The Extreme Gradient Boosting (XGBoost) algorithm trained by the corresponding simulation data is used to construct the inversion model to calculate the complex dielectric coefficient of the tested material. Besides, this paper uses linear regression algorithm (LR) to calibrate the measurement system in order to improve the measurement accuracy. The entire system was tested using different materials at room temperature, and the maximum error of the measurement accuracy is less than 8% compared to the theoretical data. The robustness of the entire system was also tested by measuring Macor materials up to 800 °C. This proposed method provides an effective way to understand the mechanism between microwaves and matter at high temperatures.This paper aims to propose an online relative complex permittivity measurement system at high temperature based on microwave interferometer. A ridge waveguide with a TE The dielectric property of material mainly relies on the frequency and temperature. In the high power application of microwaves, the microwave frequency is usually fixed, and the interaction between microwaves and matter at different temperature, especially high temperature, should be investigated ,2,3,4,5.The measurement of relative permittivity of materials in high temperature environment has very important requirements in the fields of aerospace, metallurgy, and chemical industry ,10,11,12In the study of high-temperature dielectric measurement, JG Powles used a rectangular waveguide as a measuring device in the middle of the last century, connected the load at the waveguide end, and then placed the sample under test in the test waveguide, passing the electricity in the measurement system. The parameters complete the relative complex permittivity measurement of polycrystalline ceramic materials at 0.95 GHz and 2.4 GHz ; Vasunda in 2007 , by measThis paper proposes an on-line temperature relative complex permittivity measurement system at the frequency of 2450 MHz based on microwave interferometer. A ridge waveguide is designed as the core device of the system, where the tested materials can be placed. The scattering parameters are measured using the microwave power and microwave interferometer. The Extreme Gradient Boosting (XGBoost)  simulatiIn the system, the ridge waveguide is used for heating and measuring, and the structure of ridge waveguide is shown in The system consists of a solid-state source, a circulator, a dual directional coupler, a ridge waveguide, a matched load power meter, an infrared thermometer, an interferometer module, and a data acquisition module. The measurement system is shown in S parameters. Therefore, the XGBoost algorithm is used to simulate the corresponding relationship. The process of predicting relative complex permittivity with XGBoost algorithm is shown in S parameters measured by experiments can be sent to the well-trained XGBoost algorithm model, then the relative complex permittivity can be predicted.During the heating process, the relative complex permittivity of the material changes with the increase of temperature, and the absorption and reflection of the microwave also change. The change of the reflected power and the transmitted power means the change of S parameters. In order to solve this problem, the system optimizes the structure of the ridge waveguide model during design, so that the relative complex dielectric coefficient corresponds to the S parameter one by one. The monotonicity of the data is ensured by continuously optimizing the height and width of the ridge and the diameter of the quartz tube combined with the FDTD algorithm. In the measurement of high temperature relative complex permittivity, the construction of sample space is particularly important. When XGBoost algorithm is used to inverse the dielectric properties, it often encounters multi-value problems, which means that different relative complex dielectric coefficients correspond to the same It can be seen from In order to reduce the multi-value phenomenon of the inversion calculation better, the XGBoost adopts three input and two output network structures, with It can be seen from the figure that with the real part of the relative complex permittivity, This paper used the XGBoost algorithm as the core algorithm for inverting relative complex permittivity. Boosting is a machine learning technique algorithm. It is one of the Boosting algorithms, a boost library with linear scale solver and tree learning algorithm developed by Tianqi Chen of the university of Washington in 2016 . Its big1. Add a regularization term to the target function:Loss is the error function, representing the fitted data of the model. Ω is a regularization term, which represents the parameters of the penalty complex model and is used to solve over fitting. In this paper, 2. Second-order Taylor expansion is used to expand the error function at 3. Removing all constant terms and substituting Equation (3) and regular terms (2) into Equation (4), we can get:j-th leaf node as: 4. Defining the sample set on the MSE), the mean absolute error (MAE), and the coefficient of determination (MSE and MAE estimated over i-th sample, and MSE and MAE, the better the prediction.This paper builds the XGBoost machine learning model based on the Python programming language. The mean squared error (on error . The MSEMSE, MAE, and R², as shown in After training the XGBoost model with the training data set obtained from FDTD simulation, the parameters of XGBoost model are determined by optimizing the values of After the parameters of XGBoost model are obtained, different data from the training data set is selected to test this model, so that the prediction effect of the algorithm can be evaluated. In order to ensure the accuracy of system measurement, the system needs to be calibrated before measurement to eliminate the error caused by system deviation. This paper uses linear regression algorithm in machine learning to calibrate the system . The datS parameters after system calibration. S parameters measured and simulated by FDTD algorithm.Here, we define S parameters can be obtained by inputting the measured S parameters, and then sending them to the well-trained XGBoost model for inversion. The results after calibration with regression algorithm are also shown in The drift parameter vector ω of the system can be determined by algorithm and this data. Therefore, when the calibrated system is used for measurement, the calibrated S parameter values of methanol, ethanol, and air before experiment to calibrate the system. The schematic diagram of system calibration with linear regression algorithm is shown in The results show that the system error reduced after calibration. Therefore, in order to improve the accuracy of system measurement, it is necessary to measure the Measurements are conducted to verify the system reliability under room temperature and dynamic high temperatures respectively.The easy-to-access chemical substances in the laboratory are used for the experiment. As shown in Macor materials that are less susceptible to deformation at elevated temperatures are used to verify the relative complex permittivity of the system at elevated temperatures. The experimental results were compared with the results in the reference . The speIt can be seen from the measurement results that when the temperature is below 400 °C, the measured values of the real and imaginary parts of the relative complex permittivity of Macor are close to those of the reference. When the temperature reaches 500 °C, the real and imaginary parts of the relative complex permittivity of Macor show a certain degree of increase. The measured real part has a certain deviation from the reference value, but the error is small. The value of the imaginary part of the electrical coefficient is still relatively close. When the temperature rises to 600 °C, the change trend of the imaginary part of the complex dielectric constant is still consistent with the reference. The measured value of the real part of the complex dielectric coefficient still has a certain deviation from the reference value, but the change trend is consistent. To some extent, the accuracy of the measurement of the system is proved, and compared with the measurement results in the previous chapter, after adding the phase measurement to the system, the relative complex permittivity of the Macor and the reference are calculated by inversion. The values are closer, indicating that the measurement accuracy of the system is improved compared to the measurement system of the previous chapter, and the measurement accuracy of the system is further optimized.This paper designs an on-line high temperature relative complex permittivity measurement system at a working frequency of 2450 MHz. The core device of the system is a ridge waveguide, and the heating and relative complex permittivity of the sample to be tested are measured by the ridge waveguide; the scattering parameters in the experimental system are measured using a microwave power meter and a microwave interferometer, respectively, and the XGBoost model training is completed to construct an inversion calculation network to realize the calculation and measurement of the relative complex permittivity of the measured substance. The accuracy of the system measured at room temperature was verified using chemical solvents and their mixed solvents with known relative complex dielectric coefficients. The relative complex permittivity of Macor materials up to 800 °C was measured and compared, and the online high temperature relative complex was compared. The measurement results of the electrical coefficient measurement system and the data in the reference documents verify the measurement accuracy of the high temperature relative complex permittivity measurement system in the high temperature range.


