NEPE Propellant Mesoscopic Modeling and Damage Mechanism Study Based on Inversion Algorithm.

To accurately characterize the mesoscopic properties of NEPE (Nitrate Ester Plasticized Polyether) propellant, the mechanical contraction method was used to construct a representative volume element (RVE) model. Based on this model, the macroscopic mechanical response of NEPE propellant at a strain rate of 0.0047575 s-1 was simulated and calculated, and the parameters of the cohesive zone model (CZM) were inversely optimized using the Hooke-Jeeves algorithm by comparing the simulation results with the results of the uniaxial tensile test of NEPE propellants. Additionally, the macroscopic mechanical behavior of NEPE composite solid propellants at strain rates of 0.00023776 s-1 and 0.023776 s-1 was also predicted. The mesoscopic damage evolution process of NEPE propellants was investigated by the established model. The study results indicate that the predicted curves are relatively consistent with the basic features and change trends of the test curves. Therefore, the established model can effectively simulate the mesoscopic damage process of NEPE composite solid propellants and their macroscopic mechanical properties.

A neural mechanism for conserved value computations integrating information and rewards.

Behavioral and economic theory dictate that we decide between options based on their values. However, humans and animals eagerly seek information about uncertain future rewards, even when this does not provide any objective value. This implies that decisions are made by endowing information with subjective value and integrating it with the value of extrinsic rewards, but the mechanism is unknown. Here, we show that human and monkey value judgements obey strikingly conserved computational principles during multi-attribute decisions trading off information and extrinsic reward. We then identify a neural substrate in a highly conserved ancient structure, the lateral habenula (LHb). LHb neurons signal subjective value, integrating information's value with extrinsic rewards, and the LHb predicts and causally influences ongoing decisions. Neurons in key input areas to the LHb largely signal components of these computations, not integrated value signals. Thus, our data uncover neural mechanisms of conserved computations underlying decisions to seek information about the future.

Discovering covalent inhibitors of protein-protein interactions from trillions of sulfur(VI) fluoride exchange-modified oligonucleotides.

Molecules that covalently engage target proteins are widely used as activity-based probes and covalent drugs. The performance of these covalent inhibitors is, however, often compromised by the paradox of efficacy and risk, which demands a balance between reactivity and selectivity. The challenge is more evident when targeting protein-protein interactions owing to their low ligandability and undefined reactivity. Here we report sulfur(VI) fluoride exchange (SuFEx) in vitro selection, a general platform for high-throughput discovery of covalent inhibitors from trillions of SuFEx-modified oligonucleotides. With SuFEx in vitro selection, we identified covalent inhibitors that cross-link distinct residues of the SARS-CoV-2 spike protein at its protein-protein interaction interface with the human angiotensin-converting enzyme 2. A separate suite of covalent inhibitors was isolated for the human complement C5 protein. In both cases, we observed a clear disconnection between binding affinity and cross-linking reactivity, indicating that direct search for the aimed reactivity-as enabled by SuFEx in vitro selection-is vital for discovering covalent inhibitors of high selectivity and potency.

Recent Advances for Quantum Neural Networks in Generative Learning.

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts. As such, QGLMs are receiving growing attention from the quantum physics and computer science communities, where various QGLMs that can be efficiently implemented on near-term quantum machines with potential computational advantages are proposed. In this paper, we review the current progress of QGLMs from the perspective of machine learning. Particularly, we interpret these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum variational autoencoders, as the quantum extension of classical generative learning models. In this context, we explore their intrinsic relations and their fundamental differences. We further summarize the potential applications of QGLMs in both conventional machine learning tasks and quantum physics. Last, we discuss the challenges and further research directions for QGLMs.

Quantum Imitation Learning.

Despite remarkable successes in solving various complex decision-making tasks, training an imitation learning (IL) algorithm with deep neural networks (DNNs) suffers from the high-computational burden. In this work, we propose quantum IL (QIL) with a hope to utilize quantum advantage to speed up IL. Concretely, we develop two QIL algorithms: quantum behavioral cloning (Q-BC) and quantum generative adversarial IL (Q-GAIL). Q-BC is trained with a negative log-likelihood (NLL) loss in an offline manner that suits extensive expert data cases, whereas Q-GAIL works in an inverse reinforcement learning (IRL) scheme, which is online, on-policy, and is suitable for limited expert data cases. For both QIL algorithms, we adopt variational quantum circuits (VQCs) in place of DNNs for representing policies, which are modified with data reuploading and scaling parameters to enhance the expressivity. We first encode classical data into quantum states as inputs, then perform VQCs, and finally measure quantum outputs to obtain control signals of agents. Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts, with the potential of quantum speedup. To our knowledge, we are the first to propose the concept of QIL and conduct pilot studies, which paves the way for the quantum era.

