This study discusses the influences of green absorptive capacity, green dynamic capacities, and green service innovation on firm performance. In order to fill the research gap, this study proposes the concept of green service innovation. The results are as follows: First, this study finds that green absorptive capacity has positive effects on green dynamic capacities, green service innovation, and firm performance. Second, this study points out that green dynamic capacities have positive effects on green service innovation and firm performance. Third, this study observes that green dynamic capacities and green service innovation intercede the positive connection between green absorptive capacity and firm performance.

<b><i>Background and Aims:</i></b> Immune checkpoint inhibitors (ICIs) exhibit significant clinical activity in patients with advanced hepatocellular carcinoma (HCC). This study explored whether tumor response to ICIs in HCC varies among different organs. <b><i>Methods:</i></b> We reviewed the data of patients with advanced HCC who had received ICIs. Patients with measurable diseases were enrolled. Organ-specific response criteria, adapted from RECIST 1.1 and immune-related RECIST, were used to evaluate the objective response to ICIs in tumors located in the liver, lung, lymph node, and other intra-abdominal sites. <b><i>Results:</i></b> Of the 75 enrolled patients with advanced HCC, 51 and 11 patients had chronic hepatitis B virus and chronic hepatitis C virus infection, respectively. Regarding ICI treatment, 58, 1, and 16 patients had undergone anti-PD-1/anti-PD-L1 monoclonal antibody (mAb) alone, anti-CTLA4 mAb alone, and anti-PD-1 mAb plus anti-CTLA4 mAb, respectively; 20 and 55 patients had received ICIs as first-line or ≥second-line therapy. The overall objective response rate (ORR) was 28.0%. In total, 58, 34, 19, and 18 patients had measurable hepatic tumors and lung, lymph node, and other intra-abdominal metastases, and the corresponding organ-specific ORRs were 22.4, 41.2, 26.3, and 38.9%, respectively. Of the 39 patients who had both hepatic and extrahepatic tumors, 12 had disease control in extrahepatic tumors while progressive disease (PD) in hepatic tumors, whereas only 4 exhibited disease control in hepatic tumors while PD in extrahepatic tumors (<i>p</i> = 0.046, McNemar test). Of the 16 patients with only evaluable tumors in the liver and lungs at baseline, 8 had disease control in the lungs while PD in the liver, and none experienced disease control in the liver while PD in the lungs (<i>p</i> = 0.005). <b><i>Conclusions:</i></b> The hepatic tumors of HCC may be less responsive to ICIs than extrahepatic lesions. Lung metastases responded most favorably to ICIs. The mechanisms underlying this differential response to ICIs warrant further investigation.

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The species Pseudomonas syringae encompasses plant pathogens with differing host specificities and corresponding pathovar designations. P. syringae requires the Hrp (type III protein secretion) system, encoded by a 25-kb cluster ofhrp and hrc genes, in order to elicit the hypersensitive response (HR) in nonhosts or to be pathogenic in hosts. DNA sequence analysis of the hrpC and hrpRSoperons of P. syringae pv. syringae 61 (brown spot of beans), P. syringae pv. glycinea U1 (bacterial blight of soybeans), and P. syringae pv. tomato DC3000 (bacterial speck of tomatos) revealed that the 13 genes comprising the right half of the hrp cluster (including those in the previously sequenced hrpZ operon) are conserved and identically arranged. The hrpC operon is comprised of hrpF,hrpG, hrcC, hrpT, and hrpV. hrcC encodes a putative outer membrane protein that is conserved in all type III secretion systems. The other four genes appear to be characteristic of group I Hrp systems, such as those possessed byP. syringae and Erwinia amylovora. The predicted products of these four genes in P. syringae pv. syringae 61 are HrpF (8 kDa), HrpG (15.4 kDa), HrpT (7.5 kDa), and HrpV (13.4 kDa). HrpT is a putative outer membrane lipoprotein. HrpF, HrpG, and HrpV are all hydrophilic proteins lacking N-terminal signal peptides. The HrpG, HrcC, HrpT, and HrpV proteins of P. syringae pathovars syringae and tomato (the two most divergent pathovars) had at least 76% amino acid identity with each other, whereas the HrpF proteins of these two pathovars had only 36% amino acid identity. The HrpF proteins of P. syringae pathovars syringae and glycinea also showed significant similarity to the HrpA pilin protein of P. syringae pathovar tomato. Functionally nonpolar mutations were introduced into each of the genes in thehrpC operon of P. syringae pv. syringae 61 by insertion of an nptII cartridge lacking a transcription terminator. The mutants were assayed for their ability to elicit the HR in nonhost tobacco leaves or to multiply and cause disease in host bean leaves. Mutations in hrpF, hrcC, andhrpT abolished or greatly reduced the ability of P. syringae pv. syringae 61 to elicit the HR in tobacco. ThehrpG mutant had only weakly reduced HR activity, and the activity of the hrpV mutant was indistinguishable from that of the wild type. Each of the mutations could be complemented, but surprisingly, the hrpV subclone caused a reduction in the HR elicitation ability of the ΔhrpV::nptIImutant. The hrpF and hrcC mutants caused no disease in beans, whereas the hrpG, hrpT, and hrpV mutants had reduced virulence. Similarly, thehrcC mutant grew little in beans, whereas the other mutants grew to intermediate levels in comparison with the wild type. These results indicate that HrpC and HrpF have essential functions in the Hrp system, that HrpG and HrpT contribute quantitatively but are not essential, and that HrpV is a candidate negative regulator of the Hrp system.