Lactobacillus (Lb.) strains, Lb. plantarum ATCC 8014 and Lb. casei ATCC 393, to metabolize different white wheat flour and soybeans flour combinations to compare their efficiency, together with/without Saccharomyces cerevisiae on sourdough fermentation. For this purpose, the viability, pH, organic acids, and secondary metabolites production were investigated, together with the dynamic rheological properties of the sourdough. During sourdough fermentation, LAB presented higher growth, and the pH decreased significantly from above pH 6 at 0 h to values under 4 at 24 h for each experiment. Co-cultures of LAB and yeast produced a higher quantity of lactic acid than single cultures, especially in sourdough enriched with soy-flour. In general, sourdoughs displayed a stable, elastic-like behavior, and the incorporation of soy-flour conferred higher elasticity in comparison with sourdoughs without soy-flour. The higher elasticity of sourdoughs enriched with soy-flour can be attributed to the fact that through frozen storage, soy proteins have better water holding capacity. In conclusion, sourdough supplemented with 10% soy-flour had better rheological properties, increased lactic, acetic, and citric acid production.Sourdough fermentation presents several advantageous effects in bread making, like improved nutritional quality and increased shelf life. Three types of experiments aimed to evaluate comparatively the efficiency of two  Sourdough used in the food industry, is a traditional leaven agent, for the fermentation of dough with microorganisms like yeasts and lactic acid bacteria (LAB) ,2. The uWhite wheat flour is an essential staple food worldwide and contributes to the everyday intake of daily fiber, micronutrient, and energy, but it presents fewer nutrients in comparison to whole-wheat flour ,8. In adDuring cereal fermentation, typically up to 24 h at moderate temperatures, the metabolic activity of the microorganisms present is in interaction with the grain constituents. In general, LAB produce lactic and acetic acids, lowering the pH typically below pH 5. Additionally, usually, yeasts produce carbon dioxide and ethanol. Interactions between yeasts and bacteria are essential for the metabolic activity of the sourdough, such as the bioconversion of complex carbohydrates into organic acids or other chemical compounds with bioactive potential . The chaIn functional food production, the use of LAB is favored due to its unique characteristics. LAB is competent in producing polyols, antimicrobial substances, nutraceuticals, valuable enzymes, or aromatic compounds ,27,28. LSaccharomyces cerevisiae (Sc), a commercial baker’s yeast, remains the usual selection in baking. Sc has fast growth, through dough leavening, great biomass yield on molasses medium, and presents increased CO2 production [Lactobacillus plantarum (Lp), a heterofermentative LAB is one of the native dominant strains of sourdough [Lp ferments hexoses through the Embden-Meyerhof Parnas pathway [Lactobacillus casei (Lc) ferments nearly all hexose sugars to lactic acid [Lc is a highly used LAB in food fermentation for flavor enhancement in the cheese industry and in sourdough production to manipulate acidification, gluten protein deterioration, aroma compounds, and amino acid production and several other nutritional and sensory improvement [Sc. Yeasts are a crucial element in foodstuff and beverage production [During food fermentations, often-encountered microorganisms are LAB and yeasts. oduction . Supportourdough . Lp ferm pathway  and is e pathway ,33. The tic acid . Lc is arovement . Utilizarovement ,37,38. Aoduction .L. plantarum 21B was also found in sourdough bread. Compared to bread started with S. cerevisiae 141 alone or in association with L. brevis 1D, the sourdough bread, which used L. plantarum 21 B in association with S. cerevisiae 141 delayed fungal contamination until after seven days of storage at room temperature [The antifungal activity of perature .L. plantarum 21 B to decrease the fungal contamination of sourdough baked products has interesting potential applications [The food industry to be efficient and competing has to address consumer requirements and recent trends, which have incorporated the need for high-quality foods that are not highly processed and do not involve any chemical preservatives. Considering the antimicrobial compounds generated by LAB are regarded as natural preservatives, the use of ications .Lactobacillus plantarum (Lp) and Lactobacillus casei (Lc) as single strain starter cultures, and in multiple strain starters together with baker’s yeast Saccharomyces cerevisiae (Sc); and (3) to characterize the dynamic rheological properties and effect of soy flour addition on bread dough throughout a 24 h fermentation after frozen storage.The objectives of the present study were to (1) characterize the outcome of soy flour addition to wheat flour during fermentation; (2) analyze the conversion level of wheat flour and soy flour through fermentation with two heterofermentative lactic acid bacteria (LAB) strains Culture media components and other reagents were of analytical grade and obtained from VWR International  except peptone special obtained from Sigma-Aldrich , and agar (Agar plant for cell culture) obtained from Applichem .Lactobacillus (Lb.) strains, Lb. plantarum ATCC 8014 and Lb. casei ATCC 393, obtained from the University of Agricultural Science, and Veterinary Medicine Cluj-Napoca and Sc  acquired from commerce. The medium used for LAB was MRS broth . For the Sc yeast strain, the medium used was GPY .The microorganisms used throughout this study were two Microorganisms’ reactivation before experimental usage was in 9 mL MRS media by introducing 1 mL of LAB inoculum or 1 g of dried yeast in GPY media. Vial incubation performed at 30 °C (yeast) and 37 °C (LAB) for 18–24 h. The second propagation occurred in MRS/GPY broth, through the inoculation of the activated LAB or yeast (10 mL) and afterward incubated for 18–24 h.8 CFU/mL was determined with the spectrophotometer NanoDrop 1000  through optical density measurement at 600 nm (OD600) between values 0.009 and 0.011 for bacteria or with Thoma counting chamber  under the microscope  for yeasts [8 bacteria/yeast. Samples for HPLC (5 mL), viability (1 mL), and wet biomass (1 mL) extracted at 0, 2, 4, 6, 8, 10, 12 h, and at 24 h to monitor the changes  Lc, (S3) Lp, Lc, and Sc.Microorganism concentration of 10r yeasts . In 500  changes . Two sinhttps://scdaturda.ro/onix/). The obtained soybean variety was Onix (Glycine max (L.) Merril), with conventional soil cultivation system through tillage and 60% autumn rape vegetable debris (green fertilizer). Soybeans were ground (SF) and added to the wheat flour in the amount of 5% and 10%. The preparation consisted of three types of fermentation with 100% WF (batch A), 95% WF with 5% SF (batch B), and 90% WF with 10% of soy flour addition (batch C). Before fermentation, the measured wheat quantities went through a sterilization process, and after the addition of 100 mL of autoclaved deionized water, the dough went through a homogenization step. The ratio of obtained sourdough was 1 g−1 mL, with a final yield of 200 mL.A commercial wheat flour, used for traditional bread making , with 15.3% moisture and 11.2% protein, was used. The sourdough preparation included ratio flour: water of 1:1, to produce a dough yield of 200 (dough mass/flour mass × 100). The soybean provided by the Agricultural Research and Development Center Turda  50 mM concentration, pH 2.8, and a flow rate of 0.5 mL/min, at a temperature of 20 °C. The measurement of chromatograms was possible at the wavelength λ = 210 nm.After fermentation extraction and quantification of organic acids and secondary metabolites was possible with the help of high-performance liquid chromatography  equipped with solvent degasser, quaternary pumps, DAD detector coupled with a mass detector, column thermostat, and automatic injector . The separation of organic acids could be realized on reversed-phase chromatographic column Acclaim OA , eluted for 10 min with monosodium phosphate solution  at room temperature through dissolving 5.0 g of sample in 50 mL of double-distilled water .For rheological measurements, samples were kept at −20 °C (frozen storage), and before measurements, they were defrosted at room temperature. The dough’s dynamic rheological characteristics were analyzed utilizing an Anton Paar MCR 72 rheometer  ,48, suppn = 3. The statistical evaluation was carried out using Graph Prism Version 8.0.1.  through a one-way ANOVA (Tukey multiple comparisons tests). Differences among means at a 5% level have been considered statistically significant.The results of three independent assays (performed with replicates each) were expressed as mean value ± SD, 10–1012 CFU/mL for model media and 108–109 CFU/mL for sourdough fermentation (Lc (S2) and in the co-cultures (S3) with a final concentration of above 1012 CFU/mL. The viability on three different substrates reached a final concentration above 109 CFU/mL. The highest viability observed was with the LAB Lp, and the increase was uniform throughout the experiment, while Lc at the beginning presented a slower growth, but the final concentration was similar to Lp. No negative effect on the viability of LAB could be observed in the presence of Sc. In contrary LAB together with Sc yeast, presented the highest viability on substrate with 10% and 5% soy flour addition. Comparing the results in a similar study [6 CFU/mL) and final cell density (109 CFU/mL at 24 h) in model media and the same results as in our study in sourdough.In general, for all fermentations, reached cell counts for LAB were of 10entation . These car study  LAB presLc, reaching final values of 5.7 × 109 CFU/mL in comparison to the values obtained with 100% wheat flour of 3.3 × 109 CFU/mL. In co-cultures with the addition of 10% soy flour, the viability also increased for LAB (from 1.1 × 109 CFU/mL with 100% wheat flour to 2.9 × 109 CFU/mL with 10% soy flour) and Sc (from 1.8 × 107 CFU/mL with 100% wheat flour to 4.1 × 107 CFU/mL with 10% soy flour). As demonstrated by Aguirre et al. [Lp. Depending on the applied fermentation conditions, especially refreshment time and the substrate, the pH values differed  was reached in fermentation batch C, and the highest (3.8–3.9) was reached in fermentation batch A. These data indicated the growth and metabolic activity of the LAB during the whole fermentation period.Lp and 4.57 with Lc in media containing only glucose as a carbohydrate. Similar results were obtained with maltose (Lp-4.81 and Lc-4.62), and in media with glucose and fructose (Lp-4.1 and Lc-4.85). In media containing only fructose (Lp-5.59 and Lc-5.64), the pH had not decreased substantially. In sourdoughs, the obtained pH values reached the same results as in the present study <4.0, and the pH became stable after 20–22 h [In a study from Paucean et al. , pH star 20–22 h ,51.−1 are presented in Rheological property alterations of sourdoughs with/without soy-flour addition and the effect of fermentation with single and co-cultures were evaluated . The stoLc microorganism. At a final angular frequency of 628 rad s−1 at 24 h the loss modulus increased with Lp from 599.1 ± 6.1 to 3313.6 ± 6.7, with Lc from 2581.3 ± 11.1 to 5790.3 ± 5.8 with 10%, and in co-cultures from 889.5 ± 3.9 with no soy flour to 5326 ± 10.1 with 10% of soy flour addition.In general, G’ was higher than G’’, illustrating that every sourdough sample displayed a stable, elastic-like behavior. The effect of soy flour addition to wheat flour increased the elasticity of doughs in every fermentation, but especially with the In batch A and B, G’ and G’’ decreased from 10 to 24 h of fermentation and in batch C at 24 h G’ and G’’ were higher than at the beginning of the fermentation. The higher elasticity in batch C can be explained mainly by the water holding capacity of soy proteins during frozen storage (−20 °C) and the disruption of sourdough macromolecules like gluten proteins . Soy andWheat flour combined with water results in a hydrated gluten network that gives the viscoelastic property of sourdough. The right amount of water is important in sourdough preparation, and due to non-miscible properties of wheat biopolymers, it is present in each phase of bread making . The stuLp strains proved that through fermentation of whole wheat bread, the quality and shelf life of bread could be improved. In this study, with the use of Lp LB-1 stronger extensibility, increased viscoelasticity, and water retention ability was obtained.The effect of fermentation with different The production of organic acids and the decrease of pH influence the rheological properties, and reduce mixing time, which causes an increased elastic behavior . SoybeanLAB greatly influences the sensory, textural, nutritional, and shelf-life characteristics of sourdough baked goods, especially bread . For a lLp, 0.186 ± 0.07 g/L with Lc, and the highest production was observed with co-cultures of 0.294 ± 0.10 g/L.In the present study, the ratio of acetic acid production