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Convex clustering has recently garnered increasing interest due to its attractive theoretical and computational properties, but its merits become limited in the face of high-dimensional data. In such settings, pairwise affinity terms that rely on $k$-nearest neighbors become poorly specified and Euclidean measures of fit provide weaker discriminating power. To surmount these issues, we propose to modify the convex clustering objective so that feature weights are optimized jointly with the centroids. The resulting problem becomes biconvex, and as such remains well-behaved statistically and algorithmically. In particular, we derive a fast algorithm with closed form updates and convergence guarantees, and establish finite-sample bounds on its prediction error. Under interpretable regularity conditions, the error bound analysis implies consistency of the proposed estimator. Biconvex clustering performs feature selection throughout the clustering task: as the learned weights change the effective feature representation, pairwise affinities can be updated adaptively across iterations rather than precomputed within a dubious feature space. We validate the contributions on real and simulated data, showing that our method effectively addresses the challenges of dimensionality while reducing dependence on carefully tuned heuristics typical of existing approaches.

As a prominent dimension reduction method for multivariate linear regression, the envelope model has received increased attention over the past decade due to its modeling flexibility and success in enhancing estimation and prediction efficiencies. Several enveloping approaches have been proposed in the literature; among these, the partial response envelope model [57] that focuses on only enveloping the coefficients for predictors of interest, and the simultaneous envelope model [14] that combines the predictor and the response envelope models within a unified modeling framework, are noteworthy. In this article we incorporate these two approaches within a Bayesian framework, and propose a novel Bayesian simultaneous partial envelope model that generalizes and addresses some limitations of the two approaches. Our method offers the flexibility of incorporating prior information if available, and aids coherent quantification of all modeling uncertainty through the posterior distribution of model parameters. A block Metropolis-within-Gibbs algorithm for Markov chain Monte Carlo (MCMC) sampling from the posterior is developed. The utility of our model is corroborated by theoretical results, comprehensive simulations, and a real imaging genetics data application for the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study.

The journal of physical chemistry letters 13(47), 11036 - 11043 (2022). doi:10.1021/acs.jpclett.2c02704, Two questions that remain a challenge in the field of colloidal doped core/shell nanomaterials of different morphologies are the nature of the interface and the radial location of the dopant ion due to the diffusion within the lattice. Using a model system of Cu-doped CdSe/CdS quantum dots, we develop an in-depth understanding of the extended X-ray absorption fine structure (EXAFS) spectra of the dopant and host atoms to address both issues. Our findings suggest that the interface is not sharp, in agreement with the nonstructural studies in the literature. Local structure analysis around the Cu dopant ion confirms that Cu drifts out from the core toward the outer region in the absence of the shell but stays mostly in the core after the formation of a sufficiently thick interfacial barrier ($∼2$ monolayers). This study highlights the significance of EXAFS spectroscopy in understanding the nature of the interface in nanomaterials., Published by ACS, Washington, DC

The safety of medical products due to adverse events (AE) from drugs, therapeutic biologics, and medical devices is a major public health concern worldwide. Likelihood ratio test (LRT) approaches to pharmacovigilance constitute a class of rigorous statistical tools that permit objective identification of AEs of a specific drug and/or a class of drugs cataloged in spontaneous reporting system databases. However, the existing LRT approaches encounter certain theoretical and computational challenges when an underlying Poisson model assumption is violated, including in cases of zero-inflated data. We briefly review existing LRT approaches and propose a novel class of (pseudo-) LRT methods to address these challenges. Our approach uses an alternative parametrization to formulate a unified framework with a common test statistic that can handle both Poisson and zero-inflated Poisson (ZIP) models. The proposed framework is computationally efficient, and it reveals deeper insights into the comparative behaviors of the Poisson and the ZIP models for handling AE data. Our extensive simulation studies document notably superior performances of the proposed methods over existing approaches particularly under zero-inflation, both in terms of statistical (eg, much better control of the nominal level and false discovery rate with substantially enhanced power) and computational (

H-bonded supramolecular polymers of ambipolar π-conjugated chromophores exhibit stable room temperature ferroelectricity with Tc ∼ 361 K and Ps ∼ 2.0 μC cm−2 (Ec ≤ 2.0 kV cm−1).

