Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy.

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

Evaluating complementaries among urban water, energy, infrastructure, and social Sustainable Development Goals in China.

Urban areas' performance in water, energy, infrastructure, and socio-economic sectors is intertwined and measurable through Sustainable Development Goals (SDGs) 6-13. Effective synergy among these is critical for sustainability. This study constructs an indicator framework that reflects progress towards these urban SDGs in China. Findings indicate underperformance in SDGs 8-11, suggesting the need for transformative actions. Through network analysis, the research reveals complementarities among these SDGs. Notably, the SDG space divides into socio-economic and ecological clusters, with SDG 6 (Clean Water and Sanitation) central to both. Additionally, SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure) act as bridges, while greater synergies exist between SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). An in-depth view at the indicator-level shows a core-periphery structure, emphasizing indicators like SDG 6.2 (Wastewater Treatment Rate) and SDG 6.6 (Recycled Water Production Capacity per capita) as pivotal. This study confirms the urban SDG space's stability and predictiveness, underscoring its value in steering well-aligned policy decisions for sustainable growth.

A multi-view feature representation for predicting drugs combination synergy based on ensemble and multi-task attention models.

This paper proposes a novel multi-view ensemble predictor model that is designed to address the challenge of determining synergistic drug combinations by predicting both the synergy score value values and synergy class label of drug combinations with cancer cell lines. The proposed methodology involves representing drug features through four distinct views: Simplified Molecular-Input Line-Entry System (SMILES) features, molecular graph features, fingerprint features, and drug-target features. On the other hand, cell line features are captured through four views: gene expression features, copy number features, mutation features, and proteomics features. To prevent overfitting of the model, two techniques are employed. First, each view feature of a drug is paired with each corresponding cell line view and input into a multi-task attention deep learning model. This multi-task model is trained to simultaneously predict both the synergy score value and synergy class label. This process results in sixteen input view features being fed into the multi-task model, producing sixteen prediction values. Subsequently, these prediction values are utilized as inputs for an ensemble model, which outputs the final prediction value. The 'MVME' model is assessed using the O'Neil dataset, which includes 38 distinct drugs combined across 39 distinct cancer cell lines to output 22,737 drug combination pairs. For the synergy score value, the proposed model scores a mean square error (MSE) of 206.57, a root mean square error (RMSE) of 14.30, and a Pearson score of 0.76. For the synergy class label, the model scores 0.90 for accuracy, 0.96 for precision, 0.57 for kappa, 0.96 for the area under the ROC curve (ROC-AUC), and 0.88 for the area under the precision-recall curve (PR-AUC).

BMP4 and Temozolomide Synergize in the Majority of Patient-Derived Glioblastoma Cultures.

One of the main causes of poor prognoses in patient with glioblastoma (GBM) is drug resistance to current standard treatment, which includes chemoradiation and adjuvant temozolomide (TMZ). In addition, the concept of cancer stem cells provides new insights into therapy resistance and management also in GBM and glioblastoma stem cell-like cells (GSCs), which might contribute to therapy resistance. Bone morphogenetic protein-4 (BMP4) stimulates astroglial differentiation of GSCs and thereby reduces their self-renewal capacity. Exposure of GSCs to BMP4 may also sensitize these cells to TMZ. A recent phase I trial has shown that local delivery of BMP4 is safe, but a large variation in survival is seen in these treated patients and in features of their cultured tumors. We wanted to combine TMZ and BMP4 (TMZ + BMP4) therapy and assess the inter-tumoral variability in response to TMZ + BMP4 in patient-derived GBM cultures. A phase II trial could then benefit a larger group of patients than those treated with BMP4 only. We first show that simultaneous treatment with TMZ + BMP4 is more effective than sequential treatment. Second, when applying our optimized treatment protocol, 70% of a total of 20 GBM cultures displayed TMZ + BMP4 synergy. This combination induces cellular apoptosis and does not inhibit cell proliferation. Comparative bulk RNA-sequencing indicates that treatment with TMZ + BMP4 eventually results in decreased MAPK signaling, in line with previous evidence that increased MAPK signaling is associated with resistance to TMZ. Based on these results, we advocate further clinical trial research to test patient benefit and validate pathophysiological hypothesis.