in model media was elevated, but in sourdough was very low  or thereDepending on the used LAB through the sourdough fermentation taking place, the metabolism of organic acids like citrate, fumarate, and malate. Lactate, malate, and citrate transformation uses intracellular protons and consequently enhances the LAB’s tolerance to the acidic environment . In the Lp, especially in sourdoughs from batch B with 5% soy flour. The production of lactic acid in batch B with single Lp culture was 3.787 ± 0.03 g/L and in co-cultures together with Lc and Sc was 4.879 ± 0.03 g/L. The lowest lactic acid production was observed in batch A with both single cultures (2.137–2.178 g/L), but with co-cultures lactic acid production increased significantly to 3.787 g/L.Lactic acid concentration at 0 h in sourdoughs was absent or in a very low concentration (0.3–0.5 g/L). After a fermentation of 24 h, the highest lactic acid production could be observed with Fumaric acid presented a low but continuous amount during the whole 24 h of fermentation. According to several studies, malic and fumaric acid are usually converted to succinic acid in LAB ,67,68.The production of malic acid, primarily used in the food industry as a taste intensifier and an acidulant is especially a result of yeast fermentation . Malic aLp strains from isolated dairy food and proved that organic acids present an important factor as antimicrobial substances in fermentation broths. These organic acids display the best antimicrobial activity, especially if they are mixed in different concentrations. Beside lactic and acetic acid, citric, malic, and tartaric acid also possess antimicrobial activity.When LAB remains without any carbohydrate, they might utilize citric acid as an energy supply . In the Lactobacillus strains produce various amounts of succinic acid in MRS media. In the present study, succinic acid production increased for Lp at 4 h of 9.496 ± 0.10 g/L, Lc at 10 h of 9.244 ± 0.09 g/L, and in co-culture at 4 h of 9.681 ± 0.11 g/L, after which it decreased. In sourdoughs, succinic acid was only produced where Lp was present, with an increase after 4 h of fermentation and decreased until 24 h. Succinic acid production increased with the increase of soybean flour with the highest values in batch A of 1.077 ± 0.08, batch B of 1.918 ± 0.03, and batch C of 3.993 ± 0.05 g/L. Considering Lp, has an incomplete tricarboxylic acid cycle and a natural producer of succinic acid; this LAB is extensively researched in studies of metabolic engineering for higher succinic acid production [Succinic acid production in model media was in concordance with Kaneuchi et al. , who shooduction .Lc (5.7 × 10 9 CFU/mL), and co-cultures . The metabolic activity and growth of microorganisms was also observed with the decrease of pH value with final values of 3.4–3.6 with 10% of soy flour and 3.8–3.9 where no soy flour was added.Sourdough fermentation with single and co-cultures of LAB and yeast has several beneficial effects, and soy-flour incorporation further improved sourdough quality. The addition of soy-flour to wheat flour had a positive impact, considering the fermentations with Lp  and co-cultures of Lp + Lc + Sc (lactic: 3.580 ± 0.01 g/L and succinic acid: 0.799 ± 0.10 g/L). Acetic acid production in sourdoughs was only observed in co-cultures and also increased with the increase of soy-flour at a final value of 0.294 ± 0.10 g/L.Some beneficial effects of soy-flour addition were the increase of organic acids after 24 h of fermentation production especially with Lc was present.The incorporation of soy-flour in wheat flour during sourdough preparation generated relatively important alterations in comparison with sourdough without any soy-flour addition. The addition of 10% of soy-flour presented enhanced rheological properties, like increased elastic behavior. Although every sourdough exhibited a constant elastic-like behavior, the incorporation of soy-flour presented higher elasticity, which is due to the water retention ability of soy-protein in the course of frozen storage. The highest elasticity was obtained after 24 h of fermentation and especially where


For the first time, the immunoglobulin (IG) or antibody and T cell receptor (TR) genes were officially recognized as ‘genes’ as well as were conventional genes. This major breakthrough has allowed the entry, in genomic databases, of the IG and TR variable (V), diversity (D) and joining (J) genes and alleles of Homo sapiens and of other jawed vertebrate species, based on the CLASSIFICATION axiom. The second major breakthrough has been the IMGT unique numbering and the IMGT Collier de Perles for the V and constant (C) domains of the IG and TR and other proteins of the IG superfamily (IgSF), based on the NUMEROTATION axiom. IMGT-ONTOLOGY axioms and concepts bridge genes, sequences, structures and functions, between biological and computational spheres in the IMGT® system . They provide the IMGT Scientific chart rules to identify, to describe and to analyse the IG complex molecular data, the huge diversity of repertoires, the genetic  polymorphisms, the IG dual function , the antibody humanization and engineering.IMGT Table of content:1. Introduction2. Immunoglobulin (IG) or Antibody Molecular Genetics2.1. Human Immunoglobulin (IG) or Antibody Structure and Dual Function2.1.1. Immunoglobulin Fab and Fc Basic Structure2.1.2. B Cell Differentiation2.1.3. Membrane Immunoglobulins and B Cell Receptor2.1.4. Secreted IG2.2. Human IG Classes and Subclasses: Heavy and Light Chain Types2.2.1. IG Heavy Chain Types2.2.2. IG Light Chain Types2.3. IG Chain Variable and Constant Domains2.3.1. IG Variable Domains2.3.2. IG Constant Domains2.4. Synthesis and Expression of the Immunoglobulins (IG) or Antibodies2.4.1. IG Molecular Synthesis Characteristics2.4.2. Synthesis of the H-mu Chains: D-J and V-D-J Rearrangements in the IGH Locus2.4.3. Synthesis of the L-kappa and L-lambda Chains: V-J Rearrangements in the IGK and IGL Loci2.5. Origin of the Variable Domain Diversity of the Immunoglobulins2.5.1. Overview2.5.2. Combinatorial Diversity2.5.3. Junctional Diversity2.5.4. Somatic Hypermutations2.6. Expression of the Heavy Chains and IG Classes2.6.1. Coexpression of the Membrane H-mu and H-delta Chains2.6.2. Expression of H-gamma, H-epsilon and H-alpha chains: Class Switch Recombination− IgD+ Cells2.6.3. Expression of H-delta Chains from IgM2.6.4. Expression of Membrane and Secreted Immunoglobulins2.7. Regulation of the Rearrangements and Chain Expression2.7.1. Allelic and Isotypic Exclusion. Rearrangement Chronology2.7.2. Regulation of the IG Gene Expression: Enhancers2.8. Structural and Biological Properties of the Secreted Immunoglobulins2.8.1. IgM2.8.2. IgD2.8.3. IgG2.8.4. IgAIgA1 and IgA2Secretory IgAIgA Effector Function 2.8.5. IgE® Gene and Allele Nomenclature3. Immunoglobulin Genes: IMGT® Standardized Genes and Alleles (CLASSIFICATION)3.1. IMGT® and Immunoinformatics3.1.1. IG and TR Genes and Concepts of Classification: Birth of IMGTHomo sapiens IG Genes and Concepts of Identification and Description3.1.2. 3.2. Homo sapiens IGH Locus and GenesHomo sapiens IGH Locus3.2.1. Organization of the 3.2.2. IGHC Multigene Deletions and Gene Order, IGHC and IGHV Copy Number Variation (CNV) Haplotypes3.2.3. IGH OrphonsHomo sapiens IGH Genomic Repertoire3.2.4. Potential 3.3. Homo sapiens IGK Locus and GenesHomo sapiens IGK Locus3.3.1. Organization of the 3.3.2. IGK OrphonsHomo sapiens IGK Genomic Repertoire3.3.3. Potential Homo sapiens IGKC Allotypes (Km Alleles)3.3.4. 3.4. Homo sapiens IGL Locus and GenesHomo sapiens IGL Locus3.4.1. Organization of the 3.4.2. IGL OrphonsHomo sapiens IGL Genomic Repertoire3.4.3. Potential Homo sapiens IGL Isotypes3.4.4. ® Collier de Perles4. Immunoglobulin Structures: IMGT Unique Numbering and IMGT4.1. IMGT Unique Numbering and IMGT Colliers de Perles for V-DOMAIN (NUMEROTATION)4.1.1. V Domain Definition and Main Characteristics4.1.2. V-DOMAIN IMGT Colliers de Perles4.1.3. V-DOMAIN Strands and Loops (FR-IMGT and CDR-IMGT)4.1.4. V-DOMAIN Conserved Amino Acids4.1.5. V-DOMAIN Delimitation4.1.6. Protein Displays for the V-REGION and J-REGION4.2. IMGT Unique Numbering and IMGT Colliers de Perles for C Domain (NUMEROTATION)4.2.1. C Domain Definition and Main Characteristics4.2.2. C Domain IMGT Colliers de Perles4.2.3. C Domain Strands and loops4.2.4. C Domain Conserved Amino Acids4.2.5. C Domain Genomic Delimitation4.2.6. C-REGION Protein Displays® Databases and Tools for IG Sequences and Structures5. IMGT®, the International ImMunoGeneTics Information System®5.1. IMGT® Nucleotide Sequence and Repertoire Analysis5.2. IMGT5.2.1. IMGT/V-QUEST for Nucleotide Sequence Analysis5.2.2. IMGT/HighV-QUEST® Amino Acid Sequence Analysis and Representation5.3. IMGT5.3.1. IMGT/DomainGapAlign5.3.2. IMGT/Collier-de-Perles Tool® Structure and Amino Acid Databases5.4. IMGT5.4.1. IMGT/3Dstructure-DB5.4.2. IMGT/2Dstructure-DB5.4.3. IMGT/mAb-DB6. Using the IMGT Numbering for V and C-Domain for Antibody Description and Engineering6.1. Antibody V-DOMAIN Humanization by IMGT-CDR Grafting6.1.1. CDR-IMGT Delimitation for Grafting6.1.2. Amino acid Interactions between FR-IMGT and CDR-IMGT6.2. Only-Heavy-Chain Antibodies6.2.1. Dromedary IgG2 and IgG36.2.2. Human Heavy Chain Diseases (HCD)6.2.3. Nurse Shark IgN6.3. Contact Analysis of TR-Mimic Antibodies and TR6.4. Antibody C-Domain Post-Translational Modifications, Engineering and Allotypes6.4.1. N-linked Glycosylation Sites CH2 N84.46.4.2. Knobs-into-Holes6.4.3. Interface Ball-and-Socket-Like Joints6.4.4. IGHG Alleles and Gm Allotypes6.4.5. IGHG Engineered Variants and Effector Properties7. Conclusionsgnathostomata more than 450 million years ago. It is characterized by a remarkable immune specificity and memory which are the properties of the B and T cells, and by an extreme diversity of their specific antigen receptors ..2].The expression of the surface IgM at a later stage requires the synthesis of L-kappa or L-lambda chains, that is a productive V-J rearrangement of the IGK or IGL loci. It is the expression of the pre-B cell receptor at the surface of a pre-B cell which gives the signal which inhibits further IGHV-D-J rearrangements on chromosome 14 , and theIn order to synthesize complete IG heavy and light chains, the rearranged IGHV-D-J and IGKV-J (or IGLV-J) genes are transcribed with the IGHM gene and with the IGKC (or one IGLC) genes, respectively. The transcription level, low in the first B cell development stages, becomes very high in the plasma cells which result from the clonal proliferation . The V gcis, that is on the same chromosome, and (iii) activate in vitro genes others than those to which they are normally associated in vivo and increase their transcription, even when located at a distance of several kb  [GFCC’C”] ,44,45,46The loop and strands are visualized in the IMGT Colliers de Perles ,50,51,52®http://www.imgt.org, IMGT Scientific chart > Numbering > Correspondence between V numberings).The V domain strands and loops and their delimitations and lengths, based on the IMGT unique numbering for V domain ,44,45,46®http://www.imgt.org, IMGT Scientific chart > 2. Numbering > IMGT unique numbering for V-DOMAIN and V-LIKE-DOMAIN).For a V domain, the BC loop (or CDR1-IMGT in a V-DOMAIN) encompasses positions 27 to 38, the C’C’’ loop (or CDR2-IMGT in a V-DOMAIN) positions 56 to 65, and the FG loop (or CDR3-IMGT) positions 105 to 117. In a V-DOMAIN, the CDR3-IMGT encompasses the V-(D)-J junction that results from a V-J or V-D-J rearrangement ,,237,2,23®http://www.imgt.org, IMGT Repertoire (IG and TR) > 2. Proteins and alleles > 1. Protein displays > V-REGION > IGHV, ibid. > IGKV, ibid. > IGLV; ibid. > J-REGION > IGHJ, ibid. IGKJ, ibid. IGLJ). The translation of the IGH D-REGION in the six frames are displayed in Alignments of alleles  1n8z_A and ERBB2 (‘(Ligand)’ 1n8z_C) and between the trastuzumab Fab VH [D1] 1n8z_B and ERBB2 (‘(Ligand)’ 1n8z_C) are shown in ‘Renumbered IMGT flat file’ allows to view (or download) an IMGT coordinate file renumbered according to the IMGT unique numbering and to which has been added the IMGT specific information. This IMGT information  is in the ‘REMARK 410’ lines (blue online) added in the IMGT coordinate files. Tools associated to IMGT/3Dstructure-DB include IMGT/StructuralQuery and IMGT/DomainSuperimpose, available online.