Construction of a high-density genetic map and QTL localization of body weight and wool production related traits in Alpine Merino sheep based on WGR.

BACKGROUND: The Alpine Merino is a new breed of fine-wool sheep adapted to the cold and arid climate of the plateau in the world. It has been popularized in Northwest China due to its superior adaptability as well as excellent production performance. Those traits related to body weight, wool yield, and wool fiber characteristics, which are economically essential traits in Alpine Merino sheep, are controlled by QTL (Quantitative Trait Loci). Therefore, the identification of QTL and genetic markers for these key economic traits is a critical step in establishing a MAS (Marker-Assisted Selection) breeding program. RESULTS: In this study, we constructed the high-density genetic linkage map of Alpine Merino sheep by sequencing 110 F1 generation individuals using WGR (Whole Genome Resequencing) technology. 14,942 SNPs (Single Nucleotide Polymorphism) were identified and genotyped. The map spanned 2,697.86 cM, with an average genetic marker interval of 1.44 cM. A total of 1,871 high-quality SNP markers were distributed across 27 linkage groups, with an average of 69 markers per LG (Linkage Group). Among them, the smallest genetic distance is 19.62 cM for LG2, while the largest is 237.19 cM for LG19. The average genetic distance between markers in LGs ranged from 0.24 cM (LG2) to 3.57 cM (LG17). The marker density in the LGs ranged from LG14 (39 markers) to LG1 (150 markers). CONCLUSIONS: The first genetic map of Alpine Merino sheep we constructed included 14,942 SNPs, while 46 QTLs associated with body weight, wool yield and wool fiber traits were identified, laying the foundation for genetic studies and molecular marker-assisted breeding. Notably, there were QTL intervals for overlapping traits on LG4 and LG8, providing potential opportunities for multi-trait co-breeding and further theoretical support for selection and breeding of ultra-fine and meaty Alpine Merino sheep.

Selection signatures of wool color in Gangba sheep revealed by genome-wide SNP discovery.

BACKGROUND: Gangba sheep as a famous breed of Tibetan sheep, its wool color is mainly white and black. Gangba wool is economically important as a high-quality raw material for Tibetan blankets and Tibetan serge. However, relatively few studies have been conducted on the wool color of Tibetan sheep. RESULTS: To fill this research gap, this study conducted an in-depth analysis of two populations of Gangba sheep (black and white wool color) using whole genome resequencing to identify genetic variation associated with wool color. Utilizing PCA, Genetic Admixture, and N-J Tree analyses, the present study revealed a consistent genetic relationship and structure between black and white wool colored Gangba sheep populations, which is consistent with their breed history. Analysis of selection signatures using multiple methods (FST, π ratio, Tajima's D), 370 candidate genes were screened in the black wool group (GBB vs GBW); among them, MC1R, MLPH, SPIRE2, RAB17, SMARCA4, IRF4, CAV1, USP7, TP53, MYO6, MITF, MC2R, TET2, NF1, JAK1, GABRR1 genes are mainly associated with melanin synthesis, melanin delivery, and distribution. The enrichment results of the candidate genes identified 35 GO entries and 19 KEGG pathways associated with the formation of the black phenotype. 311 candidate genes were screened in the white wool group (GBW vs GBB); among them, REST, POU2F1, ADCY10, CCNB1, EP300, BRD4, GLI3, and SDHA genes were mainly associated with interfering with the differentiation of neural crest cells into melanocytes, affecting the proliferation of melanocytes, and inhibiting melanin synthesis. 31 GO entries and 22 KEGG pathways were associated with the formation of the white phenotype. CONCLUSIONS: This study provides important information for understanding the genetic mechanism of wool color in Gangba, and provides genetic knowledge for improving and optimizing the wool color of Tibetan sheep. Genetic improvement and selective breeding to produce wool of specific colors can meet the demand for a diversity of wool products in the Tibetan wool textile market.

Rapid Mining of Fast Ion Conductors via Subgraph Isomorphism Matching.