Preoperative ultrasound identification and localization of the inferior parathyroid glands in thyroid surgery.

Introduction: The parathyroid glands are important endocrine glands for maintaining calcium and phosphorus metabolism, and they are vulnerable to accidental injuries during thyroid cancer surgery. The aim of this retrospective study was to investigate the application of high-frequency ultrasound imaging for preoperative anatomical localization of the parathyroid glands in patients with thyroid cancer and to analyze the protective effect of this technique on the parathyroid glands and its effect on reducing postoperative complications. Materials and methods: A total of 165 patients who were operated for thyroid cancer in our hospital were included. The patients were assigned into two groups according to the time period of surgery: Control group, May 2018 to February 2021 (before the application of ultrasound localization of parathyroid in our hospital); PUS group, March 2021 to May 2022. In PUS group, preoperative ultrasound was used to determine the size and location of bilateral inferior parathyroid glands to help surgeons identify and protect the parathyroid glands during operation. We compared the preoperative ultrasound results with the intraoperative observations. Preoperative and first day postoperative serum calcium and PTH were measured in both groups. Results: Our preoperative parathyroid ultrasound identification technique has more than 90% accuracy (true positive rate) to confirm the location of parathyroid gland compared to intraoperative observations. Postoperative biochemical results showed a better Ca2+ [2.12(0.17) vs. 2.05(0.31), P=0.03] and PTH [27.48(14.88) vs. 23.27(16.58), P=0.005] levels at first day post-operation in PUS group compared to control group. We also found a reduced risk of at least one type of hypoparathyroidism after surgery in control group:26 cases (31.0%) vs. 41 cases (50.6%), p=0.016. Conclusion: Ultrasound localization of the parathyroid glands can help in the localization, identification and in situ preservation of the parathyroid glands during thyroidectomy. It can effectively reduce the risk of hypoparathyroidism after thyroid surgery.

Machine learning quantitatively characterizes the deformation and destruction of explosive molecules.

Although explosives have been widely used in mines, road development, old building demolishing, and munition explosions; currently, how chemical bonds between atoms break and recombine, how the molecular structure is deformed and destroyed, how the reaction product molecules are formed, and the details for this rapid change process in explosive reactions are not yet fully understood, which limits the full use of explosive energy and safer use of explosives. This paper presents a quantitative model of molecular structure deformation using machine learning algorithms as well as a qualitative model of its relationship with molecular structure destruction, based on a molecular dynamics simulation and detailed analysis of the shock-loaded ε-CL-20, providing new perspectives for explosive community research. Specifically, the quantitative model of molecular structure deformation establishes the quantitative relationship between the molecular volume change and molecular position change, and between molecular distance change and molecular volume change using the machine learning algorithms such as Delaunay triangulation, clustering, and gradient descent. We find that the molecular spacing in explosives is strongly compressed after being shocked, and the peripheral structure can shrink inward, which is beneficial to keep the cage structure stable. When the peripheral structure is compressed to a certain extent, the cage structure volume begins to expand and is then destroyed. In addition, hydrogen atom transfer occurs within the explosive molecule. This study amplifies the structural changes and the chemical reaction process for explosive molecules after being strongly compressed by a shock wave, which can enrich the knowledge of the real detonation reaction process. The analysis method based on quantitative characterization using machine learning proposed in this study can also be used to analyze the microscopic reaction mechanism in other materials.

Bioinformatic Analysis of PTTG Family and Prognosis and Immune Cell Infiltration in Gastric Cancer.