Kernel k-means clustering is a powerful tool for unsupervised learning of non-linearly separable data. Its merits are thoroughly validated on a suite of simulated datasets and real data benchmarks that feature nonlinear and multi-view separation. Since the earliest attempts, researchers have noted that such algorithms often become trapped by local minima arising from the non-convexity of the underlying objective function. In this paper, we generalize recent results leveraging a general family of means to combat sub-optimal local solutions to the kernel and multi-kernel settings. Called Kernel Power k-Means, our algorithm uses majorization-minimization (MM) to better solve this non-convex problem. We show that the method implicitly performs annealing in kernel feature space while retaining efficient, closed-form updates. We rigorously characterize its convergence properties both from computational and statistical points of view. In particular, we characterize the large sample behavior of the proposed method by establishing strong consistency guarantees as well as finite-sample bounds on the excess risk of the estimates through modern tools in learning theory. The proposal's efficacy is demonstrated through an array of simulated and real data experiments.

Purpose:The clinical behavior of ampullary adenocarcinoma varies widely. Targeted tumor sequencing may better define biologically distinct subtypes to improve diagnosis and management.Experimental Design:The hidden-genome algorithm, a multilevel meta-feature regression model, was trained on a prospectively sequenced cohort of 3,411 patients (1,001 pancreatic adenocarcinoma, 165 distal bile-duct adenocarcinoma, 2,245 colorectal adenocarcinoma) and subsequently applied to targeted panel DNA-sequencing data from ampullary adenocarcinomas. Genomic classification (i.e., colorectal vs. pancreatic) was correlated with standard histologic classification [i.e., intestinal (INT) vs. pancreatobiliary (PB)] and clinical outcome.Results:Colorectal genomic subtype prediction was primarily influenced by mutations in APC and PIK3CA, tumor mutational burden, and DNA mismatch repair (MMR)–deficiency signature. Pancreatic genomic-subtype prediction was dictated by KRAS gene alterations, particularly KRAS G12D, KRAS G12R, and KRAS G12V. Distal bile-duct adenocarcinoma genomic subtype was most influenced by copy-number gains in the MDM2 gene. Despite high (73%) concordance between immunomorphologic subtype and genomic category, there was significant genomic heterogeneity within both histologic subtypes. Genomic scores with higher colorectal probability were associated with greater survival compared with those with a higher pancreatic probability.Conclusions:The genomic classifier provides insight into the heterogeneity of ampullary adenocarcinoma and improves stratification, which is dictated by the proportion of colorectal and pancreatic genomic alterations. This approach is reproducible with available molecular testing and obviates subjective histologic interpretation.

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Supplementary Methods

Efficient photonic and plasmonic response within a single material without the decay of either property is a challenge that applications such as surface lattice resonance (SLR)-assisted spontaneous...

Cell migration, the process by which cells move from one location to another, plays crucial roles in many biological events. While much research has been devoted to understand the process, most statistical cell migration models rely on using time-lapse microscopy data from cell trajectories alone. However, the cell and its associated nucleus work together to orchestrate cell movement, which motivates a joint analysis of coupled cell-nucleus trajectories. In this paper, we propose a Bayesian hierarchical model for analyzing cell migration. We incorporate a bivariate angular distribution to handle the coupled cell-nucleus trajectories and introduce latent motility status indicators to model a cell's motility as a time-dependent characteristic. A Markov chain Monte Carlo algorithm is provided for practical implementation of our model, which is used on real experimental data from MDA-MB-231 and NIH 3T3 cells. Through the fitted models, deeper insights into the migratory patterns of these experimental cell populations are gained and their differences are quantified.

Shot boundary detection is mainly considered as a stepping stone in the broad arena of content-based video retrieval. Ample systematic investigation has been carried out in the terrain of shot boundary detection. The attainment of shot boundary detection procedures is greatly hindered due to the presence of unforeseen illumination change and motion effects in a video. This work proposes a novel bifold-stage technique to recognize abrupt transition in videos, invariant to motion, and illumination effects. In the first stage, the local ternary patterns feature is used to extract information from each frame in a video. Then, a set of novel adaptive thresholds such as $${\gamma }$$ and $${{{\beta }}}$$ are used to find the possible transition frames. In the confirmation stage, Lab color difference along with an adaptive threshold $${\delta }$$ is used to extract actual transition frames. The experimental result depicts that the motion effect is also scaled down in the initial stage. The Lab color difference passed down in the second stage also handles the illumination and motion effects which are not managed in the initial stage. Experimentation is done using TRECVid 2001 and 2007 standard datasets and palpable that the proposed technique outperforms most of the contemporary shot boundary detection approaches.

