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Soil surface roughness (SSR) is an important factor affecting soil erosion and soil nutrient transport. Human tillage leads to increased instability in SSR, and the characteristics of SSR caused by different tillage practices await further study. This research utilizes terrestrial laser scanning (TLS) to measure the SSR of six farmland plots (25 m × 25 m) and analyzes the characteristics of SSR under different tillage practices (plowing, harrowing, ridging, crusting, etc.). The study results show: 1) Different agricultural tillage practices lead to significant differences in SSR. The plowed and harrowed plot corresponds to the maximum (2.49 cm) and minimum (1.5 cm) root mean square height (RMSH), respectively. Correlation length (CL) is more affected by different tillage practices than RMSH. The difference in CL between the ridged and harrowed plot is 2.6 times. 2) Ridging and crusting caused significant directional variation in SSR. The SSR anisotropy of the harrowed plot can be disregarded. 3) Under the condition of measuring soil profile in 12 directions and randomly sampling 70 times in each direction, the profile length must be at least 3 m to ensure that the measurement error of SSR is better than 5% compared to the “true” value. TLS can measure two-dimensional SSR. Therefore, it is only necessary to ensure that the measurement range is at least 3 m × 3 m. The study results provide a reference for the high-precision measurement of SSR (RMSH and CL) under different agricultural tillage practices.

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research.

Abstract Phosphatidylinositol 3-kinase alpha (PI3Kα) inhibitors are currently evaluated for the therapy of esophageal squamous cell carcinoma (ESCC). It is of great importance to identify potential biomarkers to predict or monitor the efficacy of PI3Kα inhibitors in an aim to improve the clinical responsive rate in ESCC. Here, ESCC PDXs with CCND1 amplification were found to be more sensitive to CYH33, a novel PI3Kα-selective inhibitor currently in clinical trials for the treatment of advanced solid tumors including ESCC. Elevated level of cyclin D1, p21 and Rb was found in CYH33-sensitive ESCC cells compared to those in resistant cells. CYH33 significantly arrested sensitive cells but not resistant cells at G1 phase, which was associated with accumulation of p21 and suppression of Rb phosphorylation by CDK4/6 and CDK2. Hypo-phosphorylation of Rb attenuated the transcriptional activation of SKP2 by E2F1, which in turn hindered SKP2-mediated degradation of p21 and reinforced accumulation of p21. Moreover, CDK4/6 inhibitors sensitized resistant ESCC cells and PDXs to CYH33. These findings provided mechanistic rationale to evaluate PI3Kα inhibitors in ESCC patients harboring amplified CCND1 and the combined regimen with CDK4/6 inhibitors in ESCC with proficient Rb.

In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing season. We focused on Daxing Farm in Fujin City, Jiamusi City, Heilongjiang Province, for cropland plot extraction using Planet satellite imagery. Utilizing Sentinel-2 satellite imagery, we analysed the differences in rice’s modified normalized difference water index (MNDWI) during specific phenological periods. A multitemporal Gaussian mixture model (GMM) was developed, integrated with the maximum expectation algorithm, to produce binarized classification outcomes. These results were employed to detect surface changes and map the corresponding rice cultivation types. The probability of various rice cultivation types within arable plots was quantified, yielding a plot-level rice-cultivation-type mapping product. The mapping achieved an overall accuracy of 91.46% in classifying rice planting types, with a Kappa coefficient of 0.89. The area extraction based on arable land parcels showed a higher R2 by 0.1109 compared to pixel-based area extraction and a lower RMSE by 0.468, indicating more accurate results aligned with real statistics and surveys, thus validating our study’s method. This approach, not requiring labelled samples or many predefined parameters, offers a new method for rapid and feasible mapping, especially suitable for direct-seeded rice areas in Northeast China. It fills the gap in mapping rice distribution for different planting types, supporting water management in rice fields and policies for planting-method changes.

