Modeling of the resilient supply chain system from a perspective of production design changes

Building an effective resilient supply chain system (RSCS) is critical and necessary to reduce the risk of supply chain disruptions in unexpected scenarios such as COVID-19 pandemic and trade wars. To overcome the impact of insufficient raw material supply on the supply chain in mass disruption scenarios, this study proposes a novel RSCS considering product design changes (PDC). An RSCS domain model is first developed from the perspective of PDC based on a general conceptual framework, i.e., function-context-behavior-principle-state-structure (FCBPSS), which can portray complex systems under unpredictable situations. Specifically, the interaction among the structure, state and behavior of the infrastructure system and substance system is captured, and then a quantitative analysis of the change impact process is presented to evaluate the resilience of both the product and supply chain. Next, a case study is conducted to demonstrate the PDC strategy and to validate the feasibility and effectiveness of the RSCS domain model. The results show that the restructured RSCS based on the proposed strategy and model can remedy the huge losses caused by the unavailability of raw materials.

Study on protective immunity induced by recombinant sars-cov-2 s1 and s protein vaccine

To evaluate the immune protection of recombinant SARS-CoV-2 S1 and S protein vaccine. Methods;Recombinant SARS-CoV-2 S1 or S protein combined with aluminum hydroxide adjuvant was inoculated at different doses of 0.1 μg, 1 μg, 5 μg and 10 μg per mouse for 6-8 weeks. Serum IgG antibody liters were detected by enzyme linked immunosorbent assay (ELISA) after second immunization. The serum neutralizing antibody titers of the immunized mice against pseudotype SARS-CoV-2-Fluc WT, B. 1.1. 7, P. 1, B. 1.617.2, B. 1.621, 501Y. V2-1 strains were compared by pseudovirus neutralization test. The cellular immune levels of sera were detected by enzyme-linked immunospot assay (ELISpot). Results;Both SARS-CoV-2 S and S1 proteins could induce strong IgG antibody levels in mouse model. The sera of mice immunized with S1 protein showed obvious neutralization activity against SARS-CoV-2-Fluc WT, B. 1. 1.7 and P. 1. The sera of mice immunized with the recombinant S protein also showed obvious neutralization activity against SARS-CoV-2-Fluc B. 1.617.2 in addition to SARS-CoV-2-Fluc WT, B. 1. 1.7 and P. 1. The serum of mice immunized with two kinds of proteins had the strongest neutralizing effect on SARS-CoV-2-Fluc WT. Mouse spleen cells immunized with S protein could significantly induce the production of interferon-γ (IFN--γ) and interleukin-4 (IL-4). The levels of IgG antibody, neutralizing antibody and cellular immunity induced by S protein were higher than those of S1. Conclusion;Recombinant SARS-CoV-2 S protein vaccine can induce protective immune responses. © 2022, China Biotechnology. All rights reserved.

