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Pancreatic cancer is currently one of the most lethal diseases. In recent years, increasing evidence has shown that reprogrammed metabolism may play a critical role in the carcinogenesis, progression, treatment and prognosis of pancreatic cancer. Affected by internal or external factors, pancreatic cancer cells adopt extensively distinct metabolic processes to meet their demand for growth. Rewired glucose, amino acid and lipid metabolism and metabolic crosstalk within the tumor microenvironment contribute to unlimited pancreatic tumor progression. In addition, the metabolic reprogramming involved in pancreatic cancer resistance is also closely related to chemotherapy, radiotherapy and immunotherapy, and results in a poor prognosis. Reflective of the key role of metabolism, the number of preclinical and clinical trials about metabolism-targeted therapies for pancreatic cancer is increasing. The poor prognosis of pancreatic cancer patients might be largely improved after employing therapies that regulate metabolism. Thus, investigations of metabolism not only benefit the understanding of carcinogenesis and cancer progression but also provide new insights for treatments against pancreatic cancer.

Attributable to its late diagnosis, early metastasis, and poor prognosis, pancreatic cancer remains one of the most lethal diseases worldwide. Unlike other solid tumors, pancreatic cancer harbors ample stromal cells and abundant extracellular matrix but lacks vascularization, resulting in persistent and severe hypoxia within the tumor. Hypoxic microenvironment has extensive effects on biological behaviors or malignant phenotypes of pancreatic cancer, including metabolic reprogramming, cancer stemness, invasion and metastasis, and pathological angiogenesis, which synergistically contribute to development and therapeutic resistance of pancreatic cancer. Through various mechanisms including but not confined to maintenance of redox homeostasis, activation of autophagy, epigenetic regulation, and those induced by hypoxia-inducible factors, intratumoral hypoxia drives the above biological processes in pancreatic cancer. Recognizing the pivotal roles of hypoxia in pancreatic cancer progression and therapies, hypoxia-based antitumoral strategies have been continuously developed over the recent years, some of which have been applied in clinical trials to evaluate their efficacy and safety in combinatory therapies for patients with pancreatic cancer. In this review, we discuss the molecular mechanisms underlying hypoxia-induced aggressive and therapeutically resistant phenotypes in both pancreatic cancerous and stromal cells. Additionally, we focus more on innovative therapies targeting the tumor hypoxic microenvironment itself, which hold great potential to overcome the resistance to chemotherapy and radiotherapy and to enhance antitumor efficacy and reduce toxicity to normal tissues.

Sex chromosomes evolved from autosomes many times across the eukaryote phylogeny. Several models have been proposed to explain this transition, some involving male and female sterility mutations linked in a region of suppressed recombination between X and Y (or Z / W , U / V ) chromosomes. Comparative and experimental analysis of a reference genome assembly for a double haploid YY male garden asparagus ( Asparagus officinalis L.) individual implicates separate but linked genes as responsible for sex determination. Dioecy has evolved recently within Asparagus and sex chromosomes are cytogenetically identical with the Y , harboring a megabase segment that is missing from the X . We show that deletion of this entire region results in a male-to-female conversion, whereas loss of a single suppressor of female development drives male-to-hermaphrodite conversion. A single copy anther-specific gene with a male sterile Arabidopsis knockout phenotype is also in the Y- specific region, supporting a two-gene model for sex chromosome evolution. Several models have been proposed to explain the emergence of sex chromosomes. Here, through comparative genomics and mutant analysis, Harkess et al. show that linked but separate genes on the Y chromosome are responsible for sex determination in Asparagus , supporting a two-gene model for sex chromosome evolution.

In order to reduce the damage of apple harvesting robot to fruits and achieve compliant picking, an adaptive variable parameter impedance control method for apple harvesting robot compliant picking is proposed in this paper. Firstly, the Burgers viscoelastic model is used to characterize the rheological properties of apples and study the variation of mechanical properties of apple grasping at different speeds. Then, a force-based impedance control system is designed. On this basis, aiming at the influence of impedance controller parameters on contact force, three impedance parameters self-tuning functions are constructed to complete the design of an improved force-based impedance control system based on the hyperbolic secant function. The simulation and experimental results show that the proposed control makes the desired force smoother, and its overshoot is about 2.3%. The response speed is faster, and the adjustment time of contact force is shorter of about 0.48 s. The contact force overshoot is about 2%, which is 37.5% less than that of the traditional force-based impedance control. This research improves the control performance for apple harvesting robot compliant picking.