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Synergistic Strategies in Prostate Cancer Therapy: Electrochemotherapy and Electromagnetic Hyperthermia.

Prostate cancer is a significant global health problem, being the second most common cancer and the fifth leading cause of death in men worldwide. Standard chemotherapy, though effective, often lacks selectivity for tumor cells, resulting in dose-limiting side effects. To address this, innovative biomedical approaches such as electrochemotherapy and electromagnetic hyperthermia have emerged. Electrochemotherapy improves drug delivery by facilitating electroporation, thereby increasing intracellular concentrations of chemotherapeutic agents. This approach reduces dosages and associated adverse effects. Meanwhile, electromagnetic hyperthermia raises the temperature of tumor cells, enhancing their sensitivity to chemotherapy. While previous research has demonstrated the inhibitory effects of magnetic hyperthermia on prostate cancer cell growth both in vitro and in vivo, and its synergy with chemotherapy has shown enhanced tumor remission, limited studies have focused on electrochemotherapy alone or in combination with hyperthermia in prostate cancer models. This study aims to assess the synergistic effects of electromagnetic hyperthermia, with superparamagnetic iron oxide nanoparticles (SPIONs) and electrochemotherapy, with electroporation and the chemotherapeutic drugs bleomycin and cisplatin, on the prostate cancer-derived cell line DU-145/GFP and prostate-derived cell line RWPE-1. Results indicate enhanced cytotoxicity with both treatments (bleomycin and cisplatin) by adding electroporation, demonstrating a particularly pronounced effect with bleomycin. Combining electroporation with hyperthermia significantly augments cytotoxicity. Moreover, electroporation effectively reduced the time of exposure to electromagnetic hyperthermia while magnifying its cytotoxic effects. Future research in in vivo trials may reveal additional insights into the combined effects of these therapies.

Antimicrobial Hydroxyethyl-Cellulose-Based Composite Films with Zinc Oxide and Mesoporous Silica Loaded with Cinnamon Essential Oil.

Background: Cellulose derivatives are gaining much attention in medical research due to their excellent properties such as biocompatibility, hydrophilicity, non-toxicity, sustainability, and low cost. Unfortunately, cellulose does not exhibit antimicrobial activity. However, derivatives like hydroxyethyl cellulose represent a proper matrix to incorporate antimicrobial agents with beneficial therapeutic effects. Methods: Combining more antimicrobial agents into a single composite material can induce stronger antibacterial activity by synergism. Results: Therefore, we have obtained a hydroxyethyl-cellulose-based material loaded with zinc oxide nanoparticles and cinnamon essential oil as the antimicrobial agents. The cinnamon essential oil was loaded in mesoporous silica particles to control its release. Conclusions: The composite films demonstrated high antibacterial activity against Staphylococcus aureus and Escherichia coli strains, impairing the bacterial cells' viability and biofilm development. Such antimicrobial films can be used in various biomedical applications such as topical dressings or as packaging for the food industry.

Template-based synergy extrapolation analysis for prediction of muscle excitations.

OBJECTIVE: Accurate prediction of unmearsured muscle excitations can reduce the required wearable surface electromyography (sEMG) sensors, which is a critical factor in the study of physiological measurement. Synergy extrapolation uses synergy excitations as building blocks to reconstruct muscle excitations. However, the practical application of synergy extrapolation is still limited as the extrapolation process utilizes unmeasured muscle excitations it seeks to reconstruct. This paper aims to propose and derive methods to provide an avenue for the practical application of synergy extrapolation with non-negative matrix factorization (NMF) methods. APPROACH: Specifically, a tunable Gaussian-Laplacian mixture distribution NMF (GLD-NMF) method and related multiplicative update rules are derived to yield appropriate synergy excitations for extrapolation. Furthermore, a template-based extrapolation structure (TBES) is proposed to extrapolate unmeasured muscle excitations based on synergy weighting matrix templates totally extracted from measured sEMG datasets, improving the extrapolation performance. Moreover, we applied the proposed GLD-NMF method and TBES to selected muscle excitations acquired from a series of single-leg stance (SLS) tests, walking tests and upper limb reaching tests. MAIN RESULTS: Experimental results show that the proposed GLD-NMF and TBES could extrapolate unmeasured muscle excitations accurately. Moreover, introducing synergy weighting matrix templates could decrease the number of sEMG sensors in a series of experiments. In addition, verification results demonstrate the feasibility of applying synergy extrapolation with NMF methods. SIGNIFICANCE: With the TBES method, synergy extrapolation could play a significant role in reducing data dimensions of sEMG sensors, which will improve the portability of sEMG sensors-based systems and promotes applications of sEMG signals in human-machine interfaces scenarios.