Gastric cancer is the sixth highest incidence rate in the world. Although treatment has made progress, the prospect of gastric cancer patients is bleak. Difficulties and future prospects of immunotherapy in cancer treatment. Adaptive cell therapy, cancer vaccines, gene therapy, and monoclonal antibody therapy have all been used in gastric cancer with some initial success. PTTGs (pituitary tumor-transforming genes) have been proven to be closely related to the prognosis of many malignant tumors. However, the prognosis and immune cell infiltration of gastric adenocarcinoma (STAD) remain unclear. We retrieved multiple databases to understand the possible activity of PTTGs and their expression in gastric cancer, as well as their relationship with clinical data, overall survival rate, first progression, and survival rate after progression. PTTGs are overexpressed in STAD tumor tissues. Many clinical variables are closely related to PTTGs. In addition, PTTG was associated with overall survival independent of disease. In addition, the expression of PTTG1/2 was positively correlated with the molecular status of the immune checkpoint and negatively correlated with the infiltration of various immune cells. Data research shows that PTTG and STAD are closely related. This paved the way for future research, revealed the complex pathophysiology of gastric cancer, and introduced an effective new treatment.

Optimization of glycosylation type of recombinant human growth hormone-Fc fusion protein expressed in CHO cells / 中国生物制品学杂志

@#Objective To optimize the expression of recombinant human growth hormone-Fc(rhGH-Fc)fusion protein in CHO cells in order to obtain better glycosylation ratio and lower content of highmannose.Methods CHO cells expressing rhGH-Fc were cultured in a 7 L bioreactor.The glycosylation modifications of rhGH-Fc were adjusted by improving the composition of feeding media(using three commercial media:Gly-1:EX-CELL Glycosylation Adjust,Gly-2:SHEFF-CHO PLUS PG ACF and Gly-3:EfficientFeed C + AGT Supplement & GlycanTune C + Total Feed),and the glycosylation type and proportion of the target proteins were analyzed by mass spectrometry.Results The G0F(main glycosylation types:G0,G1 and G2;F:fucose)of Gly-1,Gly-2 and Gly-3 were 32.89%,58.66% and 33.28%,the G1F were 31.39%,18.03%and 34.90%,and the G2F were 31.39%,18.03% and 34.90%,respectively.Gly-1 and Gly-3 made the target protein contain less G0F while more G2F;Gly-3 feeding scheme-showed less high mannose modification than the other two schemes.Conclusion Gly-1 medium changed the glycosylation modification from G0F to G1F and G2F,while Gly-2 medium changed that from G2F and G1F to G0F.However,Gly-3 medium changed the glycosylation modification from G0F to G1F and G2F,and the contentof high mannose was less than 5%,which may have a better effect on modifying glycosylation type and proportion of the target protein.

Bioinformatics analysis: relationship between adrenocortical carcinoma and KIFs.

Adrenal cortical cancer has a relatively low incidence, but a dismal 5-year survival rate. Surgical intervention is the gold standard of care today. In spite of this progress, patients continue to have a dismal outlook. The results of this study demonstrate that kinin superfamily (KIF) has strong ties to many different types of cancers. However, their prognostic and immune cell infiltration of adrenocortical carcinoma (ACC) remain unclear. Multiple databases were searched for information on the transcription level of KIFs, its correlation with clinical data of ACC patients, patients' overall survival (OS), first progression survival (FPS), and progression free interval (PFI). Its role and association with immune cells were also investigated. We observed an increase in the expression of KIF4A, KIF11, KIF20A, and KIF22. There was a strong correlation between them and the advancedness of ACC tumors. Parallel to this, KIFs are connected to the concepts of operating systems, distributed file systems, and partitioned file systems. Similarly, we found five key genes, PRC1, PLK1, KIF23, KIFC1, and KIF5A, through data analysis, all of which participate in multiple cellular pathways. Both KIF4A and KIF11 expression levels were marginally positively correlated with immune infiltration. Because KIF4A, KIF11, KIF20A, and KIF22 are involved in multiple ACC processes and can influence the onset and progression of ACC, they provide a mechanistically grounded framework for diagnosing and managing the disease.

The targets of aspirin in bladder cancer: bioinformatics analysis.