The rapidly evolving field of inorganic solid-state electrolytes (ISSEs) has been driven in recent years by advances in data-mining techniques, which facilitates the high-throughput computational screening for candidate materials in the databases. The key to the mining process is the selection of critical features that underline the similarity of a material to an existing ISSE. Unfortunately, this selection is generally subjective and frequently under debate. Here we propose a subgraph isomorphism matching method that allows an objective evaluation of the similarity between two compounds according to the topology of the local atomic environment. The matching algorithm has been applied to discover four structure types that are highly analogous to the LiTi2(PO4)3 NASICON prototype. We demonstrate that the local atomic environments similar to LiTi2(PO4)3 endow these four structures with favorable Li diffusion tunnels and ionic conductivity on par with those of the prototype. By further taking into account the electronic structure and electrochemical stability window, 13 compounds are identified to be potential ISSEs. Our findings not only offer a promising approach toward rapid mining of fast ion conductors without limitation in the compositional range but also reveal insights into the design of ISSEs according to the topology of their framework structures.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning.

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.

Halide Anions Tuning of Lead-Free Perovskite-Type Ferroelectric Semiconductors with Inverse High-Temperature Symmetry Breaking.

There is a noticeable gap in the literature regarding research on halogen-substitution-regulated ferroelectric semiconductors featuring multiple phase transitions. Here, a new category of 1D perovskite ferroelectrics (DFP)2SbX5 (DFP+ = 3,3-difluoropyrrolidium, X- = I-, Br-, abbreviated as I-1 and Br-2) with twophase transitions (PTs) is reported. The first low-temperature PT is a mmmFmm2 ferroelectric PT, while the high-temperature PT is a counterintuitive inverse temperature symmetry-breaking PT. By the substitution of iodine with bromine, the Curie temperature (Tc) significantly increases from 348 K of I-1 to 374 K of Br-2. Their ferroelectricity and pyroelectricity are improved (Ps value from 1.3 to 4.0 µC cm-2, pe value from 0.2 to 0.48 µC cm-2 K-1 for I-1 and Br-2), while their optical bandgaps increased from 2.1 to 2.7 eV. A critical slowing down phenomenon is observed in the dielectric measurement of I-1 while Br-2 exhibits the ferroelastic domain. Structural and computational analyses elucidate that the order-disorder movement of cations and the distortion of the chain perovskite [SbX5]2- anions skeleton lead to PT. The semiconductor properties are determined by [SbX5]2- anions. The findings contribute to the development of ferroelectric semiconductors and materials with multiple PTs and provide materials for potential applications in the optoelectronic field.

Event recognition method based on feature synthesizing for a zero-shot intelligent distributed optical fiber sensor.

Phase-sensitive optical time domain reflectometer (Φ-OTDR) is an emergent distributed optical sensing system with the advantages of high localization accuracy and high sensitivity. It has been widely used for intrusion identification, pipeline monitoring, under-ground tunnel monitoring, etc. Deep learning-based classification methods work well for Φ-OTDR event recognition tasks with sufficient samples. However, the lack of training data samples is sometimes a serious problem for these data-driven algorithms. This paper proposes a novel feature synthesizing approach to solve this problem. A mixed class approach and a reinforcement learning-based guided training method are proposed to realize high-quality feature synthesis. Experiment results in the task of eight event classifications, including one unknown class, show that the proposed method can achieve an average classification accuracy of 42% for the unknown class and obtain its event type, meanwhile achieving a 74% average overall classification accuracy. This is 29% and 7% higher, respectively, than those of the ordinary instance synthesizing method. Moreover, this is the first time that the Φ-OTDR system can recognize a specific event and tell its event type without collecting its data sample in advance.

Suppression of DNMT2/3 by proinflammatory cytokines inhibits CtBP1/2-dependent genes to promote the occurrence of atrophic nonunion.

