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.     

Omics Analysis of Blood-Responsive Regulon in Bordetella pertussis Identifies a Novel Essential T3SS Substrate

Bacterial pathogens sense specific cues associated with different host niches and integrate these signals to appropriately adjust the global gene expression. Bordetella pertussis is a Gram-negative, strictly human pathogen of the respiratory tract and the etiological agent of whooping cough (pertussis). Though B. pertussis does not cause invasive infections, previous results indicated that this reemerging pathogen responds to blood exposure. Here, omics RNA-seq and LC–MS/MS techniques were applied to determine the blood-responsive regulon of B. pertussis. These analyses revealed that direct contact with blood rewired global gene expression profiles in B. pertussis as the expression of almost 20% of all genes was significantly modulated. However, upon loss of contact with blood, the majority of blood-specific effects vanished, with the exception of several genes encoding the T3SS-secreted substrates. For the first time, the T3SS regulator BtrA was identified in culture supernatants of B. pertussis. Furthermore, proteomic analysis identified BP2259 protein as a novel secreted T3SS substrate, which is required for T3SS functionality. Collectively, presented data indicate that contact with blood represents an important cue for B. pertussis cells.     

High-resolution FIB-TEM-STEM structural characterization of grain boundaries in the high dielectric constant perovskite CaCu3Ti4O12

In this work, the grain boundaries composition of the polycrystalline CaCu₃Ti₄O₁₂ (CCTO) was investigated. A Focused Ion Beam (FIB)/lift-out technique was used to prepare site-specific thin samples of the grain boundaries interface of CCTO ceramics. Scanning transmission electron microscopy (STEM) coupled with energy dispersive X-ray spectrometry (EDXS) and Electron Energy Loss Spectroscopy (EELS) systems were used to characterize the composition and nanostructure of the grain and grain boundaries region. It is known that during conventional sintering, discontinuous grain growth occurs and a Cu-rich phase appears at grain boundaries. This Cu-rich phase may affect the final dielectric properties of CCTO but its structure and chemical composition remained unknown. For the first time, this high-resolution FIB-TEM-STEM study of CCTO interfacial region highlights the composition of the phases segregated at grain boundaries namely CuO, Cu₂O and the metastable phase Cu₃TiO_4.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

GOLPH3 Regulates EGFR in T98G Glioblastoma Cells by Modulating Its Glycosylation and Ubiquitylation

Protein trafficking is altered when normal cells acquire a tumor phenotype. A key subcellular compartment in regulating protein trafficking is the Golgi apparatus, but its role in carcinogenesis is still not well defined. Golgi phosphoprotein 3 (GOLPH3), a peripheral membrane protein mostly localized at the trans-Golgi network, is overexpressed in several tumor types including glioblastoma multiforme (GBM), the most lethal primary brain tumor. Moreover, GOLPH3 is currently considered an oncoprotein, however its precise function in GBM is not fully understood. Here, we analyzed in T98G cells of GBM, which express high levels of epidermal growth factor receptor (EGFR), the effect of stable RNAi-mediated knockdown of GOLPH3. We found that silencing GOLPH3 caused a significant reduction in the proliferation of T98G cells and an unexpected increase in total EGFR levels, even at the cell surface, which was however less prone to ligand-induced autophosphorylation. Furthermore, silencing GOLPH3 decreased EGFR sialylation and fucosylation, which correlated with delayed ligand-induced EGFR downregulation and its accumulation at endo-lysosomal compartments. Finally, we found that EGF failed at promoting EGFR ubiquitylation when the levels of GOLPH3 were reduced. Altogether, our results show that GOLPH3 in T98G cells regulates the endocytic trafficking and activation of EGFR likely by affecting its extent of glycosylation and ubiquitylation.     

Fatigue life improvement of 12Cr1МoV steel by irradiation with Zr+ ion beam

Cyclic tension and bend tests were performed on heat-resistant 12Cr1MoV steel specimens in as-supplied condition as well as after Zr⁺ ion beam surface irradiation. Distinct differences in strain induced relief, as well in cracking pattern of modified surface layer were observed by optical microscopy and interference profilometry. Changes in subsurface layer are characterized by means of nano- and microindentation and fractography of fracture surfaces (with the help of scanning electron microscopy). It is shown that the main influence on mechanical properties is mostly induced by thermal treatment during irradiation rather than formation of a 2 μm thick layer doped with Zr. The differences in deformation behavior may be explained by physical mesomechanics concepts.     

Geopolymer concrete-filled pultruded composite beams – Concrete mix design and application

The limited research on the geopolymer concrete mix design for targeting a specific strength is identified an obstacle for their effective design and wide use. In this paper, a mix design procedure has been proposed for fly-ash based geopolymer concrete and its use as infill hybrid composite beam is investigated. Then, the structural performance of geopolymer concrete filled hybrid composite beam is investigated to determine their possible application in civil infrastructure. Firstly, a detailed procedure of mix design for fly-ash based geopolymer concrete is presented. Secondly, three hybrid beams filled with geopolymer concrete were prepared and tested in a four-point bending setup to evaluate their flexural modulus and modulus of rupture. Numerical and analytical evaluation of the behaviour of hybrid beam were performed and results showed a good agreement with the experimental investigation. Thirdly, the suitability of the beam for a composite railway sleeper is evaluated and compared with existing timber and composite sleepers. Finally, the beams’ performance in a ballast railway track is analysed using Strand7 finite element simulation software and the results showed that the new concept of using geopolymer concrete as infill to pultruded composite section satisfied the stiffness and strength requirements for a railway sleeper.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.