform trend in SOS by controlling the SOS patterns at low elevations. A more uniform SOS may have important effects on local ecosystem stability. Our findings could provide a theoretical basis for establishing ecological restoration measures in areas experiencing similar trends.

Effects of drought and climate factors on vegetation dynamics in Central Asia from 1982 to 2020.

Ecological security and ecosystem stability in Central Asia depend heavily on the local vegetation. Vegetation dynamics and the response and hysteresis relationships to climate factors and drought on multiple scales over long time series in the region still need to be further explored. Using the net primary productivity (NPP) values as the vegetation change index of interest, in this study, we analyzed vegetation dynamics in Central Asia from 1982 to 2020 and assessed the responses and time lags of vegetation to climate factors and drought. The results showed that NPP gradually decreased from north to south and from east to west. Vegetation was distributed along both sides of the mountains. The temperatures rose from northeast to southwest, while precipitation gradually increased from southwest to northeast. The proportion of dry and wet years was as follows: normal (56.41%) > slightly dry (28.2%) > slightly humid (15.39%). Precipitation and drought conditions were positively correlated with NPP during the growing season, while temperature was negatively correlated with NPP. Increased spring temperature, precipitation, and drought conditions positively affected vegetation, while sustained summer temperature resulted in suppressed vegetation growth. Autumn vegetation was positively affected by temperature and drought, and precipitation was negatively correlated with autumn vegetation. Increasing winter temperatures promoted vegetation growth. The time lag between NPP and temperature gradually increased from northeast to southwest, and the time lag between NPP and precipitation gradually increased from south to north. Spring temperatures had the greatest beneficial impact on forestlands; summer climatic factors and drought had little effect on shrublands; the autumn climate exhibited small differences in its influence of each plant type; and winter temperatures had the greatest positive effect on grasslands. No time lag effect was found between any of the four vegetation types and precipitation. A one-month lag was found between cultivated lands and temperature; a two-month lag was found between forestlands and temperature; and a one-month lag was found between forestlands and drought and between shrublands and drought. The results can provide a scientific foundation for the sustainable development and management of ecosystems.

Selectively Targeting STAT3 Using a Small Molecule Inhibitor is a Potential Therapeutic Strategy for Pancreatic Cancer.

PURPOSE: Pancreatic cancer is the worst prognosis among all human cancers, and novel effective treatments are urgently needed. Signal transducer and activator of transcription 3 (STAT3) has been demonstrated as a promising target for pancreatic cancer. Meanwhile, selectively targeted STAT3 with small molecule remains been challenging. EXPERIMENTAL DESIGN: To specifically identify STAT3 inhibitors, more than 1.3 million compounds were screened by structure-based virtual screening and confirmed with the direct binding assay. The amino acid residues that WB436B bound to were verified by induced-fit molecular docking simulation, RosettaLigand computations, and site-directed mutagenesis. On-target effects of WB436B were examined by microscale thermophoresis, surface plasmon resonance, in vitro kinase assay, RNA sequencing, and selective cell growth inhibition assessment. In vivo studies were performed in four animal models to evaluate effects of WB436B on tumor growth and metastasis. Kaplan-Meier analyses were used to assess survival. RESULTS: WB436B selectively bound to STAT3 over other STAT families protein, and in vitro antitumor activities were improved by 10 to 1,000 fold than the representative STAT3 inhibitors. WB436B selectively inhibits STAT3-Tyr705 phosphorylation, STAT3 target gene expression, and the viability of STAT3-dependent pancreatic cancer cells. WB436B significantly suppresses tumor growth and metastasis in vivo and prolongs survival of tumor-bearing mice. Mechanistic studies showed that WB436B have unique binding sites located in STAT3 Src homology 2 domain. CONCLUSIONS: Our work presents the first-in-class selective STAT3 inhibitor WB436B as a potential therapeutic candidate for the treatment of pancreatic cancer.

Silencing METTL3 Stabilizes Atherosclerotic Plaques by Regulating the Phenotypic Transformation of Vascular Smooth Muscle Cells via the miR-375-3p/PDK1 Axis.