Supramolecular assembly of biomolecules/macromolecules stems from the desire to mimic complex biological structures and functions of living organisms. While DNA nanotechnology is already in an advanced stage, protein assembly is still in its infancy as it is a significantly difficult task due to their large molecular weight, conformational complexity and structural instability towards variation in temperature, pH or ionic strength. This article reports highly stable redox-responsive supramolecular assembly of a protein Bovine serum albumin (BSA) which is functionalized with a supramolecular structure directing unit (SSDU). The SSDU consists of a benzamide functionalized naphthalene-diimide (NDI) chromophore which is attached with the protein by a bio-reducible disulfide linker. The SSDU attached protein (NDI-BSA) exhibits spontaneous supramolecular assembly in water by off-set π-stacking among the NDI chromophores, leading to the formation of spherical nanoparticles (diameter: 150–200 nm). The same SSDU when connected with a small hydrophilic wedge (NDI-1) instead of the large globular protein, exhibits a different π-stacking mode with relatively less longitudinal displacement which results in a fibrillar network and hydrogelation. Supramolecular co-assembly of NDI-BSA and NDI-1 (3 : 7) produces similar π-stacking and an entangled 1D morphology. Both the spherical assembly of NDI-BSA or the fibrillar co-assembly of NDI-BSA + NDI-1 (3 : 7) provide sufficient thermal stability to the protein as its thermal denaturation could be completely surpassed while the secondary structure remained intact. However, the esterase like activity of the protein reduced significantly as a result of such supramolecular assembly indicating limited access by the substrate to the active site of the enzyme located in the confined environment. In the presence of glutathione (GSH), a biologically important tri-peptide, due to the cleavage of the disulfide bond, the protein became free and was released, resulting in fully regaining its enzymatic activity. Such supramolecular assembly provided excellent protection to the protein against enzymatic hydrolysis as the relative hydrolysis was estimated to be, Supramolecular structure directing unit regulated co-assembly of a protein produces a highly stable fibrillar nanostructure and glutathione responsive release of the protein in its active state.

Advances in perioperative care, surgical instrumentation, and understanding of the patho-physiology of trauma have engendered new treatment paradigms for these injuries within the last two decades. Evaluation of outcome of LATERAL MASS FIXATION in sub-axial cervical spine injury in terms of improvement of neurological deficit (ASIA Scale) in one, three, six and nine months follow up. Sub-axial cervical spine injury patients attending OPD, Emergency. Age group: between 15 to 75 years, both male and female. The study was a both retro and prospective, non randomized, uncontrolled, interventional study group. The study was performed over a cohort of adult patients. Our study attested to the safety and acceptability of lateral mass fixation as a established mean of posterior fixation with no failure rate after one year follow up and a lesser operative complication rate.

The paper proposes a novel video segmentation system with maiden application of CIEDE2000 colour-difference and mean luminace pattern. CIEDE2000 colour-difference uses Lab colour space which is a stable and efficient colour space. The main advantage of Lab colour space model is that it can approximate all the available colours perceived by our human eye. CIEDE2000 colour difference is used for detecting abrupt transitions in the video. The novel contribution of the paper is the maiden use of the mean luminance pattern, increasing and decreasing patterns of the mean value of frame luminance, for detecting the gradual transition. The approach is validated on standard databases TRECVid 2001 and 2007 test video database. The performance of the proposed technique is compared with recently reported techniques and found to be superior as compared to other techniques. The accuracy achieved with the proposed method on the standard databases is 95.9% for cut transition, 78.6% for gradual transition and 92.1% overall.

In this paper, we propose a novel shot boundary detection technique using gradient and colour information. The gradient similarity and luminance distortion are calculated to measure the contrast and structural changes of each frame including luminance changes. In the proposed system, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to extract the possible transitions using an adaptive threshold across the videos. In the verification part, CIEDE2000 colour-difference values of the possible transition frames are compared for declaration of abrupt and gradual transitions. Our system takes effectively less computational time to detect abrupt and gradual transition for a video as compared with contemporary solutions. Our proposed system also gives dominate the performance as compared with latest techniques in terms of F1 score using TRECVid 2001 and 2007 selected dataset. We have performed a series of rigorous experimentation to validate our claims.

