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Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike protein and the membrane (M) protein, which may imply an enhanced ability to evade immune responses. In this study, an examination of co-mutations within the spike RBD and their potential correlation with mutations in the M protein was conducted. The EVmutation method was utilized to analyze the distribution of the mutations to elucidate the relationship between the mutations in the spike RBD and the alterations in the M protein. Additionally, the Sequence-to-Sequence Transformer Model (S2STM) was employed to establish mapping between the amino acid sequences of the spike RBD and M proteins, offering a novel and efficient approach for streamlined sequence analysis and the exploration of their interrelationship. Certain mutations in the spike RBD, G339D-S373P-S375F and Q493R-Q498R-Y505, are associated with a heightened propensity for inducing mutations at specific sites within the M protein, especially sites 3 and 19/63. These results shed light on the concept of mutational synergy between the spike RBD and M proteins, illuminating a potential mechanism that could be driving the evolution of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Alzheimer's disease and Type 2 diabetes are two epidemiologically linked diseases which are closely associated with the misfolding and aggregation of amyloid proteins amyloid-β (Aβ) and human islet amyloid polypeptide (hIAPP), respectively. The co-aggregation of the two amyloid proteins is regarded as the fundamental molecular mechanism underlying their pathological association. The green tea extract epigallocatechin-3-gallate (EGCG) has been extensively demonstrated to inhibit the amyloid aggregation of Aβ and hIAPP proteins. However, its potential role in amyloid co-aggregation has not been thoroughly investigated. In this study, we employed the enhanced-sampling replica exchange molecular dynamics simulation (REMD) method to investigate the effect of EGCG on the co-aggregation of Aβ and hIAPP. We found that EGCG molecules substantially diminish the β-sheet structures within the amyloid core regions of Aβ and hIAPP in their co-aggregates. Through hydrogen-bond, π–π and cation–π interactions targeting polar and aromatic residues of Aβ and hIAPP, EGCG effectively attenuates both inter-chain and intra-chain interactions within the co-aggregates. All these findings indicated that EGCG can effectively inhibit the co-aggregation of Aβ and hIAPP. Our study expands the potential applications of EGCG as an anti-amyloidosis agent and provides therapeutic options for the pathological association of amyloid misfolding disorders. [ABSTRACT FROM AUTHOR]

While undergoing structural deformation, DNA experiences changes in the interactions between its internal base pairs, presenting challenges to conventional elastic methods. To address this, we propose the Discrete Critical State (DCS) model in this paper. This model combines surface discrete frame theory with gauge theory and Landau phase transition theory to investigate DNA's structural deformation, phase transitions, and chirality. Notably, the DCS model considers both the internal interactions within DNA and formulates an overall equation using unified physical and geometric parameters. By employing the discrete frame, we derive the evolution of physical quantities along the helical axis of DNA, including geodesic curvature, geodesic torsion, and others. Our findings indicate that B-DNA has a significantly lower free energy density compared to Z-DNA, which is in agreement with experimental observations. This research reveals that the direction of base pairs is primarily governed by the geodesic curve within the helical plane, aligning closely with the orientation of the base pairs. Moreover, the geodesic curve has a profound influence on the arrangement of base pairs at the microscopic level and effectively regulates the configuration and geometry of DNA through macroscopic-level free energy considerations. [ABSTRACT FROM AUTHOR]

The three distinct medicinal parts of Angelica sinensis (Oliv.) Diels (Ang) roots are the head, body, and tail (ARH, ARB, and ART, respectively). How endophytic fungi shape the differences in metabolic components among these parts remains unclear. We quantified the distribution of active components and endophytic fungi along the ARH, ARB, and ART and their relationships. Based on the metabolic components and their abundances detected via non-target metabolism, the different medicinal parts were distinguishable. The largest number of dominant metabolic components was present in ART. The difference between ART and ARH was the greatest, and ARB was in a transitional state. The dominant active molecules in ART highlight their effects in haemodynamics improvement, antibacterial, anti-inflammatory, and hormone regulation, while ARH and ARB indicated more haemostasis, blood enrichment, neuromodulation, neuroprotection and tranquilisation, hepatoprotection, and antitumour activities than that of ART. The ARHs, ARBs, and ARTs can also be distinguished from each other based on the endophytic fungi at the microbiome level. The most dominant endophytic fungi were distributed in ART; the differences between ART and ARH were the largest, and ARB was in a transition state, which is consistent with the metabolite distributions. Structural equation modelling showed that the endophytic fungi were highly indicative of the metabolic components. Correlation analysis further identified the endophytic fungi significantly positively correlated with important active components, including Condenascus tortuosus, Sodiomyces alcalophilus, and Pleotrichocladium opacum. The bidirectional multivariate interactions between endophytic fungi and the metabolic components shape their spatial variations along the longitudinal direction in the Ang root. [ABSTRACT FROM AUTHOR]

