Low-Power and Wireless Communication Research on Underground Displacement Three-Dimensional Monitoring System.

Underground displacement monitoring is a crucial means of preventing geological disasters. Compared to existing one-dimensional methods (measuring only horizontal or vertical displacement), the underground displacement three-dimensional measurement method and monitoring system proposed by the author's research team can more accurately reflect the internal movement of rock and soil mass, thereby improving the timeliness and accuracy of geological disaster prediction. To ensure the reliability and long-term operation of the underground displacement three-dimensional monitoring system, this article further introduces low-power design theory and Bluetooth wireless transmission technology into the system. By optimizing the power consumption of each sensing unit, the current during the sleep period of a single sensing unit is reduced to only 0.09 mA. Dynamic power management technology is employed to minimize power consumption during each detection cycle. By using Bluetooth wireless transmission technology, the original wired communication of the system is upgraded to a relay-type wireless network communication, effectively solving the problem of the entire sensing array's operation being affected when a single sensing unit is damaged. These optimized designs not only maintain monitoring accuracy (horizontal and vertical displacement errors not exceeding 1 mm) but also enable the monitoring system to operate stably for an extended period under harsh weather conditions.

Significance of phosphate adsorbed on the cellular surface as a storage pool and its regulation in marine microalgae.

The increasing prevalence of phosphorus limitation in coastal waters has drawn attention to the bioavailability of cellular surface-adsorbed phosphorus (SP) as a reservoir of phosphorus in phytoplankton. This study examined the storage, utilization, and regulation of SP in the coastal waters of the East China Sea, as well as three cultivated algal bloom species (Skeletonema marinoi, Prorocentrum shikokuense, and Karenia mikimotoi) prevalent in the area. SP accounted for 14.3%-45.5% of particulate phosphorus in the field and laboratory species. After the depletion of external phosphate, the studied species can rapidly transport SP within 3-24 h. The storage of SP is regulated by both external phosphate conditions and the internal growth stage of cells, but it is not influenced by the various cellular surface structures of the studied species. This study highlights the significance of SP as a crucial phosphorus reservoir and the potential use of the SP level as an indicator of phosphorus deficiency in phytoplankton.

Robust Differential Abundance Analysis of Microbiome Sequencing Data.

It is well known that the microbiome data are ridden with outliers and have heavy distribution tails, but the impact of outliers and heavy-tailedness has yet to be examined systematically. This paper investigates the impact of outliers and heavy-tailedness on differential abundance analysis (DAA) using the linear models for the differential abundance analysis (LinDA) method and proposes effective strategies to mitigate their influence. The presence of outliers and heavy-tailedness can significantly decrease the power of LinDA. We investigate various techniques to address outliers and heavy-tailedness, including generalizing LinDA into a more flexible framework that allows for the use of robust regression and winsorizing the data before applying LinDA. Our extensive numerical experiments and real-data analyses demonstrate that robust Huber regression has overall the best performance in addressing outliers and heavy-tailedness.

A general framework for powerful confounder adjustment in omics association studies.

MOTIVATION: Genomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction. RESULTS: This study shows that the traditional approach is suboptimal and proposes a new two-dimensional false discovery rate control framework (2DFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2DFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2DFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. Promising finite sample performance is demonstrated via extensive simulations and real data applications. AVAILABILITY AND IMPLEMENTATION: R codes and vignettes are available at https://github.com/asmita112358/tdfdr.np.

Growth hormone-releasing hormone agonist attenuates vascular calcification in diabetic db/db mice.