Doped perovskite lead halide nanocrystals (PHNCs) are promising materials for various optoelectronic applications, but the major challenge faced by the researchers is the inability to dope foreign elements into perovskite lattice because of the strong lead-halide bond energies. In this work, we have used Fe as a dopant in CsPbCl

Irregular nuclear shapes are a hallmark of human cancers. Recent studies suggest that alterations to chromatin regulators may cause irregular nuclear morphologies. Here we screened an epigenetic small molecule library consisting of 145 compounds against chromatin regulators for their ability to revert abnormal nuclear shapes that were induced by gene knockdown in noncancerous MCF10A human mammary breast epithelial cells. We leveraged a previously validated quantitative Fourier approach to quantify the elliptical Fourier coefficient (EFC ratio) as a measure of nuclear irregularities, which allowed us to perform rigorous statistical analyses of screening data. Top hit compounds fell into three major mode of action categories, targeting three separate epigenetic modulation routes: 1) histone deacetylase inhibitors, 2) bromodomain and extraterminal domain protein inhibitors, and 3) methyl-transferase inhibitors. Some of the top hit compounds were also efficacious in reverting nuclear irregularities in MDA-MB-231 triple negative breast cancer cells and in PANC-1 pancreatic cancer cells in a cell-type-dependent manner. Regularization of nuclear shapes was compound-specific, cell-type specific, and dependent on the specific molecular perturbation that induced nuclear irregularities. Our approach of targeting nuclear abnormalities may be potentially useful in screening new types of cancer therapies targeted toward chromatin structure.

Background The lack of dystrophin in cardiomyocytes in Duchenne muscular dystrophy (DMD) is associated with progressive decline in cardiac function eventually leading to death by 20–40 years of age. The aim of this prospective study was to determine rate of progressive decline in left ventricular (LV) function in Duchenne muscular dystrophy (DMD) over 5 years. Methods Short axis cine and grid tagged images of the LV were acquired in individuals with DMD (n = 59; age = 5.3–18.0 years) yearly, and healthy controls at baseline (n = 16, age = 6.0–18.3 years) on a 3 T MRI scanner. Grid-tagged images were analyzed for composite circumferential strain (ℇcc%) and ℇcc% in six mid LV segments. Cine images were analyzed for left ventricular ejection fraction (LVEF), LV mass (LVM), end-diastolic volume (EDV), end-systolic volume (ESV), LV atrioventricular plane displacement (LVAPD), and circumferential uniformity ratio estimate (CURE). LVM, EDV, and ESV were normalized to body surface area for a normalized index of LVM (LVMI), EDV (EDVI) and ESV (ESVI). Results At baseline, LV ℇcc% was significantly worse in DMD compared to controls and five of the six mid LV segments demonstrated abnormal strain in DMD. Longitudinal measurements revealed that ℇcc% consistently declined in individuals with DMD with the inferior segments being more affected. LVEF progressively declined between 3 to 5 years post baseline visit. In a multivariate analysis, the use of cardioprotective drugs trended towards positively impacting cardiac measures while loss of ambulation and baseline age were associated with negative impact. Eight out of 17 cardiac parameters reached a minimal clinically important difference with a threshold of 1/3 standard deviation. Conclusion The study shows a worsening of circumferential strain in dystrophic myocardium. The findings emphasize the significance of early and longitudinal assessment of cardiac function in DMD and identify early biomarkers of cardiac dysfunction to help design clinical trials to mitigate cardiac pathology. This study provides valuable non-invasive and non-contrast based natural history data of cardiac changes which can be used to design clinical trials or interpret the results of current trials aimed at mitigating the effects of decreased cardiac function in DMD.