Current structural and functional investigations of cholesteryl ester transfer protein (CETP) inhibitor design are nearly entirely based on a fully active mutation (CETPMutant) constructed for protein crystallization, limiting the study of the dynamic structural features of authentic CETP involved in lipid transport under physiological conditions. In this study, we conducted comprehensive molecular dynamics (MD) simulations of both authentic CETP (CETPAuthentic) and CETPMutant. Considering the structural differences between the N- and C-terminal domains of CETPAuthentic and CETPMutant, and their crucial roles in lipid transfer, we identified the two domains as binding pockets of the ligands for virtual screening to discover potential lead compounds targeting CETP. Our results revealed that CETPAuthentic displays greater flexibility and pronounced curvature compared to CETPMutant. Employing virtual screening and MD simulation strategies, we found that ZINC000006242926 has a higher binding affinity for the N- and C-termini, leading to reduced N- and C-opening sizes, disruption of the continuous tunnel, and increased curvature of CETP. In conclusion, CETPAuthentic facilitates the formation of a continuous tunnel in the "neck" region, while CETPMutant does not exhibit such characteristics. The ligand ZINC000006242926 screened for binding to the N- and C-termini induces structural changes in the CETP unfavorable to lipid transport. This study sheds new light on the relationship between the structural and functional mechanisms of CETP. Furthermore, it provides novel ideas for the precise regulation of CETP functions. [ABSTRACT FROM AUTHOR]

The tractor is the primary power device of the agricultural production process. For the problem that the traditional electro-hydraulic hitch control method for tractors cannot simultaneously meet the requirements of maintaining a constant ploughing depth and improving traction performance and electric tractor overall efficiency, this paper proposes a hydraulic cylinder pressure control method of the electro-hydraulic hitch system for electric tractors. We establish a tractor-implement system dynamic model, calculate the rear axle load of the tractor in real-time according to the actual working parameters under the premise of ensuring the constant ploughing depth, construct a traction performance objective optimization function, and use the genetic algorithm to solve the optimal hydraulic cylinder pressure value of the electro-hydraulic hitch system. Hardware-in-the-loop (HIL) simulation results show that the electric tractor under the traditional position control method and the hydraulic cylinder pressure control method has an average wheel slip of 18.50% and 16.93%, an average traction efficiency of 71.35% and 73.08%, and an average overall efficiency of 50.81% and 52.40%. The hydraulic cylinder pressure control method proposed in this paper reduces the wheel slip by 9.27%, increases the traction efficiency by 2.42%, improves the electric tractor overall efficiency by 3.13%, and reduces the electric tractor overall energy loss by 7.67% compared with the traditional position-control method. Therefore, the hydraulic cylinder pressure control method of the electro-hydraulic hitch system proposed in this paper can achieve the purpose of effectively improving tractor traction performance and reducing tractor energy loss while maintaining a constant ploughing depth. This study offers technological references for electric tractors to improve traction performance and reduce the overall energy loss of electric tractors. [ABSTRACT FROM AUTHOR]

