Supplementation of oligosaccharide-based polymer enhanced growth and disease resistance of weaned pigs by modulating intestinal integrity and systemic immunity

Background:There is a great demand for antibiotic alternatives to maintain animal health and productivity.The objective of this experiment was to determine the efficacy of dietary supplementation of a blood group A6 type 1antigen oligosaccharides-based polymer(Coligo)on growth performance,diarrhea severity,intestinal health,and systemic immunity of weaned pigs experimentally infected with an enterotoxigenic Escherichia coli(ETEC),when compared with antibiotics.Results:Pigs in antibiotic carbadox or Coligo treatment groups had greater(P<0.05)body weight on d 5 or d 11post-inoculation(PI)than pigs in the control group,respectively.Supplementation of antibiotics or Coligo enhanced(P<0.05)feed efficiency from d 0 to 5 PI and reduced(P<0.05)frequency of diarrhea throughout the experiment,compared with pigs in the control group.Supplementation of antibiotics reduced(P<0.05)fecalβ-hemolytic coliforms on d 2,5,and 8 PI.Pigs in antibiotics or Coligo groups had reduced(P<0.05)neutrophil counts and serum haptoglobin concentration compared to pigs in the control group on d 2 and 5 PI.Pigs in Coligo had reduced(P<0.05)total coliforms in mesenteric lymph nodes on d 5 and 11 PI,whereas pigs in antibiotics or Coligo groups had reduced(P<0.05)total coliforms in spleen on d 11 PI compared with pigs in the control group.On d 5 PI,pigs in the Coligo group had greater(P<0.05)gene expression of ZO1 in jejunal mucosa,but less(P<0.05)m RNA expression of IL1B,IL6,and TNF in ileal mucosa,in comparison with pigs in the control group.Supplementation of antibiotics enhanced(P<0.05)the gene expression of OCLN in jejunal mucosa but decreased(P<0.05)IL1B and IL6 gene expression in ileal mucosa,compared with the control.On d 11 PI,supplementation of antibiotics or Coligo up-regulated(P<0.05)gene expression of CLDN1 in jejunal mucosa,but Coligo reduced(P<0.05)IL6 gene expression in ileal mucosa compared to pigs in the control group.Conclusions:Supplementation of Coligo improved growth performance,alleviated diarrhea severity,and enhanced gut health in weaned pigs infected with ETEC F18 in a manner similar to in-feed antibiotics.

Correlation between preoperative systemic immune inflammation index, nutritional risk index, and prognosis of radical resection of liver cancer

BACKGROUND Radical surgery is the most commonly used treatment for hepatocellular carcinoma(HCC).However,the surgical effect remains not ideal,and prognostic evaluation is insufficient.Furthermore,clinical intervention is rife with uncertainty and not conducive to prolonging patient survival.AIM To explore correlations between the systemic immune inflammatory index(SII)and geriatric nutritional risk index(GNRI)and HCC operation prognosis.METHODS This retrospective study included and collected follow up data from 100 HCC.Kaplan–Meier survival curves were used to analyze the correlation between SII and GNRI scores and survival.SII and GNRI were calculated as follows:SII=neutrophil count×platelet count/lymphocyte count;GNRI=[1.489×albumin(g/L)+41.7×actual weight/ideal weight].We analyzed the predictive efficacy of the SII and GNRI in HCC patients using receiver operating characteristic(ROC)curves,and the relationships between the SII,GNRI,and survival rate using Kaplan–Meier survival curves.Cox regression analysis was utilized to analyze independent risk factors influencing prognosis.RESULTS After 1 year of follow-up,24 patients died and 76 survived.The area under the curve(AUC),sensitivity,specificity,and the optimal cutoff value of SII were 0.728(95%confidence interval:0.600-0.856),79.2%,63.2%,and 309.14,respectively.According to ROC curve analysis results for predicting postoperative death in HCC patients,the AUC of SII and GNRI combination was higher than that of SII or GNRI alone,and SII was higher than that of GNRI(P<0.05).The proportion of advanced differentiated tumors,tumor maximum diameter(5–10 cm,>10 cm),lymph node metastasis,and TNM stage III-IV in patients with SII>309.14 was higher than that in patients with SII≤309.14(P<0.05).The proportion of patients aged>70 years was higher in patients with GNRI≤98 than that in patients with GNRI>98(P<0.05).The 1-year survival rate of the SII>309.14 group(compared with the SII≤309.14 group)and GNRI≤98 group(compared with the GNRI>98 group)was lower(P<0.05).CONCLUSION The prognosis after radical resection of HCC is related to the SII and GNRI and poor in high SII or low GNRI patients.