Circular economy, economic growth, and e-waste generation in EU27 countries: Further evidence from the novel circular economy index and threshold effect.

The circular economy has been identified as a critical keyword for achieving the Sustainable Development Goals. Nevertheless, there is a lack of in-depth empirical literature on the impact mechanisms of the circular economy (CE) and economic growth (GDP) in mitigating e-waste generation (waste electrical and electronic equipment - WEEE). Given Europe's leading position in e-waste generation per capita, the study aims to scrutinize the interplay between CE, GDP, and WEEE for 2010-2020. The research applies advanced econometric methods, primarily centered around the system generalized method of moment and dynamic panel threshold. It was noteworthy that different CE indicators exhibited varying effects on WEEE through the econometric analysis. Therefore, the research uniquely utilized the entropy weight method to compute a holistic composite index for the circular economy (CEI) and gained some interesting findings. Firstly, CEI significantly reduced WEEE, while GDP drove its increase. However, an overly developed CEI of 0.7616 counteracted its beneficial effect. Secondly, the synergy of CEI*GDP engendered the circular economy rebound effect, diminishing environmental benefits. Thirdly, in the circular context, the environmental Kuznets curve was validated, showcasing an inverted U-shaped pattern. Finally, the study found CEI to have different threshold effects, with thresholds of 0.2161 to inhibit WEEE, 0.2114 to avert the circular economy rebound effect, and 0.2360 to leverage GDP in reducing WEEE. These outcomes give insights to policymakers in designing sound policies targeting circular economy development and decoupling e-waste generation from economic growth towards the United Nations' SDGs.

Synergy of histone acetyltransferase inhibitor (HATi) with quercetin inhibits biofilm formation in Candida tropicalis.

Histone acetyltransferase inhibitors (HATi) are mechanism-based inhibitors that show promise in the treatment of several illnesses, including diabetes, hyperlipidemia, cancer, and Alzheimer's disease. The work emphasizes the significance of HATi as a possible treatment strategy against Candida species biofilms. Here, in this study, we found that combining a HATi, anacardic acid (AA), and quercetin, a known flavonoid, significantly prevented biofilm formation by C. tropicalis. We further show that C. tropicalis exhibited a considerable downregulation of drug-resistance gene expression (CDR1 and MDR1) when co-administrated. Additionally, in silico studies revealed that the AA interacts strongly with a histone acetyltransferase, Rtt109, which may account for the observed biofilm inhibitory effect. In conclusion, the study illustrates how HATi may be used to potentiate the inhibitory action of phytoactives or antifungals against drug-resistant yeast infections.

Synergy between virus and three kingdom pathogens, fungus, bacterium and virus is lost in rice mutant lines of OsRDR1/6.

Co-infection, caused by multiple pathogen attacks on an organism, can lead to disease development or immunity. This complex interaction can be synergetic, co-existing, or antagonistic, ultimately influencing disease severity. The interaction between fungus, bacterium, and virus (three kingdom pathogens) is most prevalent. However, the underlying mechanisms of co-infection need to be explored further. In this study, we investigated the co-infection phenomenon in rice plants exposed to multiple pathogen species, specifically Rice necrosis mosaic virus (RNMV) and rice blast fungus (Magnaporthe oryzae, MO), bacterial leaf blight (Xanthomonas oryzae pv. oryzae, XO) or Cucumber mosaic virus (CMV). Our research showed that RNMV interacts synergistically with MO, XO, or CMV, increasing pathogen growth and lesion size. These findings suggest positive synergy in RNMV co-infections with three kingdom pathogens, increasing accumulation and symptoms. Additionally, to investigate the role of RNAi in pathogen synergism, we analyzed rice mutant lines deficient in RNA-dependent RNA polymerase 1 (OsRDR1) or 6 (OsRDR6). Notably, we observed the loss of synergy in each mutant line, highlighting the crucial role of OsRDR1 and OsRDR6 in maintaining the positive interaction between RNMV and three kingdom pathogens. Hence, our study emphasized the role of the RNA silencing pathway in the intricate landscape of pathogen interactions; the study's outcome could be applied to understand the plant defense response to improve crop yields.