Radar emitter signal recognition under noisy background is one of the focus areas in research on radar signal processing. In this study, the soft thresholding function is embedded into deep learning network models as a novel nonlinear activation function, achieving advanced radar emitter signal recognition results. Specifically, an embedded sub-network is used to learn the threshold of soft thresholding function according to the input feature, which results in each input feature having its own independent nonlinear activation function. Compared with conventional activation functions, the soft thresholding function is characterized by flexible nonlinear conversion and the ability to obtain more discriminative features. By this way, the noise features can be flexibly filtered while retaining signal features, thus improving recognition accuracy. Under the condition of Gaussian and Laplacian noise with signal-to-noise ratio of −8 dB to −2 dB, experimental results show that the overall average accuracy of soft thresholding function reached 88.55%, which was 11.82%, 8.12%, 2.16%, and 1.46% higher than those of Sigmoid, PReLU, ReLU, ELU, and SELU, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Colon cancer is a complex disease with high incidence rates and mortality worldwide. Although some medical methods have been used for screening, prevention and treatment, its molecular mechanism is still unclear. Among all dysfunctional factors, the change of mutual regulation relationship between RNAs is an important factor affecting the development of cancer. Therefore, the purpose of this study is to find RNAs related to colon cancer that have not been verified. We used differential expression analysis to screen mRNAs, miRNAs and lncRNAs and further constructed a heterogeneous interaction network among these three kinds of RNAs. The network propagation algorithm RW-DIR was then developed to mine the biological information contained in the network and to identify RNAs closely related to colon cancer. The research results have provided some theoretical support for disease research and provide a basis for narrowing the research scope of medical experiments. Colon cancer is considered as a complex disease that consists of metastatic seeding in early stages. Such disease is not simply caused by the action of a single RNA, but is associated with disorders of many kinds of RNAs and their regulation relationships. Hence, it is of great significance to study the complex regulatory roles among mRNAs, miRNAs and lncRNAs for further understanding the pathogenic mechanism of colon cancer. In this study, we constructed a heterogeneous network consisting of differentially expressed mRNAs, miRNAs and lncRNAs. This contains three kinds of vertices and six types of edges. All RNAs were re-divided into three categories, which were "related", "irrelevant" and "unlabeled". They were processed by dynamic excitation restart random walk (RW-DIR) for identifying colon cancer-related RNAs. Ten RNAs were finally obtained related to colon cancer, which were hsa-miR-2682-5p, hsa-miR-1277-3p, ANGPTL1, SLC22A18AS, FENDRR, PHLPP2, hsa-miR-302a-5p, APCDD1, MEX3A and hsa-miR-509-3-5p. Numerical experiments have indicated that the proposed network construction framework and the following RW-DIR algorithm are effective for identifying colon cancer-related RNAs, and this kind of analysis framework can also be easily extended to other diseases, effectively narrowing the scope of biological experimental research. [ABSTRACT FROM AUTHOR]

To date, many experiments have revealed that the functional balance between hemagglutinin (HA) and neuraminidase (NA) plays a crucial role in viral mobility, production, and transmission. However, whether and how HA and NA maintain balance at the sequence level needs further investigation. Here, we applied principal component analysis and hierarchical clustering analysis on thousands of HA and NA sequences of A/H1N1 and A/H3N2. We discovered significant coevolution between HA and NA at the sequence level, which is closely related to the type of host species and virus epidemic years. Furthermore, we propose a sequence-to-sequence transformer model (S2STM), which mainly consists of an encoder and a decoder that adopts a multi-head attention mechanism for establishing the mapping relationship between HA and NA sequences. The training results reveal that the S2STM can effectively realize the "translation" from HA to NA or vice versa, thereby building a relationship network between them. Our work combines unsupervised and supervised machine learning methods to identify the sequence matching between HA and NA, which will advance our understanding of IAVs' evolution and also provide a novel idea for sequence analysis methods. [ABSTRACT FROM AUTHOR]

This paper proposes a 110% relative bandwidth (RBW) low-noise amplifier (LNA) for broadband receivers with flat gain, low noise and high linearity. Bias and parasitic parameters derived wide bandpass (BPDWB) matching networks and a cascode with dual feedbacks are introduced for broadband performance. Matching network design procedures are demonstrated, and results show that the frequency response of the network fits the target impedance well from 1 GHz to 3.5 GHz. The proposed BPDWB network improves the design efficiency and enhances the prediction accuracy of impedance matching. The proposed LNA in 0.25 μm GaAs pseudomorphic high electron mobility transistor (GaAs pHEMT) technology realizes a minimum NF of 0.45 dB at 1.6 GHz where the NF is less than 0.55 dB within the operating frequency band. A flat gain of 22.5–25.2 dB is achieved with the input voltage standing wave ratio (VSWR) below 1.22 and output VSWR less than 2.5. In addition, the proposed LNA has good linearity where the output third-order intercept point (OIP3) is better than +31.5 dBm, and the output 1 dB compression point (OP1dB) is better than +19 dBm over the wide frequency range. [ABSTRACT FROM AUTHOR]

