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Overexpression of interferon induced transmembrane protein-1 (IFITM1) enhances tumor progression in multiple cancers, but its role in triple-negative breast cancer (TNBC) is unknown. Here, we explore the functional significance and regulation of IFITM1 in TNBC and strategies to target its expression. Immunohistochemistry staining of a tissue microarray demonstrates that IFITM1 is overexpressed in TNBC samples which is confirmed by TCGA analysis. Targeting IFITM1 by siRNA or CRISPR/Cas9 in TNBC cell lines significantly inhibits proliferation, colony formation, and wound healing in vitro. Orthotopic mammary fat pad and mammary intraductal studies reveal that loss of IFITM1 reduces TNBC tumor growth and invasion in vivo. RNA-seq analysis of IFITM1/KO cells reveals significant downregulation of several genes involved in proliferation, migration, and invasion and functional studies identified NF-κB as an important downstream target of IFITM1. Notably, siRNA knockdown of p65 reduces IFITM1 expression and a drug-repurposing screen of FDA approved compounds identified parthenolide, an NFκB inhibitor, as a cytotoxic agent for TNBC and an inhibitor of IFITM1 in vitro and in vivo. Overall, our findings suggest that targeting IFITM1 by suppressing interferon-alpha/NFκB signaling represents a novel therapeutic strategy for TNBC treatment.

The maximum diameter criterion is the most important factor in the clinical management of abdominal aortic aneurysms (AAA). Consequently, interventional repair is recommended when an aneurysm reaches a critical diameter, typically 5.0 cm in the United States. Nevertheless, biomechanical measures of the aneurysmal abdominal aorta have long been implicated in AAA risk of rupture. The purpose of this study is to assess whether other geometric characteristics, in addition to maximum diameter, may be highly correlated with the AAA peak wall stress (PWS). Using in-house segmentation and meshing algorithms, 30 patient-specific AAA models were generated for finite element analysis using an isotropic constitutive material for the AAA wall. PWS, evaluated as the spatial maximum of the first principal stress, was calculated at a systolic pressure of 120 mmHg. The models were also used to calculate 47 geometric indices characteristic of the aneurysm geometry. Statistical analyses were conducted using a feature reduction algorithm in which the 47 indices were reduced to 11 based on their statistical significance in differentiating the models in the population (p < 0.05). A subsequent discriminant analysis was performed and 7 of these indices were identified as having no error in discriminating the AAA models with a significant nonlinear regression correlation with PWS. These indices were: D(max) (maximum diameter), T (tortuosity), DDr (maximum diameter to neck diameter ratio), S (wall surface area), K(median) (median of the Gaussian surface curvature), C(max) (maximum lumen compactness), and M(mode) (mode of the Mean surface curvature). Therefore, these characteristics of an individual AAA geometry are the highest correlated with the most clinically relevant biomechanical parameter for rupture risk assessment. We conclude that the indices can serve as surrogates of PWS in lieu of a finite element modeling approach for AAA biomechanical evaluation.

The article addresses the best unbiased estimators of the block compound symmetric covariance structure for m −variate observations with equal mean vector over u sites under the assumption of multivariate quasi-normality. The free-coordinate approach is used to prove that the quadratic estimation of covariance parameters is equivalent to linear estimation with a properly defined inner product in the space of symmetric matrices. Without assumption of normality but quasi-normality, meaning that up to fourth moments are the same as in the normal case, the estimators are best linear and best quadratic for mean and covariance parameters, respectively. Finally, strong consistency is proven. The properties of the estimators in the proposed model are compared against a similar model available in the literature. An application of the proposed approach to a clinical study data is presented.

A component-wise formula for the inverse of the covariance matrix of an MA(2) process is provided in Shaman (1973) requiring O ( n 3 ) calculations per column. This paper simplifies his formula resulting in only O ( n ) calculations per column. Using a well-known result concerning the inverse of symmetric Toeplitz matrices, we show that the inverse of the covariance matrix of an MA(2) process may be written as a function of its first column, requiring only O ( n ) calculations to obtain instead of the O ( n 2 ) calculations required from the Levinson–Durbin algorithm [1] .

