A new iterative algorithm for geolocating a known altitude target using TDOA and FDOA measurements in the presence of satellite location uncertainty

AbstractThis paper considers the problem of geolocating a target on the Earth surface whose altitude is known previously using the target signal time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained at satellites. The number of satellites available for the geolocation task is more than sufficient and their locations are subject to random errors. This paper derives the constrained Cramér-Rao lower bound (CCRLB) of the target position, and on the basis of the CCRLB analysis, an approximately efficient constrained maximum likelihood estimator (CMLE) for geolocating the target is established. A new iterative algorithm for solving the CMLE is then proposed, where the updated target position estimate is shown to be the globally optimal solution to a generalized trust region sub-problem (GTRS) which can be found via a simple bisection search. First-order mean square error (MSE) analysis is conducted to quantify the performance degradation when the known target altitude is assumed to be precise but indeed has an unknown but deterministic error. Computer simulations are used to compare the performance of the proposed iterative geolocation technique with those of two benchmark algorithms. They verify the approximate efficiency of the proposed algorithm and the validity of the MSE analysis

Controllability, Reachability, and Stabilizability of Finite Automata: A Controllability Matrix Method

This paper investigates the controllability, reachability, and stabilizability of finite automata by using the semitensor product of matrices. Firstly, by expressing the states, inputs, and outputs as vector forms, an algebraic form is obtained for finite automata. Secondly, based on the algebraic form, a controllability matrix is constructed for finite automata. Thirdly, some necessary and sufficient conditions are presented for the controllability, reachability, and stabilizability of finite automata by using the controllability matrix. Finally, an illustrative example is given to support the obtained new results

Shared genetics and causal relationships between major depressive disorder and COVID-19 related traits: a large-scale genome-wide cross-trait meta-analysis

IntroductionThe comorbidity between major depressive disorder (MDD) and coronavirus disease of 2019 (COVID-19) related traits have long been identified in clinical settings, but their shared genetic foundation and causal relationships are unknown. Here, we investigated the genetic mechanisms behind COVID-19 related traits and MDD using the cross-trait meta-analysis, and evaluated the underlying causal relationships between MDD and 3 different COVID-19 outcomes (severe COVID-19, hospitalized COVID-19, and COVID-19 infection).MethodsIn this study, we conducted a comprehensive analysis using the most up-to-date and publicly available GWAS summary statistics to explore shared genetic etiology and the causality between MDD and COVID-19 outcomes. We first used genome-wide cross-trait meta-analysis to identify the pleiotropic genomic SNPs and the genes shared by MDD and COVID-19 outcomes, and then explore the potential bidirectional causal relationships between MDD and COVID-19 outcomes by implementing a bidirectional MR study design. We further conducted functional annotations analyses to obtain biological insight for shared genes from the results of cross-trait meta-analysis.ResultsWe have identified 71 SNPs located on 25 different genes are shared between MDD and COVID-19 outcomes. We have also found that genetic liability to MDD is a causal factor for COVID-19 outcomes. In particular, we found that MDD has causal effect on severe COVID-19 (OR = 1.832, 95% CI = 1.037–3.236) and hospitalized COVID-19 (OR = 1.412, 95% CI = 1.021–1.953). Functional analysis suggested that the shared genes are enriched in Cushing syndrome, neuroactive ligand-receptor interaction.DiscussionOur findings provide convincing evidence on shared genetic etiology and causal relationships between MDD and COVID-19 outcomes, which is crucial to prevention, and therapeutic treatment of MDD and COVID-19

Short-chain fatty acids in breast milk and their relationship with the infant gut microbiota

IntroductionThe short-chain fatty acids (SCFAs) contained in breast milk play a key role in infant growth, affecting metabolism and enhancing intestinal immunity by regulating inflammation.MethodsIn order to examine the associations between the microbiota and SCFA levels in breast milk, and explore the roles of SCFAs in regulating the infant gut microbiota, we enrolled 50 paired mothers and infants and collected both breast milk and infant fecal samples. Breast milk SCFA contents were determined by UPLC-MS, and whole genome shotgun sequencing was applied to determine the microbial composition of breast milk and infant feces. The SCFA levels in breast milk were grouped into tertiles as high, medium, or low, and the differences of intestinal microbiota and KEGG pathways were compared among groups.ResultsThe results demonstrated that breast milk butyric acid (C4) is significantly associated with Clostridium leptum richness in breastmilk. Additionally, the specific Bifidobacterium may have an interactive symbiosis with the main species of C4-producing bacteria in human milk. Women with a low breast milk C4 tertile are associated with a high abundance of Salmonella and Salmonella enterica in their infants' feces. KEGG pathway analysis further showed that the content of C4 in breast milk is significantly correlated with the infants' metabolic pathways of lysine and arginine biosynthesis.DiscussionThis study suggests that interactive symbiosis of the microbiota exists in breast milk. Certain breast milk microbes could be beneficial by producing C4 and further influence the abundance of certain gut microbes in infants, playing an important role in early immune and metabolic development