COVID-19 VACCINE RESPONSE IN PATIENTS ON CANCER THERAPY - EVIDENCE FROM AUSTRALIAN DATA

Background: Cancer patients have increased risk of serious illness or death from COVID-19. Vaccination protects against severe disease, but cancer patients were excluded from COVID-19 vaccine registration trials. Different cancer therapies may have varying impact on immune response. We assessed seroconversion post COVID-19 vaccination among cancer patients in a setting of high vaccine uptake with minimal community transmission. Methods: Solid tumour patients and healthy controls from Canberra who received COVID-19 vaccination between 3/2021 - 1/2022 were included. Patients received active cancer therapy within two weeks of COVID-19 vaccination. Blood was collected at baseline, pre 2nd vaccine dose, then one, three and six months post 2nd dose. SARS-CoV-2 anti-spike receptor binding domain and anti-nucleocapsid immunoglobulin G(IgG) levels were measured by enzyme-linked immunosorbent assay and calibrated with the National Institutes of Health serology standard. Primary endpoint was seroconversion three months post 2nd vaccine dose, or within two weeks prior to 3rd vaccine dose in patients. Results: There were 96 solid tumour patients (76 evaluable for the primary endpoint) and 19 healthy controls. Median age 62 years with 70 (61%) being female. COVID-19 vaccines included AZD1222 (65%) and BNT162b2 (35%). Majority (69%) of patients had metastatic cancer. Baseline lymphopenia (<1.2x10-9/L) was seen in 41% of patients. Median Charlson comorbidity index score was 7 (2 - 12). Among primary endpoint evaluable patients, 47 (62%) patients received chemotherapy, alone or in combination with other cancer therapy;8 (11%) received immunotherapy alone;21 (28%) had targeted therapy. Seroconversion at three months post vaccination occurred in 86% of cancer patients and 100% of controls (p=0.11). Mean anti-spike antibody titre was 88 binding antibody units (BAU)/ml in cancer patients and 179 BAU/ml in controls, p=0.10. No subjects had positive antinucleocapsid IgG confirming absence of past COVID-19 infection. Seroconversion occurred in patients who received chemotherapy alone or in combination (83%), immunotherapy (75%) and targeted therapy (95%;p=0.2). Mean anti-spike IgG levels were 77, 63 and 137 BAU/mL with chemotherapy, immunotherapy and targeted therapy respectively. Age, metastatic disease and lymphocyte count were not associated with antispike antibody level. Among cancer patients, 40% and 95% were seropositive after 1 and 2 vaccine doses respectively. A decline in anti-spike antibody titre was seen from three months post the 2nd vaccine dose. Cancer patients had an increase in anti-spike post 3rd vaccine dose, while levels declined in controls (pre booster), at 6 months post the 2nd vaccine dose. Conclusions: Cancer patients achieved comparable seroconversion rates three months post vaccination compared with healthy controls. Although the anti-spike antibody titre was numerically lower among cancer patients than controls, the difference was not statistically significant. Recent cancer therapy did not appear to significantly affect vaccine response, however, the anti-spike antibody level was numerically lower among recipients of chemotherapy compared with targeted therapy. Patients on immunotherapy had the lowest antibody level, although the small sample size limits definitive conclusion in this subgroup. Reassuringly, a rise in anti-spike antibody occurred after the 3rd primary dose in cancer patients, surpassing the level among controls prior to receipt of booster vaccination.

A hybrid machine learning approach of fuzzy-rough-k-nearest neighbor, latent semantic analysis, and ranker search for efficient disease diagnosis

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12% of the primary tumor, 96.45% of breast cancer Wisconsin, 94.44% of cryotherapy, 93.81% of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.

Epidemiological characteristics of 17 Coronavirus disease 2019 in Puyang, Henan

Objective To explore the epidemiological characteristics of confirmed cases of coronavirus diseases 2019 (COVID-19) in Puyang, Henan Province, so as to provide basis for diseases control and preventive. Methods The epidemiological data of 17 cases of COVID-19 in Puyang were collected, and the time, regional and population distribution characteristics of COVID-19 were described and analyzed by drawing disease sequence diagram and case relationship diagram. Results Among the 17 cases, 7 were male (41.2%);the median age was 36 years old with age ranged from 12 to 66 years. A total of 4 clustered outbreaks occurred, involving 12 cases (70.6%), all of which were family recurrent cases. The median incubation period was 6.5 days, the shortest 3 days and the longest 13 days. The onset time of a second-generation case was 11 days earlier than that of the indicator case. The median time between onset and treatment was 4 days, the shortest was 0 days and the longest was 12 days. Of the 17 cases, 6 had a sojourning history in Hubei Province within 14 days, and 1 had a history of overseas tourism. The other cases were all local infections, 8 of them were close contacts of the confirmed cases, and 2 of them were from unknown sources. Among the close contacts, the cases involved in the family clustering epidemic were transmitted through close contact and respiratory tract. After detailed investigation and inquiry, it was inferred that the transmission route was the staircase droplet transmission. Conclusion In Puyang City, most of the cases were from Hubei Province, and was dominated by family clustering epidemic. There was a possibility of infection in the incubation period. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Passive Adaptation or Active Learning: A Survey on the Subjective Feelings of University Teachers in Mainland China in the Online Education Environment during the COVID-19 Pandemic

This paper proposes that we should focus on the subjective feelings of university teachers in the online education environment during the COVID-19 pandemic. Using the questionnaire survey method and choosing university teachers in mainland China as the objects of investigation, it obtained a total of 256 survey samples and puts forward two assumptions. One is that changes brought by the online education environment during the pandemic have a significant influence on university teachers' subjective feelings. The other is that university teachers with different backgrounds have different subjective feelings in the online education environment. The survey results showed university teachers' subjective feelings overall were at the upper middle level, but their attitudes towards and adaption to online classes were diverse. Among the five dimensions of teachers' subjectivity, the score of self-consciousness was the highest, and the score of autonomy was the lowest. During the pandemic, the online teaching environment has a significant impact on the subjective feelings of university teachers in mainland China. Differences in the subjective feelings of university teachers with different backgrounds mainly exist in gender variables, while there is no significant difference in age, years of teaching, and teaching subjects.