An efficient nanobioconjugate has been developed as electrode coating material by self-assembling our newly reported porous Au-seeded Ag nanorod networks (Hy-Au@AgNR840-Ass) with a thiol modified DNA aptamer (HS-Apt) for the early-stage specific detection and quantification of Dengue virus (serotype 2). Characterization of the precursors (Hy-Au@AgNR840 and Hy-Au@AgNR840-Ass) for effective fabrication of the bioconjugate (Hy-Au@AgNR840-Ass-S-Apt) was performed by using microscopic (TEM and AFM), spectroscopic (Absorbance, XPS, and SERS), scattering (DLS) and diffractometric (XRD) techniques and has been detailed in our previous publication whereas the binding affinity between the bioconjugate and different serotypes of dengue is studied here using low cost electrochemical impedimetric spectroscopy (EIS). Despite the structural similarity between virion particles of different dengue serotypes, we can detect the most prevalent DENV-2 specifically by using our nonenzymatic nanobioconjugate assay for the first time through the impedimetric technique. The cross-reactivity is checked in presence of closely related serotypes like DENV-1 & DENV-3. The key achievement of this study lies in its ability in the direct detection of DENV-2 serotype without the aid of an enzyme or antibody in dengue-infected blood up to ~ 100 infectious units per mL.

The article addresses the best unbiased estimators of doubly exchangeable covariance structure, an extension of block exchangeable covariance structure, for three-level multivariate data. Under multivariate normality, the free-coordinate approach is used to obtain linear and quadratic estimates for the model parameters that are unbiased, sufficient, complete and consistent. Data from a clinical trial study is analyzed to illustrate the application of the obtained results.

An evolutionarily conserved sequence at the 5′ terminus of hantaviral genomic RNA plays an important role in viral transcription initiation and packaging of the viral genome into viral nucleocapsids. Interaction of viral nucleocapsid protein (N) with this conserved sequence facilitates mRNA translation by a unique N-mediated translation strategy. Whereas this evolutionarily conserved sequence facilitates virus replication with the assistance of N in eukaryotic hosts having multifaceted antiviral defense, we demonstrate its interaction with N presents a novel target for therapeutic intervention of hantavirus disease. Using a high throughput screening approach, we identified three lead inhibitors that bind and induce structural perturbations in N. The inhibitors interrupt N-RNA interaction and abrogate both viral genomic RNA synthesis and N-mediated translation strategy without affecting the canonical translation machinery of the host cell. The inhibitors are well tolerated by cells and inhibit hantavirus replication with the same potency as ribavarin, a commercially available antiviral. We report the identification of a unique chemical scaffold that disrupts a critical RNA-protein interaction in hantaviruses and holds promise for the development of the first anti-hantaviral therapeutic with broad spectrum antiviral activity.

Likelihood ratio tests (LRTs) for separability of a covariance structure for doubly multivariate data are widely studied in the literature. There are three types of LRT: biased tests based on an asymptotic chi-square null distribution; unbiased/unmodified tests based on an empirical null distribution; and unbiased/modified tests with a test statistic modified to follow a theoretical chi-square null distribution. The Rao's score test (RST) statistic, an alternative for both biased and unbiased/unmodified versions of the corresponding LRT test statistics, is derived for a common case. In this paper the separability of a covariance structure with the first component as a compound symmetric correlation matrix under the assumption of multivariate normality is tested. For this purpose Monte Carlo simulation studies, which compare the biased LRT to biased RST, and unbiased/unmodified LRT to unbiased/unmodified RST, are conducted. It is shown that the RSTs outperform their corresponding LRTs in the sense of empirical Type I error as well as empirical null distribution. Moreover, since the RST does not require estimation of a general variance-covariance matrix (the alternative hypothesis), RST can be performed for small sample sizes, where the variance-covariance matrix could not be estimated for the corresponding LRT, making the LRT infeasible. Three examples are presented to illustrate and compare statistical inference based on LRT and RST.

The ClpB–DnaK bichaperone system reactivates aggregated cellular proteins and is essential for survival of bacteria, fungi, protozoa, and plants under stress. AAA+ ATPase ClpB is a promising target for the development of antimicrobials because a loss of its activity is detrimental for survival of many pathogens and no apparent ClpB orthologs are found in metazoans. We investigated ClpB activity in the presence of several compounds that were previously described as inhibitor leads for the human AAA+ ATPase p97, an antitumor target. We discovered that N2,N4-dibenzylquinazoline-2,4-diamine (DBeQ), the least potent among the tested p97 inhibitors, binds to ClpB with a Kd∼60 μM and inhibits the casein-activated, but not the basal, ATPase activity of ClpB with an IC50∼5 μM. The remaining p97 ligands, which displayed a higher affinity toward p97, did not affect the ClpB ATPase. DBeQ also interacted with DnaK with a Kd∼100 μM and did not affect the DnaK ATPase but inhibited the DnaK chaperone activity in vitro. DBeQ inhibited the reactivation of aggregated proteins by the ClpB–DnaK bichaperone system in vitro with an IC50∼5 μM and suppressed the growth of cultured Escherichia coli. The DBeQ-induced loss of E. coli proliferation was exacerbated by heat shock but was nearly eliminated in a ClpB-deficient E. coli strain, which demonstrates a significant selectivity of DBeQ toward ClpB in cells. Our results provide chemical validation of ClpB as a target for developing novel antimicrobials. We identified DBeQ as a promising lead compound for structural optimization aimed at selective targeting of ClpB and/or DnaK.