Failure of bone healing after fracture often results in nonunion, but the underlying mechanism of nonunion pathogenesis is poorly understood. Herein, we provide evidence to clarify that the inflammatory microenvironment of atrophic nonunion (AN) mice suppresses the expression levels of DNA methyltransferases 2 (DNMT2) and 3A (DNMT3a), preventing the methylation of CpG islands on the promoters of C-terminal binding protein 1/2 (CtBP1/2) and resulting in their overexpression. Increased CtBP1/2 acts as transcriptional corepressors that, along with histone acetyltransferase p300 and Runt-related transcription factor 2 (Runx2), suppress the expression levels of six genes involved in bone healing: BGLAP (bone gamma-carboxyglutamate protein), ALPL (alkaline phosphatase), SPP1 (secreted phosphoprotein 1), COL1A1 (collagen 1a1), IBSP (integrin binding sialoprotein), and MMP13 (matrix metallopeptidase 13). We also observe a similar phenomenon in osteoblast cells treated with proinflammatory cytokines or treated with a DNMT inhibitor (5-azacytidine). Forced expression of DNMT2/3a or blockage of CtBP1/2 with their inhibitors can reverse the expression levels of BGLAP/ALPL/SPP1/COL1A1/IBSP/MMP13 in the presence of proinflammatory cytokines. Administration of CtBP1/2 inhibitors in fractured mice can prevent the incidence of AN. Thus, we demonstrate that the downregulation of bone healing genes dependent on proinflammatory cytokines/DNMT2/3a/CtBP1/2-p300-Runx2 axis signaling plays a critical role in the pathogenesis of AN. Disruption of this signaling may represent a new therapeutic strategy to prevent AN incidence after bone fracture.

Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning.

OBJECTIVE: To develop and validate a risk scoring scale model (RSSM) for stratifying prognostic risk after intra-arterial therapies (IATs) for hepatocellular carcinoma (HCC). METHODS: Between February 2014 and October 2022, 2338 patients with HCC who underwent initial IATs were consecutively enrolled. These patients were divided into training datasets (TD, n = 1700), internal validation datasets (ITD, n = 428), and external validation datasets (ETD, n = 200). Five-years death was used to predict outcome. Thirty-four clinical information were input and five supervised machine learning (ML) algorithms, including eXtreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LGBT), and Random Forest (RF), were compared using the areas under the receiver operating characteristic (AUC) with DeLong test. The variables with top important ML scores were used to build the RSSM by stepwise Cox regression. RESULTS: The CatBoost model achieved the best discrimination when 12 top variables were input, with the AUC of 0.851 (95% confidence intervals (CI), 0.833-0.868) for TD, 0.817 (95%CI, 0.759-0.857) for ITD, and 0.791 (95%CI, 0.748-0.834) for ETD. The RSSM was developed based on the immune checkpoint inhibitors (ICI) (hazard ratios (HR), 0.678; 95%CI 0.549, 0.837), tyrosine kinase inhibitors (TKI) (HR, 0.702; 95%CI 0.605, 0.814), local therapy (HR, 0.104; 95%CI 0.014, 0.747), response to the first IAT (HR, 4.221; 95%CI 2.229, 7.994), tumor size (HR, 1.054; 95%CI 1.038, 1.070), and BCLC grade (HR, 2.375; 95%CI 1.950, 2.894). Kaplan-Meier analysis confirmed the role of RSSM in risk stratification (p < 0.001). CONCLUSIONS: The RSSM can stratify accurately prognostic risk for HCC patients received IAT. On the basis, an online calculator permits easy implementation of this model. CLINICAL RELEVANCE STATEMENT: The risk scoring scale model could be easily implemented for physicians to stratify risk and predict prognosis quickly and accurately, thereby serving as a more favorable tool to strengthen individualized intra-arterial therapies and management in patients with unresectable hepatocellular carcinoma. KEY POINTS: • The Categorical Gradient Boosting (CatBoost) algorithm achieved the optimal and robust predictive ability (AUC, 0.851 (95%CI, 0.833-0.868) in training datasets, 0.817 (95%CI, 0.759-0.857) in internal validation datasets, and 0.791 (95%CI, 0.748-0.834) in external validation datasets) for prediction of 5-years death of hepatocellular carcinoma (HCC) after intra-arterial therapies (IATs) among five machine learning models. • We used the SHapley Additive exPlanations algorithms to explain the CatBoost model so as to resolve the black boxes of machine learning principles. • A simpler restricted variable, risk scoring scale model (RSSM), derived by stepwise Cox regression for risk stratification after intra-arterial therapies for hepatocellular carcinoma, provides the potential forewarning to adopt combination strategies for high-risk patients.