Introduction: Vascular calcification (VC) is an independent risk factor for cardiovascular diseases. VC increases mortality of all-causes. VC is one of most common cardiovascular complications in type II diabetes. So far, no therapy has been proven to be effective in treatment of clinical VC. The present study investigated the therapeutic effects of MR409, an agonistic analog of growth hormone-releasing hormone (GHRH-A), on VC in diabetic db/db mice. Method and result: Diabetic mice were injected with MR409 subcutaneously every day for 8 weeks. Long-term treatment with MR409 improved serum lipid profile and endothelium-dependent relaxation to acetylcholine, and reduced vascular structural injury in diabetic mice without affecting serum growth hormone level. Echocardiography showed that calcium plaques present in heart valve of diabetic mice disappeared in diabetic mice after treatment with MR409. MR409 inhibited vascular calcium deposition associated with a marked reduction in the expressions of osteogenic-regulated alkaline phosphatase (ALP) and transcription osteogenic marker gene Runx2 in diabetic mice. MR409 also inhibited vascular reactive oxygen species (ROS) generation and upregulated the expressions of anti-calcifying protein Klotho in diabetic mice. Discussion: Our results demonstrate that GHRH-A MR409 can effectively attenuate VC and heart valve calcification, and protect against endothelial dysfunction and vascular injury in diabetic mice without significantly affecting pituitary-growth hormone axis. The mechanisms may involve upregulation of anti-calcifying protein Klotho and reduction in vascular ROS and the expression of redox sensitive osteogenic genes Runx2 and ALP. GHRH-A may represent a new pharmacological strategy for treatment of VC and diabetics associated cardiovascular complications.

Batch-effect correction with sample remeasurement in highly confounded case-control studies.

Batch effects are pervasive in biomedical studies. One approach to address the batch effects is repeatedly measuring a subset of samples in each batch. These remeasured samples are used to estimate and correct the batch effects. However, rigorous statistical methods for batch-effect correction with remeasured samples are severely underdeveloped. Here we developed a framework for batch-effect correction using remeasured samples in highly confounded case-control studies. We provided theoretical analyses of the proposed procedure, evaluated its power characteristics and provided a power calculation tool to aid in the study design. We found that the number of samples that need to be remeasured depends strongly on the between-batch correlation. When the correlation is high, remeasuring a small subset of samples is possible to rescue most of the power.

dICC: distance-based intraclass correlation coefficient for metagenomic reproducibility studies.

SUMMARY: Due to the sparsity and high dimensionality, microbiome data are routinely summarized into pairwise distances capturing the compositional differences. Many biological insights can be gained by analyzing the distance matrix in relation to some covariates. A microbiome sampling method that characterizes the inter-sample relationship more reproducibly is expected to yield higher statistical power. Traditionally, the intraclass correlation coefficient (ICC) has been used to quantify the degree of reproducibility for a univariate measurement using technical replicates. In this work, we extend the traditional ICC to distance measures and propose a distance-based ICC (dICC). We derive the asymptotic distribution of the sample-based dICC to facilitate statistical inference. We illustrate dICC using a real dataset from a metagenomic reproducibility study. AVAILABILITY AND IMPLEMENTATION: dICC is implemented in the R CRAN package GUniFrac. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LinDA: linear models for differential abundance analysis of microbiome compositional data.

Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control challenging. Here, we show that the compositional effects can be addressed by a simple, yet highly flexible and scalable, approach. The proposed method, LinDA, only requires fitting linear regression models on the centered log-ratio transformed data, and correcting the bias due to compositional effects. We show that LinDA enjoys asymptotic FDR control and can be extended to mixed-effect models for correlated microbiome data. Using simulations and real examples, we demonstrate the effectiveness of LinDA.

Synthesis of potent antagonists of receptors for growth hormone-releasing hormone with antitumor and anti-inflammatory activity.