Youth Engagement Synergy in Mental Health Legislation and Programming.

Youth engagement in implementing mental health legislation and programming is an intuitive best practice. However, well-intentioned efforts to work inclusively with youth are often perfunctory. Drawing from select community partnership models and insights from providers, public health advisors, and youth, we examine the typology, processes, and outcomes of youth engagement. We critically apply the theory of partnership synergy to understand how the convergence of youth and adult experiences and knowledge can advance mental health programming beyond what could be achieved by a single group.

Oxidative lipid damage by naringenin selectively sensitizes chronic myeloid leukemia cell lines and patient samples to Bcr-Abl tyrosine kinase inhibitors.

Chronic myeloid leukemia (CML) treatment with Bcr-Abl tyrosine kinase inhibitors (TKIs) has significantly improved patient outcomes, yet challenges such as drug resistance and persistence of leukemic stem cells persist. This study explores the potential of naringenin, a natural flavonoid, to enhance the efficacy of Bcr-Abl TKIs in CML therapy. We showed that naringenin reduces viability of a panel of CML cell lines regardless of varying cellular origin and genetic mutations, and acts synergistically with dasatinib and ponatinib. Importantly, naringenin is effective in targeting blast crisis CML CD34+ cells by decreasing their colony formation, self-renewal and viability. Compared to CML, naringenin is significantly less effective against normal bone marrow (NBM) counterparts. In addition, naringenin significantly enhances the inhibitory effects of dasatinib in CML but not NBM CD34+ cells. Mechanism studies showed that naringenin's inhibitory effects were associated with the induction of oxidative stress and lipid damage, as evidenced by increased reactive oxygen species (ROS) and malondialdehyde (MDA) levels. Notably, naringenin upregulated genes related to mitochondrial biogenesis while downregulating antioxidant defense genes. Pretreatment with α-tocopherol, which inhibits lipid-mediated ROS production, completely abolished the ROS increase and restored cell viability, indicating that lysosomal lipid peroxidation plays a crucial role in naringenin's mechanism of action. In a CML xenograft mouse model, the combination of naringenin and dasatinib resulted in remarkably more tumor growth suppression compared to single drug alone. Importantly, this combination was well-tolerated, with no adverse effects on body weight observed. These findings suggest that naringenin, by inducing oxidative lipid damage, enhances the anti-leukemic effects of Bcr-Abl TKIs, offering a promising therapeutic strategy for CML.

Investigation on the Synergy between Membrane Permeabilizing Amphiphilic α-Hydrazido Acids and Commonly Used Antibiotics against Drug-Resistant Bacteria.

The growth of (multi)drug resistance in bacteria is among the most urgent global health issues. Monocationic amphiphilic α-hydrazido acid derivatives are structurally simple mimics of antimicrobial peptides (AMPs) with fewer drawbacks. Their mechanism of membrane permeabilization at subtoxic concentrations was found to begin with an initial electrostatic attraction of isolated amphiphile molecules to the phospholipid heads, followed by a rapid insertion of the apolar portions. As the accumulation into the bilayer proceeded, the membrane increased its fluidity and permeability without being subjected to major structural damage. After having ascertained that α-hydrazido acid amphiphiles do not interact with bacterial DNA, they were subjected to synergy evaluation for combinations with conventional antibiotics. Synergy was observed for combinations with tetracycline against sensitive S. aureus and E. coli, as well as with ciprofloxacin and colistin against resistant strains. Additivity with a remarkable recovery in activity of conventional antibiotics (from 2-fold to ≥32-fold) together with largely subtoxic concentrations of α-hydrazido acid derivatives was found for combinations with ciprofloxacin toward susceptible S. aureus and methicillin toward MRSa. However, no potentiation of conventional antibiotics was observed for combinations with linezolid and gentamicin against the corresponding resistant S. aureus and E. coli strains.