Differences between Chronically Hepatitis B Virus-Infected Pregnant Women with and without Intrafamilial Infection: From Viral Gene Sequences to Clinical Manifestations.

INTRODUCTION: This study aimed to investigate the differences between pregnant women with chronic hepatitis B virus (HBV) infection and intrafamilial infection and those without intrafamilial infection. METHODS: HBV-DNA was extracted from the sera of 16 pregnant women with chronic hepatitis B (CHB) and their family members for gene sequencing and phylogenetic analyses. A total of 74 pregnant women with CHB were followed up from the second trimester to 3 months postpartum. Viral markers and other laboratory indicators were compared between pregnant women with CHB with and without intrafamilial infection. RESULTS: The phylogenetic tree showed that HBV lines in the mother-spread pedigree shared a node, whereas there was an unrelated genetic background for HBV lines in individuals without intrafamilial infection. From delivery to 3 months postpartum, compared with those without intrafamilial infection, pregnant women with intrafamilial infection were related negatively to HBV-DNA (ß = -0.43, 95% confidence interval [CI]: -0.76 to -0.12, p = 0.009), HBeAg (ß = -195.15, 95% CI: -366.35 to -23.96, p = 0.027), and hemoglobin changes (ß = -8.09, 95% CI: -15.54 to -0.64, p = 0.035) and positively to changes in the levels of alanine aminotransferase (ß = 73.9, 95% CI: 38.92-108.95, p < 0.001) and albumin (ß = 2.73, 95% CI: 0.23-5.23, p = 0.033). CONCLUSION: The mother-spread pedigree spread model differs from that of non-intrafamilial infections. Pregnant women with intrafamilial HBV infection have less hepatitis flares and liver damage, but their HBV-DNA and HBeAg levels rebound faster after delivery, than those without intrafamilial infection by the virus.

Genetic dissection and identification of stripe rust resistance genes in the wheat cultivar Lanhangxuan 121, a cultivar selected from a space mutation population.

Stripe rust is a devastating disease of wheat worldwide. Chinese wheat cultivar Lanhangxuan 121 (LHX121), selected from an advanced line L92-47 population that had been subjected to space mutation breeding displayed a consistently higher level of resistance to stipe rust than its parent in multiple field environments. The aim of this research was to establish the number and types of resistance genes in parental lines L92-47 and LHX121 using separate segregating populations. The first population developed from a cross between LHX121 and susceptible cultivar Xinong 822 comprised 278 F2:3 lines. The second validation population comprised 301 F2:3 lines from a cross between L92-47 and susceptible cultivar Xinong 979. Lines of two population were evaluated for stripe rust response at three sites during the 2018-2020 cropping season. Affymetrix 660 K SNP arrays were used to genotype the lines and parents. Inclusive composite interval mapping detected QTL QYrLHX.nwafu-2BS, QYrLHX.nwafu-3BS, and QYrLHX.nwafu-5BS for resistance in all three environments. Based on previous studies and pedigree information, QYrLHX.nwafu-2BS and QYrLHX.nwafu-3BS were likely to be Yr27 and Yr30 that are present in the L92-47 parent. QYrLHX.nwafu-5BS (YrL121) detected only in LHX121 was mapped to a 7.60 cM interval and explained 10.67-22.57% of the phenotypic variation. Compared to stripe rust resistance genes previously mapped to chromosome 5B, YrL121 might be a new adult plant resistance QTL. Furthermore, there were a number of variations signals using 35 K SNP array and differentially expressed genes using RNA-seq between L92-47 and LHX121 in the YrL121 region, indicating that they probably impair the presence and/or function of YrL121. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01461-0.