The paper deals with the best unbiased estimators of the blocked compound symmetric covariance structure for m -variate observations over u sites under the assumption of multivariate normality. The free-coordinate approach is used to prove that the quadratic estimation of covariance parameters is equivalent to linear estimation with a properly defined inner product in the space of symmetric matrices. Complete statistics are then derived to prove that the estimators are best unbiased. Finally, strong consistency is proven. The proposed method is implemented with a real data set.

Centers and vertices principal component analyses are common methods to explain variations within multivariate interval data. We introduce multivariate equicorrelated structures to vertices’ covariance. Assuming the structure, we show equivalence between centers and vertices methods by proving their eigensystems proportional.

Antibiotic resistance is a growing health concern, and new avenues of antimicrobial drug design are being actively sought. One suggested pathway to be targeted for inhibitor design is that of iron scavenging through siderophores. Here we present a high throughput screen to the isochorismate-pyruvate lyase of Pseudomonas aeruginosa, an enzyme required for the production of the siderophore pyochelin. Compounds identified in the screen are high nanomolar to low micromolar inhibitors of the enzyme and produce growth inhibition in PAO1 P. aeruginosa in the millimolar range under iron-limiting conditions. The identified compounds were also tested for enzymatic inhibition of Escherichia coli chorismate mutase, a protein of similar fold and similar chemistry, and of Yersinia enterocolitica salicylate synthase, a protein of differing fold but catalyzing the same lyase reaction. In both cases, subsets of the inhibitors from the screen were found to be inhibitory to enzymatic activity (mutase or synthase) in the micromolar range and capable of growth inhibition in their respective organisms (E. coli or Y. enterocolitica).

Cu–BTC metal organic framework, HKUST-1 was synthesized by solvothermal method and characterized using surface area analyser, scanning electron microscope, powder X-ray diffractometer, Fourier transform infrared spectrophotometer and thermo gravimetric analyser. HKUST-1 thus produced and characterized, was studied for the degradation of sulfur mustard (HD) and its surrogate 2-chloroethyl ethyl sulfide (CEES) using gas chromatograph coupled with flame ionization detector. From the reaction kinetics it was observed that the CEES degraded faster relative to HD. The rate constant and half life were found to be 0.02 min−1, 34.65 min and 0.043 min−1, 16.11 min for HD and CEES respectively. Degradation products were analyzed by gas chromatograph coupled with mass spectrometer. At the same time reusability of the HKUST-1 has also been explored.

This paper builds up a pattern recognition system to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognition system results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques.

In this article, we study a new linear discriminant function for three-level m-variate observations under the assumption of multivariate normality. We assume that the m-variate observations have a doubly exchangeable covariance structure consisting of three unstructured covariance matrices for three multivariate levels and a separable additive structure on the mean vector. The new discriminant function is very efficient in discriminating individuals in a small sample scenario. An iterative algorithm is proposed to calculate the maximum likelihood estimates of the unknown population parameters as closed form solutions do not exist for these unknown parameters. The new discriminant function is applied to a real data set as well as to simulated data sets. We compare our findings with other linear discriminant functions for three-level multivariate data as well as with the traditional linear discriminant function.

In this article we study a linear discriminant function of multiple m -variate observations at u -sites and over v -time points under the assumption of multivariate normality. We assume that the m -variate observations have a separable mean vector structure and a “jointly equicorrelated covariance” structure. The new discriminant function is very effective in discriminating individuals in a small sample scenario. No closed-form expression exists for the maximum likelihood estimates of the unknown population parameters, and their direct computation is nontrivial. An iterative algorithm is proposed to calculate the maximum likelihood estimates of these unknown parameters. A discriminant function is also developed for unstructured mean vectors. The new discriminant functions are applied to simulated data sets as well as to a real data set. Results illustrating the benefits of the new classification methods over the traditional one are presented.

