Adaptive identification of time delays in nonlinear dynamical models

This paper develops an adaptive synchronization strategy to identify both discrete and distributed time delays in nonlinear dynamical models. In contrast with adaptive techniques for parameter estimation in the literature, the adaptive strategy developed here for time-delay identification invites more precise results that have physical and dynamical importance. It is analytically and numerically found that distributed time delays in a model with an asymptotically stable steady state can be adaptively identified, and which is different from the case of discrete time-delays identification. Other aspects of the strategy developed here, for time-delay identification, are illustrated by several representative dynamical models. Aside from illustrations for toy models and their generated data, the strategy developed is used with experimental data, to identify a time delay, called transcriptional delay, in a model describing the transcription of messenger RNAs (mRNAs) for Notch signaling molecules

Review of Alkali-Based Pretreatment To Enhance Enzymatic Saccharification for Lignocellulosic Biomass Conversion

Lignocelluloses have been the focus of much attention, because of their conversion to fermentable sugars for cellulosic ethanol production, both from the viewpoint of energy and the environment. Pretreatment plays a crucial rule in biomass conversion, to overcome the chemical and structural difficulties, which have evolved in lignocelluloses, and to produce a cost-effective fermentable sugar via enzymatic saccharification. Among the developed pretreatment approaches, alkali-based pretreatment technology, which can utilize the equipment and chemical recovery system in the pulping industry, has been considered one of the most promising pretreatment methods, mainly because of its high efficiency in delignification and high final total sugar yields. This paper reviews the classification, mechanism, advantages, disadvantages, and the progress of alkali-based pretreatment technologies, in order to better understand the fundamental principles of alkali-based pretreatments. This is of vital importance for the process improvement and commercial production of alkali-based pretreatment for producing cellulosic ethanol

Characterization of the Detailed Relationships of the Key Variables in the Process of the Alkaline Sulfite Pretreatment of Corn Stover by Multivariate Analysis

In biomass pretreatment processes, both the properties of feedstock and process parameters play important roles in the yield of downstream enzymatic hydrolysis. More importantly, like many other industrial processes, the pretreatment system is multivariate and the variables in the system are inter-related to different extents, which means that studying the relationships of the key variables is of critical importance for the improvement of downstream enzymatic saccharification yield. In this work, two multivariate analysis methods of the Principal Component Analysis (PCA) and Partial Least Square (PLS) were employed to characterize the detailed relationships of the key process variables of alkaline sulfite pretreatment of corn stover. The results showed that the total alkali charge is positively correlated with the sugar content in pretreated biomass, lignin removal efficiency, and final sugar yield; pretreatment temperature has negative impact on the recovery of polysaccharides; and total alkali charge is more influential than other pretreatment process variables (such as Na2SO3/NaOH and temperature) under the conditions studied

Comprehensive analysis of important parameters of choline chloride-based deep eutectic solvent pretreatment of lignocellulosic biomass

Choline chloride based deep eutectic solvents have showed great potential in lignocellulosic biomass pretreatment. In this study, for DES pretreatment with different hydrogen bond donners of different raw materials under different reaction conditions, multivariate analysis methods including principal component analysis and partial least squares analysis were used for reveal the pretreatment mechanism by evaluating the inner relationships among 42 key process factors. Furthermore, based on molecular simulation, the detailed relationships between key variables were further analyzed. Meanwhile, four-dimensional color graphs were used to intuitively reveal the synergistic influence of multivariate conditions variables on pretreatment effect to obtain better economic benefits and energy consumption indicators for DES pretreatment. The results showed that HBD hydrophilic ability, HBD polarity, HBD acidity, HBD ability to form hydrogen bonds, molar ratio of HBD to choline chloride and pretreatment severity had great influence on the Choline chloride based deep eutectic solvents pretreatment effect