Mapping seedling and adult plant leaf rust resistance genes in the durum wheat cultivar Strongfield and other Triticum turgidum (L.) lines.

Durum wheat (T. turgidum L.) is threatened by the appearance of new virulent races of leaf rust, caused by Puccinia triticina, in recent years. This study was conducted to determine the leaf rust resistance in a modern Canadian durum cultivar Strongfield. Six populations derived from crosses of Strongfield with six tetraploid wheat lines, respectively, were tested at seedling plant stage with different P. triticina races. Two of the populations were evaluated for adult plant leaf rust infection in Canada and Mexico. A stepwise regression joint linkage QTL mapping and analysis by MapQTL were performed. Strongfield contributed the majority of QTL detected, contributing seven QTL detected in field tests, and eight QTL conditioning seedling resistance. A 1B QTL, QLr-Spa-1B.1, from Strongfield had a significant effect in both Canadian and Mexican field tests, and corresponded with Lr46/Yr29. The remaining field QTL were found in only the Canadian or the Mexican environment, not both. The QTL from Strongfield on 3A, QLr-Spa-3A, conferred seedling resistance to all races tested and had a significant effect in the field in Canada. This is the first report of the QLr-Spa-3A and Lr46/Yr29 as key components of the genetic resistance in Canadian durum wheat. KASP markers were developed to detect the QLr-Spa-3A for use in marker assisted leaf rust resistance breeding. The susceptible parental lines contributed QTL on 1A, 2B and 5B that were effective in Mexican field tests that may be good targets to integrate into modern durum varieties to improve resistance to new durum virulent races.

Study on the correlation of C-reactive protein/albumin ratio with sudden sensorineural hearing loss complicated by hypertension: a prospective study.

BACKGROUND: Understanding the pathophysiology of sudden sensorineural hearing loss (SSNHL) and identifying its clinical symptoms and associated risk factors are crucial for doctors in order to create effective prevention and therapeutic methods for this prevalent otolaryngologic emergency. METHODS: This study focuses on investigating the correlation between the C-reactive protein/albumin ratio (CAR) and SSNHL complicated by hypertension. In this study, 120 patients diagnosed with SSNHL were divided into groups with and without hypertension, and propensity score matching was used to compare and analyze the severity, type, prognosis, and CAR levels in SSNHL. RESULTS: The results showed that the SSNHL group with hypertension had significantly higher CAR levels, age, hearing curve abnormalities, and more severe hearing loss compared to the control group with isolated SSNHL. These differences were statistically significant (p < 0.001). Among different subtypes of SSNHL, CAR levels increased progressively with the advancement of the condition, and these differences were also statistically significant (p < 0.001). CONCLUSION: In summary, in patients with SSNHL, those with hypertension had higher CAR levels than those without a history of hypertension, and they experienced more severe hearing loss. Moreover, there was a clear correlation between CAR levels and the extent of SSNHL, indicating that greater CAR levels in patients with SSNHL are connected to more severe hearing loss in various hearing patterns and perhaps indicative of a poorer prognosis.

Overexpression of YAP confers radioresistance to esophageal cancer by altering the tumor microenvironment.

This study aimed to investigate the role of yes-associated protein (YAP) in the radiotherapy sensitivity of esophageal squamous cell carcinoma (ESCC). The clonogenic ability of ESCC cells was reduced after YAP silencing and radiotherapy. Overexpression of YAP promoted cell survival and had a synergistic effect with the hypoxic microenvironment. YAP was found to directly regulate hypoxia-inducible factor 1α (HIF-1α). Bioinformatics analysis revealed the involvement of YAP in modulating the tumor immune microenvironment. Inhibition of YAP expression reduced myeloid-derived suppressor cells (MDSCs) and influenced the immunosuppressive state, leading to radio resistance. These findings provide insights into the YAP-HIF-1α interaction and support YAP as a potential target for enhancing radiotherapy sensitivity in esophageal cancer.