Esophageal cancer is one of the most lethal cancers worldwide because of its rapid progression and poor prognosis. Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are two major subtypes of esophageal cancer. ESCC predominantly affects African and Asian populations, which is closely related to chronic smoking and alcohol consumption. EAC typically arises in Barrett's esophagus with a predilection for Western countries. While surgical operation and chemoradiotherapy have been applied to combat this deadly cancer, molecularly targeted therapy is still at the early stages. With the development of large-scale next-generation sequencing, various genomic alterations in ESCC and EAC have been revealed and their potential roles in the initiation and progression of esophageal cancer have been studied. Potential therapeutic targets have been identified and novel approaches have been developed to combat esophageal cancer. In this review, we comprehensively analyze the genomic alterations in EAC and ESCC and summarize the potential role of the genetic alterations in the development of esophageal cancer. Progresses in the therapeutics based on the different tissue types and molecular signatures have also been reviewed and discussed.

The auxin/indole-3-acetic acid (Aux/IAA) gene family plays a critical role in auxin-mediated responses and fruit development. However, studies on its role in watermelon are limited. In this study, 29 ClIAA gene members were identified in the watermelon genome and classified into eleven groups. Of note, ClIAA16, which was found to be up-regulated during fruit ripening, was targeted using CRISPR/Cas9 gene editing. Knockout mutants of ClIAA16 exhibited a 3–4 day delay in ripening compared to the wild type, highlighting the regulatory importance of ClIAA16. Our findings shed light on the importance of ClIAA genes in watermelon fruit ripening and pave the way for further functional studies.

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security.

Agricultural drought significantly impacts agricultural production, highlighting the need for accurate monitoring. Accurate agricultural-drought-monitoring models are critical for timely warning and prevention. The random forest (RF) is a popular artificial intelligence method but has not been extensively studied for agricultural drought monitoring. Here, multi-source remote sensing data, including surface temperature, vegetation index, and soil moisture data, were used as independent variables; the 3-month standardized precipitation evapotranspiration index (SPEI_3) was used as the dependent variable. Soil texture and terrain data were used as auxiliary variables. The bias-corrected RF model was used to construct a random forest synthesized drought index (RFSDI). The drought-degree determination coefficients (R2) of the training and test sets reached 0.86 and 0.89, respectively. The RFSDI and SPEI_3 fit closely, with a correlation coefficient (R) above 0.92. The RFSDI accurately reflected typical drought years and effectively monitored agricultural drought in Northeast China (NEC). In the past 18 years, agricultural drought in NEC has generally decelerated. The degree and scope of drought impacts from 2003 to 2010 were greater than those from 2010 to 2020. Agricultural drought occurrence in NEC was associated with dominant climatic variables such as precipitation (PRE), surface temperature (Ts), relative humidity (RHU), and sunshine duration (SSD), alongside elevation and soil texture differences. The agricultural drought occurrence percentage at 50–500 m elevations reached 94.91%, and the percentage of occurrence in loam and sandy soils reached 90.31%. Water and temperature changes were significantly correlated with the occurrence of agricultural drought. Additionally, NEC showed an alternating cycle of drought and waterlogging of about 10 years. These results have significant application potential for agricultural drought monitoring and drought prevention in NEC and demonstrate a new approach to comprehensively evaluating agricultural drought.

Abstract Phosphoinositide‐3 kinase alpha (PI3Kα) has been confirmed to be a potential therapeutic target for esophageal squamous cell carcinoma (ESCC), while the potency of PI3Kα inhibitors is often attenuated by concurrent oncogenic signalling pathways. We performed genome‐wide gain‐of‐function screening with a CRISPR‐SAM library and identified enhancer of zeste homolog 2 (EZH2) rendering ESCC cells resistant to the PI3Kα inhibitor CYH33. Enhanced expression of EZH2 frequently occurs in ESCC and is related to poor prognosis. Overexpression of full‐length EZH2 but not methyltransferase‐deficient EZH2 conferred resistance to CYH33, while downregulating EZH2 expression restored sensitivity. EZH2 expression was negatively related to the activity of CYH33 against the proliferation of ESCC cell lines and patient‐derived cells. Transcriptomic analysis revealed that EZH2 abrogated CYH33‐mediated cell cycle regulation. EZH2 epigenetically suppressed the transcription of CDKN1A, promoting RB phosphorylation and cell cycle progression. Concurrently targeting EZH2 significantly potentiated CYH33 to inhibit the growth of ESCC cells and patient‐derived xenografts accompanied by enhanced cell cycle arrest. Taken together, our study demonstrated that an EZH2‐p21‐RB axis remodeled cell cycle regulation and rendered resistance to PI3Kα inhibitors in ESCC. Simultaneously targeting PI3Kα and EZH2 may provide an effective strategy for ESCC therapy with high expression of EZH2.