This paper presents a broadband GaN pseudo high-electron-mobility transistor (pHEMT) two-stage driver amplifier based on an enhanced feedback technique for a wideband system. Through well-designed parameter values of the feedback and the matching structure of the circuit, a relatively flat frequency response was obtained over a wide frequency band. Simultaneously, in order to reduce the fluctuation of current caused by the environmental temperature, a bias circuit with quiescent current temperature compensation was designed. The driver power amplifier, which was implemented in the form of a monolithic microwave integrated circuit (MMIC), was designed to drive a broadband high-power amplifier. The designed broadband driver amplifier for the 6 GHz to 20 GHz frequency band had a very small die size of 1.5 × 1.2 mm 2 due to the use of an optimized impedance matching structure. It exhibited a small-signal gain of 12.5 dB and output power of 26 dBm. The flatness of this driver amplifier for gain and output power was achieved as ±2.5 dB and ±1 dB over the entire frequency band, respectively. The experimental results showed up to 35 dBm in the OIP3, and the current variation range was ±5 mA after using the temperature compensation bias circuit. [ABSTRACT FROM AUTHOR]

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed. [ABSTRACT FROM AUTHOR]

This study investigated the influence of ultrafine metakaolin replacing cement as a cementitious material on the properties of concrete and mortar. Two substitution levels of ultrafine metakaolin (9% and 15% by mass) were chosen. The reference samples were plain cement concrete sample and silica fume concrete sample with the same metakaolin substitution rates and superplasticizer contents. The results indicate that simultaneously adding ultrafine metakaolin and a certain amount of polycarboxylate superplasticizer can effectively ensure the workability of concrete. Additionally, the effect of adding ultrafine metakaolin on the workability is better than that of adding silica fume. Adding ultrafine metakaolin or silica fume can effectively increase the compressive strength, splitting tensile strength, resistance to chloride ion penetration and freeze–thaw properties of concrete due to improved pore structure. The sulphate attack resistance of mortar can be improved more obviously by simultaneously adding ultrafine metakaolin and prolonging the initial moisture curing time. [ABSTRACT FROM AUTHOR]

Simple Summary: In order to evaluate the potential application of genomic selection (GS) for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. We found the predictive accuracies to be low when the reference and validation populations were sampled from different breeds. When using multiple breeds for the reference population, the predictive accuracies were higher if the reference was comprised of breeds with close relationships. In addition, the accuracy increased in all scenarios when the heritability increased, and the genetic architecture of the QTL can affect genomic prediction. Our study suggested that the application of meta-populations can increase accuracy in scenarios with a reduced size of reference populations. Genomic selection (GS) has been widely considered as a valuable strategy for enhancing the rate of genetic gain in farm animals. However, the construction of a large reference population is a big challenge for small populations like indigenous cattle. In order to evaluate the potential application of GS for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. Also, we examined the effect of different genetic architecture on prediction accuracy. In this study, we simulated a set of genotype data by a resampling approach which can reflect the realistic linkage disequilibrium pattern for multiple populations. We found within-breed evaluations yielded the highest accuracies ranged from 0.64 to 0.68 for four different simulated genetic architectures. For scenarios using multiple breeds as reference, the predictive accuracies were higher when the reference was comprised of breeds with a close relationship, while the accuracies were low when prediction were carried out among breeds. In addition, the accuracy increased in all scenarios with the heritability increased. Our results suggested that using meta-population as reference can increase accuracy of genomic predictions for small populations. Moreover, multi-breed genomic selection was feasible for Chinese indigenous populations with genetic relationships. [ABSTRACT FROM AUTHOR]

The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 min. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model. [ABSTRACT FROM AUTHOR]