Single-cell transcriptome sequencing analysis reveals intra-tumor heterogeneity in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive system that poses a significant threat to human life and health. It is crucial to thoroughly investigate the mechanisms of esophageal carcinogenesis and identify potential key molecular events in its carcinogenesis. Single-cell transcriptome sequencing is an emerging technology that has gained prominence in recent years for studying molecular mechanisms, which may help to further explore the underlying mechanisms of the ESCC tumor microenvironment in depth. The single-cell dataset was obtained from GSE160269 in the Gene Expression Omnibus database, including 60 tumor samples and four paracancer samples. The single-cell data underwent dimensional reduction clustering analysis to identify clusters and annotate expression profiles. Subcluster analysis was conducted for each cellular taxon. Copy number variation analysis of tumor cell subpopulations was performed to primarily identify malignant cells within them. A proposed chronological analysis was performed to obtain the process of cell differentiation. In addition, cell communication, transcription factor analysis, and tumor pathway analysis were also performed. Relevant risk models and key genes were established by univariate COX regression and LASSO analysis. The key genes obtained from the screen were subjected to appropriate silencing and cellular assays, including CCK-8, 5-ethynyl-2'-deoxyuridine, colony formation, and western blot. Single-cell analysis revealed that normal samples contained a large number of fibroblasts, T cells, and B cells, with fewer other cell types, whereas tumor samples exhibited a relatively balanced distribution of cell types. Subclassification analysis of immune cells, fibroblasts, endothelial cells, and epithelial cells revealed their specific spatial characteristics. The prognostic risk model, we constructed successfully, achieved accurate prognostic stratification for ESCC patients. The screened key gene, UPF3A, was found to be significantly associated with the development of ESCC by cellular assays. This process might be linked to the phosphorylation of ERK and P38. Single-cell transcriptome analysis successfully revealed the distribution of cell types and major expressed factors in ESCC patients, which could facilitate future in-depth studies on the therapeutic mechanisms of ESCC.

Engineering an enzyme-like catalytic sensor for on-site dual-mode evaluation of total antioxidant capacity.

A novel Fe-MoOx nanozyme, engineered with enhanced peroxidase (POD)-like activity through strategic doping and the creation of oxygen vacancies, is introduced to catalyze the oxidation of TMB with high efficiency. Furthermore, Fe-MoOx is responsive to single electron transfer (SET) and hydrogen atom transfer (HAT) mechanisms related to antioxidants and can serve as a desirable nanozyme for total antioxidant capacity (TAC) determination. The TAC colorimetric platform can reach a low LOD of 0.512 µM in solution and 24.316 µM in the smartphone-mediated RGB hydrogel (AA as the standard). As proof of concept, the practical application in real samples was explored. The work paves a promising avenue to design diverse nanozymes for visual on-site inspection of food quality.

Real-Time Measurement and Uncertainty Evaluation of Optical Path Difference in Fiber Optic Interferometer Based on Auxiliary Interferometer.

Optical interferometers are the main elements of interferometric sensing and measurement systems. Measuring their optical path difference (OPD) in real time and evaluating the measurement uncertainty are key to optimizing system noise and ensuring system consistency. With the continuous sinusoidal wavelength modulation of the laser, real-time OPD measurement of the main interferometer is achieved through phase comparison of the main and auxiliary interferometers. The measurement uncertainty of the main interferometer OPD is evaluated. It is the first evaluation of the impact of different auxiliary interferometer calibration methods on OPD measurements. A homodyne quadrature laser interferometer (HQLI) is used as the main interferometer, and a 3 × 3 interferometer is used as the auxiliary interferometer. The calibration of the auxiliary interferometer using optical spectrum analyzer scanning and ruler measurement is compared. The evaluation shows that the auxiliary interferometer is the most significant source of uncertainty and causes the total uncertainty to increase linearly with increasing OPD. It is proven that a high-precision calibration and large-OPD auxiliary interferometer can improve the real-time accuracy of OPD measurements based on the auxiliary interferometer. The scheme can determine the minimum uncertainty to optimize the system noise and consistency for vibration, hydroacoustic, and magnetic field measurements with OPDs of the ~m level.