Opinion Mining about Online Education Basing on GDELT and Twitter Data

This public health incident transformed teaching activities from offline to online. The media content and comments on social media provide a dataset for digging the public opinion on online learning. This study uses the GDELT and TWITTER platforms' data, searching "COVID" and "online education" as keywords;the relevant information is collected and analyzed in python. The results of this public opinion mining will play an essential role in discovering the problem of online teaching in this pandemic.

A Hybrid Data Mining Method for EEG Analysis in Online Education Enhanced by Background Music

Oitline teaching arc facing drikmatic challenges due to the COVIDI9 pandemic requiring massive online education. Students are experiencing mental and physical isolation during this period. This research :Aims to find an efficient may to discover students emotion status through 1,;El: patient recognition (1'10. Traditional PR !method,' haie been applied eNtensikeh in 1":Ft;recognition including Artificial Neuron Networks iANNi, Support Vector Nlak'hinekSVNI Nearest Xeighhorc (KNN), and so on. In this paper, a association rule -based PR method has been introduced through incorporating clustering and Apriori association rube methods. The experimental results demonst rale that Ute tgdimized rule -based 1":F:t;PR model Can improve real-time recognition eflicienc. Tlic proposed model can Ike used for identifying students cognitive statuses and improve educational perfikrrnunce in (.0\11/19 period

The Chinese auto industry will overcome the industry downturn and minimize the impact of the COVID19 epidemic

The article explores the problems of the Chinese automobile industry that was formed three years ago. The highest annual growth rate in Chinese car production and sales in the Chinese market in history occurred in 2010, after which it declined sharply and since 2011 the auto! motive industry has been experiencing its worst days - the annual growth rate of car sales in China was the lowest in 15 years, and in 2018 the growth rate became negative. Such a noticeable decrease in the annual growth rate of production and sales of cars was the result of a slowdown in the country's economic development, the end of the state program to provide benefits and various kinds of preferences to car buyers, the use of restrictive measures when buying cars with internal combustion engines in many cities in China, and the expansion of the development of such a form of shared consumption economy as carsharing, trade contradictions in Sino-American economic relations and the impact of the consequences of the COVID-19 epidemic. Today, the automotive industry in China has been able to resume production against the background of improving epidemiological situation, but the automotive industry still faces great difficulties, in particular with the problem of weak consumer demand. Due to insufficient market demand, the warehouse stocks of enterprises are constantly increasing, which in the future may lead to the bankruptcy of some Chinese automakers.

[Exploring the mechanism of liver enzyme abnormalities in patients with novel coronavirus-infected pneumonia].

Objective: To explore and analyze the possible mechanism of liver injury in patients with coronavirus disease 2019 (novel coronavirus pneumonia, NCP). Methods: The correlation between ALT, AST and other liver enzyme changes condition and NCP patients' disease status reported in the literature was comprehensively analyzed. ACE2 expression in liver tissue for novel coronavirus was analyzed based on single cell sequencing (GSE115469) data. RNA-Seq method was used to analyze Ace2 expression and transcription factors related to its expression in liver tissues at various time-points after hepatectomy in mouse model of acute liver injury with partial hepatectomy. t-test or Spearman rank correlation analysis was used for statistical analysis. Results: ALT and AST were abnormally elevated in some patients with novel coronavirus infection, and the rate and extent of ALT and AST elevation in severe NCP patients were higher than those in non-severe patients. Liver tissue results of single cell sequencing and immunohistochemistry showed that ACE2 was only expressed in bile duct epithelial cells of normal liver tissues, and very low in hepatocytes. In a mouse model of acute liver injury with partial hepatectomy, Ace2 expression was down-regulated on the first day, but it was elevated up to twice of the normal level on the third day, and returned to normal level on seventh day when the liver recovered and hepatocyte proliferation stopped. Whether this phenomenon suggests that the bile duct epithelial cells with positive expression of Ace2 participate in the process of liver regeneration after partial hepatectomy deserves further study. In RNA-Seq data, 77 transcription factors were positively correlated with the expression of Ace2 (r > 0.2, FDR < 0.05), which were mainly enriched in the development, differentiation, morphogenesis and cell proliferation of glandular epithelial cells. Conclusion: We assumed that in addition to the over activated inflammatory response in patients with NCP, the up-regulation of ACE2 expression in liver tissue caused by compensatory proliferation of hepatocytes derived from bile duct epithelial cells may also be the possible mechanism of liver tissue injury caused by 2019 novel coronavirus infection.