Simple Summary: Analysis of the genomic regions under consideration can provide important insights into the genetic basis of complex traits. In this study, we utilized a high-density SNP (Single nucleotide polymorphism) array for analysis of genome selection signatures in Chinese Wagyu cattle. In total, 239 selected regions responding to 162 candidate genes were identified, which related to economic traits, including fatty acid, meat quality, growth and developmental traits. Moreover, we found that these selected genes were enriched for the four Gene Ontology (GO) terms of biological regulation and metabolic process. These results also contribute to the understanding of genetic basis of these traits during the formation of this population. Selective breeding can lead to genetic diversity and diverse phenotypes in farm animals. Analysis of the genomic regions under selection can provide important insights into the genetic basis of complex traits. In this study, a high-density SNP array was used for analysis of genome selection signatures in Chinese Wagyu cattle. In total, we obtained 478,903 SNPs and 24,820 no-overlap regions for |iHS| (integrated haplotype score) estimations. Under the threshold of the top 1%, 239 regions were finally identified as candidate selected regions and 162 candidate genes were found based on the UMD3.1 genome assembly. These genes were reported to be associated with fatty acids, such as Bos taurus nitric oxide synthase 1 adaptor protein (NOS1AP), Bos taurus hydroxysteroid 17-beta dehydrogenase 7 (HSD17B7), Bos taurus WD repeat domain 7 (WDR7), Bos taurus ELOVL fatty acid elongase 2 (ELOVL2), Bos taurus calpain 1 (CAPN1), Bos taurus parkin RBR E3 ubiquitin protein ligase (PRKN, also known as PARK2), Bos taurus mitogen-activated protein kinase kinase 6 (MAP2K6), meat quality, including Bos taurus ADAM metallopeptidase domain 12 (ADAM12), Bos taurus 5′-aminolevulinate synthase 1 (ALAS1), Bos taurus small integral membrane protein 13 (SMIM13) and Bos taurus potassium two pore domain channel subfamily K member 2 (KCNK2), growth, and developmental traits, such as Bos taurus insulin like growth factor 2 receptor (IGF2R), Bos taurus RAR related orphan receptor A (RORA), Bos taurus fibroblast growth factor 14 (FGF14), Bos taurus paired box 6 (PAX6) and Bos taurus LIM homeobox 6 (LHX6). In addition, we identified several genes that are associated with body size and weight, including Bos taurus sorting nexin 29 (SNX29), Bos taurus zinc finger imprinted 2 (ZIM2), Bos taurus family with sequence similarity 110 member A (FAM110A), immune system, including Bos taurus toll like receptor 9 (TLR9), Bos taurus TAFA chemokine like family member 1 (TAFA1), Bos taurus glutathione peroxidase 8 (putative) (GPX8), Bos taurus interleukin 5 (IL5), Bos taurus PR domain containing 9 (PRDM9), Bos taurus glutamate ionotropic receptor kainate type subunit 2 (GRIK2) and feed intake efficiency, Bos taurus sodium voltage-gated channel alpha subunit 9 (SCN9A), Bos taurus relaxin family peptide/INSL5 receptor 4 (RXFP4), Bos taurus RNA polymerase II associated protein 3 (RPAP3). Moreover, four GO terms of biological regulation (GO:0009987, GO:0008152) and metabolic process (GO:0003824, GO:0005488) were found based on these genes. In addition, we found that 232 candidate regions (~18 Mb) overlapped with the Quantitative trait loci (QTL)regions extracted from cattle QTLdb. Our findings imply that many genes were selected for important traits in Chinese Wagyu cattle. Moreover, these results can contribute to the understanding of the genetic basis of the studied traits during the formation of this population. [ABSTRACT FROM AUTHOR]

Simple Summary: Evaluation of the population structure and linkage disequilibrium can offer important insights to fully understand the genetic diversity and population history of cattle, which can enable us to appropriately design and implement GWAS and GS in cattle. In this study, we characterized the extent of genome-wide LD and the haplotype block structure, and estimated the persistence of phase of Chinese indigenous cattle with Illumina BovineHD BeadChip. According to our study, 58K, 87K, 95K, 52K, and 52K markers would be necessary for SCHC, NCC, SWC, SIM, and WAG, respectively, in the implementation of GWAS and GS and combining a multipopulation with high persistence of phase is feasible for the implication of genomic selection for Chinese beef cattle. Understanding the linkage disequilibrium (LD) across the genome, haplotype structure, and persistence of phase between breeds can enable us to appropriately design and implement the genome-wide association (GWAS) and genomic selection (GS) in beef cattle. We estimated the extent of genome-wide LD, haplotype block structure, and the persistence of phase in 10 Chinese cattle population using high density BovinHD BeadChip. The overall LD measured by r2 between adjacent SNPs were 0.60, 0.67, 0.58, 0.73, and 0.71 for South Chinese cattle (SCHC), North Chinese cattle (NCC), Southwest Chinese cattle (SWC), Simmental (SIM), and Wagyu (WAG). The highest correlation (0.53) for persistence of phase across groups was observed for SCHC vs. SWC at distances of 0–50 kb, while the lowest correlation was 0.13 for SIM vs. SCHC at the same distances. In addition, the estimated current effective population sizes were 27, 14, 31, 34, and 43 for SCHC, NCC, SWC, SIM, and WAG, respectively. Our result showed that 58K, 87K, 95K, 52K, and 52K markers were required for implementation of GWAS and GS in SCHC, NCC, SWC, SIM, and WAG, respectively. Also, our findings suggested that the implication of genomic selection for multipopulation with high persistence of phase is feasible for Chinese cattle. [ABSTRACT FROM AUTHOR]