In this article we study the problem of classification of three-level multivariate data, where multiple q -variate observations are measured on u -sites and over p -time points, under the assumption of multivariate normality. The new classification rules with certain structured and unstructured mean vectors and covariance structures are very efficient in small sample scenario, when the number of observations is not adequate to estimate the unknown variance–covariance matrix. These classification rules successfully model the correlation structure on successive repeated measurements over time. Computation algorithms for maximum likelihood estimates of the unknown population parameters are presented. Simulation results show that the introduction of sites in the classification rules improves their performance over the existing classification rules without the sites.

In this article we study the problems of tests of hypotheses on Kronecker product structured covariance matrices with multiple q-variate observations over u sites and over p time/spatial points under the assumption of multivariate normality. We provide the maximum likelihood estimates of the unknown population parameters, and the computation algorithms to calculate the test statistics. The tests are implemented with three real data sets. A simulation study is also performed to check the finite sample performance.

Collagen crosslinks are important to the quality of bone and may be contributors to the age-related increase in bone fracture. This study was performed to investigate whether age and gender effects on collagen crosslinks are similar in osteonal and interstitial bone tissues. Forty human cadaveric femurs were collected and divided into two age groups: Middle aged (42–63 years of age) and Elderly (69–90 years of age) with ten males and ten females in each group (n = 10). Micro-cores of bone tissue from both secondary osteons (newly formed) and interstitial regions (biologically old) in the medial quadrant of the diaphysis were extracted using a custom-modified, computer numerical controlled machine. The bone specimens were then analyzed using high performance liquid chromatography to determine the effects of age and gender on the concentration of mature, enzymatic crosslinks (hydroxylysyl-pyridinoline – HP and lysylpyridinoline – LP) and a non-enzymatic crosslink (pentosidine – PE) at these two bony sites. The results indicate that age has a significant effect on the concentration of LP and PE, while gender has a significant effect on HP and LP. In addition, the concentration of the crosslinks in the secondary osteons is significantly different from that in the interstitial bone regions. These results suggest that the rate of non-enzymatic crosslinking may increase while the formation of maturate enzymatic crosslinks may decrease with age. Such changes could potentially reduce the inherent quality of the bone tissue in the elderly skeleton.

Although the effects of dehydration on the mechanical behavior of cortical bone are known, the underlying mechanisms for such effects are not clear. We hypothesize that the interactions of water with the collagen and mineral phases each have a unique influence on mechanical behavior. To study this, strength, toughness, and stiffness were measured with three-point bend specimens made from the mid-diaphysis of human cadaveric femurs and divided into six test groups: control (hydrated), drying in a vacuum oven at room temperature (21 degrees C) for 30 min and at 21, 50, 70, or 110 degrees C for 4 h. The experimental data indicated that water loss significantly increased with each increase in drying condition. Bone strength increased with a 5% loss of water by weight, which was caused by drying at 21 degrees C for 4 h. With water loss exceeding 9%, caused by higher drying temperatures (or =70 degrees C), strength actually decreased. Drying at 21 degrees C (irrespective of time in vacuum) significantly decreased bone toughness through a loss of plasticity. However, drying at 70 degrees C and above caused toughness to decrease through decreases in strength and fracture strain. Stiffness linearly increased with an increase in water loss. From an energy perspective, the water-mineral interaction is removed at higher temperatures than the water-collagen interaction. Therefore, we speculate that loss of water in the collagen phase decreases the toughness of bone, whereas loss of water associated with the mineral phase decreases both bone strength and toughness.

Under the assumption of multivariate normality the likelihood ratio test is derived to test a hypothesis for Kronecker product structure on a covariance matrix in the context of multivariate repeated measures data. Although the proposed hypothesis testing can be computationally performed by indirect use of Proc Mixed of SAS, the Proc Mixed algorithm often fails to converge. We provide an alternative algorithm. The algorithm is illustrated with two real data sets. A simulation study is also conducted for the purpose of sample size consideration.

We study the problem of classification with multiple q-variate observations with and without time effect on each individual. We develop new classification rules for populations with certain structured and unstructured mean vectors and under certain covariance structures. The new classification rules are effective when the number of observations is not large enough to estimate the variance–covariance matrix. Computational schemes for maximum likelihood estimates of required population parameters are given. We apply our findings to two real data sets as well as to a simulated data set.