The syntheses and biological evaluation of GHRH antagonists of AVR series with high anticancer and anti-inflammatory activities are described. Compared to our previously reported GHRH antagonist 602 of MIAMI series, AVR analogs contain additional modifications at positions 0, 6, 8, 10, 11, 12, 20, 21, 29 and 30, which induce greater antitumor activities. Five of nineteen tested AVR analogs presented binding affinities to the membrane GHRH receptors on human pituitary, 2-4-fold better than MIA-602. The antineoplastic properties of these analogs were evaluated in vitro using proliferation assays and in vivo in nude mice xenografted with various human cancer cell lines including lung (NSCLC-ADC HCC827 and NSCLC H460), gastric (NCI-N87), pancreatic (PANC-1 and CFPAC-1), colorectal (HT-29), breast (MX-1), glioblastoma (U87), ovarian (SK-OV-3 and OVCAR-3) and prostatic (PC3) cancers. In vitro AVR analogs showed inhibition of cell viability equal to or greater than MIA-602. After subcutaneous administration at 5 µg/day doses, some AVR antagonists demonstrated better inhibition of tumor growth in nude mice bearing various human cancers, with analog AVR-353 inducing stronger suppression than MIA-602 in lung, gastric, pancreatic and colorectal cancers and AVR-352 in ovarian cancers and glioblastoma. Both antagonists induced greater inhibition of GH release than MIA-602 in vitro in cultured rat pituitary cells and in vivo in rats. AVR-352 also demonstrated stronger anti-inflammatory effects in lung granulomas from mice with lung inflammation. Our studies demonstrate the merit of further investigation of AVR GHRH antagonists and support their potential use for clinical therapy of human cancers and other diseases.

OPTIMAL FALSE DISCOVERY RATE CONTROL FOR LARGE SCALE MULTIPLE TESTING WITH AUXILIARY INFORMATION.

Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting the structural relationship among the hypotheses, are available. Exploiting such auxiliary information can boost statistical power. To this end, we propose a framework based on a two-group mixture model with varying probabilities of being null for different hypotheses a priori, where a shape-constrained relationship is imposed between the auxiliary information and the prior probabilities of being null. An optimal rejection rule is designed to maximize the expected number of true positives when average false discovery rate is controlled. Focusing on the ordered structure, we develop a robust EM algorithm to estimate the prior probabilities of being null and the distribution of p-values under the alternative hypothesis simultaneously. We show that the proposed method has better power than state-of-the-art competitors while controlling the false discovery rate, both empirically and theoretically. Extensive simulations demonstrate the advantage of the proposed method. Datasets from genome-wide association studies are used to illustrate the new methodology.

Agonistic analog of growth hormone-releasing hormone promotes neurofunctional recovery and neural regeneration in ischemic stroke.

Ischemic stroke can induce neurogenesis. However, most stroke-generated newborn neurons cannot survive. It has been shown that MR-409, a potent synthetic agonistic analog of growth hormone-releasing hormone (GHRH), can protect against some life-threatening pathological conditions by promoting cell proliferation and survival. The present study shows that long-term treatment with MR-409 (5 or 10 µg/mouse/d) by subcutaneous (s.c.) injection significantly reduces the mortality, ischemic insult, and hippocampal atrophy, and improves neurological functional recovery in mice operated on for transient middle cerebral artery occlusion (tMCAO). Besides, MR-409 can stimulate endogenous neurogenesis and improve the tMCAO-induced loss of neuroplasticity. MR-409 also enhances the proliferation and inhibits apoptosis of neural stem cells treated with oxygen and glucose deprivation-reperfusion. The neuroprotective effects of MR-409 are closely related to the activation of AKT/CREB and BDNF/TrkB pathways. In conclusion, the present study demonstrates that GHRH agonist MR-409 has remarkable neuroprotective effects through enhancing endogenous neurogenesis in cerebral ischemic mice.

Covariate adaptive familywise error rate control for genome-wide association studies.

The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel covariate-adaptive procedure to control the familywise error rate that incorporates external covariates which are potentially informative of either the statistical power or the prior null probability. An efficient algorithm is developed to implement the proposed method. We prove its asymptotic validity and obtain the rate of convergence through a perturbation-type argument. Our numerical studies show that the new procedure is more powerful than competing methods and maintains robustness across different settings. We apply the proposed approach to the UK Biobank data and analyse 27 traits with 9 million single-nucleotide polymorphisms tested for associations. Seventy-five genomic annotations are used as covariates. Our approach detects more genome-wide significant loci than other methods in 21 out of the 27 traits.

2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies.

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies.