BACKGROUND: The anti-carcinogenic properties of aspirin have been observed in some solid tumors. However, the molecular mechanism of therapeutic effects of aspirin on bladder cancer is still indistinct. We introduced a bioinformatics analysis approach, to explore the targets of aspirin in bladder cancer (BC). METHODS: To find out the potential targets of aspirin in BC, we analyzed direct protein targets (DPTs) of aspirin in Drug Bank 5.0. The protein-protein interaction (PPI) network and signaling pathway of aspirin DPTs were then analyzed subsequently. A detailed analysis of the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway has shown that aspirin is linked to BC. We identified overexpressed genes in BC comparing with normal samples by Oncomine and genes that interlinked with aspirin target genes in BC by STRING. RESULTS: Firstly, we explored 16 direct protein targets (DPT) of aspirin. We analyzed the protein-protein interaction (PPI) network and signaling pathways of aspirin DPT. We found that aspirin is closely associated with a variety of cancers, including BC. Then, we classified mutations in 3 aspirin DPTs (CCND1, MYC and TP53) in BC using the cBio Portal database. In addition, we extracted the top 50 overexpressed genes in bladder cancer by Oncomine and predicted the genes associated with the 3 aspirin DPTs (CCND1, MYC and TP53) in BC by STRING. Finally, 5 exact genes were identified as potential therapeutic targets of aspirin in bladder cancer. CONCLUSION: The analysis of relevant databases will improve our mechanistic understanding of the role of aspirin in bladder cancer. This will guide the direction of our next drug-disease interaction studies.

Case report: Unique ultrasound feature of thyroid metastases in occult breast cancer.

Occult breast cancer is an uncommon type of breast cancer. Metastases of occult breast cancer to other tissues are rather rare. We present a rare case of thyroid metastases in a 46-year-old woman who underwent occult breast cancer. The first ultrasound (US) examination of the thyroid showed that the left lobe was enlarged but had normal thyroid function. At first, this case was misdiagnosed as thyroiditis based on the thyroid US features. However, the cytological and histological results showed that nests of the neoplastic cells were found. Further immunohistochemistry results confirmed that these neoplasms were derived from breast tissue. Analysis using the successive US scans revealed that the sizes and echo of the thyroid repeatedly changed after the radiotherapy and chemotherapy treatment. To our knowledge, this is the first reported case of occult breast carcinoma presenting with thyroid metastases. This case can easily be misdiagnosed as thyroiditis due to the metastasis area not manifesting as regular suspicious nodules or diffused punctate calcifications.

A Nonlinear Viscoelastic Constitutive Model for Solid Propellant with Rate-Dependent Cumulative Damage.

Solid propellant is a composite material exhibiting classic nonlinear viscoelastic mechanical characteristic, which is due in a large part to a cumulative damage process caused by the formation and growth of microflaws inside. The standard relaxation tests and uniaxial tension tests under different velocities of hydroxyl-terminated polybutadiene (HTPB) propellant are carried out in this paper, where Digital Image Correlation (DIC) technique is applied to record deformation. The experimental results show that the material mechanical behavior is rate-dependent. It is also observed that the yield stress and failure stress are significantly rate-dependent on the tensile velocity. Based on these experimental results, it can be inferred that the stiffness degradation and damage evolution of HTPB propellant are a rate-dependent processes. Therefore, the damage accumulation of HTPB propellant is considered rate-dependent in this research. In order to describe the mechanical characteristic precisely, a nonlinear viscoelastic constitutive model with rate-dependent cumulative damage is developed. The damage model is developed based on the concept of pseudo strain, in which a Prony series representation of viscoelastic material functions is applied. Besides, a rate-dependent damage variable is introduced into the model through considering the rate-dependent characteristics of cumulative damage process. In addition, a new normalized failure criterion is derived on the basis of the proposed damage model, which is independent of strain-rate after normalization. Finally, it is implemented in commercial finite element software for stress analysis to verify the predictive capacities of the damage model. The accuracy of the constitutive model and failure criterion is validated under uniaxial tensile tests of various strain rates.

Bacteriophage protein PEIP is a potent Bacillus subtilis enolase inhibitor.