(1)Homo sapiens  sheet at the hydrophobic VH-VL interface in conventional antibodies of Camelidae as well as of any vertebrate species  , or llam12.8.18] ). Howeve [8.8.8] ). The serophilic . The ter®http://www.imgt.org, IMGT Education > Pathology of the immune system > Molecular defects in Immunoglobulin Heavy Chain Diseases (HCD); ibid., IMGT Lexique > Heavy Chain Diseases (HCD)).The camelidae ‘only-heavy-chain’ antibodies synthesis is remarkably reminiscent of what is observed in human heavy chain diseases (HCD). These proliferative disorders of B lymphoid cells produce truncated monoclonal immunoglobulin heavy chains which lack associated light chains. In most HCD, the absence of the heavy chain CH1 domain by deletion or splicing defect may be responsible for the lack of assembly of the light chain . SimilarGinglymostoma cirratum (‘Gincir’ in the IMGT 6-letter abbreviaton code for species) IgN antibodies  [®http://www.imgt.org, IMGT Repertoire (IG and TR) > 2. Proteins and alleles > 1. Protein displays > IGHV > nurse shark (Ginglymostoma cirratum), ibid., IGHC; ibid., > 1. Locus and genes > 7. Gene tables > IGHV > Chondrichthyes > nurse shark (Ginglymostoma cirratum), ibid., IGHC).A convergence mechanism in evolution is observed in nurse shark ceptor’)  which arThe IMGT unique numbering has recently allowed the contact analysis comparison between an antibody and a T cell receptor, with a same ligand . Both im® using the IMGT unique numbering [The constant region of the IG heavy chain is made of several CH domains, which are analysed and described in IMGTumbering ,44,45,46The N-linked glycosylation site present in the CH2 domain of the constant region of the four human IG H-gamma chains is located at CH2 N84.4. As shown in the IMGT Collier de Perles, this asparagine is localized at the DE turn .The human glycans are mainly classified as ‘biantennary complex’ structure with a core fucose (Fuc) and are often terminated with N-acetylneuraminic acid (Neu5Ac), a sialic acid . The lar®http://www.imgt.org, IMGT Lexique > Immunoglobulin (IG) or antibody glycosylation).The graphical representation of the 36 possible structures of IG N-glycans expressed in human cells are available in the IMGT Lexique, with correspondence between the ‘GA’ nomenclature, based on the number of galactose (G)  and sialic acid (A)  at the terminal ends of the arms of the IG N-glycans, and the ‘HN’ nomenclature based on the number of carbohydrates in the glycan composition , with, if present, S number of sialic acids . .The knobs-into-holes methodology has been proposed for obtaining bispecific antibodies . The aim domains . Interac domains A. The knHomo sapiens IGHG2 Fc with the interface in 3D structures of IGHG1 Fc revealed that in all Fc of gamma chains the movement of the CH2 results from a pivoting around a highly conserved ball-and-socket-like joint [Homo sapiens gamma isotypes and the IGHG genes and alleles except for IGHG3 H115 that shows a polymorphism associated to different G3m allotypes [®http://www.imgt.org, IMGT Repertoire > 2. Proteins and alleles > 1. Protein displays > C-DOMAIN with CHS, M and HINGE regions; ibid., IMGT Repertoire > 2. Proteins and alleles > 2. Alignments of alleles > IGHC; ibid., IMGT/3Dstructure-DB > query on Fab).The comparison of the interface between the CH2 and CH3 domains from 3D structures of ke joint . Using tllotypes . This ballotypes , in whicAllotypes are polymorphic markers of an IG subclass that correspond to amino acid changes and are detected serologically by antibody reagents . In therThe H-gamma1 chains may express four G1m alleles : G1m3, G1m3,1, G1m17,1, and G1m17,1,2  . The corAmino acids involved in the expression of G1m allotypes and localized on the CH1 and CH3 domains of H-gamma1 chains are shown in IMGT Colliers de Perles on two layers and in 3D structures . In the Trastuzumab has been engineered in order to obtain the less immunogenic (being most frequent in different populations) allotype G1m17 (CH1 K120), associated to the nG1m1  , definedThe H-gamma2 chains express only one allotype G2m23. G2m23 and the H-gamma2 chains are either G2m23 or G2m. . The C region of the H-gamma3 chains is encoded by IGHG3*01, G3m5*  with CH3 S44 (439), M84 (452), Q98 (474), I101 (477), R115 (490) and F116 (491).The therapeutical antibody omodenbamab  is an ®http://www.imgt.org, The IMGT Biotechnology page > Amino acid positions involved in ADCC, ADCP, CDC, half-life and half-IG exchange).Amino acids in the IGHG constant regions of the IG heavy chains are frequently engineered to modify the effector properties of the therapeutic monoclonal antibodies. Amino acids changes are engineered at positions involved in antibody-dependent cellular (ADCC), antibody-dependent cellular phagocytosis (ADCP), complement-dependent cytotoxicity (CDC), half-life increase, half-IG exchange, and B cell inhibition by coengagement of antigen and FcγR on the same cell ,321 (IMGHomo sapiens IGHG1v1), and then the domain(s) with AA change(s) defined by the letter of the novel AA and position in the domain, e.g., CH2, P1.4. In The IMGT engineered variant nomenclature  has beenExamples of Fc engineered variants from ® bridging genes, structures and functions provides a unique frame for three research axes: deciphering the IG and TR locus, genes and alleles in genomes of vertebrates from fish to humans, identifying clonality and exploring high-throughput repertoires with IMGT/HighV-QUEST and exploiting data from IMGT/mAb-DB, IMGT/2Dstructure-DB and IMGT/3Dstructure-DB towards targeted and customized therapeutic antibodies. Regarding the first axis, IMGT® genomic annotated data are classically displayed in IMGT Repertoire Web Resources . So far the number of higher vertebrate species present in the IMGT Web Resources reaches forty. The curated IG and TR genes and alleles are entered in the IMGT/GENE-DB database and the corresponding IMGT® reference directories and used for coherent gene and sequence annotations of IG and TR loci of newly sequenced genomes. Thus the annotation of the IG and TR loci [IMGT TR loci ,326,327  TR loci ,361,362 ® biocuration of the IG and TR loci [IMGT/V-QUEST is the reference tool for the clonality sequence analysis in leukemia and lymphoma ,365,366, TR loci ,350,351, TR loci ,370,371.® integrated and standardized approach provides the genetic knowledge for allowing antibody informatics to answer the needs of targeted and customized therapy in the context of personalized medicine. The INN definition for the -mab integrates the format, target(s), IMGT gene and allele nomenclature, the CDR-IMGT lengths delimitation, the post-translational modifications and the engineered AA changes [Given the importance of the interactions in the antibody specificity and affinity on the one hand and in the antibody pharmacokinetics/pharmacodynamics and half-life on the other hand, the IMGT changes ,86,87.The extension of the IMGT unique numbering to the IgSF ,375,376


The multiple longest common subsequence (MLCS) problem involves finding all the longest common subsequences of multiple character sequences. This problem is encountered in a variety of areas, including data mining, text processing, and bioinformatics, and is particularly important for biological sequence analysis. By taking the MLCS problem and algorithms for its solution as research domain, this study analyzes the domain of multiple longest common subsequence algorithms, extracts features that algorithms in the domain do and do not have in common, and creates a domain feature model for the MLCS problem by using generic programming, domain engineering, abstraction, and related technologies. A component library for the domain is designed based on the feature model for the MLCS problem, and the partition and recur (PAR) platform is used to ensure that highly reliable MLCS algorithms can be quickly assembled through component assembly. This study provides a valuable reference for obtaining rapid solutions to problems of biological sequence analysis and improves the reliability and assembly flexibility of assembling algorithms. The MLCS problem was shown to be an NP-hard problem in  to denote a unit in the dynamic programming scoring matrix L of n sequence, where pi(i ≤ n) represents the position of a character of sequence ai, and the value of position P in matrix L is L[P].We use point P is called the match point, if a1[p1]=a2[p2]=⋯=an[pn]. That is, the characters of each sequence are consistent at point P.The point P= and Q=, if there is pj ≤ qj for all j(1 ≤ j ≤ n), then we call P dominating Q; if there is pj < qj for all j(1 ≤ j ≤ n), then we call P strong dominating Q.For points P is a k-dominant point when the following conditions are true:P is a match pointPoint L[P]=kQ(Q ≠ P), the value in the matrix L is L[Q]=k, and Q dominates PThere is no point Point service (S), function (F), and behavior characteristics (B) in the domain of MLCS algorithm. The longest common subsequence search service is the core service in the field of MLCS. The main functions in this domain include dynamic programming (dp) algorithm operation, heuristic multiple longest common subsequence (HMLCS) algorithm operation, and the dominant point-based multiple longest common subsequence (DP-MLCS) operation. Among the core service, the dp algorithm operation and the HMLCS operation are optional functions, and the DP-MLCS algorithms operation is a mandatory function. The description of optional functions only focuses on its main components. For the mandatory function DP-MLCS, the main flow of its algorithm operation is described in detail. The dynamic programming mode selection (dp_mode) is a significant behavioral characteristic of the operation of the dynamic programming algorithm, including two main behavioral characteristics: standard dynamic programming algorithm  and dynamic programming algorithm that optimizes memory consumption (space_opti). Heuristic mode selection (heur_mode) is a significant behavior characteristic of HMLCS. Here, only ACO algorithm and BS algorithm are taken as its main behavior characteristics. The operation process of the mandatory function DP-MLCS is as follows:Check the validity of the sequence. The input of the algorithm is DNA sequence, and protein sequence. Before the algorithm is executed, the sequence information needs to be checked for validity to determine whether the input sequence is biologically significant.Establish a preprocessing matrix for the input sequence. The data structure (successor table) was first proposed in  for prepUse different pruning functions and heuristic functions to filter the match points, remove