PURPOSE: Atherosclerosis (AS) is a primary cause of cardiovascular diseases. This study investigated the mechanism of methyltransferase-like 3 (METTL3) in AS plaques via modulating the phenotypic transformation of vascular smooth muscle cells (VSMCs). METHODS: AS mouse models and MOVAS cell models were established through high-fat diet and the treatment of ox-LDL, respectively. METTL3 expression in AS models was detected via RT-qPCR and Western blot. The AS plaques, lipid deposition, and collagen fibers were examined via histological staining. The levels of Ly-6c, α-SMA, and OPN were examined via Western blot. The blood lipid indexes in mouse aortic tissues were determined using kits. The proliferation and migration of MOVAS cells were detected via CCK-8 and Transwell assays. The m6A modification level of mRNA was quantified. The binding relationship between pri-miR-375 and DGCR8, and the enrichment of m6A on pri-miR-375 were detected via RIP. The binding relationship between miR-375-3p and 3-phosphoinositide-dependent protein kinase-1 (PDK1) was verified via dual-luciferase assay. Joint experiments were designed to investigate the role of miR-375-3P/PDK1 in the phenotypic transformation of VSMCs. RESULTS: METTL3 was highly expressed in AS. Silencing METTL3 alleviated AS progression and stabilized AS plaques in mice, and limited the phenotypic transformation of VSMCs induced by ox-LDL. Silencing METTL3 inhibited m6A level and decreased the binding of DGCR8 to pri-miR-375 and further limited miR-375-3p expression. miR-375-3p targeted PDK1 transcription. miR-375-3p upregulation or PDK1 downregulation facilitated the phenotypic transformation of VSMCs. CONCLUSION: METTL3-mediated m6A modification promoted VSMC phenotype transformation and made AS plaques more vulnerable via the miR-375-3p/PDK1 axis.

Predictions Based on Different Climate Change Scenarios: The Habitat of Typical Locust Species Is Shrinking in Kazakhstan and Xinjiang, China.

Climate change, especially climate extremes, can increase the uncertainty of locust outbreaks. The Italian locust (Calliptamus italicus (Linnaeus, 1758)), Asian migratory locust (Locusta migratoria migratoria Linnaeus, 1758), and Siberian locust (Gomphocerus sibiricus (Linnaeus, 1767)) are common pests widely distributed in the semidesert grasslands of Central Asia and its surrounding regions. Predicting the geographic distribution changes and future habitats of locusts in the context of climate warming is essential to effectively prevent large and sudden locust outbreaks. In this study, the optimized maximum entropy (MaxEnt) model, employing a combination of climatic, soil, and topographic factors, was used to predict the potential fitness areas of typical locusts in the 2030s and 2050s, assuming four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. Modeling results showed that the mean area under the curve (AUC) and true statistical skill (TSS) of the MaxEnt model reached 0.933 and 0.7651, respectively, indicating that the model exhibited good prediction performance. Our results showed that soil surface sand content, slope, mean precipitation during the hottest season, and precipitation seasonality were the key environmental variables affecting locust distribution in the region. The three locust species were mainly distributed in the upstream region of the Irtysh River, the Alatao Mountain region, the northern slopes of the Tianshan Mountains, around Sayram Lake, the eastern part of the Alakol Lake region, the Tekes River region, the western part of Ulungur Lake, the Ili River, and the upstream region of the Tarim River. According to several climate projections, the area of potential habitat for the three most common locust species will decrease by 3.9 × 104-4.6 × 104 km2 by the 2030s and by 6.4 × 104-10.6 × 104 km2 by the 2050s. As the climate becomes more extreme, the suitable area will shrink, but the highly suitable area will expand; thus, the risk of infestation should be taken seriously. Our study present a timely investigation to add to extensive literature currently appearing regarding the myriad ways climate change may affect species. While this naturally details a limited range of taxa, methods and potential impacts may be more broadly applicable to other locust species.

Angiotensin-Converting Enzyme 2 SNPs as Common Genetic Loci and Optimal Early Identification Genetic Markers for COVID-19.

Background: Angiotensin-converting enzyme 2 (ACE2) is implicated as a host cell receptor that causes infection in the pathogenesis of coronavirus disease 2019 (COVID-19), and its genetic polymorphisms in the ACE2 gene may promote cardiovascular disease and systemic inflammatory injury in COVID-19 patients. Hence, the genetic background may potentially explain the broad interindividual variation in disease susceptibility and/or severity. Methods: Genetic susceptibility to COVID-19 was analyzed by examining single-nucleotide polymorphisms (SNPs) of ACE2 in 246 patients with COVID-19 and 210 normal controls using the TaqMan genotyping assay. Results: We demonstrated that the ACE2 SNPs rs4646142, rs6632677, and rs2074192 were associated with COVID-19 (for all, p < 0.05), and the differences in the ACE2 SNPs rs4646142 and rs6632677 were correlated with COVID-19-related systemic inflammatory injury and cardiovascular risk. Specifically, rs4646142 was associated with high-sensitivity C-reactive protein (hs-CRP), prealbumin (PAB), apolipoprotein A (APOA), high-density lipoprotein (HDL), and acid glycoprotein (AGP) levels. Rs6632677 was also associated with elevated CRP, acid glycoprotein (AGP), and haptoglobin (HPT). Conclusions: Our results suggest that the ACE2 SNPs rs4646142 and rs6632677 may be common genetic loci and optimal early identification genetic markers for COVID-19 with cardiovascular risk.

Feature tuning improves MAXENT predictions of the potential distribution of Pedicularis longiflora Rudolph and its variant.