SUMMARY: PERMANOVA (permutational multivariate analysis of variance based on distances) has been widely used for testing the association between the microbiome and a covariate of interest. Statistical significance is established by permutation, which is computationally intensive for large sample sizes. As large-scale microbiome studies, such as American Gut Project (AGP), become increasingly popular, a computationally efficient version of PERMANOVA is much needed. To achieve this end, we derive the asymptotic distribution of the PERMANOVA pseudo-F statistic and provide analytical P-value calculation based on chi-square approximation. We show that the asymptotic P-value is close to the PERMANOVA P-value even under a moderate sample size. Moreover, it is more accurate and an order-of-magnitude faster than the permutation-free method MDMR. We demonstrated the use of our procedure D-MANOVA on the AGP dataset. AVAILABILITY AND IMPLEMENTATION: D-MANOVA is implemented by the dmanova function in the CRAN package GUniFrac. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Initial measurement of electron nonextensive parameter with electric probe.

Theoretical analysis and a large number of experiments have proved that plasma components do not satisfy Boltzmann-Gibbs statistics and can be well described by nonextensive statistical mechanics, while new plasma parameters, electron nonextensive parameters, which are introduced to describe the nonextensive properties of plasma, cannot be diagnosed yet. Here we show measurement of electron nonextensive parameters of plasma with a nonextensive single electric probe. Our results show that nonextensive electric probe may play a role in plasma diagnosis, measuring nonextensivity of plasma and improving diagnostic accuracy of other plasma parameters. We expect the proposed nonextensive single electric probe can be starting point of more complex nonextensive electric probe. In addition, nonextensive electric probe is an important means to study various plasma waves and instability, turbulence, and anomalous transport, and a definite and quantitative test of the theory of nonextensive geodetic acoustic models will be relevant to such development.

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing.

BACKGROUND: Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS: In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS: Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.

Splice variant of growth hormone-releasing hormone receptor drives esophageal squamous cell carcinoma conferring a therapeutic target.

The extrahypothalamic growth hormone-releasing hormone (GHRH) and its cognate receptors (GHRH-Rs) and splice variants are expressed in a variety of cancers. It has been shown that the pituitary type of GHRH-R (pGHRH-R) mediates the inhibition of tumor growth induced by GHRH-R antagonists. However, GHRH-R antagonists can also suppress some cancers that do not express pGHRH-R, yet the underlying mechanisms have not been determined. Here, using human esophageal squamous cell carcinoma (ESCC) as a model, we were able to reveal that SV1, a known splice variant of GHRH-R, is responsible for the inhibition induced by GHRH-R antagonist MIA-602. We demonstrated that GHRH-R splice variant 1 (SV1) is a hypoxia-driven promoter of tumor progression. Hypoxia-elevated SV1 activates a key glycolytic enzyme, muscle-type phosphofructokinase (PFKM), through the nuclear factor kappa B (NF-κB) pathway, which enhances glycolytic metabolism and promotes progression of ESCC. The malignant actions induced by the SV1-NF-κB-PFKM pathway could be reversed by MIA-602. Altogether, our studies demonstrate a mechanism by which GHRH-R antagonists target SV1. Our findings suggest that SV1 is a hypoxia-induced oncogenic promoter which can be an alternative target of GHRH-R antagonists.

Actions and Potential Therapeutic Applications of Growth Hormone-Releasing Hormone Agonists.