redundant match points, and calculate the dominant points contained in the match point set.Use the obtained set of dominant points to establish directed acyclic graph (DAG), and each longest path in the DAG is a path of MLCS.Backtrack to obtain all the longest common subsequence characters through a depth-first search and output the results.This section invokes research on the commonly used MLCS algorithm and analyzes the domain of such algorithms. The FODA feature modeling method is used to perform feature modeling on the The flow chart of DP-MLCS algorithms is shown in successor table  operation, the domination point calculation mode (domi_mode) selection operation, the DAG operation, and the result output (result_op) operation are subfunctions of the DP-MLCS algorithms operation. The sequence legality check operation is the basic operation of the MLCS algorithm and is not described in the model. Calculate match point (CMP) and pruning mode (prune_mode) selection are the two significant behavior characteristics of the domi_mode selection operation. There are two main behavior characteristics under the prune_mode: prune function (PF) and heuristic function (HF). Backtrack (BT) is used as the significant behavioral characteristics of the result_op operation. The MLCS domain model is shown in Through accurate analysis of a series of DP-MLCS algorithms, the sequence legality check component (seq_check component), successor table component (successor_matrix_mani component), dominant point mode selection component (dominant_mode component), directed acyclic graph component (DAG component), result output component (result_op component), matching point calculation component , and backtrack component. The line with arrows indicates the dependency relationship of one component to another component, and the dependency relationship between components is shown in The domain is designed according to the above domain model of MLCS algorithms. The constraints and dependencies between the features are designed and analyzed, and the dependency graph of the algorithm component is established. This section only introduces the component dependencies of a series of DP-MLCS algorithms in the MLCS algorithms as a simple example. The algorithm input in this domain is molecular sequence. Before the algorithm is executed, the sequence needs to be checked for legality. Combined with the introduction of the operation steps of the DP-MLCS algorithms in   1 cal_match_point component   |  |n] =  ∧ n ≥ 2   AQ: init _point: match_ point ∈ match _matrix: seqs [i1] = seqs [i2] = … = seqs[n][in]   AR: ∀ match_point i1, i,…, in: mAccording to the above domain feature model and component dependency relationships of MLCS algorithms, the PAR platform is used to formally implement the domain component library. The Radl language is first used to accurately describe the functional specifications of components of the MLCS field, which are then integrated according to the dependencies between them to form an ADT. Finally, the abstract programming language Apla is used to implement all ADTs. The specifications of the key components cal_match_point and prune_mode in the DP-MLCS algorithms are as follows:in, out, and aux are three keywords defined by the PAR platform, which represent input, output, and auxiliary variables. The auxiliary variables do not need to appear in the program implementation. The array, boolean, integer are predefined types in the PAR platform. AQ represents the preconditions required by the component, and AR represents the postconditions of the component. The cal_match_point component implements the function to calculate the match points of input sequence by inputting the successor table and the initial match points. The successor matrix is the set of successor tables; n is the number of input sequences; match_matrix is the set of calculated match points; and the auxiliary variable seqs represents the input sequence, which stores all characters of the input sequence.   2 prune_mode componentk: integer; match_matrix: set of all match_ point that level = k;   | : array of integer;    Q : array of integer;       out flag: boolean] |k ≥ 0∧P∈ match_matrix   AQ: i ≤ n: p[i] < q[i])   AR: flag = (∀Q : Q∈(match_matrix-P): ∃i: 0 < In the formal specification, k is the rank of the input matching point (the position of the point in the longest common subsequence); match_matrix is the set of matching points with level k; P and Q are the matching points with input level k. The output flag is a Boolean value. If it is true, it indicates that the point P is the dominant point. Otherwise, the point P is a redundant point and should be deleted.The prune_mode component deletes the redundant points in the matching points through the pruning function, thereby calculating the dominant point of the sequence. In the specification, By using abstract data types and abstract processes that write programs directly, the Apla language can describe algorithmic problems abstractly. This makes it easy to verify its correctness and in turn ensures the reliability of the program. As the input language of the program generation system of the PAR platform, Apla can be quickly and easily converted into C++, Java, Python, and other programming languages and platforms.  1 Seqs ADT  define ADT Seqs (sometype elem);   type Seqs = private;   procedure read_seqs ;    function seq_check(Seqs): boolean;    procedure Generate_successor_matrix );i: integer; j: integer; k: integer);    procedure set_value : integer;    function get_value ;   type MatchPoint = private;   function cal_match_point (sometype elemMatrix): sometype elemMatrix;   function pruning_tech : boolean;   function memory_dominant_point (b : array[integer]): sometype elemMatrix;   enddefThe subroutine MatchPoint encapsulates the dominant_mode component related to operations of the match points. The ADT contains three functions. The first is the cal_match_point function to calculate the matching point, where elemMatrix is a type parameter. The second is generic function pruning_tech that chooses different pruning techniques according to the input matrix. This is a Boolean function used to determine whether the matching points satisfy the condition to become the dominant point. The third function is memory_dominant_point, used to store information relevant to the hierarchically dominant point.   3 DAG ADT   define ADT DAG (sometype elem)   type DAG = private;   function Generat_graph (sometype elemMatrix): sometype elemMatrix;   procedure backtrack ;   enddefThe ADT DAG encapsulates three operational components related to directed acyclic graphs: the DAG, backtrack, and result_op. The function Generat_graph generates a DAG based on the set of the input dominant points, and the subprogram backtrack uses a depth-first search strategy to traverse the DAG. According to the matrix of dominant point, the subprogram will choose different traversal methods, and it also includes a subfunction print_MLCS, which is used to output the obtained MLCS, and record and output the number of MLCS.The ADT   Program MLCS:∗ file path of input sequence∗/   const path_infile: string;/∗ file path of output MLCS∗/   const path_outfile: string; /   var∗save the input sequence ∗/;   seqs: Array [String];/   char_num, seq_num: integer;   /∗ successor matrix initialization, omit the matrix initialization code ∗/   function successor_matrix ;   vari, j: integer;      begini, j: 0 ≤ i ≤ char_num, 0 ≤ j ≤ n)   foreach ;   ADT dag: new DAG (Memory_dominant_point);   /∗MLCS Searching manipulate procedure∗/   procedure MLCS_search_mani ;   var   match_matrix, dominant_matrix:array[array[integer]]   begin   Fast_Seqs. read_seqs (path_infile);   Fast_Seqs. seq_check (seqs);   Fast_Seqs. Generate_successor_matrix (seqs);   match_point.cal_match_point (successor_matrix);   match_point.pruning_tech (match_matrix);   match_point.memory_dominant_point(dominant_matrix);   graph = dag. Generat_graph (dominant_matrix);   dag. backtrack );   endWe use the ADT of MLCS algorithm component established in By using the Apla-C++ program generation system of the PAR platform, we convert the Apla program of the assembly algorithm into C++ code. The generic programs and functions defined in Apla were converted into separate class member functions in C++ to reduce the coupling between components. In particular, the calling functions were converted into indicator functions in C++, and the generic parameter was converted into the pointer parameter to implement the polymorphism of the Apla program. After converting all ADT components to C++, the Apla code of the MLCS search operation was converted into a main function, and finally an algorithm program that can be run was generated by manually assembling the components.We tested our assembly algorithm on the rice gene sequence of the GenBank database and compared the performance of our assembly algorithm with that of Clustal-W algorithm , which iBecause of the excellent verifiability of high-level language Apla, the MLCS algorithm formed by component assembly in the domain of MLCS not only improves the reliability, execution efficiency, and maintainability of the assembly algorithm program, but also can be manually assembled to form the specified algorithm according to customer needs, and also enhances the generality of algorithm components.Sequence alignment is a key method and problem in research on bioinformatics. Finding the longest common subsequence among multiple biological sequences is an effective technique for sequence alignment. The MLCS algorithms are widely used in bioinformatics, data mining, information retrieval, and pattern recognition. This article is the first to consider algorithms to solve the MLCS problem as a special field, research them at a high level of abstraction, improve their reliability and developmental efficiency, and reduce the likelihood of occurrence of suboptimal solutions and errors. We used FODA method to analyze the domain of the MLCS algorithms, extract its general and variable features, and transform them into components. The formalized specification language Radl was used to accurately describe the functional specifications of components of MLCS algorithms, and the high-level language Apla was used to implement the function components. Automatic or semiautomatic methods are expected to be available to solve the problems of the assembly and generation of highly reliable components for specific problems. Finally, through a series of program conversion systems on the PAR platform, it is quickly converted into executable algorithms. The final experimental results show that our research has high practicability.This paper combines the theories and techniques of domain engineering, generative programming, formal methods, etc., and the practical research carried out on the MLCS algorithm family can provide new ideas for algorithm research in other fields of bioinformatics. We have applied this research method to the study of pairwise sequence alignment problems and multiple-sequence alignment problems and have achieved substantial achievement. This shows that the algorithmic assembly generation method in this paper is extended to other types of problems and is expected to develop into a method to solve a series of similar problems. Next, we will design a graphical user interface (GUI) for the component library to enable users to obtain the required algorithms by selecting components on the visual interface.