Prognostic impact of preoperative nutritional and immune inflammatory parameters on liver cancer

The immune response to tissue damage or infection involves inflammation,a multifaceted biological process distinguished by immune cell activation,mediator secretion,and immune cell recruitment to the site of injury.Several blood-based immune-inflammatory biomarkers with prognostic significance in malignancies have been identified.In this issue of the World Journal of Gastrointestinal Surgery,they examined the prognosis of liver cancer radical resection in relation to pre-operative systemic immune-inflammation and nutritional risk indices.Comparing older and younger individuals often reveals compromised nutritional and immu-nological statuses in the former.Therefore,performing preoperative evaluations of the nutritional status and immunity in geriatric patients is critical.In addition to being a primary treatment modality,radical resection is associated with a sig-nificant mortality rate following surgery.Insufficient dietary consump-tion and an elevated metabolic rate within tumor cells contribute to the increased proba-bility of malnutrition associated with the ailment,consequently leading to a sub-stantial deterioration in prognosis.Recent studies,reinforce the importance of nu-tritional and immune-inflammatory biomarkers.Prior to surgical intervention,geriatric nutritional risk and systemic immune-inflammatory indices should be prioritized,particularly in older patients with malignant diseases.

Dynamic changes in the systemic immune responses of spinal cord injury model mice

Intraspinal inflammatory and immune responses are considered to play central roles in the pathological development of spinal cord injury.This study aimed to decipher the dynamics of systemic immune responses,initiated by spinal cord injury.The spinal cord in mice was completely transected at T8.Changes in the in vivo inflammatory response,between the acute and subacute stages,were observed.A rapid decrease in C-reactive protein levels,circulating leukocytes and lymphocytes,spleen-derived CD4~+interferon-γ+T-helper cells,and inflammatory cytokines,and a marked increase in neutrophils,monocytes,and CD4~+CD25~+FOXP3~+regulatory T-cells were observed during the acute phase.These systemic immune alterations were gradually restored to basal levels during the sub-acute phase.During the acute phase of spinal cord injury,systemic immune cells and factors showed significant inhibition;however,this inhibition was transient,and the indicators of these serious disorders gradually returned to baseline levels during the subacute phase.All experiments were performed in accordance with the institutional animal care guidelines,approved by the Institutional Animal Care and Use Committee of Experimental Animal Center of Drum Tower Hospital,China(approval No.2019 AE01040)on June 25,2019.

Systemic immune inflammation index, ratio of lymphocytes to monocytes, lactate dehydrogenase and prognosis of diffuse large Bcell lymphoma patients

BACKGROUND In malignant tumors,inflammation plays a vital role in the development,invasion,and metastasis of cancer cells.Diffuse large B-cell lymphoma(DLBCL),the most common malignant proliferative disease of the lymphatic system,is commonly associated with inflammation.The international prognostic index(IPI),which includes age,lactate dehydrogenase(LDH),number of extranodal lesions,Ann Arbor score,and Eastern Cooperative Oncology Group(ECOG)score,can evaluate the prognosis of DLBCL.However,its use in accurately identifying highrisk patients and guiding treatment is poor.Therefore,it is important to find novel immune markers in predicting the prognosis of DLBCL patients.AIM To determine the association between the systemic immune inflammation index(SII),ratio of lymphocytes to monocytes(LMR),ratio of LMR to LDH(LMR/LDH),and prognosis of patients with DLBCL.METHODS A total of 68 patients diagnosed with DLBCL,treated in our hospital between January 2016 and January 2020,were included.χ2 test,Pearson’s R correlation,Kaplan Meier curves,and Cox proportional risk regression analysis were used.The differences in the SII,LMR,and LMR/LDH among patients with different clinicopathological features were analyzed.The differences in progression-free survival time among patients with different SII,LMR,and LMR/LDH expressions and influencing factors affecting the prognosis of DLBCL patients,were also analyzed.RESULTS The LMR and LMR/LDH in patients with Ann Arbor stage III–IV,ECOG score≥2,and SII,IPI score 2–5 were significantly higher than those of patients with Ann Arbor stage I-II and ECOG score<2(P<0.05).Patients with high SII,LMR,and LMR/LDH had progression-free survival times of 34 mo(95%CI:32.52–38.50),35 mo(95%CI:33.42–36.58)and 35 mo(95%CI:33.49–36.51),respectively,which were significantly lower than those with low SII,LMR,and LMR/LDH(P<0.05);the SII,LMR,and LMR/LDH were positively correlated(P<0.05).Cox proportional risk regression analysis showed that the SII,LMR,and LMR/LDH were influencing factors for the prognosis of DLBCL patients(hazard ratio=1.143,1.665,and 1.704,respectively;P<0.05).CONCLUSION The SII,LMR,and LMR/LDH are related to the clinicopathological features of DLCBL,and they also influence the prognosis of patients with the disease.