In this article, we briefly review the identification of GHRH, provide an abridged overview of GHRH antagonists, and focus on studies with GHRH agonists. Potent GHRH agonists of JI and MR class were synthesized and evaluated biologically. Besides the induction of the release of pituitary GH, GHRH analogs promote cell proliferation and exert stimulatory effects on various tissues, which express GHRH receptors (GHRH-Rs). A large body of work shows that GHRH agonists, such as MR-409, improve pancreatic ß-cell proliferation and metabolic functions and facilitate engraftment of islets after transplantation in rodents. Accordingly, GHRH agonists offer a new therapeutic approach to treating diabetes. Various studies demonstrate that GHRH agonists promote repair of cardiac tissue, producing improvement of ejection fraction and reduction of infarct size in rats, reduction of infarct scar in swine, and attenuation of cardiac hypertrophy in mice, suggesting clinical applications. The presence of GHRH-Rs in ocular tissues and neuroprotective effects of GHRH analogs in experimental diabetic retinopathy indicates their possible therapeutic applications for eye diseases. Other effects of GHRH agonists, include acceleration of wound healing, activation of immune cells, and action on the central nervous system. As GHRH might function as a growth factor, we examined effects of GHRH agonists on tumors. In vitro, GHRH agonists stimulate growth of human cancer cells and upregulate GHRH-Rs. However, in vivo, GHRH agonists inhibit growth of human cancers xenografted into nude mice and downregulate pituitary and tumoral GHRH-Rs. Therapeutic applications of GHRH analogs are discussed. The development of GHRH analogs should lead to their clinical use.

Initial severity-dependent longitudinal model with application to a randomized controlled trial of women with depression.

The assessment of initial severity of a disease is arguably one of the most important factors in identifying appropriate therapies. In this paper, we propose an initial severity-dependent longitudinal model to account for the influence of the initial severity of a disease on the posttreatment severity and the efficacy of medical treatments. The proposed model has the flexibility of nonparametric modeling, as it allows coefficients to vary with the initial severity of the disease. It also provides attractive and practical patient-specific interpretation of initial severity-dependent coefficients. As a result, the proposed model enables patient-specific modeling and treatment recommendations consistent with the assessment of the patient's initial severity, and thus, it can be used as a decision support tool for clinicians. A new empirical likelihood approach is employed for efficient estimation and statistical inference about the initial severity-dependent coefficients. In contrast to the literature on marginal regression models, the proposed estimation procedure allows nuisance parameters associated with the working correlation matrix and the error variances to vary smoothly with the initial severity. The effectiveness of the proposed procedure is demonstrated via simulation studies. We further apply the proposed method by analyzing a data set arising from a randomized controlled trial of women with depression and discover an interesting phenomenon; antidepressant medication intervention is effective for patients with moderate or severe depression, whereas psychotherapy intervention using manual-guided cognitive behavior therapy is effective for patients with a severe case of depression.

Agonists of growth hormone-releasing hormone (GHRH) inhibit human experimental cancers in vivo by down-regulating receptors for GHRH.

The effects of the growth hormone-releasing hormone (GHRH) agonist MR409 on various human cancer cells were investigated. In H446 small cell lung cancer (SCLC) and HCC827 and H460 (non-SCLC) cells, MR409 promoted cell viability, reduced cell apoptosis, and induced the production of cellular cAMP in vitro. Western blot analyses showed that treatment of cancer cells with MR409 up-regulated the expression of cyclins D1 and D2 and cyclin-dependent kinases 4 and 6, down-regulated p27kip1, and significantly increased the expression of the pituitary-type GHRH receptor (pGHRH-R) and its splice-variant (SV1). Hence, in vitro MR409 exerts agonistic action on lung cancer cells in contrast to GHRH antagonists. However, in vivo, MR409 inhibited growth of lung cancers xenografted into nude mice. MR409 given s.c. at 5 µg/day for 4 to 8 weeks significantly suppressed growth of HCC827, H460, and H446 tumors by 48.2%, 48.7%, and 65.6%, respectively. This inhibition of tumor growth by MR409 was accompanied by the down-regulation of the expression of pGHRH-R and SV1 in the pituitary gland and tumors. Tumor inhibitory effects of MR409 in vivo were also observed in other human cancers, including gastric, pancreatic, urothelial, prostatic, mammary, and colorectal. This inhibition of tumor growth parallel to the down-regulation of GHRH-Rs is similar and comparable to the suppression of sex hormone-dependent cancers after the down-regulation of receptors for luteinizing hormone-releasing hormone (LHRH) by LHRH agonists. Further oncological investigations with GHRH agonists are needed to elucidate the underlying mechanisms.