Modelling carbon uptake of Australian evergreen ecosystems under rising CO2 concentration and water limitation

The current rise in the atmospheric CO2 concentration (Ca) provides both challenges and opportunities to terrestrial plant communities. Higher Ca provides a benefit to plants by allowing them to achieve higher photosynthetic rates at lower stomatal conductance (gs). On the other hand, the negative impact of rising Ca is global warming. Rising temperatures directly affect plants but also increase the dryness (vapour pressure deficit, D) of the air. Higher D could reduce gs and thus photosynthesis, leading to a loss of plant fitness. Terrestrial vegetation models can be used to quantify the combined impact of these environmental changes but need to be evaluated for their performance against observations. This thesis focuses on evaluating Ca responses of Australian ecosystems, which feature evergreen trees adapted to frequent water deficits. In the following chapters, I focus on three major components of terrestrial vegetation models: leaf area index (LAI); the response of gs to D; and the response of gs and photosynthesis to elevated Ca. These three components are particularly important for the modelling of rising Ca because the leaf scale response is captured by the responses of gs and photosynthesis to water deficit and Ca, while LAI is particularly important for the up-scaling of leaf level carbon and water fluxes to the whole ecosystem. Improvements in these components are thus likely to reduce the uncertainties in current terrestrial vegetation models. In Chapter 2, I test the concept of ecohydrological equilibrium for its ability to predict key traits of Australian evergreen ecosystems. This theory posits that long-term equilibrium LAI (Lequ) is determined by water availability. The predicted LAI values and the response to Ca both compared well to those of satellite-derived data. These results indicate that Lequ could be a useful alternative to satellite-derived data to terrestrial vegetation models to guide foliage carbon allocation. In the second research chapter (Chapter 3), I compared existing gs models and commonly used assumptions (i.e., hydraulic and non-stomatal limitations) for their ability to predict leaf and canopy-scale carbon and water fluxes under high D. I found that incorporating an empirical non-stomatal limitation of apparent photosynthetic capacity with increasing D improved model performance against data and outperformed models incorporating hydraulic limitation . The results suggest that future models should consider non-stomatal limitations to photosynthesis, especially in high-D environments. The Chapter 4 of this thesis aimed to determine the gross primary productivity (GPP) under ambient and elevated Ca (+38%; 150 µmol mol-1) at the Eucalyptus Free Air CO2 Enrichment (EucFACE) experiment. I parameterised the process-based model, MAESTRA, with a suite of in situ measurements of canopy structure and plant physiology shared with me by the EucFACE scientific community. I also conducted an attribution analysis to explore the determinants of the response of GPP to elevated Ca. My findings indicate a relatively small elevated Ca response of GPP (+8%) in the evergreen woodland. My results are key to understanding the response of this ecosystem to elevated Ca. In summary, the findings from this thesis provide some key insights into current gaps in the modelling of terrestrial vegetation. The results show viable options to improve the leaf gas exchange and LAI submodels that are used by terrestrial vegetation models. Overall, this thesis suggests ways for future terrestrial vegetation models to address these gaps for more realistic predictions under changing climate and rising Ca

Phytoremediation : a sustainable remedial method for soil contaminated by vanadium

Vanadium amassing in the soil increased with its widespread usage in multi-field. Elevated soil vanadium confers adverse effects on living organisms involved in plants, animals, and microorganisms. Moreover, vanadium can enter the human body through the food chain and lead to potential health risks stemming from its toxicity and carcinogenicity. Therefore, the remediation of soil contaminated by vanadium is imperative. Phytoremediation, a clean phytotechnology, is gaining increasing grace in modern society that prefers spirit-enjoy persuing. However, due to the blemishes of the remediation plants per se, the remediation efficiency relying on plants alone is not attractive. Therefore, the strengthened screening of vanadium accumulator and hyperaccumulator plants should step forward. Simultaneously, it is necessary to improve phytoremediation efficiency by some complementary measures, such as inoculating plant growth-promoting bacteria, vanadium reducing bacteria, and the proper application of plant growth regulators. Overall, microbe-assisted and moderate usage of plant growth-promoting factors are promising for the phytoremediation of vanadium-contaminated soil

Drug Sensitivity Screening and Targeted Pathway Analysis Reveal a Multi-Driver Proliferative Mechanism and Suggest a Strategy of Combination Targeted Therapy for Colorectal Cancer Cells

Treatment of colorectal cancer mostly relies on traditional therapeutic approaches, such as surgery and chemotherapy. Limited options of targeted therapy for colorectal cancer narrowly focus on blocking cancer-generic targets VEGFR and EGFR. Identifying the oncogenic drivers, understanding their contribution to proliferation, and finding inhibitors to block such drivers are the keys to developing targeted therapy for colorectal cancer. In this study, ten colorectal cancer cell lines were screened against a panel of protein kinase inhibitors blocking key oncogenic signaling pathways. The results show that four of the 10 cell lines did not respond to any kinase inhibitors significantly, the other six were mildly inhibited by AZD-6244, BMS-754807, and/or dasatinib. Mechanistic analyses demonstrate that these inhibitors independently block the MAP kinase pathway, IR/IGF-1R/AKT pathway, and Src kinases, suggesting a multi-driver nature of proliferative signaling in these cells. Most of these cell lines were potently and synergistically inhibited by pair-wise combinations of these drugs. Furthermore, seven of the 10 cell lines were inhibited by the triple combination of AZD-6244/BMS-754807/dasatinib with IC50’s between 10 and 84 nM. These results suggest that combination targeted therapy may be an effective strategy against colorectal cancer

