Ablative Radiotherapy Reprograms the Tumor Microenvironment of a Pancreatic Tumor in Favoring the Immune Checkpoint Blockade Therapy

The low overall survival rate of patients with pancreatic cancer has driven research to seek a new therapeutic protocol. Radiotherapy (RT) is frequently an option in the neoadjuvant or palliative settings for pancreatic cancer treatment. This study explored the effect of RT protocols on the tumor microenvironment (TME) and their consequent impact on anti-programmed cell death ligand-1 (PD-L1) therapy. Using a murine orthotopic pancreatic tumor model, UN-KC-6141, RT-disturbed TME was examined by immunohistochemical staining. The results showed that ablative RT is more effective than fractionated RT at recruiting T cells. On the other hand, fractionated RT induces more myeloid-derived suppressor cell infiltration than ablative RT. The RT-disturbed TME presents a higher perfusion rate per vessel. The increase in vessel perfusion is associated with a higher amount of anti-PD-L1 antibody being delivered to the tumor. Animal survival is increased by anti-PD-L1 therapy after ablative RT, with 67% of treated animals surviving more than 30 days after tumor inoculation compared to a median survival time of 16.5 days for the control group. Splenocytes isolated from surviving animals were specifically cytotoxic for UN-KC-6141 cells. We conclude that the ablative RT-induced TME is more suited than conventional RT-induced TME to combination therapy with immune checkpoint blockade.     

Towards artificial intelligence at scale in the chemical industry

In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing.     

Study on emotion recognition and companion Chatbot using deep neural network

Multimedia Tools and Applications - With the development of technology, the importance of the research on speech emotion recognition and semantic analysis has increased. The research is primarily...     

Micro-controlled design of portable cattle mastitis detection system

Microsystem Technologies - This work presents a novel handheld mastitis detection device for detecting mastitis in dairy cattle, as well as a cattle management information system for managing...     

Thermally stable and durable superhydrophobic surfaces on stainless steel sheets with microholes via acid pretreatment and modification with self-assembled monolayers

Microfluidics and Nanofluidics - Single-cell nucleic acid analysis aims at discovering the genetic differences between individual cells which is well known as the cellular heterogeneity. This...     

Extruded meat alternatives made from Maillard-reacted beef bone hydrolysate and plant proteins: part I – Effect of moisture content

This study investigated the effects of moisture content (MC) on the physicochemical properties of extruded meat alternatives made from Maillard-reacted beef bone hydrolysate and plant proteins. Samples were extruded at 170 °C (maximum barrel temperature), at 3.6 kg h⁻¹ (liquid feed rate) and at 1.8, 2.2, 2.6 and 3.0 kg h⁻¹ (dry feed rates) to obtain MC of 60%MC, 56%MC, 52%MC and 49%MC, respectively. Meat alternatives at 52%MC showed the greatest degree of texturisation. However, meat alternatives at 49%MC were the closest in terms of both textural and microstructural properties to reference sample, boiled chicken breast. Results from protein solubility suggested that a large amount of aggregated proteins were associated with hydrogen bonds, while disulphide bonds were the main contributor in the formation of fibrous structure in meat alternatives. Results showed that the change in MC as process parameter played an important role in the formation of fibrous structure in extruded meat alternatives.     

Hydrogen production enhancement using hot gas cleaning system combined with prepared Ni-based catalyst in biomass gasification

This paper investigates the hot gas temperature effect on enhancing hydrogen generation and minimizing tar yield using zeolite and prepared Ni-based catalysts in rice straw gasification. Results obtained from this work have shown that increasing hot gas temperature and applying catalysts can enhance energy yield efficiency. When zeolite catalyst and hot gas temperature were adjusted from 250 °C to 400 °C, H₂ and CO increased slightly from 7.31% to 14.57%–8.03% and 17.34%, respectively. The tar removal efficiency varies in the 70%–90% range. When the zeolite was replaced with prepared Ni-based catalysts and hot gas cleaning (HGC) operated at 250 °C, H₂ contents were significantly increased from 6.63% to 12.24% resulting in decreasing the hydrocarbon (tar), and methane content. This implied that NiO could promote the water-gas shift reaction and CH₄ reforming reaction. Under other conditions in which the hot gas temperature was 400 °C, deactivated effects on prepared Ni-based catalyst were observed for inhibiting syngas and tar reduction in the HGC system. The prepared Ni-based catalyst worked at 250 °C demonstrate higher stability, catalyst activity, and less coke decomposition in dry reforming. In summary, the optimum catalytic performance in syngas production and tar elimination was achieved when the catalytic temperature was 250 °C in the presence of prepared Ni-based catalysts, producing 5.92 MJ/kg of lower heating value (LHV) and 73.9% tar removal efficiency.     

A few-shot transfer learning approach using text-label embedding with legal attributes for law article prediction

Applied Intelligence - In this paper, we proposed an intelligent law article prediction method to address the data imbalance and missing value problems in law text analysis. The proposed method...