Pedicularis longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) are alpine plants and traditional Chinese medicines with important medicinal value, and future climate changes may have an adverse impact on their geographic distribution. The maximum entropy (MAXENT) model has the outstanding ability to predict the potential distribution region of species under climate change. Therefore, given the importance of the parameter settings of feature classes (FCs) and the regularization multiplier (RM) of the MAXENT model and the importance of add indicators to evaluate model performance, we used ENMeval to improve the MAXENT niche model and conducted an in-depth study on the potential distributions of these two alpine medicinal plants. We adjusted the parameters of FC and RM in the MAXENT model, evaluated the adjusted MAXENT model using six indicators, determined the most important ecogeographical factors (EGFs) that affect the potential distributions of these plants, and compared their current potential distributions between the adjusted model and the default model. The adjusted model performed better; thus, we used the improved MAXENT model to predict their future potential distributions. The model predicted that P. longiflora Rudolph and its variant (P. longiflora var. tubiformis (Klotzsch) Tsoong) would move northward and showed a decrease in extent under future climate scenarios. This result is important to predict their potential distribution regions under changing climate scenarios to develop effective long-term resource conservation and management plans for these species.

Influence of natural and anthropogenic controls on runoff in the Keriya River, central Tarim Basin, China.

The potential impact of natural factors on the runoff of intermittent rivers and ephemeral streams (IRES) has been largely ignored in the Tarim Basin, China. A representative example is the Keriya River. To quantify the long-term dynamic variations in lower reach surface runoff of IRES, river length, defined as the distance between a selected fix point along the perennial river segment to its dynamic, ephemeral end, was used as an indicator. Using a total of 272 remote sensing images, we digitized and measured the distance (river length) between the center of Yutian County and the river's end point on each image, and then calculated monthly inter-annual and intra-annual variations in length of the lower Keriya River from 2000 to 2019. Hydrometeorological data were combined with descriptors of anthropogenic disturbances to assess the relative influence of natural factors and anthropogenic disturbances on lower reach river runoff. The results showed that intra-annual variations in river length fluctuated seasonally, with the minimum value occurring in June; two main peaks occurred in March and August. The minimum June value in river length was closely linked to an increase in agricultural water demand and a decrease in upper reach runoff. The August peak in river length was related to the peak values in upper reach runoff and agricultural water demand; upper reach runoff made a significant contribution because the former was about 20% more than the latter in summer. The March peak corresponded to elevated lower reach groundwater levels and to the melting of ice along river channels. Inter-annual variations in river length were due to inter-annual variations in upper reach runoff and middle reach agricultural water use which increased slightly during the study period. Inter-annual variations in frequency and amplitude of the fluctuations in river length were mainly controlled by changes in upper reach runoff. The minimum in river length in 2009 was consistent with the low in upper reach runoff of the Keriya River and other rivers in the Tarim Basin. The most significant factors controlling variations in river length are natural in origin.

Analysis of spatial patterns and driving factors of provincial tourism demand in China.

Modeling and forecasting tourism demand across destinations has become a priority in tourism research. Most tourism demand studies rely on annual statistics with small sample sizes and lack research on spatial heterogeneity and drivers of tourism demand. This study proposes a new framework for measuring inter-provincial tourism demand's spatiotemporal distribution using search engine indices based on a geographic perspective. A combination of spatial autocorrelation and Geodetector is utilized to recognize the spatiotemporal distribution patterns of tourism demand in 2011 and 2018 in 31 provinces of mainland China and detect its driving mechanisms. The results reveal that the spatial distribution of tourism demand manifests a vital stratification phenomenon with significant spatial aggregation in the southwest and northeast of China. Traffic conditions, social-economic development level, and physical conditions compose a constant and robust interaction network, which dominates the spatial distribution of tourism demand in different development stages through different interactions.

Screening of MicroRNA Biomarkers Related to the Progression of Bladder Cancer.

Objective: This study aimed to identify the novel microRNAs (miRNAs) for early diagnosis of bladder cancer. Materials and Methods: Differentially expressed miRNAs between early and advanced bladder cancer were identified by differential expression analysis, using miRNA-seq data from The Cancer Genome Atlas (TCGA). The optimal subset of feature miRNAs for pathologic stage prediction was acquired by Random Forest algorithm and was used to construct a support vector machine (SVM) classifier. The performance of the SVM classifier in predicting the progression of bladder cancer samples was validated using an independent validating dataset. An miRNA-regulated target gene network was finally constructed and functional annotation were performed for the target genes. Results: A total of 52 significantly differentially expressed miRNAs were identified between early and advanced bladder cancer samples and 17 of these miRNAs were identified to be feature miRNAs. The 17 feature miRNAs were used to construct an SVM classifier, which showed a high performance in pathologic stage prediction for both training and validating dataset. Besides, our functional annotation analysis showed that the feature miRNAs were significantly involved in biological processes and pathways related to extracellular matrix process and PI3K/Akt signaling. Conclusions: The optimal subset of miRNAs may act as potential therapeutic targets and diagnostic markers of bladder cancer.