The reporting quality of randomized controlled trials in Chinese herbal medicine (CHM) formulas for diabetes based on the consort statement and its extension for CHM formulas

Background: This study aimed to assess the overall reporting quality of randomized controlled trials (RCTs) in Chinese herbal medicine (CHM) formulas for patients with diabetes, and to identify factors associated with better reporting quality.Methods: Four databases including PubMed, Embase, Cochrane Library and Web of Science were systematically searched from their inception to December 2022. The reporting quality was assessed based on the Consolidated Standards of Reporting Trials (CONSORT) statement and its CHM formula extension. The overall CONSORT and its CHM formula extension scores were calculated and expressed as proportions separately. We also analyzed the pre-specified study characteristics and performed exploratory regressions to determine their associations with the reporting quality.Results: Seventy-two RCTs were included. Overall reporting quality (mean adherence) were 53.56% and 45.71% on the CONSORT statement and its CHM formula extension, respectively. The strongest associations with reporting quality based on the CONSORT statement were multiple centers and larger author numbers. Compliance with the CHM formula extension, particularly regarding the disclosure of the targeted traditional Chinese medicine (TCM) pattern (s), was generally insufficient.Conclusion: The reporting quality of RCTs in CHM formulas for diabetes remains unsatisfactory, and the adherence to the CHM formula extension is even poorer. In order to ensure transparent and standardized reporting of RCTs, it is essential to advocate for or even mandate adherence of the CONSORT statement and its CHM formula extension when reporting trials in CHM formulas for diabetes by both authors and editors

Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms

Chimpanzee reservoirs of pandemic and nonpandemic HIV-1

Human immunodeficiency virus type 1 (HIV-1), the cause of human acquired immunodeficiency syndrome ( AIDS), is a zoonotic infection of staggering proportions and social impact. Yet uncertainty persists regarding its natural reservoir. The virus most closely related to HIV-1 is a simian immunodeficiency virus ( SIV) thus far identified only in captive members of the chimpanzee subspecies Pan troglodytes troglodytes. Here we report the detection of SIVcpz antibodies and nucleic acids in fecal samples from wild-living P.t. troglodytes apes in southern Cameroon, where prevalence rates in some communities reached 29 to 35%. By sequence analysis of endemic SIVcpz strains, we could trace the origins of pandemic ( group M) and nonpandemic ( group N) HIV-1 to distinct, geographically isolated chimpanzee communities. These findings establish P. t. troglodytes as a natural reservoir of HIV-1

Milk fat globule membrane promotes brain development in piglets by enhancing the connection of white matter fiber trace

IntroductionBrain development during infancy is crucial for later health and development. Although Milk Fat Globule Membrane (MFGM) has been demonstrated to enhance brain development, further investigation is needed to determine the optimal dose.MethodsIn this study, 80 piglets aged 2 days were randomly assigned to four groups: Control group, MFGM-L (1.74 g MFGM per 100 g diet), MFGM-M (4.64 g MFGM per 100 g diet), and MFGM-H (6.09 g MFGM per 100 g diet). Daily body weight and milk intake of the piglets were recorded until 31 days postnatal. Learning and memory abilities were evaluated using the spatial T-maze test on day 15. MRI analysis was conducted to assess functional and structural changes in brain tissues. Additionally, mRNA and protein expression of brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NTF-3) in the hippocampus and prefrontal cortex were evaluated.ResultsThe results indicated that the MFGM supplemented diet significantly improved the accuracy of the piglets in the T-maze test, with the MFGM-L group exhibiting the best performance. MRI showed no volumetric differences in the gray and white matter between the groups. However, the fractional anisotropy in the left and right hippocampus of piglets in the MFGM-L group was significantly higher than in the other three groups. Furthermore, there was a strong correlation between the accuracy of the T-maze test and hippocampal fractional anisotropy.DiscussionThe MFGM supplemented diet also increased the expression of BDNF in the cerebral cortex. However, the changes in BDNF were not consistent with the results of the T-maze test. In conclusion, adding 1.74 g MFGM per 100 g diet can significantly improve neonatal piglets’ learning and memory abilities, potentially by enhancing the connection of white matter fiber bundles in the brain