Over fertilization has resulted in serious soil compaction and acidification, as well as nonpoint pollution. A strategy for variable fertilization according to Site-Specific Management Zones (SSMZ) is urgently needed. We developed an object-oriented method using image segmentation for SSMZ delineation for a farming cooperative founded in 2013 in northeastern China. The method is based on Normalized Difference Vegetation Index (NDVI) during the crop growth period from 2011 to 2016. We analyzed and validated SSMZ results with plant sampling data, NDVI, soil temperature and moisture. The results indicated that (i) SSMZs derived from NDVI and image segmentation enhanced the difference between SSMZs and the homogeneity within SSMZs; (ii) crop dry biomass was consistent with NDVI, and NDVI showed a clear change in the regular SSMZ pattern with terrain variation after 2013; and (iii) SSMZ patterns were mainly affected by different farming practices or by topographic factors before and after the cooperative was founded. This new approach for SSMZ delineation will accelerate the application of precision agriculture in relief areas. If the study results are applied to other areas, the main factors of SSMZ need to be determined because of climate differences as well as specific soil and terrain conditions.

Objective Activating KRAS mutations are the most common drivers in the development of non-small cell lung cancer (NSCLC). However, unsuccess of treatment by direct inhibition of KRAS has been proven. Deregulation of PI3K signaling plays an important role in tumorigenesis and drug resistance in NSCLC. The activity of PI3Kα-selective inhibition against KRAS-mutated NSCLC remains largely unknown.Methods Cell proliferation was detected by sulforhodamine B assay. Cell cycle distribution and apoptosis were measured by flow cytometry. Cell signaling was assessed by Western blot and immunohistochemistry. RNA interference was used to down-regulate the expression of cyclin D1. Human NSCLC xenografts were employed to detect therapeutic efficacy in vivo.Results CYH33 possessed variable activity against a panel of KRAS-mutated NSCLC cell lines. Although CYH33 blocked AKT phosphorylation in all tested cells, Rb phosphorylation decreased in CYH33-sensitive, but not in CYH33-resistant cells, which was consistent with G1 phase arrest in sensitive cells. Combined treatment with the CDK4/6 inhibitor, PD0332991, and CYH33 displayed synergistic activity against the proliferation of both CYH33-sensitive and CYH33-resistant cells, which was accompanied by enhanced G1-phase arrest. Moreover, down-regulation of cyclin D1 sensitized NSCLC cells to CYH33. Reciprocally, CYH33 abrogated the PD0332991-induced up-regulation of cyclin D1 and phosphorylation of AKT in A549 cells. Co-treatment with these two drugs demonstrated synergistic activity against A549 and H23 xenografts, with enhanced inhibition of Rb phosphorylation.Conclusions Simultaneous inhibition of PI3Kα and CDK4/6 displayed synergistic activity against KRAS-mutated NSCLC. These data provide a mechanistic rationale for the combination of a PI3Kα inhibitor and a CDK4/6 inhibitor for the treatment of KRAS-mutated NSCLC.

Crop biomass is an agricultural indicator of productivity, and knowledge of the temporal and spatial variation in biomass in different topography is critical for the application of precision management techniques. This study integrates microtopography with multitemporal remote sensing observations to reveal biomass- and yield-limiting variables. Three SPOT-6 high spatial resolution images and six topographic variables were combined to model the spatial variation in soybean biomass at multiple stages during the growing season. The results showed the following. (i) The multiple regression model for biomass estimation that combines topographic variables with vegetation indices can achieve higher accuracy than a vegetation index model. (ii) Biomass varied dramatically with topography during the growing season. (iii) Microtopographic variables, such as curvature and slope, had distinctive impacts on crop conditions over a growing season. Early in the growing season, sunny upper slopes produced more biomass than shady lower slopes, whereas this trend reversed over the season. Gentle concave slopes (–1.2 m−1 < curvature < 0 m−1) showed greater productivity later in the season, while slopes with high concavity (curvature < –1.2 m−1) or high convexity (curvature > 0.75 m−1) suppressed crop growth. These conclusions can be used directly to precisely manage nutrients and water applications.

Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.