Enolase is a highly conserved enzyme that presents in all organisms capable of glycolysis or fermentation. Its immediate product phosphoenolpyruvate is essential for other important processes like peptidoglycan synthesis and the phosphotransferase system in bacteria. Therefore, enolase inhibitors are of great interest. Here, we report that Gp60, a phage-encoded enolase inhibitor protein (PEIP) of bacteriophage SPO1 for Bacillus subtilis, is an enolase inhibitor. PEIP-expressing bacteria exhibit growth attenuation, thinner cell walls, and safranin color in Gram staining owing to impaired peptidoglycan synthesis. We solve the structure of PEIP-enolase tetramer and show that PEIP disassembles enolase by disrupting the basic dimer unit. The structure reveals that PEIP does not compete for substrate binding but induces a cascade of conformational changes that limit accessibility to the enolase catalytic site. This phage-inspired disassembly of enolase represents an alternative strategy for the development of anti-microbial drugs.

The TAS Test project: a prospective longitudinal validation of new online motor-cognitive tests to detect preclinical Alzheimer's disease and estimate 5-year risks of cognitive decline and dementia.

BACKGROUND: The worldwide prevalence of dementia is rapidly rising. Alzheimer's disease (AD), accounts for 70% of cases and has a 10-20-year preclinical period, when brain pathology covertly progresses before cognitive symptoms appear. The 2020 Lancet Commission estimates that 40% of dementia cases could be prevented by modifying lifestyle/medical risk factors. To optimise dementia prevention effectiveness, there is urgent need to identify individuals with preclinical AD for targeted risk reduction. Current preclinical AD tests are too invasive, specialist or costly for population-level assessments. We have developed a new online test, TAS Test, that assesses a range of motor-cognitive functions and has capacity to be delivered at significant scale. TAS Test combines two innovations: using hand movement analysis to detect preclinical AD, and computer-human interface technologies to enable robust 'self-testing' data collection. The aims are to validate TAS Test to [1] identify preclinical AD, and [2] predict risk of cognitive decline and AD dementia. METHODS: Aim 1 will be addressed through a cross-sectional study of 500 cognitively healthy older adults, who will complete TAS Test items comprising measures of motor control, processing speed, attention, visuospatial ability, memory and language. TAS Test measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 (p-tau181). Aim 2 will be addressed through a 5-year prospective cohort study of 10,000 older adults. Participants will complete TAS Test annually and subtests of the Cambridge Neuropsychological Test Battery (CANTAB) biennially. 300 participants will undergo in-person clinical assessments. We will use machine learning of motor-cognitive performance on TAS Test to develop an algorithm that classifies preclinical AD risk (p-tau181-defined) and determine the precision to prospectively estimate 5-year risks of cognitive decline and AD. DISCUSSION: This study will establish the precision of TAS Test to identify preclinical AD and estimate risk of cognitive decline and AD. If accurate, TAS Test will provide a low-cost, accessible enrichment strategy to pre-screen individuals for their likelihood of AD pathology prior to more expensive tests such as blood or imaging biomarkers. This would have wide applications in public health initiatives and clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05194787 , 18 January 2022. Retrospectively registered.

Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network.

In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

Enzymatic hydrolysis lignin functionalized Ti3C2Tx nanosheets for effective removal of MB and Cu2+ ions.

Functionalized two-dimensional Ti3C2Tx (TN-EHL) was prepared as an effective adsorbent for removal of methylene blue dye (MB) and copper ions (Cu2+). Enzymatic hydrolysis lignin (EHL), a reproducible natural resource, was used to functionalize the Ti3C2Tx nanosheets. EHL can not only introduce active functional groups into TN-EHL but also prevent the oxidation of Ti3C2Tx, thus promoting the adsorption performance of TN-EHL. The maximum adsorption capacities of TN-EHL50 (in which the EHL content is 50 wt%) for MB and Cu2+ were 293.7 mg g-1 and 49.96 mg g-1, respectively. The higher correlation coefficients (R2) of MB (0.9996) and Cu2+ (0.9995) indicating that their adsorption processes can be described by the pseudo-second-order kinetic model. The MB adsorption data fit the Freundlich isotherm with R2 of 0.9953, whereas the Cu2+ ions adsorption data fit the Langmuir isotherm with R2 of 0.9998. The thermodynamic analysis indicates that the adsorption process of MB and Cu2+ on TN-EHL50 is spontaneous and endothermic. Significantly, the Cu2+ ions were reduced to Cu2O and CuO particles during the adsorption process. Therefore, TN-EHL has a great potential as an environmentally friendly adsorbent for MB removal and recovery of Cu2+ ions from wastewater.