Comparative Study on the Performance of Gel Grease for High-End Equipment Based on the Synergistic Effect of Friction-Reducing Agents.

In the field of high-end equipment, the synergistic effect of friction-reducing agents plays an important role in the performance study of gel grease. Exploring its tribological and rheological properties can not only significantly reduce the coefficient of friction of mechanical components and enhance its viscosity at high temperatures but also effectively reduce energy consumption, thus improving the service life of high-end equipment. In this study, Schaeffler Load 460 gel grease was mixed with polysiloxane viscosity modifier (PV611) and molybdenum dialkyl dithiocarbamate (RFM3000) according to (3:1, 1:1, and 1:3), and its tribological properties and rheological properties were investigated by the MRS-10G friction and wear tester, MCR302 rotational rheometer, and crossover test. Comparative analyses of tribological and rheological properties were carried out. The results showed that the average coefficient of friction of Schaeffler Load 460 grease was reduced by 57.2%, 60%, and 71.9%, respectively, with the addition of two different ratios of friction reducers; the average diameter of abrasive spots was reduced by 44.5%, 55.4%, and 61.3%; and the shear stress and viscosity were increased by 117.94 Pa and 1295.02 mPa∙s, respectively, compared with that of the original grease, which is a good example for the lubrication of gel grease in the high-end equipment industry. This study provides a new direction and idea for the lubrication research of gel grease in the high-end equipment industry.

Synergistic effect of essential oils and chlorhexidine against planktonic and biofilm-forming cells of Malassezia pachydermatis.

Malassezia (M.) pachydermatis, is often associated with secondary infection of the skin and external auditory canal in dogs and cats. The treatment of Malassezia infections is based on the local application of antifungals often combined with antiseptics. Due to increased resistance of yeast to commonly used antimycotics, especially in biofilm-forming cells, the use of natural substances, e.g. plant essential oils, appears as a new promised option. In this study, the efficacy of selected plant essential oils (EO) - oregano, rosemary, bergamot, clove, cinnamon, and thyme - in combination with chlorhexidine on both planktonic and biofilm-forming cells of M. pachydermatis, was investigated. The checkerboard test was used to determine the effect of chlorhexidine combined with individual EOs. According to the FICI (fractional inhibitory concentration index) in planktonic cells, most combinations showed additive effect, except for thyme and rosemary EO, where a synergistic effect was found (33.3 % and 16.7 % respectively). In the biofilm-forming cells, a synergistic effect was noted in chlorhexidine combined with bergamot EO, recorded in 6 isolates (33.3 %), and with thyme and oregano EO, detected in 3 isolates (16.7 %). A significant decrease (p ˂ 0.05) was found in FIC (fractional inhibitory concentration) compared to MIC (minimum inhibitory concentration), for both planktonic and biofilm-forming cells. Based on the obtained results, we can conclude that the combination of chlorhexidine with EOs achieved better efficiency than when using each agent alone and made it possible to reduce the concentration of both, and a sufficient antifungal and antibiofilm effect was achieved in M. pachydermatis strains.

Assessing traffic emissions during the summer world university games 2023: Insights for multisectoral synergetic decontamination.

Implementing temporary traffic control measures is a common strategy to prevent air pollution and alleviate traffic congestion during mega-events. Accurate assessment of event-time vehicular emissions is useful for local authorities to develop effective policies. However, many previous assessments were based on policy-based scenarios, which often failed to capture the synergistic impact from other sectors (e.g., the industrial sector). Here, we conducted both traditional scenario-based ex-ante evaluation and data-driven real-time emissions tracking to evaluate the impacts of traffic restriction policies during the Summer World University Games 2023 in Chengdu, China. Real-time tracking revealed that average vehicle kilometers traveled (VKT), traffic emissions of nitrogen oxides (NOX), and volatile organic compound (VOC) decreased by 18.3 %, 37.9 %, and 18.4 %, respectively, compared to the non-event period. The scenario-based ex-ante evaluation substantially underestimated NOX emission reduction from the truck fleet due to the lack of consideration for multisectoral synergistic effects. Event-time interventions in the industrial sector accounted for an additional 18.1 % reduction in truck NOX emissions. This study underscores the importance of considering cross-sectoral synergy when establishing policies for future mega events.