In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to predict the SOC from 1989 to 2018, and the geographic detector method (GDM) was applied to analyze quantitatively the natural and anthropogenic factors that are affecting Aohan banner. The results indicated that: (1) After adding the terrain factors, the R2 and residual predictive deviation (RPD) of the RF model increased by 1.178 and 0.39%, with root mean square errors (RMSEs) of 1.42 g/kg and 1.05 g/kg, respectively; (2) The spatial distribution of SOC was higher in the south and lower in the north; the negative growth of SOC accounted for 55.923% of the total area, showing a trend of degradation; (3) Precipitation was the main driving factor of SOC spatial variation in the typical agro-pastoral ecotone of northern China, which was also affected by temperature, elevation, soil type and soil texture (p < 0.01). (4). Anthropogenic factors (carbon input and gross domestic product (GDP)) had a greater impact on SOC than did climate factors (temperature and precipitation), making anthropogenic factors the dominant factors affecting SOC temporal variation (p < 0.01). The results of this work constitute a basis for a regional assessment of the temporal evolution of organic carbon in the soil surface, which is a key tool for monitoring the sustainable development of agropastoral ecotones.

Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the ‘mixed pixels’ nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.

Multi-day vegetation index (VI) composite images, the basis for crop yield estimation, are subject to temporal information losses. Using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Landsat_5_TM and Landsat_7_ETM, we reconstructed time-series VI using temporal information and a mathematical model to estimate yield. The results indicated the following: (i) The reconstructed model describes real changes in VI during crop growth. (ii) The Extreme model is best for cotton growth process delineation. Compared to NDVI, EVI is appropriate for the reconstruction of time series VI curves, obtaining a model-fitting coefficient of 0.97 and a root mean square error (RMSE) of 0.05. (iii) The correlation between reconstructed EVI (REVI) and yield was more significant than Landsat VI and yield, with a correlation coefficient of 0.90 during the growth stage. The determination coefficient of the model using EVI on the DOY 198 as an input variable was 0.81, and the RMSE was 481.19. (iv) The Extreme model reconstruction reduces background differences between different plots, providing the basis for regional yield estimation using remote sensing. Our method resolves issues associated with multi-day composite VI. With increases in

The redistribution of solar radiation, temperature, soil moisture and heat by topography affects the physical and chemical properties of the soil and the spatial distribution characteristics of crop growth. Analyses of the relationship between topography and these variables may help to improve the accuracy of digital elevation models (DEMs). The purpose of correcting Shuttle Radar Topography Mission (SRTM) data is to obtain high-precision DEM data in cultivated land. A typical black soil area was studied. A high-precision reference DEM was generated from an unmanned aerial vehicle (UAV) and extensive measured ground elevation data. The normalized differential vegetation index (NDVI), perpendicular drought index (PDI) extracted from SPOT-6 remote sensing images and potential solar radiation (PSR) extracted from SRTM. The interactions between topography and NDVI, PDI, and PSR were analyzed. The NDVI, PDI and PSR in June, July, August and September of 2016 and the SRTM were used as independent variables, and the UAV DEM was used as the dependent variable. Linear stepwise regression (LSR) and a back-propagation neural network (BPNN) were used to establish an elevation prediction model. The results indicated that (1) The correlation between topography and NDVI, PSR, PDI was significant at 0.01 level. The PDI and PSR improved the spatial resolution of SRTM data and reduce the vertical error. (2) The BPNN (R21 = 0.98, root mean square error, RMSE1 = 0.54) yielded a higher SRTM accuracy than did the studied linear model (RMSE1 = 1.00, R21 = 0.90). (3) A series of significant improvements in the SRTM were observed when assessed with the reference DEMs for two different areas, with RMSE reductions of 91% (from 14.95 m to 1.23 m) and 93% (from 15.6 m to 0.94 m). The proposed method improved the accuracy of existing DEMs and could provide support for accurate farmland management.

Protein kinases are therapeutic targets for human cancer. However, “gatekeeper” mutations in tyrosine kinases cause acquired clinical resistance, limiting long-term treatment benefits. mTOR is a key cancer driver and drug target. Numerous small-molecule mTOR kinase inhibitors have been developed, with some already in human clinical trials. Given our clinical experience with targeted therapeutics, acquired drug resistance in mTOR is thought likely, but not yet documented. Herein, we describe identification of a hot spot (L2185) for drug-resistant mutations, which is distinct from the gatekeeper site, and a chemical scaffold refractory to drug-resistant mutations. We also provide new insights into mTOR kinase structure and function. The hot spot mutations are potentially useful as surrogate biomarkers for acquired drug resistance in ongoing clinical trials and future treatments and for the design of the next generation of mTOR-targeted drugs. Our study provides a foundation for further research into mTOR kinase function and targeting.