Pengetahuan, kemahiran dan amalan guru membina item kemahiran berfikir aras tinggi (KBAT) dalam instrumen pentaksiran pembelajaran

Kajian ini dijalankan bagi mengenal pasti tahap pengetahuan, kemahiran dan amalan guru membina item kemahiran berfikir aras tinggi (KBAT) dalam instrumen pentaksiran pembelajaran. Kajian ini juga melihat perbezaan tahap pengetahuan, kemahiran dan amalan guru membina item KBAT berdasarkan kepada kumpulan guru mengajar matapelajaran tingkatan 3 dan kumpulan guru mengajar matapelajaran tingkatan 5. Sebanyak enam persoalan kajian telah dibangunkan bagi mengkaji permasalahan kajian. Rekabentuk kajian ini melibatkan analisis deskriptif dan inferensi dalam bentuk tinjauan yang melibatkan data kuantitatif dengan menggunakan borang soal selidik berskala likert lima mata sebagai instrumen kajian. Seramai 161 sampel guru daripada 3 buah sekolah di daerah Muar, Johor terpilih menjadi responden dalam kajian ini. Model Pengukuran Rasch telah digunakan bagi menentukan kesahan dan kebolehpercayaan instrumen kajian yang telah dibina sendiri. Hasil analisis kajian menunjukkan tahap pengetahuan, kemahiran dan amalan guru membina item KBAT adalah berada pada tahap tinggi. Dapatan kajian juga menunjukkan tidak terdapat perbezaan yang signifikan di antara kumpulan guru mengajar matapelajaran tingkatan 3 dan kumpulan guru mengajar matapelajaran tingkatan 5 terhadap tahap pengetahuan, kemahiran dan amalan membina item KBAT. Hasil kajian ini dapat dijadikan sebagai satu garis panduan kepada guru-guru yang mengubal item pentaksiran dan program peningkatan profesionalisme guru di sekolah. Kajian lanjutan juga boleh dilaksanakan bagi memperbaiki kekurangan dalam kajian ini

Biphasic Mathematical Model of Cell–Drug Interaction That Separates Target-Specific and Off-Target Inhibition and Suggests Potent Targeted Drug Combinations for Multi-Driver Colorectal Cancer Cells

Quantifying the response of cancer cells to a drug, and understanding the mechanistic basis of the response, are the cornerstones for anti-cancer drug discovery. Classical single target-based IC50 measurements are inadequate at describing cancer cell responses to targeted drugs. In this study, based on an analysis of targeted inhibition of colorectal cancer cell lines, we develop a new biphasic mathematical model that accurately describes the cell–drug response. The model describes the drug response using three kinetic parameters: ratio of target-specific inhibition, F1, potency of target-specific inhibition, Kd1, and potency of off-target toxicity, Kd2. Determination of these kinetic parameters also provides a mechanistic basis for predicting effective combination targeted therapy for multi-driver cancer cells. The experiments confirmed that a combination of inhibitors, each blocking a driver pathway and having a distinct target-specific effect, resulted in a potent and synergistic blockade of cell viability, improving potency over mono-agent treatment by one to two orders of magnitude. We further demonstrate that mono-driver cancer cells represent a special scenario in which F1 becomes nearly 100%, and the drug response becomes monophasic. Application of this model to the responses of \u3e400 cell lines to kinase inhibitor dasatinib revealed that the ratio of biphasic versus monophasic responses is about 4:1. This study develops a new mathematical model of quantifying cancer cell response to targeted therapy, and suggests a new framework for developing rational combination targeted therapy for colorectal and other multi-driver cancers

VDD: Varied Drone Dataset for Semantic Segmentation

Semantic segmentation of drone images is critical to many aerial vision tasks as it provides essential semantic details that can compensate for the lack of depth information from monocular cameras. However, maintaining high accuracy of semantic segmentation models for drones requires diverse, large-scale, and high-resolution datasets, which are rare in the field of aerial image processing. Existing datasets are typically small and focus primarily on urban scenes, neglecting rural and industrial areas. Models trained on such datasets are not sufficiently equipped to handle the variety of inputs seen in drone imagery. In the VDD-Varied Drone Dataset, we offer a large-scale and densely labeled dataset comprising 400 high-resolution images that feature carefully chosen scenes, camera angles, and varied light and weather conditions. Furthermore, we have adapted existing drone datasets to conform to our annotation standards and integrated them with VDD to create a dataset 1.5 times the size of fine annotation of Cityscapes. We have developed a novel DeepLabT model, which combines CNN and Transformer backbones, to provide a reliable baseline for semantic segmentation in drone imagery. Our experiments indicate that DeepLabT performs admirably on VDD and other drone datasets. We expect that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks. VDD is freely available on our website at https://vddvdd.com