Perrault syndrome: Clinical report and retrospective analysis

Abstract Background Perrault syndrome (PRLTS4; OMIM# 615300) is a rare autosomal recessive disorder and only a few cases have been reported worldwide. We report a Chinese female characterized by sensorineural hearing loss and premature ovarian insufficiency. Methods We evaluated audiological, endocrine, and ultrasound examinations and examined the genetic causes using whole‐exome sequencing. We reviewed the literature to discuss the pathogenesis, genotype–phenotype correlation, treatment, and prevention of PRLTS4. Results Bioinformatic analysis revealed compound heterozygous mutations in the LARS2 gene, c.880G>A (p.Glu294Lys), and c.2108T>C (p.Ile703Thr) which is a novel missense mutation, co‐segregated in this family. Taken together, the patient was clinically diagnosed as PRLTS4. The literature review showed that the phenotype for PRLTS4 varies widely, but the sensorineural hearing loss, increased gonadotropin levels, and amenorrhea occurred frequently. All reported mutations are highly conserved in mammals based on conservation analysis, and there is a mutation hotspot for PRLTS4. Conclusion This study expanded the mutation spectrum of LARS2 and is the first report of PRLTS4 in a Chinese family. Genetic testing plays an important role in early diagnosis of syndromic deafness and clinical genetic evaluation is essential to guide prevention

Auditory Neuropathy Spectrum Disorder (ANSD)—Clinical Characteristics and Pathogenic Variant Analysis of Three Nonsyndromic Deafness Families

Objective. To analyze the phenotypic features and pathogenic variants of three unrelated families presenting with nonsyndromic auditory neuropathy spectrum disorder (ANSD). Methods. Three recruited families that were affected by congenital deafness were clinically evaluated, including a detailed family history and audiological and radiological examination. The peripheral blood of all patients and their parents was collected for DNA extraction, and then, the exonic and flanking regions were enriched and sequenced using targeted capture and high-throughput sequencing technology. Bioinformatics analyses and the Sanger sequencing were carried out to screen and validate candidate pathogenic variants. The pathogenicity of candidate variants was evaluated by an approach that was based on the standards and guidelines for interpreting genetic variants as proposed by the American College of Medical Genetics and Genomics (ACMG). Results. Four patients in three families were diagnosed as nonsyndromic ANSD, and all exhibited OTOF gene mutations. Among them, two individuals in family 1 (i.e., fam 1-II-2 and fam 1-II-3) carried homozygous variants c.[2688del];[2688del] (NM_194248.3). Two individuals from family 2 (fam 2-II-1) and family 3 (fam 3-II-4) carried compound heterozygous variants c.[4960G>A];[1469C>G] and c.[2675A>G];[2977_2978del], respectively. Conclusions. Three unrelated pedigrees with ANSD were caused by pathogenic variants in the OTOF gene. Five mutations were found and included c.2688del, c.2675A>G, c.2977_2978del, c.4960G>A, and c.1469C>G, of which the first two are novel and expanded mutational spectrum of the OTOF gene, thus having important implications for genetic counseling of the family

Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters

Incorporating density functional theory (DFT) and machine learning (ML) methodologies, an intrinsic relationship model was developed utilizing the Kamlet–Taft parameters and polarity values of 104 deep eutectic solvents (DES). DES with high lignocellulosic pretreatment efficiency were expected to be screened through the synergistic combination of hydrogen bond acidity (α), hydrogen bond basicity (β), polarization (Π*) and molecular polarity index (MPI). Partial least-squares (PLS) models and a variety of ML models were used to predict cellulose retention and delignification. The XGBoost model has the highest predictive performance with R2 of 0.97 and 0.91, respectively. Feature importance analysis and partial dependence analysis were used to explain the importance of variables based on the XGBoost model. Feature importance analysis showed that α, β, Π* of DES and MPI of hydrogen bond donor determined the pretreatment efficiency. The partial dependence analysis showed that the relationship among 4 parameters and the pretreatment efficiency is nonlinear, and there are multiple extreme values in different intervals. The model gave a parameter range corresponding to the high pretreatment efficiency. Based on the range of 4 parameters given in this study, the composition and ratio of DES can be selected to ensure that at least 80% of the cellulose is retained and 50% of the lignin is removed. Molecular simulation results showed that these highly efficient DES often contain a large number of hydrogen bonds and highly polar groups