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The main purpose of this study is to how to enhance the green product development performance from the perspectives of green vision and organizational culture. This study explores the influences of a green shared vision and the environmental organizational culture on the green product development performance in Taiwan's manufacturing industry. It also examines the mediation effect of the green absorptive capacity. The empirical results indicate that both the green shared vision and environmental organizational culture have positive effects on the green absorptive capacity, which in turn positively affects the green product development performance. In particular, the green absorptive capacity completely mediates the relationship between the green shared vision and green product development performance, whereas it partially mediates the relationship between the environmental organizational culture and green product development performance.

Purpose: Treatment planning systems (TPSs) from different vendors can involve different implementations of Monte Carlo dose calculation (MCDC) algorithms for pencil beam scanning (PBS) proton therapy. There are currently no guidelines for validating non-water materials in TPSs. Furthermore, PBSspecific parameters can vary by 1-2 orders of magnitude among different treatment delivery systems (TDSs). This paper proposes a standardized framework on the use of commissioning data and steps to validate TDS-specific parameters and TPS-specific heterogeneity modeling to potentially reduce these uncertainties. Methods: A standardized commissioning framework was developed to commission the MCDC algorithms of RayStation 8A and Eclipse AcurosPT v13.7.20 using water and non-water materials. Measurements included Bragg peak depth-dose and lateral spot profiles and scanning field outputs for Varian ProBeam. The phase-space parameters were obtained from in-air measurements and the number of protons per MU from output measurements of 10 9 10 cm 2 square fields at a 2 cm depth. Spot profiles and various PBS field measurements at additional depths were used to validate TPS. Human tissues in TPS, Gammex phantom materials, and artificial materials were used for the TPS benchmark and validation. Results: The maximum differences of phase parameters, spot sigma, and divergence between MCDC algorithms are below 4.5 µm and 0.26 mrad in air, respectively. Comparing TPS to measurements at depths, both MC algorithms predict the spot sigma within 0.5 mm uncertainty intervals, the resolution of the measurement device. Beam Configuration in AcurosPT is found to underestimate number of protons per MU by~2.5% and requires user adjustment to match measured data, while RayStation is within 1% of measurements using Auto model. A solid water phantom was used to validate the range accuracy of non-water materials within 1% in AcurosPT. Conclusions: The proposed standardized commissioning framework can detect potential issues during PBS TPS MCDC commissioning processes, and potentially can shorten commissioning time and improve dosimetric accuracies. Secondary MCDC can be used to identify the root sources of disagreement between primary MCDC and measurement.

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power (RSP) maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including fully connected neural networks (FCNN), dual-FCNN (DFCNN), and three variants of ResNet: ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). Two empirical DECT models were implemented to compare with the PIDL method. An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.