Supply network position and firm performance: evidence from Chinese listed manufacturing companies

The aim of this paper is to examine the relationship between supply network position and firm performance. A-share manufacturing companies listed from 2013 to 2015 are chosen as the initial samples, and large sample supply networks are constructed with relational embeddedness and structural embeddedness. The location of supply network is depicted by network centrality and structural hole with social network analysis, and the influence of supply network position on the corporate performance is examined with multiple OLS regression analysis. This paper observes that a firms’ supply network position is an important factor affecting its performance. The higher the network centrality is, the richer the structural holes are, and the worse the company’s performance is. The results suggest that firms that have a high level of centrality or rich structural holes in their supply networks will gain limited information, resource and control benefits and face great business risks that may negatively influence their performance

The expression, clinical relevance, and prognostic significance of HJURP in cholangiocarcinoma

BackgroundCholangiocarcinoma (CCA) is the malignancy originating from the biliary epithelium, including intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA) CCA. The prognosis of CCA is very poor, and the biomarkers of different CCA subsets should be investigated separately. Holliday junction recognition protein (HJURP) is a key component of the pre-nucleosomal complex, which is responsible for normal mitosis. The ectopic expression of HJURP has been reported in several cancers, but not CCA.Materials and methodsIn our study, we investigated the expression of HJURP in 127 CCA patients which were composed of 32 iCCAs, 71 pCCAs, and 24 dCCAs with immunohistochemistry and divided these patients into subgroups with a low or high expression of HJURP. With chi-square test and univariate and multivariate analyses, we evaluated the clinical relevance and prognostic significance of HJURP in iCCAs, pCCAs, and dCCAs.ResultsHJURP was ectopically upregulated in CCAs compared with the para-tumor tissues based on TCGA and other mRNA-seq databases. A high expression of HJURP was correlated with low overall survival rates of iCCA and pCCA, but not in dCCA. Moreover, HJURP was an independent prognostic biomarker in both iCCA and pCCA. Patients with high HJURP were more likely to suffer CCA-related death after operation.ConclusionsHJURP was an independent prognostic biomarker in both iCCA and pCCA, but not in dCCA. Our results provide more evidence of the molecular features of different CCA subsets and suggest that patients with high HJURP are more high-risk, which can guide more precision follow-up and treatment of CCA

Applying the concept of ecohydrological equilibrium to predict steady state leaf area index

Leaf area index (LAI) is a key variable in modeling terrestrial vegetation because it has a major impact on carbon and water fluxes. However, several recent intercomparisons have shown that modeled LAI differs significantly among models and between models and satellite‐derived estimates. Empirical studies show that LAI is strongly related to precipitation. This observation is predicted by the ecohydrological equilibrium theory, which provides an alternative means to predict steady state LAI. We implemented this theory in a simple optimization model. We hypothesized that, when water availability is limited, plants should adjust steady state LAI and stomatal behavior to maximize net canopy carbon export, under the constraint that canopy transpiration is a fixed fraction of total precipitation. We evaluated the predicted LAI (Lopt) for Australia against ground‐based observations of LAI at 135 sites and continental‐scale satellite‐derived estimates. For the site‐level data, the root‐mean‐square error of predicted Lopt was 1.07 m2 m−2, similar to the root‐mean‐square error of a comparison of the data against 9‐year mean satellite‐derived LAI (Lsat) at those sites. Continentally, Lopt had an R2 of 0.7 when compared to Lsat. The predicted Lopt increased continental‐wide with rising atmospheric [CO2] over 1982–2010, which agreed with satellite‐derived estimations, while the predicted stomatal behavior responded differently in dry and wet regions. Our results indicate that long‐term equilibrium LAI can be successfully predicted from a simple application of ecohydrological theory. We suggest that this theory could be usefully incorporated into terrestrial vegetation models to improve their predictions of LAI

Consistent diurnal pattern of leaf respiration in the light among contrasting species and climates

Leaf daytime respiration (leaf respiration in the light, R (L)) is often assumed to constitute a fixed fraction of leaf dark respiration (R (D)) (i.e. a fixed light inhibition of respiration (R (D))) and vary diurnally due to temperature fluctuations. These assumptions were tested by measuring R (L), R (D) and the light inhibition of R (D) in the field at a constant temperature using the Kok method. Measurements were conducted diurnally on 21 different species: 13 deciduous, four evergreen and four herbaceous from humid continental and humid subtropical climates. R (L) and R (D) showed significant diurnal variations and the diurnal pattern differed in trajectory and magnitude between climates, but not between plant functional types (PFTs). The light inhibition of R (D) varied diurnally and differed between climates and in trajectory between PFTs. The results highlight the entrainment of leaf daytime respiration to the diurnal cycle and that time of day should be accounted for in studies seeking to examine the environmental and biological drivers of leaf daytime respiration