Solar-driven, selective biomass hydrogenation is recognized as a promising route to renewable chemicals production, but remains challenging. Here, we report a TiO 2 supported Cu single-atom catalyst with a four-coordinated Cu 1 −O 4 structure, which can be universally applied for solar-driven production of various renewable chemicals from lignocellulosic biomass-derived platform molecules with good yields using green methanol as a hydrogen donor, to address this challenge. It is significant that the biomass upgrading driven by natural sunlight on a gram scale demonstrates the great practical potential. By combining in situ soft X-ray absorption spectroscopy with theoretical calculations, we successfully identify the dynamic evolution of Cu sites along with the biomass hydrogenation and methanol oxidation, where the tandem process is enabled by the photogenerated electrons and holes to complete a chemical cycle. The concept of solar-driven biomass hydrogenation proposed here provides an efficient and sustainable methodology for the sustainable production of renewable chemicals. The development of highly efficient catalysts for the selective upgrading of biomass remains a huge challenge. Here, the authors report a Cu single-atom catalyst that facilitates the solar-driven synthesis of renewable chemicals from lignocellulosic biomass and green methanol as a hydrogen donor.

The biosynthesis of (bacterio)chlorophyll pigments is among the most productive biological pathways on Earth. Photosynthesis relies on these modified tetrapyrroles for the capture of solar radiation and its conversion to chemical energy. (Bacterio)chlorophylls have an isocyclic fifth ring, the formation of which has remained enigmatic for more than 60 y. This reaction is catalyzed by two unrelated cyclase enzymes using different chemistries. The majority of anoxygenic phototrophic bacteria use BchE, an O₂-sensitive [4Fe-4S] cluster protein, whereas plants, cyanobacteria, and some phototrophic bacteria possess an O₂-dependent enzyme, the major catalytic component of which is a diiron protein, AcsF. Plant and cyanobacterial mutants in ycf54 display impaired function of the O₂-dependent enzyme, accumulating the reaction substrate. Swapping cyclases between cyanobacteria and purple phototrophic bacteria reveals three classes of the O₂-dependent enzyme. AcsF from the purple betaproteobacterium Rubrivivax (Rvi.) gelatinosus rescues the loss not only of its cyanobacterial ortholog, cycI, in Synechocystis sp. PCC 6803, but also of ycf54; conversely, coexpression of cyanobacterial cycI and ycf54 is required to complement the loss of acsF in Rvi. gelatinosus. These results indicate that Ycf54 is a cyclase subunit in oxygenic phototrophs, and that different classes of the enzyme exist based on their requirement for an additional subunit. AcsF is the cyclase in Rvi. gelatinosus, whereas alphaproteobacterial cyclases require a newly discovered protein that we term BciE, encoded by a gene conserved in these organisms. These data delineate three classes of O₂-dependent cyclase in chlorophototrophic organisms from higher plants to bacteria, and their evolution is discussed herein.

The aerosol liquid water (ALW) content (ALWC), an important component of atmospheric particles, has a significant effect on atmospheric optical properties, visibility and multiphase chemical reactions. In this study, ALWC is determined from aerosol hygroscopic growth factor (GF) and particle number size distribution (PNSD) measurements and is also simulated by ISORROPIA II, a thermodynamic equilibrium model, with measured aerosol chemical composition data taken at an urban site in Beijing from 8 November to 15 December 2017. Rich measurements made during the experiment concerning virtually all aerosol properties allow us not only to derive the ALWC but also to study the contributions by various species for which little has been done in this region. The simulated ALWC including the contribution of organics and the calculated ALWC are highly correlated (coefficient of determination R2=0.92). The ALWC contributed by organics (ALWCOrg) accounts for 30 %±22 % of the total ALWC during the sampling period. These results suggest a significant contribution of organics to ALWC, which is rather different from previous studies that showed negligible contributions by organics. Our results also show that ALWC correlates well with the mass concentrations of sulfate, nitrate, and secondary organic aerosols (SOAs) (R2=0.66, 0.56 and 0.60, respectively). We further noted that accumulation mode particles play a key role in determining ALWC, dominating among all the aerosol modes. ALWC is an exponential function of ambient relative humidity (RH), whose strong diurnal variation influence the diurnal variation of ALWC. However, there is a 3 h lag between the extremes of ALWC and RH values, due to the diurnal variations in PNSD and aerosol chemical composition. Finally, a case study reveals that ALWCOrg plays an important role in the formation of secondary aerosols through multiphase reactions at the initial stage of a heavy-haze episode.