BACKGROUND Catheterization facilitates continuous bacteriuria, for which the clinical significance remains unclear. This study aimed to determine the clinical presentation, epidemiology, and dynamics of bacteriuria in a cohort of long-term catheterized nursing home residents. METHODS Prospective urine culture, urinalysis, chart review, and assessment of signs and symptoms of infection were performed weekly for 19 study participants over 7 months. All bacteria ≥ 1 × 103 cfu/mL were cultured, isolated, identified, and tested for susceptibility to select antimicrobials. RESULTS In total, 226 of the 234 urine samples were polymicrobial (97%), with an average of 4.7 isolates per weekly specimen. A total of 228 urine samples (97%) exhibited ≥ 1 × 106 CFU/mL, 220 (94%) exhibited abnormal urinalysis, 126 (54%) were associated with at least 1 possible sign or symptom of infection, and 82 (35%) would potentially meet a standardized definition of catheter-associated urinary tract infection (CAUTI), but only 3 had a caregiver diagnosis of CAUTI. Bacterial isolates (286; 30%) were resistant to a tested antimicrobial agent, and bacteriuria composition was remarkably stable despite a combined total of 54 catheter changes and 23 weeks of antimicrobial use. CONCLUSION Bacteriuria composition was largely polymicrobial, including persistent colonization by organisms previously considered to be urine culture contaminants. Neither antimicrobial use nor catheter changes sterilized the urine, at most resulting in transient reductions in bacterial burden followed by new acquisition of resistant isolates. Thus, this patient population exhibits a high prevalence of bacteriuria coupled with potential indicators of infection, necessitating further exploration to identify sensitive markers of true infection. FUNDING This work was supported by the NIH (R00 DK105205, R01 DK123158, UL1 TR001412).

ObjectiveTo quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles.MethodsMRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal changes in biomarkers were modeled with a cumulative distribution function using a nonlinear mixed-effects approach.ResultsMRS FF and MRI qT2 increased with DMD disease duration, with the progression time constants differing markedly between individuals and across muscles. The average age at half-maximal muscle involvement (μ) occurred 4.8 years earlier in vastus lateralis than soleus, and these measures were strongly associated with loss-of-ambulation age. Corticosteroid treatment was found to delay μ by 2.5 years on average across muscles, although there were marked differences between muscles with more slowly progressing muscles showing larger delay.ConclusionsMRS FF and MRI qT2 provide sensitive noninvasive measures of DMD progression. Modeling changes in these biomarkers across multiple muscles can be used to detect and monitor the therapeutic effects of corticosteroids on disease progression and to provide prognostic information on functional outcomes. This modeling approach provides a method to transform these MRI biomarkers into well-understood metrics, allowing concise summaries of DMD disease progression at individual and population levels.ClinicalTrials.gov identifier:NCT01484678.

Oligothiophene derivatives have been extensively studied as p-type semiconducting materials in organic electronics applications. This work reports the synthesis, self-assembly and photophysical properties of acceptor-donor-acceptor (A-D-A)-type oligothiophene derivatives by end-group engineering of quaterthiophene (QT) with naphthalene monoimide (NMI) chromophores that are further connected to a trialkoxy benzamide wedge. Conjugation to the NMI units reduces the HOMO-LUMO gap significantly, and consequently the absorption spectrum exhibits a bathochromic shift of about 50 nm compared with QT. Furthermore, extended H-bonding interactions among the amido groups of the peripheral wedges produce entangled fibrillar nanostructures and gelation in hydrocarbon solvents such as methylcyclohexane, wherein the A-D-A chromophore exhibits typical H-aggregation. On the contrary, the fact that the same chromophore, lacking only the amido units, does not produce gels or H-aggregates indicates strong impact of H-bonding on the self-assembly. Computational studies revealed the electronic properties of the chromophore and predicted the geometry of a dimer in the H-aggregate that reasonably matches with the experimental results. Bulk electrical conductivity measurements determined an excellent conductivity of 2.3×10-2 S cm-1 for the H-aggregated system (OT-1), which is two orders of magnitude higher than that of the same chromophore lacking the amido groups (OT-2).

Many researches have been done on shot boundary detection, but the performance of shot boundary detection approaches is yet to be addressed for the videos having sudden illumination and object/camera motion effects efficiently. In this paper, a novel dual-stage approach for an abrupt transition detection is proposed which is able to withstand under certain illumination and motion effects. Firstly, an adaptive Wiener filter is applied to the lightness component of the frame to retain some important information on both frequencies and LBP-HF is extracted to reduce the illumination effect. From the experimentation, it is also confirmed that the motion effect is also reduced in the first stage. Secondly, Canny edge difference is used to further remove the illumination and motion effects which are not handled in the first stage. TRECVid 2001 and TRECVid 2007 datasets are applied to analyze and validate our proposed algorithm. Experimental results manifest that the proposed system outperforms the state-of-the-art shot boundary detection techniques.