The rapidly digestible starch (RDS) content in GF cookies decreased with increasing steam explosion severity, whereas slowly digestible starch (SDS) and resistant starch (RS) contents significantly increased from 36.91% to 40.92% and from 2.50% to 9.06%, respectively. Steam explosion is an effective technique for enhancing the release of nutrients like dietary fiber and total phenolics, and okra seed flour, especially SEOS flour, can be alternative choices to provide food functional materials for developing various GF food products.Health‐conscious consumers are increasingly interested in gluten‐free (GF) foods. Raw okra seed (ROS) flour and steam‐exploded okra seed (SEOS) flour were explored for developing GF cookies with high nutritional values and in vitro enzymatic digestion. Results indicated that the steam explosion exhibited significant effects on enhancing the release of dietary fibers and lipids in okra seed flour at moderate explosion pressure. Although steam explosion caused the loss of flavonoid compounds, moderate high explosion pressure enhanced the release of total phenolics ranged from 294.57 to 619.07 mg GAE/100 g DM with significantly improved DPPH Raw okra seed flour and steam‐exploded okra seed (SEOS) flour were explored for developing gluten‐free (GF) cookies with high nutritional values and in vitro enzymatic digestion. Steam explosion is an effective technique for enhancing the release of nutrients like dietary fiber and total phenolics, and okra seed flour, especially SEOS flour, can be alternative choices to provide food functional materials for developing various GF food products. The food industry has made positive responses to improve the formulas of GF foods and remove gluten from the diet for people who are gluten intolerant  is originally cultivated in Africa and now grown in different tropical and warm subtropical regions of Europe, Africa, the Middle East, India, and Southeast Asia. Previous studies have shown that okra seeds showed high contents of dietary fiber, protein, polyphenols, and flavonoids and thus possessed antioxidative, antidiabetic, and antidepressant benefits  as a dietary fiber ingredient in GF cookies. The in vitro enzymatic digestion, textural and sensory characteristics of GF cookies in the presence of steam explosion‐treated okra seed flour were also studied as compared with those of pure GF cookies without the addition of okra seed flour and those of GF cookies with the addition of nonsteam explosion okra seed flour. This study will provide knowledge about the employment of steam explosion for improving the nutritional values of legumes and seeds, which is useful for food raw material producers and manufacturers to develop new formulations of GF food products.22.1A. esculentus [Linn.] Moench) were purchased from the local market in Bozhou City , and the commercial rice flour from Indica rice (Oryza sativa L.) was purchased from Jianhao Food Co., Ltd. . The proximate composition of rice flour is as follows: moisture 6.02% ± 0.08%, protein 12.60% ± 0.37%, lipid 0.01% ± 0.00%, ash 0.13% ± 0.01%, and total starch 74.97% ± 0.47%. All other materials were of food‐grade and were obtained from the local market in Tianjin City (China). Unless mentioned otherwise, all the chemicals used were of analytical grade and were purchased from National Pharmaceutical Group Corporation . All the enzyme assay kits were purchased from Megazyme Ltd. .The commercially available dried okra seeds . The steam explosion process was performed on a QBS‐200B type steam explosion device (Hebei Gentle ICSE EnvironTech Co. Ltd.) with the reactor volume of 5,000 ml. The device consists of a reactor chamber, a receiver, and a steam generator. 500 g rehydrated okra seeds were loaded into the reactor chamber, treated with saturated steam at steam pressures of 1.0, 1.5, and 2.0 MPa for 5 min, respectively and suddenly exploded and decompressed into the receiver within 0.1 s. The SEOSs samples were collected and dried in the oven at 50°C for 4 hr. The raw okra seed (ROS) and SEOS samples were then ground to pass through a 250 μm diameter mesh screen, and the ROS flour and SEOS flour products were stored at 4°C for further use.2.32.3.1The moisture content was determined according to AOAC 934.01 loss on drying method AOAC, . The cru2.3.2The total dietary fiber (TDF), SDF, and IDF contents were determined using Megazyme assay kit K‐RINTDF 10/15 and following manufacturer’s assay procedure.2.3.32 flow of 2 ml/min. The oven temperature was programmed as follows: 60°C for 5 min, then 10°C/min until 245°C for 20 min. The MS was performed with electron ionization at the temperature of 220°C, the scan speed of 1,000 u/s, acquisition mass range of 35–500 u, scan interval of 0.5 s, and solvent delay of 4 min. The peak was identified using a Supelco 37 component FAME mix (Sigma‐Aldrich). The percentages of fatty acids were calculated with GC‐MS peak areas by area normalization method according to AOAC 963.22 method  according to the direct method reported by O'Fallon, Busboom, Nelson, and Gaskins . The FAMod AOAC, .2.42.4.1g for 10 min at 4°C. The resulting supernatant was then dissolved with distilled water to a constant volume of 25 ml and regarded as okra seed extracts for further total phenolic, TFC, and antioxidative activity determination.One gram of ROS and SEOS flour was mixed with 20 ml distilled water and then treated by high‐speed shearing on an IKA‐T25 homogenizer (UltraTurrax IKA) at 8,000 rpm for 5 min. Extracts were centrifuged at 2,673 Total phenolic content and TFC of ROS and SEOS flour were determined according to Sarker and Oba  with the2.4.2• and O2·- radical scavenging activity (RSA), and ferric reducing antioxidant power (FRAP). DPPH• RSA was determined following the method of Sharma, Saxena, and Riar . The microstructure of ROS and SEOS flour samples were visualized on a Hitachi SU‐1510 scanning electron microscopy at a beam accelerating voltage of 15 kV with magnifications of ×500 and ×1,000.2.62.6.1The base ingredient with different addition ratios of okra seed flour to rice flour  was prepared based on rice flour + okra seed flour 100 g, butter 30 g, egg 55 g, baking powder 2 g, salt 1 g, and distilled water 6 g. For all formulas, no sugar was added to limit the amount of glycemic carbohydrates , loss modulus (G″), and loss factor (tanδ) in the frequency range of 0.1–100 rad/s were determined by a preliminary strain sweep test at 1 Hz and 25°C  equipped with a 20 mm in diameter parallel plate geometry gapped by 1 mm. The changes in storage modulus (2.6.4L* (lightness), a* (redness‐greenness) and b* (yellowness‐blueness) values were obtained separately.The color determination of GF cookies was performed on an NR10QC Chroma Meter (Shenzhen 3NH Technology Co.Ltd.) and the 2.6.5Textural analyses, including hardness and brittleness, were performed on a TA. XT. Plus texture analyzer (Stable Micro System) equipped with an HDP/3PB probe, using 3 mm/s of pretest speed, 3 mms/s of test speed, and 10 mm/s post‐test speed in automatic trigger mode at 5 g of trigger force.2.7α‐amylase  was dissolved in 11.85 ml distilled water with stirring at 37°C for 10 min and centrifuged at 2,673 g for 10 min. 8 ml supernatant was then mixed with 0.1 ml amyloglucosidase . The amount of released glucose was measured at 0, 20, and 120 min with a D‐glucose assay kit . The rapidlydigestible starch (RDS), slowly digestible starch (SDS), and resistant starch (RS) contents were calculated following the below equations. 20 and G120 were the released glucose amount after 20 min and 120 min enzymatic hydrolysis, respectively.All GF cookies samples were ground to pass through an 80‐mesh screen to simulate the chewing process. In vitro enzymatic digestion of all GF cookies was determined according to the method of Guo, Yu, Copeland, Wang, and Wang  with sli2.8Sensory properties of GF cookies were evaluated by 10 panelists  from the College of Food Science and Engineering, using a 9‐point hedonic scale for four attributes . 9‐point represented “like extremely,” whereas 1‐point represented “dislike extremely.” Finally, the overall performance was calculated as a summed score of the four indicators and used to evaluate the sensory quality of GF cookies.2.9p < .05.Unless otherwise specified, experiments were performed in triplicate with results expressed as mean ± standard deviation. Statistical analyses were conducted using one‐way analysis of variance (ANOVA) and Duncan's multiple range tests with the help of SPSS software . Differences were considered significant at 33.13.1.1p < .05) higher content of protein (38.00%) and lipid (14.14%) as compared to rice four (12.60% and 0.01%), as well as a higher percentage of TDF (37.67%), including SDF (5.10%) and IDF (32.56%). The moisture, protein, and ash content of the studied ROS flour were within the range of different genotypes of okra seeds reported in previous literature  contents of okra flour was observed at each explosion pressure value . Correspondingly, SEOS flour samples showed higher PUFA value respect to ROS flour counterparts, especially for n‐3 and n‐6 PUFA, which are essential and must be supplied by the food diet  and 2.0 MPa (SEOS2.0), while C22:1 cis were formed in the SEOS flour sample obtained at 1.5 MPa (SEOS1.5). Studies have reported that a high intake of trans fatty acids contributed to the risk of coronary heart disease  higher dietary fiber content, but possessed lower protein content than that of ROS flour. Besides, the SEOS flour treated under explosion pressure above 1.0 MPa had higher lipid content than that of ROS flour. The results indicated that steam explosion treatment was conducive to the release of dietary fiber and lipids at moderate explosion pressure, accompanied by the formation of large cavities and intercellular spaces, which effectively help extraction of the soluble components in okra seeds. Besides, in comparison with ROS, SEOS1.5 showed the closest fatty acid composition.Taking the above results into account, SEOS flour showed significantly (3.23.2.1p < .05) TPC than that of ROS, implying that the exposure of ROS to high‐pressure saturated steam led to the loss of phenolic structures and activities. However, TPC released tended to be higher as steam explosion treatment severity conditions increased to 1.5 and 2.0 MPa in materials pre‐impregnated with water. As suggested in the report by Conde et al. (p < .05) higher in the SEOS1.5 and SEOS2.0 in comparison with ROS, followed by SEOS1.0, suggesting that steam explosion could produce a considerable increase of TPC in okra seed flour and the extent depended upon the steam explosion conditions.Total phenolic contents of ROS and SEOS flour Table  ranged fe et al. , part ofp < .05) different, ranging from 22.74 to 42.22 mg RE/100 g DM. ROS showed the highest TFC value of 42.22 mg RE/100 g DM in comparison with SEOS, and the TFC value of okra seed flour decreased with the explosion pressure increased, suggesting that high‐pressure saturated steam caused the damage of flavonoid compounds during the steam explosion, which is a typically hydrothermal treatment , 2.34% to 6.35% (O2·‐ RSA), and 13.37 to 149.04 mmol/L (FRAP). It could be seen that increasing the steam explosion severity led to an increase in DPPH• RSA and FRAP. SEOS2.0 showed the highest antioxidant efficiency in DPPH• RSA, followed by SEOS1.5, SEOS1.0, and ROS, with significant differences (p < .05) observed. The order of FRAP for ROS, SEOS1.0, SEOS1.5, and SEOS2.0 resembled that of DPPH• assay, while the changing trend of O2·‐ RSA was not consistent with that of DPPH• RSA and FRAP, showing the order of SEOS2.0 > SEOS1.5 > ROS > SEOS1.0.In vitro antioxidative activities, including DPPHed Table  and rang3.3• RSA, O2·‐ RSA, and FRAP) were conducted to describe the effects of different explosion pressures  on antioxidative activities of okra seed flour. As shown in Table R2 = −.945), confirming that celluloses, hemicelluloses, and lignins were degraded into oligosaccharides, monosaccharides, and other degradation products at relatively higher explosion pressure of 1.5 and 2.0 MPa. It should be noted that TPC assay is not specific for polyphenols, as any reducing agent, such as lignin derivatives . Moreover, TFC exhibited a reverse correlation with SDF content in certain pressure ranges (0−1.5 MPa) of the steam explosion, which confirmed that relatively moderate steam explosion caused an increase of water‐soluble carbohydrates but notably damage of flavonoid compounds.Correlations among IDF, SDF, TPC, TFC, and antioxidative activities (DPPHR2 = −.960) was observed between DPPH• RSA and TFC. Despite the decrease of TFC along with the explosion pressure, DPPH• RSA exhibited a significantly upward trend and showed certain correlations with SDP content (R2 = .699) and TPC (R2 = .468), respectively. This result indicated that SEOS1.0 might be subjected to severe destruction of the cell wall and, to a certain extent, modification of IDF into SDF, leading to the enhanced release of other antioxidant compounds, along with the increase of SDF, for enhancing DPPH• RSA. The higher DPPH• RSA of SEOS1.5 and SEOS2.0 might be mainly attributed to hydrothermal reaction products formed during the steam explosion. In particular, part of lignins was degraded and converted into pigment derivatives, and insoluble phenolic compounds were released from hemicellulose, causing the production of free phenolic compounds  and significantly correlations with TPC (R2 = .951), which indicated that the phenolics were main contributors to the antioxidant activities of SEOS flour for O2·‐ RSA. In addition, FRAP showed a similar trend with that of DPPH• RSA (R2 = .964), possessing negatively correlation with TFC and positively correlation with TPC. TFC had higher correlation coefficient with O2·‐ RSA than DPPH• RSA and FRAP, which suggested that some nonphenolics, such as reducing sugars formed during steam explosion, were also contributed to the antioxidant activities of okra seed flour for DPPH• RSA and FRAP. This trend was different from the result of . The difference could be due to the different resources studied and the different steam explosion parameters used. Therefore, although steam explosion caused the loss of flavonoid compounds, moderate high explosion pressure enhanced the release and production of soluble reducing sugars and total phenolics with good antioxidative activities.Similarly, Garrote, Cruz, Domínguez, and Parajó  reported3.4SEM micrographs of SEOS flour were shown in Figure 3.53.5.1G″ values than that of pure GF cookies. For all amounts of okra seed flour , both G′ and G″ values of ROS and SEOS‐fortified dough decreased with the increase of explosion steam severity, except for the addition of 4% of SEOS1.5 and 6% of SEOS2.0. According to Figure G′ was higher than loss modulus G″ (tanδ < 1), indicating the elastic‐like behavior of GF cookie dough. However, tanδ values of GF cookie dough notably increased by adding SEOS flour, yield a GF cookie dough, which became less elastic  of okra seed flour are shown in Table 3.7The sensory evaluation of GF cookies was conducted by a 9‐point hedonic scale for four attributes, including color, texture, flavor, and taste. The results of sensory evaluation are shown in Table 4Based on the above results, it can be concluded that the steam explosion exhibited significant effects on enhancing the release of dietary fibers and lipids in okra seed flour at moderate explosion pressure. Although the steam explosion resulted in the loss of flavonoids in okra seed flour, relatively high explosion pressure improved the production of soluble reducing sugars and total phenolics with strong antioxidative activities. The SEOS flour presented a more porous structure at higher explosion pressure with the formation of obvious cavities and voids. ROS and SEOS‐fortified cookie dough yield weaker cookies with more extensible dimensions and lower hardness. Furthermore, RDS content in GF cookies decreased with increasing the steam explosion severity, whereas SDS and RS accordingly increased. Combined with sensory evaluation results, SEOS1.5 flour showed relatively higher dietary fiber and lipid contents without transfatty acids, as well as moderately higher in vitro antioxidative activities and porous microstructure, which contributed to significantly higher SDS and RS content of SEOS1.5‐GF‐6 cookies, in comparison with those of pure and ROS‐fortified GF cookies. Therefore, the steam explosion is an effective technique for enhancing the release of nutritional components like dietary fiber and total phenolics in okra seeds, and okra seed flour, especially SEOS flour, can be alternatives to provide new food functional materials for developing various GF food products with a formulation adapted to the needs of gluten‐sensitive and health‐diet conscious consumers.The authors declare no conflicts of interest.Figure S1Click here for additional data file.