Recently, vehicular networks have emerged to facilitate intelligent transportation systems (ITS). They enable vehicles to communicate with each other in order to provide various services such as traffic safety, autonomous driving, and entertainments. The vehicle-to-vehicle (V2V) communication channel is doubly selective, where the channel changes within the transmission bandwidth and the frame duration. This necessitates robust algorithms to provide reliable V2V communications. In this paper, we propose a scheme that provides joint adaptive modulation, coding and payload length selection (AMCPLS) for V2V communications. Our AMCPLS scheme selects both the modulation and coding scheme (MCS) and the payload length of transmission frames for V2V communication links, according to the V2V channel condition. Our aim is to achieve both reliability and spectrum efficiency. Our proposed AMCPLS scheme improves the V2V effective throughput performance while satisfying a predefined frame error rate (FER). Furthermore, we present a deep learning approach that exploits deep convolutional neural networks (DCNN) for implementing the proposed AMCPLS. Simulation results reveal that the proposed DCNN-based AMCPLS approach outperforms other competing machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM) in terms of FER, effective throughput, and prediction time. [ABSTRACT FROM AUTHOR]

Heat shock 70 kDa protein 8 (HSPA8) and erb-b2 receptor tyrosine kinase 2 (ERBB2) were the promising candidates for milk protein concentration in dairy cattle revealed through previous RNA sequencing (RNA-Seq) study. The objective of this post-RNA-Seq study was to confirm genetic effects of HSPA8 and ERBB2 on milk protein concentration in a large Chinese Holstein population and to evaluate the genetic effects of both genes on other milk production traits. There were 2 single-nucleotide polymorphisms (SNPs) identified for HSPA8 and 11 SNPs for ERBB2 by sequencing 17 unrelated Chinese Holstein sires. The SNP-rs136632043 in HSPA8 had significant associations with all five milk production traits (p = 0.0086 to p < 0.0001), whereas SNP-rs132976221 was remarkably associated with three yield traits (p < 0.0001). Nine (ss1996900615, rs109017161, rs109122971, ss1996900614, rs110133654, rs109941438, rs110552983, rs133031530, and rs109763505) of 11 SNPs in ERBB2 were significantly associated with milk protein percentage (p = 0.0177 to p < 0.0001). A 12 Kb haplotype block was formed in ERBB2 and haplotype associations revealed similar effects on milk protein traits. Our findings confirmed the significant genetic effects of HSPA8 and ERBB2 on milk protein concentration and other milk production traits and SNP phenotypic variances above 1% may serve as genetic markers in dairy cattle breeding programs. [ABSTRACT FROM AUTHOR]

While undergoing structural deformation, DNA experiences changes in the interactions between its internal base pairs, presenting challenges to conventional elastic methods. To address this, we propose the Discrete Critical State (DCS) model in this paper. This model combines surface discrete frame theory with gauge theory and Landau phase transition theory to investigate DNA's structural deformation, phase transitions, and chirality. Notably, the DCS model considers both the internal interactions within DNA and formulates an overall equation using unified physical and geometric parameters. By employing the discrete frame, we derive the evolution of physical quantities along the helical axis of DNA, including geodesic curvature, geodesic torsion, and others. Our findings indicate that B-DNA has a significantly lower free energy density compared to Z-DNA, which is in agreement with experimental observations. This research reveals that the direction of base pairs is primarily governed by the geodesic curve within the helical plane, aligning closely with the orientation of the base pairs. Moreover, the geodesic curve has a profound influence on the arrangement of base pairs at the microscopic level and effectively regulates the configuration and geometry of DNA through macroscopic-level free energy considerations.