Thiolated pH-responsive poly(acrylic acid) functionalized gold nanoparticles (AuNP-SPAA) were investigated as recoverable and reusable catalysts in catalyzing the 4-nitrophenol reduction reaction. ...

Purpose: The clinical behavior of ampullary adenocarcinoma varies widely. Targeted tumor sequencing may better define biologically distinct subtypes to improve diagnosis and management. Experimental Design: The hidden-genome algorithm, a multilevel meta-feature regression model, was trained on a prospectively sequenced cohort of 3,411 patients (1,001 pancreatic adenocarcinoma, 165 distal bile-duct adenocarcinoma, 2,245 colorectal adenocarcinoma) and subsequently applied to targeted panel DNA-sequencing data from ampullary adenocarcinomas. Genomic classification (i.e., colorectal vs. pancreatic) was correlated with standard histologic classification [i.e., intestinal (INT) vs. pancreatobiliary (PB)] and clinical outcome. Results: Colorectal genomic subtype prediction was primarily influenced by mutations in APC and PIK3CA, tumor mutational burden, and DNA mismatch repair (MMR)–deficiency signature. Pancreatic genomic-subtype prediction was dictated by KRAS gene alterations, particularly KRAS G12D, KRAS G12R, and KRAS G12V. Distal bile-duct adenocarcinoma genomic subtype was most influenced by copy-number gains in the MDM2 gene. Despite high (73%) concordance between immunomorphologic subtype and genomic category, there was significant genomic heterogeneity within both histologic subtypes. Genomic scores with higher colorectal probability were associated with greater survival compared with those with a higher pancreatic probability. Conclusions: The genomic classifier provides insight into the heterogeneity of ampullary adenocarcinoma and improves stratification, which is dictated by the proportion of colorectal and pancreatic genomic alterations. This approach is reproducible with available molecular testing and obviates subjective histologic interpretation.

ConspectusBioinspired self-assembly has been explored with diverse synthetic scaffolds, among which amphiphiles are perhaps the most extensively studied systems. Classical surfactants or amphiphilic block copolymers, depending on the hydrophobic-hydrophilic balance, produce distinct nanostructures, which hold promise for applications ranging from biology to materials sciences. Nevertheless, their immiscibility-driven aggregation does not provide the opportunity to precisely regulate the internal order, morphology, or functional group display, which is highly desirable, especially in the context of biological applications.A new class of amphiphiles have emerged in the recent past in which the hydrophilic segment(s) is appended with a hydrophobic supramolecular-structure-directing-unit (SSDU), consisting of a π-conjugated chromophore and a H-bonding group. Self-recognition of the SSDU by attractive directional interactions governs the supramolecular assembly, which is fundamentally different than the repulsive solvent-immiscibility driven aggregation of traditional amphiphiles. Such SSDU-appended hydrophilic polymers exhibit entropy-driven highly stable self-assembly producing distinct nanostructures depending on the H-bonding functional group. For example, polymers with the hydrazide-functionalized SSDU attached form a polymersome, while in a sharp contrast, the same polymers when connected to an amide containing SSDU produce a cylindrical micelle via a spherical-micelle intermediate. This relationship holds true for a series of SSDU-attached hydrophilic polymers irrespective of the hydrophobic/hydrophilic balance or chemical structure, indicating that the supramolecular-assembly is primarily controlled by the specific molecular-recognition motif of the SSDU, instead of the packing parameter-based norms. Beyond synthetic polymers, SSDU-attached proteins also exhibit similar molecular-recognition driven self-assembly as well as coassembly with SSDU-attached polymers or hydrophilic wedges, producing multi-stimuli-responsive nanostructures in which the protein gains remarkable protection from thermal denaturation or enzymatic hydrolysis and exhibits redox-responsive enzymatic activity.Furthermore, SSDU-derived bola-shape π-amphiphiles have been recognized as a useful scaffold for the synthesis of unsymmetric polymersomes, rarely reported in the literature. The building block consists of a hydrophobic naphthalene-diimide (NDI) π-system attached to a hydrophilic functional group (ionic or nonionic) and a nonionic wedge on its two opposite arms. Extended H-bonding among the hydrazide groups, placed only on one side of the central chromophore by design, ensures stacking of the NDIs with parallel orientation and induces a preferred direction of curvature so that the H-bonded chain and consequently the functional groups attached to the same side remain at the inner-wall of the supramolecular polymersome. Automatically, the functional groups, located on the other side, are displayed at the outer surface. This design works for different amphiphiles, which by virtue of efficient and predictable functional group display, strongly influences the multivalent binding with different biological targets resulting in efficient enzyme inhibition, glycocluster effect, or antibacterial activity, depending on the nature of the functional group. By taking advantage of the electron accepting nature of the NDI, electron rich pyrene-containing amphiphiles can be costacked in alternating sequence, producing temperature and redox-responsive supramolecular polymers with NDI/pyrene stoichiometry-dependent morphology, lower critical solution temperature (LCST), functional group display, and antibacterial activity.

