There is a need to develop new, more efficient therapies for head and neck cancer (HNSCC) patients. It is currently unclear whether defects in DNA repair genes play a role in HNSCCs' resistance to therapy. PARP1 inhibitors (PARPi) were found to be "synthetic lethal" in cancers deficient in BRCA1/2 with impaired homologous recombination. Since tumors rarely have these particular mutations, there is considerable interest in finding alternative determinants of PARPi sensitivity. Effectiveness of combined irradiation and PARPi olaparib was evaluated in ten HNSCC cell lines, subdivided into HR-proficient and HR-deficient cell lines using a GFP-based reporter assay. Both groups were equally sensitive to PARPi alone. Combined treatment revealed stronger synergistic interactions in the HR-deficient group. Because HR is mainly active in S-Phase, replication processes were analyzed. A stronger impact of treatment on replication processes (p = 0.04) and an increased number of radial chromosomes (p = 0.003) were observed in the HR-deficient group. We could show that radiosensitization by inhibition of PARP1 strongly correlates with HR competence in a replication-dependent manner. Our observations indicate that PARP1 inhibitors are promising candidates for enhancing the therapeutic ratio achieved by radiotherapy via disabling DNA replication processes in HR-deficient HNSCCs.

There is a need to develop new, more efficient therapies for head and neck cancer (HNSCC) patients. It is currently unclear whether defects in DNA repair genes play a role in HNSCCs' resistance to therapy. PARP1 inhibitors (PARPi) were found to be "synthetic lethal" in cancers deficient in BRCA1/2 with impaired homologous recombination. Since tumors rarely have these particular mutations, there is considerable interest in finding alternative determinants of PARPi sensitivity. Effectiveness of combined irradiation and PARPi olaparib was evaluated in ten HNSCC cell lines, subdivided into HR-proficient and HR-deficient cell lines using a GFP-based reporter assay. Both groups were equally sensitive to PARPi alone. Combined treatment revealed stronger synergistic interactions in the HR-deficient group. Because HR is mainly active in S-Phase, replication processes were analyzed. A stronger impact of treatment on replication processes (p = 0.04) and an increased number of radial chromosomes (p = 0.003) were observed in the HR-deficient group. We could show that radiosensitization by inhibition of PARP1 strongly correlates with HR competence in a replication-dependent manner. Our observations indicate that PARP1 inhibitors are promising candidates for enhancing the therapeutic ratio achieved by radiotherapy via disabling DNA replication processes in HR-deficient HNSCCs.

Metabolomics is a relatively new component in systems biology that focuses on the high-throughput characterization of small molecular metabolites in biological systems. It is widely used in several scientific fields, particularly in that of food. Due to its excellent detection and prediction capacities, metabolomics well suited to analyze such complex matrix. This review emphasizes the most commonly used food metabolomics analytical technologies with a focus on novel approaches that have emerged in recent years, highlighting their suitability for food samples analysis as aided by chemometric data visualization. A comparison is presented among different metabolomics platforms and their prioritization for which metabolite classes in food. Application of metabolomics are presented in the context of food composition analysis, food quality safety, and food traceability. Furthermore, the constraints and limitations of actual metabolomics applications are explored, bringing novel insights into metabolomics use in food science to maximize its application potential in that major industrial sector.

Agricultural water appropriation has undergone rapid changes in recent decades, but estimates of global water appropriation have not been updated with the latest data and consistent methods. Documenting these changes is challenging given the heterogeneous water use landscape and the growing influence of human activities worldwide, and this complexity cannot be well addressed with the existing methodology, which is subject to large model uncertainties. Here, a spatial analysis and aggregated method was proposed to quantify and refine estimates of global agricultural water appropriation (GAWA) in terms of consumptive freshwater use, with data derived from Earth observations, independent of estimates from hydrological models. The results show the global water appropriation at the pixel scale, in agroecological zones and in the main water-consuming countries, including global maps of rainfed and irrigated cropland evapotranspiration (ET), net water consumption due to irrigation, natural ET and renewable freshwater resources (RFWR), and indicate that agriculture remains the largest user in terms of both water consumption and withdrawals worldwide, representing 87% of global water consumption, with approximately 60% of global freshwater withdrawals devoted to irrigation circa 2015. The percentage of withdrawals devoted to irrigation has decreased in recent decades when compared to the previous estimate of 70%. The results reveal the actual global crop consumptive use (8053.6 km3/yr) of blue and green water and the total human water consumption (8442 km3/yr), which represents the part of the water cycle affected by human intervention, mainly (95.4%) by agriculture. This study reveals that high-resolution irrigated croplands are essential for accurate estimations of water use appropriation and demonstrates that earth observation-derived data can provide a new understanding of global water use landscape. The study can support decision making in sustaining food and water security, and implementing water-adapted sustainable agricultural policies.

Accurate global irrigation information is essential for managing water scarcity and improving food security. However, the mapping of high-resolution irrigation at the global scale is challenging due to the wide range of climate conditions, crop types and phenology, ambiguous and heterogeneous spectral features, and farming practices. Here, a robust method is proposed using irrigation performance under drought stress as a proxy for crop productivity stabilization and crop water consumption. For each irrigation mapping zone (IMZ), dry months in the 2017–2019 period and the driest months in the 2010–2019 period were identified over the growing season. The thresholds of the normalized difference vegetation index (NDVI) in the dry months from 2017 to 2019 and the NDVI deviation (NDVIdev) in the driest month were identified to separate irrigated and rainfed cropland with samples. The final threshold from either the NDVI or the NDVIdev of the IMZ was determined with a higher overall accuracy in separating irrigated and non-irrigated areas. The results show that the global maximum irrigation extent (GMIE) at a 30-m resolution was 23.38% of global cropland in 2010–2019, with an overall accuracy of 83.6% globally and significant regional differences in irrigation proportions ranging from 1.1% in western Africa to 100% in Old World deserts among the 110 IMZs and from 0.4% in Belarus to 80.2% in Pakistan and 100% in Egypt among 45 countries. The study quantitatively distinguished annually and intermittently irrigated regions, which had values of 42% and 58% of global cropland, respectively, by applying indicators. This method, using the NDVI and NDVIdev thresholds, is simple, concrete and reproducible and better for zones with homogeneous weather conditions. The study offers independent, consistent and comparable information for defining the baseline, tracking changes in irrigation infrastructure, and leading future changes in how stakeholders plan and design irrigation systems.

To improve the searching effectiveness of the harmony search (HS) algorithm, an enhanced harmony search algorithm with circular region perturbation (EHS_CRP) is proposed in this paper. In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. Thus, the EHS_CRP algorithm is a viable and reliable alternative for some difficult and multidimensional real-world problems.