There is great interest in developing small molecules agents capable of reversing tumor immune escape to restore the body’s immune system. As an immunosuppressive enzyme, indoleamine 2,3-dioxygenase 1 (IDO-1) is considered a promising target for oncology immunotherapy. Currently, none of IDO-1 inhibitors have been launched for clinical practice yet. Thus, the discovery of new IDO-1 inhibitors is still in great demand. Herein, a series of diverse ortho-naphthaquinone containing natural product derivatives were synthesized as novel IDO-1 inhibitors. Among them, 1-ene-3-ketone-17-hydroxyl derivative 12 exhibited significantly improved enzymatic and cellular inhibitory activity against IDO-1 when compared to initial lead compounds. Besides, the molecular docking study disclosed that the two most potent compounds 11 and 12 have more interactions within the binding pocket of IDO-1 via hydrogen-bonding, which may account for their higher IDO-1 inhibitory activity.

Various cultivars of Perilla frutescens (L.) (var. crispa and var. frutescens) Britt. were harvested in China and Japan. They were easily differentiated on the basis of their foliage color, that varied from red to green. Water extracts of dried plants were investigated for their antioxidant activity (AA) and their polyphenolic compounds compared. Among them, cinnamic acid derivatives (coumaroyl tartaric acid, caffeic acid and rosmarinic acid), flavonoids (apigenin 7-O-caffeoylglucoside, scutellarein 7-O-diglucuronide, luteolin 7-O-diglucuronide, apigenin 7-O-diglucuronide, luteolin 7-O-glucuronide, and scutellarein 7-O-glucuronide) and anthocyanins (mainly cis-shisonin, shisonin, malonylshisonin and cyanidin 3-O-(E)-caffeoylglucoside-5-O-malonylglucoside) were quantified. AA assays are based on the inhibition of the free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH). The DPPH radical scavenging activity was calculated as Trolox ® [(±)-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid] equivalent antioxidant capacity (TEAC). The mean amount of total phenolics of the water extracts (4-29 µmol/100 mL) and the TEAC value calculated (23-167 µmol TE/100 mL) confirmed the high antioxidant activity of these leaf water extracts. These results were highly correlated within some o-dihydroxylated polyphenolic compounds and AA.

Despite the emerging success of multi-targeted protein tyrosine kinase (PTK) inhibitors in cancer therapy, significant side effects and resistance concerns seems to be avoided unlikely. The aim of the present study was to identify novel multi-targeting PTK inhibitors. The kinase enzymatic activities were measured by enzyme-linked immunosorbent assay (ELISA). The antiproliferative activities in human microvascular endothelial cells (HMECs) were evaluated by sulforhodamine (SRB) assay. The phosphorylation of kinases and their downstream molecules was probed by western blot analysis. The binding mode between MdOS and PTKs was profiled by surface plasmon resonance (SPR) approach and molecular simulation. Tube formation assay, rat aortic ring method and chicken chorioallantoic membrane assay were combined to illustrate the in vitro and in vivo anti-angiogenic effects. Results indicated that MdOS, a novel marine-derived oligosaccharide sulfate, exhibited a broad-spectrum PTK inhibitory action. At an enzymatic level, MdOS inhibited HER2, EGFR, VEGFR, PDGFR, c-Kit, FGFR1 and c-Src, with little impact on FGFR2. In cellular settings, MdOS inhibited phosphorylation of PTKs, exemplified by HER2, EGFR and VEGFR2, and downstream molecules of Erk1/2 and AKT. Further studies demonstrated that MdOS acted as an ATP-competitive inhibitor via directly binding to the residues of entrance rather than those of the ATP-binding pocket. Furthermore, MdOS inhibited proliferation and tube formation of HMECs, arrested microvessel outgrowth of rat aortic rings and hindered the neovascularization of chick allantoic membrane. Taken together, results presented here indicated that MdOS exhibited anti-angiogenic activity in a PTK-dependent manner and make it a promising agent for further evaluation in PTK-associated cancer therapy.