Moho Imaging with Fiber Borehole Strainmeters Based on Ambient Noise Autocorrelation.

Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu'an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations.

Strain and Temperature Sensing Based on Different Temperature Coefficients fs-FBG Arrays for Intelligent Buoyancy Materials.

The temperature and strain fields monitoring during the preparation process of buoyancy materials, as well as the health status after molding, are important for mastering the mechanical properties of buoyancy materials and ensuring the safety of operators and equipment. This paper proposes a short and high-density femtosecond fiber Bragg grating (fs-FBG) array based on different temperature coefficients fibers. By optimizing the parameters of femtosecond laser point-by-point writing technology, high-performance fs-FBG arrays with millimeter level gating length and millimeter level spatial resolution were prepared on two types of fibers. These were successfully embedded in buoyancy materials to achieve in-situ online monitoring of the curing process and after molding. The experimental results show that the fs-FBG array sensor has good anti-chirp performance and achieves online monitoring of millimeter-level spatial resolution. Intelligent buoyancy materials can provide real-time feedback on the health status of equipment in harsh underwater environments. The system can achieve temperature monitoring with an accuracy of 0.56 °C and deformation monitoring with sub-millimeter accuracy; the error is in the order of micrometers, which is of great significance in the field of deep-sea exploration.

Real-Time Direction Judgment System for Dual-Frequency Laser Interferometer.

Current real-time direction judgment systems are inaccurate and insensitive, as well as limited by the sampling rate of analog-to-digital converters. To address this problem, we propose a dynamic real-time direction judgment system based on an integral dual-frequency laser interferometer and field-programmable gate array technology. The optoelectronic signals resulting from the introduction of a phase subdivision method based on the amplitude resolution of the laser interferometer when measuring displacement are analyzed. The proposed system integrates the optoelectronic signals to increase the accuracy of its direction judgments and ensures these direction judgments are made in real time by dynamically controlling the integration time. Several experiments were conducted to verify the performance of the proposed system. The results show that, compared with current real-time direction judgment systems, the proposed system makes accurate judgements during low-speed motions and can update directions within 0.125 cycles of the phase difference change at different speeds. Moreover, a sweep frequency experiment confirmed the system's ability to effectively judge dynamic directions. The proposed system is capable of accurate and real-time directional judgment during low-speed movements of a table in motion.

Maternal supplementation with konjac glucomannan improves maternal microbiota for healthier offspring during lactation.

BACKGROUND: The maternal diet during gestation and lactation affects the health of the offspring. Konjac glucomannan (KGM) is a significantly functional polysaccharide in food research, possessing both antioxidant and prebiotic properties. However, the mechanisms of how KGM regulates maternal nutrition remain insufficient and limited. This study aimed to investigate maternal supplementation with KGM during late gestation and lactation to benefit both maternal and offspring generations. RESULTS: Our findings indicate that KGM improves serum low density lipoprotein cholesterol (LDL-C) and antioxidant capacity. Furthermore, the KGM group displayed a significant increase in the feed intake-related hormones neuropeptide tyrosine (NPY), Ghrelin, and adenosine monophosphate-activated kinase (AMPK) levels. KGM modified the relative abundance of Clostridium, Candidatus Saccharimonas, unclassified Firmicutes, and unclassified Christensenellaceae in sow feces. Acetate, valerate, and isobutyrate were also improved in the feces of sows in the KGM group. These are potential target bacterial genera that may modulate the host's health. Furthermore, Spearman's correlation analysis unveiled significant correlations between the altered bacteria genus and feed intake-related hormones. More importantly, KGM reduced interleukin-6 (IL-6) levels in milk, further improved IL-10 levels, and reduced zonulin levels in the serum of offspring. CONCLUSION: In conclusion, maternal dietary supplementation with KGM during late gestation and lactation improves maternal nutritional status by modifying maternal microbial and increasing lactation feed intake, which benefits the anti-inflammatory capacity of the offspring serum. © 2024 Society of Chemical Industry.