Domain generation algorithms (DGAs) use specific parameters as random seeds to generate a large number of random domain names to prevent malicious domain name detection. This greatly increases the difficulty of detecting and defending against botnets and malware. Traditional models for detecting algorithmically generated domain names generally rely on manually extracting statistical characteristics from the domain names or network traffic and then employing classifiers to distinguish the algorithmically generated domain names. These models always require labor intensive manual feature engineering. In contrast, most state-of-the-art models based on deep neural networks are sensitive to imbalance in the sample distribution and cannot fully exploit the discriminative class features in domain names or network traffic, leading to decreased detection accuracy. To address these issues, we employ the borderline synthetic minority over-sampling algorithm (SMOTE) to improve sample balance. We also propose a recurrent convolutional neural network with spatial pyramid pooling (RCNN-SPP) to extract discriminative and distinctive class features. The recurrent convolutional neural network combines a convolutional neural network (CNN) and a bi-directional long short-term memory network (Bi-LSTM) to extract both the semantic and contextual information from domain names. We then employ the spatial pyramid pooling strategy to refine the contextual representation by capturing multi-scale contextual information from domain names. The experimental results from different domain name datasets demonstrate that our model can achieve 92.36% accuracy, an 89.55% recall rate, a 90.46% F1-score, and 95.39% AUC in identifying DGA and legitimate domain names, and it can achieve 92.45% accuracy rate, a 90.12% recall rate, a 90.86% F1-score, and 96.59% AUC in multi-classification problems. It achieves significant improvement over existing models in terms of accuracy and robustness. Domain generation algorithms (DGA) provide methods for generating large numbers of pseudo-random domain names using specific parameters such as the date, the time, or text as seeds for random initialization. DGAs are often associated with malicious network behaviors. Recent botnets  use DGAs to quickly generate candidate remote command-and-control server domain lists ,2. They In recent years, researchers have proposed several types of models to detect algorithmically generated domain names. Traditional models require manual reverse engineering of the DGA-based malwares, which is time consuming and laborious. The malwares can easily escape detection by changing their DGAs during examination. Therefore, reverse engineering models cannot meet the accuracy and timeliness requirements. Models based on blacklist filtering have a limited coverage of algorithmically generated domain names and cannot adapt to the growth of the malicious domain name set. Models based on traditional statistical machine learning methods have become mainstream in detecting algorithmically generated domain names. These models are based on the analysis of domain names or DNS requests. Models based on the analysis of DNS requests detect algorithmically generated domain names by analyzing the differences in the statistical characteristics of the requested domains, request interval, number of request failures, etc., when sending DNS requests to legitimate domain names and algorithmically generated domain names. Models based on the analysis of domain names detect algorithmically generated domain names by analyzing the differences in the distribution characteristics of characters, words, word lengths, numbers of words, etc., between legitimate and algorithmically generated domain names. The main drawback of these models is that they inevitably require intensive manual feature engineering for building the feature set. When the DGA produces variants, these models require the feature set to be reconstructed. This makes it difficult for the models to adapt to large and frequent changes in the DGAs. Furthermore, models based on the analysis of DNS requests usually rely on third-party credit systems and have very high detection costs.Neural models have recently achieved remarkable progress in various research fields including computer vision, natural language processing, and network security. Neural models can automatically extract the discriminative category features from domain names and effectively detect algorithmically generated domain names by constructing neural networks with multiple hidden layers. However, neural models rely on large-scale domain name datasets for training and are more susceptible to an imbalanced sample distribution than other models.To address the problem of an imbalanced sample distribution, we employ an improved borderline synthetic minority over-sampling algorithm (Borderline-SMOTE) to optimize sample balance in the domain name datasets.To address the problem of feature extraction, we propose a hybrid neural network that combines a convolutional neural network, a bi-directional long short-term memory (Bi-LSTM) network, and a spatial pyramid pooling strategy. We first employ a convolutional neural network and Bi-LSTM to extract semantic and contextual features from domain names simultaneously and then refine the contextual representation by utilizing the spatial pyramid pooling strategy to capture multi-scale contextual information from the domain names. Therefore, the features captured by the proposed hybrid neural network have more discriminative power and are less sensitive to noise.We conduct extensive experiments and analysis to validate the effectiveness of the sample equalization strategy and the performance of the proposed model RCNN-spatial pyramid pooling (SPP). The experiment results demonstrate that the sample equalization method can provide a benefit to performance, and RCNN-SPP can significantly outperform competing models in terms of accuracy, robustness, and convergence speed.To address the aforementioned issues, we propose a model to detect algorithmically generated domain names. Our main contributions are as follows:The remainder of this paper is organized as follows. Existing models for detecting algorithmically generated domain names are primarily based on reverse engineering, blacklist filtering, statistical machine learning methods, and neural networks.As an example of a reverse engineering-based model, Plohmann et al.  performeBuilding a blacklist that includes domains and IP addresses involved in malicious operations is a common and simple way of detecting algorithmically generated domain names. Kührer et al.  conducteOther models formulate the detection of algorithmically generated domain names as a classification problem and apply statistical machine learning methods to solve the classification problem. Some models distinguish the algorithmically generated domain names by obtaining discriminative information from DNS requests. Wang et al.  proposedConsidering the remarkable differences between algorithmically generated domain names and human generated domain names in terms of the distribution of alphanumeric characters, domain name length, number of characters, and other features, some models rely on the analysis of domain names to detect algorithmically generated domain names. Yadav et al.  analyzedDeep neural networks have achieved significant success in various fields including network security. Woodbridge et al.  employedThe model for detecting algorithmically generated domain names is shown in l is the upper bounded length of the domain name and the ith element of ith character in the character sequence according to the character dictionary. Then, the final fixed-length vector representation The domain name encoding module encodes the character sequence of an input domain name. First, we create a character dictionary by taking into account the occurrence frequency of each character in the domain name dataset and assign a unique number to each character according to its occurrence frequency. A domain name is then denoted as a character sequence, and its initial vector representation is The human generated domain name samples constitute the main part of the dataset. In contrast, the domain names generated by a certain DGA usually constitute only a small proportion of the whole dataset because there is a large number of DGAs. Considering that the neural model requires a large-scale dataset for parameter tuning and is sensitive to sample distribution imbalance , the samThe SMOTE algorithm is a random oversampling algorithm. The key idea of SMOTE is to select samples from the minority class randomly and synthesize new samples from the minority class by interpolating between nearest samples, thereby increasing the sample size of the minority class and relieving the imbalance in the sample distribution. The Borderline-SMOTE algorithm  further In order to balance the distribution of class samples in the training set of domain name character sequences with class labels, we define the majority class and the minority class. We regard the human generated domain names as belonging to a majority class in the domain name dataset. If the number of samples generated by the malicious domain name algorithm is less than the specified threshold, the corresponding training samples are regarded as a minority class. For the domain names generated by a certain DGA, we use the harmonic mean to determine whether it belongs to a minority or majority class. The harmonic mean equalizes the weights of each class so that it can better reflect the average sample sizes of various DGAs in the dataset.n different DGAs so that the classes in the dataset can be denoted as ith DGA. The harmonic mean is defined as follows:Suppose that there are After identifying the minority classes in the domain name dataset, the synthesized domain name samples are generated as follows:K nearest neighbor samples of Step 1: For each minority class Step 2: For each domain name sample s in the range , and select the first s nearest samples from s domain name samples for class Step 3: For each Step 4: Finally, add all the synthesized samples to the original domain name dataset to improve the sample balance.Algorithm 1 Sample synthesis for minority classes.Input: class set TOutput:T1:2:3:fordo4:if then 5:  /*synthesize domain name samples for class 6:fordo  7:   8:   9:ifthen   10:    11:forsdo    12:     13:S */     /* add the synthesized domain name sample 14:     15:end for    16:end if   17:end for  18:end if 19:end for20:With the aforementioned analyses, the procedure of sample synthesis for minority classes is summarized in Algorithm 1.The most essential task in our model is to learn a distinctive, robust, and discriminative feature representation that can distinguish the differences between human generated and algorithmically generated domain names. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two of the most widely-adopted neural networks for learning discriminative features in many research fields . Both CNTo address the above issues, we propose a hybrid neural network that combines a convolutional neural network, a bi-directional long short-term memory network, and spatial pyramid pooling (RCNN-SPP). The RCNN-SPP adopts the recurrent convolution neural network (RCNN) proposed by Lai  as the bRCNN-SPP employs a bi-directional recurrent structure to capture forward and backward context for a domain name denoted as Equation . The outAfter obtaining the latent feature vector of each character in a domain name, the feature representation of a domain name can be represented as F represents the convolutional filter of size Then, The structure of the recurrent convolutional neural network is shown in A typical CNN always uses a pooling layer to compress the feature map while retaining the discriminative feature information in the feature map. There are generally two types of pooling operations: average pooling and max pooling. However, average pooling and max pooling cannot capture multi-scale feature information from the domain names that is critical for identifying algorithmically generated domain names. Therefore, we employ the spatial pyramid pooling as the pooling layer to capture multi-scale contextual information in the domain names. As shown in D is the DGA class set, The feature vector output by the spatial pyramid pooling layer serves as the final representation. We adopt a softmax layer  as the oFor model training, we employ the cross entropy  with theWe selected 800,000 DGA domain names from the 360 Netlab OpenData project , 750,000Dataset from Netlab OpenData: This dataset is provided by 360 Network Security Research Lab and includes 800,000 DGA domain names generated by 50 different types of DGAs collected from botnet and normal Internet traffic data inspected by the mirai scanner. Note that because of the variety of DGAs and very short lifespan of some DGA domain names, some DGAs have only less than 100 domain name samples in this dataset.Dataset from Bambenek Consulting: This dataset contains 750,000 DGA domain names generated from time-dependent random seeds by reverse engineering 30 different malware families.Dataset from Alexa: Alexa provides a ranking list of popular websites on the Internet. The ranking is calculated by estimating a site’s average number of daily unique visitors and the number of pageviews over the past 3 months. We acquired the top 1 million domain names in the ranking list for 16th May 2018. It is reasonable to assume that these domain names are legitimate domain names  because they are related to real sites that have many human visitors and pageviews.In this study, each experiment was carried out on a large number of domain names randomly selected from the three datasets described above. We selected the DGA domain names  from Netlab OpenData and Bambenek Consulting, while the legitimate domain names  were selected from Alexa. In each experiment, we used 80% of the domain name samples for training, 10% for validation, and 10% for testing. It is necessary to note that we adjusted the ratio of legitimate domain names to DGA domain names in accordance with the different purposes of the respective experiments.In the experiments, we validated our model’s ability to distinguish DGA domain names from legitimate domain names and its ability to determine the DGA classes accurately. Therefore, the detection of DGA domain names is essentially a binary classification or multi-classification task. The ROC curve refers to the receiver operating characteristic curve. AUC is the area under the ROC curve, which is used to measure the performance of algorithms for binary classification problems. For classification tasks, precision, recall, F1-score, and ROC are commonly used as evaluation metrics. Therefore, we used these four metrics to compare the performance of our model with other compared models.The GPU used in our experiments was an NVIDIA GeForce GTX 1050Ti. The sample equalization method and the RCNN-SPP model were implemented using Keras . The chaIn this section, we conduct several experiments to analyze the binary classification and multi-classification performance of RCNN-SPP. We also study the impact of sample imbalance on the performance of RCNN-SPP and the compared models CNN and LSTM by selecting different domain name samples from the three datasets.In the first scenario, we studied the performance of RCNN-SPP and the compared models under the condition of a balanced sample class distribution. Therefore, we selected DGA domain name samples from Bambenek Consulting because each DGA class in this dataset has enough samples for model training, whereas some DAG classes in Netlab OpenData have only very few samples.The total number of domain name samples for the first experiment was 20,000, comprising 20% DGA domain names in 10 DGA classes from Bambenek Consulting with 400 samples for each DGA class and 80% legitimate domain names from Alexa.The first experiment was conducted to validate the performance of RCNN-SPP and the two compared models in binary classification, i.e., in distinguishing DGA domain names from legitimate domain names. The performance result is shown The second experiment was conducted to validate the performance of RCNN-SPP and the two compared models in multi-classification, i.e., accurately determining the DGA class to which a domain name belongs. In the second scenario, we studied the performance of RCNN-SPP and the two compared models under the condition of an imbalanced class sample distribution. Therefore, we selected 10 DGA classes from Bambenek Consulting with 1000 samples for each DGA class and nine DGA classes from Netlab OpenData with 100 samples for each DGA class. We also selected 9100 legitimate domain name samples from Alexa. The total number of domain name samples was 20,000, the same as the first scenario.In this section, we discuss the convergence speed of RCNN-SPP and the two compared models based on the second experiment of the first scenario in In this section, we discuss the impact of sample size on the performance of RCNN-SPP. We followed the same strategy as the first scenario. The proportions of the samples from the DGA domain names in the 10 DGA classes from Bambenek Consulting and the legitimate domain names from Alexa were fixed at 20% and 80%, respectively, while the total number of domain name samples varied from 1000 to 1,000,000. The binary classification and multi-classification results are shown in In this subsection, we analyze the impact of sample equalization on the performance of RCNN-SPP under the condition of an imbalanced class sample distribution. We selected nine DGA classes from 360 Netlab OpenData with a total number of 900 domain name samples and 10,000 legitimate domain name samples from Alexa. The classification results are shown in In this section, we discuss the impact of different pooling strategies on the performance of RCNN-SPP based on the dataset used in the first scenario. The experiment results are shown in In this paper, we propose a novel model for detecting algorithmically generated domain names. We employ the borderline synthetic minority over-sampling algorithm (SMOTE) to improve sample balance. We also propose a recurrent convolutional neural network to fully exploit the contextual and semantic information in different DGA categories. Furthermore, we adopt the spatial pyramid pooling strategy to refine the category feature representation, which further improves the ability of our model to identify different DGA categories. We also conduct extensive experiments and analysis on several datasets. The experiments demonstrate that our model achieves perfect performance. Future work will consider the optimization of its performance and compare it with the recent work ,38,39 to


Ubiquitin signals play various roles in proteolytic and non-proteolytic functions. Ubiquitin signals are recognized as targets of the ubiquitin–proteasome system and the autophagy–lysosome pathway. In autophagy, ubiquitin signals are required for selective incorporation of cargoes, such as proteins, organelles, and microbial invaders, into autophagosomes. Autophagy receptors possessing an LC3-binding domain and a ubiquitin binding domain are involved in this process. Autophagy activity can decline as a result of genetic variation, aging, or lifestyle, resulting in the onset of various neurodegenerative diseases. This review summarizes the selective autophagy of neurodegenerative disease-associated protein aggregates via autophagy receptors and discusses its therapeutic application for neurodegenerative diseases. Many neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD), involve accumulation of harmful and aggregation-prone proteins. These aggregated proteins are known to be ubiquitinated in many neurodegenerative diseases. Although harmful proteins are immediately degraded by proteolytic systems in healthy individuals, any perturbation of these systems caused by genetic variation, aging, or lifestyle results in accumulation of harmful protein aggregates and the onset of various diseases including neurodegenerative diseases. Ubiquitination is the most important targeting signal for proteolytic systems . Indeed,AD is a progressive neurodegenerative disorder that leads to cognitive decline . The maiAβ accumulation is the initial event in AD progression. Subsequently, Tau pathology develops in a delayed fashion . Tau proParkinson’s disease and dementia with Lewy bodies are neurodegenerative diseases that are characterized by the presence of intracellular abnormal deposits called Lewy bodies and Lewy neurites. These deposits mainly consist of α-synuclein, which is a natively unfolded protein localized to the nucleus and presynaptic nerve terminals ,22,23. SA382T ALS-FTD variation causes a significant reduction in stress granule assembly in human fibroblasts [P362L ALS-FTD variation delays stress granule disassembly and promotes the accumulation of non-dynamic stress granules [Amyotrophic lateral sclerosis (ALS) and FTD are neurodegenerative diseases characterized by motor and cognitive impairment, respectively. Both diseases have genetic and pathological overlaps. For example, variations in various genes, such as TDP-43, FUS, p62 and C9 or f72, are attributed to the etiology of both diseases, and TDP-43 pathology is often observed in both diseases (in ~97% of ALS and ~50% of FTD cases) ,41. In mroblasts . In contgranules . These rgranules . Both prgranules . The FUSgranules . Recentlgranules .Other neurodegenerative diseases are also characterized by neuronal protein aggregates. Expanded polyglutamine (polyQ) tracts are aggregation-prone and expanded polyQ-containing proteins, such as huntingtin and ataxins, cause HD and spinocerebellar ataxia, respectively . HuntingAccumulation of harmful proteins is a hallmark of various neurodegenerative diseases, as described above. Cells are protected from harmful proteins by protein quality control mechanisms, including molecular chaperone and protein degradation systems . EukaryoThe ALP is an intracellular metabolic process in which cytoplasmic proteins and organelles sequestrated by autophagosomes are degraded in lysosomes . AutophaAtg5- and Atg7-null mice, indicating that multiple ubiquitin signals might be involved in autophagic degradation of various cargoes  is modified by Lys-6-, Lys-11-, Lys-48-, Lys-63-poly-ubiquitin chains and mono-ubiquitin in AD brains or cultured cells  83,84,8,884,85. α-Synuclein undergoes ubiquitination by various E3 ubiquitin ligases . Seven iALS-causing TDP-43 and SOD1 aggregates are also detected by anti-ubiquitin antibodies. TDP-43 is targeted by Znf179 ubiquitin ligase and is modified by poly-ubiquitin chains . Znf179 Turnover of huntingtin is regulated by ubiquitination, via Lys-48- and Lys-63-poly-ubiquitin. Although aggregated mutant huntingtin mainly includes Lys-63-poly-ubiquitin chains, overexpression of Lys-48-specific ubiquitin ligase, Ube3a, reduces Lys-63-ubiquitination and huntingtin aggregation, enhancing its degradation via the Lys-48 ubiquitin–proteasome system . SimilarSmall protein aggregates are thought to be degraded by ALP. Autophagy receptors recognize ubiquitin chains bound to cargoes and transport them to autophagosomes A. RecentActivation of autophagy receptors is mediated by various kinases. For example, p62 is phosphorylated by various kinases, such as mTORC1, casein kinase 1, and TBK1 ,115,116.Autophagy activation accelerates elimination of neurodegenerative disease-associated protein aggregates and inclusions. The mTORC1 inhibitor, rapamycin, is well known to induce autophagy activity . The effProtein quality control systems, such as UPS and ALP, decline with age, which is a leading cause of neurodegenerative diseases. Clinical trials of several autophagy activators have been conducted for AD and ALS patients. For example, resveratrol is a natural polyphenol that induces autophagy activity by directly inhibiting mTOR . In indi