Multimodal Evolution Approach to Multidimensional Intrusion Detection

An artificial immunity based multimodal evolution algorithm is developed to generate detectors with variable coverage for multidimensional intrusion detection. In this algorithm, a proper fitness function is used to drive the detectors to fill in those detection holes close to self set or among self spheres, and genetic algorithm is adopted to reduce the negative effects that different distribution of self imposes on the detector generating process. The validity of the algorithm is tested with spherical and rectangular detectors, respectively, and experiments performed on two real data sets （machine learning database and DAPRA99） indicate that the proposed algorithm can obtain good results on spherical detectors, and that its performances in detection rate, false alarm rate, stabih＇ty, time cost, and adaptability to incomplete training set on spherical detectors are all better than on rectangular ones.

The Motif Tracking Algorithm

The search for patterns or motifs in data represents a problem area of key interest to finance and economic researchers. In this paper, we introduce the motif tracking algorithm （MTA）, a novel immune inspired （IS） pattern identification tool that is able to identify unknown motifs of a non specified length which repeat within time series data. The power of the algorithm comes from the fact that it uses a small number of parameters with minimal assumptions regarding the data being examined or the underlying motifs. Our interest lies in applying the algorithm to financial time series data to identify unknown patterns that exist. The algorithm is tested using three separate data sets. Particular suitability to financial data is shown by applying it to oil price data. In all cases, the algorithm identifies the presence of a motif population in a fast and efficient manner due to the utilization of an intuitive symbolic representation. The resulting population of motifs is shown to have considerable potential value for other applications such as forecasting and algorithm seeding.

Bacillus cereus AR156 primes induced systemic resistance by suppressing miR825/825 and activating defense-related genes in Arabidopsis

Small RNAs play an important role in plant immune responses. However, their regulatory function in induced systemic resistance（ISR） is nascent. Bacillus cereus AR156 is a plant growth-promoting rhizobacterium that induces ISR in Arabidopsis against bacterial infection. Here,by comparing small RNA profiles of Pseudomonas syringae pv. tomato（Pst） DC3000-infected Arabidopsis with and without AR156 pretreatment, we identified a group of Arabidopsis micro RNAs（mi RNAs） that are differentially regulated by AR156 pretreatment. mi R825 and mi R825 are two mi RNA generated from a single mi RNA gene.Northern blot analysis indicated that they were significantly downregulated in Pst DC3000-infected plants pretreated with AR156, in contrast to the plants without AR156 pretreatment. mi R825 targets two ubiquitin-protein ligases,while mi R825 targets toll-interleukin-like receptor（TIR）-nucleotide binding site（NBS） and leucine-rich repeat（LRR）type resistance（R） genes. The expression of these target genes negatively correlated with the expression of mi R825 and mi R825. Moreover, transgenic plants showing reduced expression of mi R825 and mi R825 displayed enhanced resistance to Pst DC3000 infection, whereas transgenic plants overexpressing mi R825 and mi R825 were more susceptible. Taken together, our data indicates that Bacillus cereus AR156 pretreatment primes ISR to Pst infection by suppressing mi R825 and mi R825 and activating the defense related genes they targeted.

Optimal approximation of linear systems by artificial immune response

This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

Multiobjective optimization using an immunodominance and clonal selection inspired algorithm

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm （IDCMA）. IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals （antibodies） are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant （active） antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-Ⅱ, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.