IgG4-related disease (IgG4-RD) is a chronic systemic disease involved in many organs and tissues. As only limited autoantigens have been found since the beginning of this century, the aim of this study was to reveal new candidate autoantigens of IgG4-RD. Multiple cell lines including HT-29, EA.hy926, HEK 293 and HepG2 were used to test the binding ability of circulating autoantibodies from IgG4-RD sera. The amino-acid sequence was then analyzed by matrix-assisted laser desorption/ionization time-of-flight tandem (MALDI-TOF/TOF) mass spectrometry. After the cloning and expression of recombinant putative autoantigen in a bacterial expression system, the corresponding immuno assay was set up and utilized to observe the prevalence of serum autoantibodies in a large set of confirmed clinical samples. One positive autoantigen was identified as prohibitin. ELISA analysis showed that a majority of patients with IgG4-RD have antibodies against prohibitin. Anti-prohibitin antibodies were present in the sera of patients with definite autoimmune pancreatitis (25/34; 73.5%), Mikulicz's disease (8/15; 53.3%), retroperitoneal fibrosis (6/11; 54.5%), other probable IgG4-RD (26/29; 89.7%) and Sjögren's syndrome (4/30; 13.3%) but not in apparently healthy donors (1/70; 1.4%). An association between prohibitin and patients with some IgG4-RD was observed, although the results were quite heterogeneous among different individuals within autoimmune pancreatitis, Mikulicz's disease and retroperitoneal fibrosis.

The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. Firstly, the preprocessing method of spectral characteristic bands combined with Savitzky-Golay (SG) + Standard Normal Variate (SNV) was determined. Subsequently, all sample data were divided into six groups: N-deficient, K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess. The aforementioned models were then used for classification prediction. The results show that RF and CNN + LSTM models demonstrated good predictive performance. Specifically, RF achieved accuracy rates of 70.14%, 90.81%, 88.59%, and 85.37% in the classification tasks of Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. The CNN + LSTM model achieved accuracy rates of 93.33%, 63.33%, 99.2%, 83.33%, and 98.52% in the classification tasks of K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. Finally, in the Leave-One-Group-Out Validation (LOGOV) for validating the model’s generalisation performance, RF performed better in the N-deficient, K-deficient, and Ca-deficient tasks, achieving diagnostic accuracy rates of 80.19%, 81.43%, and 77.02%, respectively. The CNN + LSTM model showed a diagnostic accuracy rate of 66.72% in the N-excess classification task. The study concludes that, given complete training data, the CNN + LSTM model can effectively diagnose mild nutrient stress (N, K, and Ca) in facility-grown tomatoes in most scenarios.

PurposeGovernments all over the world are increasingly entering into partnership agreement with the private sector through public–private partnership (PPP) models for the development and management of public projects and services. Thus, the purpose of this study is to first examine the factors that enter into government's attitude, value for money (VFM) and PPP-based waste management projects (WMPs) in Ghana. Further, this study examines the relationship between PPP-based waste management and VFM, along with the mediating role of government's attitude.Design/methodology/approachData were collected through a survey questionnaire from four metropolitan municipal assemblies of Ghana. This study’s sample consisted of 230 respondents, and structural equation modeling was employed to conduct an empirical analysis. The structured model had evaluated the critical success factors (CSFs). Subsequently, the measurement model helped in the path analysis of the proposed model.FindingsThe authors found a nonpositive relationship between PPP-based WMPs and VFM; however, government's attitude will influence the achievement of VFM. Reliance on quality targets is ranked as the highest CSF in PPP-based WMPs in Ghana. Also, incentives for private sector participation and effective private sector participation are ranked as the least CSFs.LimitationsMost of the questionnaire's items were self -rated by employees of municipal assemblies who were not policymakers. This must have increased the possibility of common method bias. In addition, the study was limited to Ghana (a developing economy); therefore, the generalization of the results should be done with much caution.Practical implicationsThis study suggests to develop a network of private sector consortiums considering international and national participants who are engaged in PPPs in order to ensure successful delivery and hence valuing for money.Originality/valueThis study offers a novel perspective to investigate the relationship of PPP-based WMPs and VFM and shows how the government's attitude mediates this relationship.