Prior coordination solutions shape motor learning and transfer in redundant tasks.

Motor learning does not occur on a 'blank slate', but in the context of prior coordination solutions. The role of prior coordination solutions is likely critical in redundant tasks where there are multiple solutions to achieve the task goal - yet their influence on subsequent learning is currently not well understood. Here we addressed this issue by having human participants learn a redundant virtual shuffleboard task, where they held a bimanual manipulandum and made a discrete throwing motion to slide a virtual puck towards a target. The task was redundant because the distance traveled by the puck was determined by the sum of the left- and right-hand speeds at the time of release. On the first day, 37 participants in different groups practiced symmetric or asymmetric solutions. On the second day, all participants transferred to a common criterion task, which required an asymmetric solution. Results showed that: (i) the symmetry of the practiced solution affected motor variability during practice, with more asymmetric solutions showing higher exploration of the null space, (ii) when transferring to the common criterion task, participants in the symmetric group showed much higher null space exploration, and (iii) when no constraints were placed on the solution, participants tended to return to the symmetric solution regardless of the solution originally practiced. Overall, these results suggest that the stability of prior coordination solutions plays an important role in shaping learning in redundant motor tasks.

Cell-specific models reveal conformation-specific RAF inhibitor combinations that synergistically inhibit ERK signaling in pancreatic cancer cells.

Pancreatic ductal adenocarcinoma (PDAC) presents significant challenges for targeted clinical interventions due to prevalent KRAS mutations, rendering PDAC resistant to RAF and MEK inhibitors (RAFi and MEKi). In addition, responses to targeted therapies vary between patients. Here, we explored the differential sensitivities of PDAC cell lines to RAFi and MEKi and developed an isogenic pair comprising the most sensitive and resistant PDAC cells. To simulate patient- or tumor-specific variations, we constructed cell-line-specific mechanistic models based on protein expression profiling and differential properties of KRAS mutants. These models predicted synergy between two RAFi with different conformation specificity (type I½ and type II RAFi) in inhibiting phospho-ERK (ppERK) and reducing PDAC cell viability. This synergy was experimentally validated across all four studied PDAC cell lines. Our findings underscore the need for combination approaches to inhibit the ERK pathway in PDAC.

[Spatio-temporal Evolution and Trade-off/Synergy Analysis of Ecosystem Services in Regions of Rapid Urbanization： A Case Study of the Lower Yellow River Region].

As the forefront of implementing China's "Yellow River Major National Strategy," the lower Yellow River area has caused irreversible "constructive destruction" to the regional natural ecosystem and ecological functions while accelerating the process of urbanization and has become an area of sharp contradiction between ecological protection and high-quality development of the river basin. Therefore, based on ArcGIS and MATLAB software, this study used the InVEST and RUSLE models to quantitatively assess water yield, habitat quality, and soil conservation services of the lower Yellow River Region from 1990 to 2020 and analyzed the spatial and temporal characteristics and their interaction relationships of various ecosystem services. The results showed that： ① In the period from 1990 to 2020, the land urbanization process accelerated significantly, with the expansion of construction land increasing by 39.89%, whereas the area of other major land types had declined to varying degrees. ② From 1990 to 2020, the distribution patterns on the county scale and grid-scale in the lower Yellow River Region were relatively consistent. The water yield and soil conservation experienced a changing trend of first decreasing and then increasing, and the spatial distribution pattern of water yield gradually shifted to more in the east and less in the west. The spatial distribution patterns of soil conservation and habitat quality remained unchanged throughout the period, with the high values distributed in the hilly or mountainous regions of the higher terrain and the low values mainly in the plains of the gentle terrain. ③ At both the grid scale and county scale, the interaction relationships between various ecosystem services had been dominated by synergy and showed significant spatial heterogeneity. Especially at the county level, strong trade-offs were occurring in a few counties. For example, the relationship between water yields and habitat quality was a significant and strong trade-off between Weishan County and Huaiyin District. The study quantified the spatial and temporal evolution characteristics of ecosystem services in the lower Yellow River Region and clarified the trade-off synergistic relationships between ecosystem services, which can provide a scientific basis for ecological protection and watershed management under the rapid urbanization process.