Intelligent multi-user detection using an artificial immune system

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

Real-time immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neural networks for plug-in hybrid electric vehicles fuel economy

The main proposition of the current investigation is to develop a computational intelligence-based framework which can be used for the real-time estimation of optimum battery state-of-charge （SOC） trajectory in plug-in hybrid electric vehicles （PHEVs）. The estimated SOC trajectory can be then employed for an intelligent power management to significantly improve the fuel economy of the vehicle. The devised intelligent SOC trajectory builder takes advantage of the upcoming route information preview to achieve the lowest possible total cost of electricity and fossil fuel. To reduce the complexity of real-time optimization, the authors propose an immune system-based clustering approach which allows categoriz- ing the route information into a predefined number of segments. The intelligent real-time optimizer is also inspired on the basis of interactions in biological immune systems, and is called artificial immune algorithm （AIA）. The objective function of the optimizer is derived from a computationally efficient artificial neural network （ANN） which is trained by a database obtained from a high-fidelity model of the vehicle built in the Autonomic software. The simulation results demonstrate that the integration of immune inspired clustering tool, AIA and ANN, will result in a powerful framework which can generate a near global optimum SOC trajectory for the baseline vehicle, that is, the Toyota Prius PHEV. The outcomes of the current investigation prove that by taking advantage of intelligent approaches, it is possible to design a computationally efficient and powerful SOC trajectory builder for the intelligent power management of PHEVs.

Photosynthetic microorganisms coupled photodynamic therapy for enhanced antitumor immune effect

The ideal photodynamic therapy(PDT)should effectively remove the primary tumor,and produce a stronger immune memory effect to inhibit the tumor recurrence and tumor metastasis.However,limited by the hypoxic and immunosuppressive microenvironment,the PDT efficiency is apparently low.Here,Chlorella(Chl.)is exploited to enhance local effect by producing oxygen to reverse hypoxia,and release adjuvants to reverse immunosuppressive microenvironment to enhance abscopal effect afterwards.Results from different animal models indicated that Chl.could enhance local effect and PDT related immune response.Ultimately,Chl.coupled PDT elicited anti-tumor effects toward established primary tumors(inhibition rate:90%)and abscopal tumors(75%),controlled the challenged tumors(100%)and alleviated metastatic tumors(90%).This Chl.coupled PDT strategy can also produce a stronger anti-tumor immune memory effect.Overall,this Chl.coupled PDT strategy generates enhanced local tumor killing,boosts PDT-induced immune responses and promotes anti-tumor immune memory effect,which may be a great progress for realizing systemic effect of PDT.

Enabling data-driven anomaly detection by design in cyber-physical production systems

Designing and developing distributed cyber-physical production systems(CPPS)is a time-consuming,complex,and error-prone process.These systems are typically heterogeneous,i.e.,they consist of multiple components implemented with different languages and development tools.One of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s maintainability.Adopting the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design,deployment,and management of CPPS in a model-based engineering methodology.We propose a method for CPPS design based on the IEC 61499 standard.The method allows designers to embed a bio-inspired anomaly-based host intrusion detection system(A-HIDS)in Edge devices.This A-HIDS is based on the incremental Dendritic Cell Algorithm(iDCA)and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’Edge layer.This study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions,and cost-effective security by design system implementation.According to the experimental data,the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

Risk dynamics for marine systems:towards a bio-inspired framework for dynamic risk assessment

The development of innovative, complex marine systems, such as autonomous ship concepts, has led to risk-based approaches indesign and operation that provide safety level quantification and continuous risk assessment. The existing approaches to dynamicrisk assessmentmainly aim at updating accident probabilities for specific risk scenarios, based on knowledge of system operation andfailure, aswell as on past accident and failure information. However, for innovative marine systems that include complex interactions,our ability to identify anything that might go wrong is very limited, which may lead to unidentified risks, and failure data may not beavailable. This paper presents the foundations of a framework for dynamic risk assessment, which is equally applicable to mannedand autonomous ships and mainly relies on information about the safe operational envelope and real-time information regardingdeviations from safety. Inspiration is drawn from how the biological immune system identifies the risk of infection in a dynamicenvironment. The objective is to show the feasibility and benefits of our approach for quantifying the operational risk of marinesystems. This paper provides the conceptual basis for developing ship specific applications and describes a process for dynamic riskassessment that is methodologically based on artificial immune systems. To demonstrate the implementation of our framework, wedescribed, an illustrative example that involves a ship in a grounding scenario. The results show that the bio-inspired assessmentprocess and risk description can reflect the changes of the risk level of a marine system.