Statistical analyses of directional or angular data have applications in a variety of fields, such as geology, meteorology and bioinformatics. There is substantial literature on descriptive and inferential techniques for univariate angular data, with the bivariate (or more generally, multivariate) cases receiving more attention in recent years. More specifically, the bivariate wrapped normal, von Mises sine and von Mises cosine distributions, and mixtures thereof, have been proposed for practical use. However, there is a lack of software implementing these distributions and the associated inferential techniques. In this article, we introduce BAMBI, an R package for analyzing bivariate (and univariate) angular data. We implement random data generation, density evaluation, and computation of theoretical summary measures (variances and correlation coefficients) for the three aforementioned bivariate angular distributions, as well as two univariate angular distributions: the univariate wrapped normal and the univariate von Mises distribution. The major contribution of BAMBI to statistical computing is in providing Bayesian methods for modeling angular data using finite mixtures of these distributions. We also provide functions for visual and numerical diagnostics and Bayesian inference for the fitted models. In this article, we first provide a brief review of the distributions and techniques used in BAMBI, then describe the capabilities of the package, and finally conclude with demonstrations of mixture model fitting using BAMBI on the two real data sets included in the package, one univariate and one bivariate.

The concept of Entropy plays a key role in Information Theory, Statistics, and Machine Learning. This paper introduces a new entropy measure, called the t-entropy, which exploits the concavity of the inverse-tan function. We analytically show that the proposed $t$ -entropy satisfies the prominent axiomatic properties of an entropy measure. We demonstrate an application of the proposed entropy measure for multi-level thresholding of images. We also propose the entropic-loss as a measure of the divergence between two probability distributions, which leads to robust estimators in the context of parametric statistical inference. The consistency and asymptotic breakdown point of the proposed estimator are mathematically analysed. Finally, we also show an application of the $t$ -entropy to feature weighted data clustering.

In this paper, we propose a Lasso Weighted k-means ( LW-k-means) algorithm, as a simple yet efficient sparse clustering procedure for high-dimensional data where the number of features ( p) can be much higher than the number of observations (n). The LW-k-means method imposes an l1 regularization term involving the feature weights directly to induce feature selection in a sparse clustering framework. We develop a simple block-coordinate descent type algorithm with time-complexity resembling that of Lloyd's method, to optimize the proposed objective. In addition, we establish the strong consistency of the LW-k-means procedure. Such consistency proof is not available for the conventional spare k-means algorithms, in general. LW-k-means is tested on a number of synthetic and real-life datasets and through a detailed experimental analysis, we find that the performance of the method is highly competitive against the baselines as well as the state-of-the-art procedures for center-based high-dimensional clustering, not only in terms of clustering accuracy but also with respect to computational time.

BackgroundCatheterization facilitates continuous bacteriuria, for which the clinical significance remains unclear. This study aimed to determine the clinical presentation, epidemiology, and dynamics of bacteriuria in a cohort of long-term catheterized nursing home residents.MethodsProspective urine culture, urinalysis, chart review, and assessment of signs and symptoms of infection were performed weekly for 19 study participants over 7 months. All bacteria ≥103 cfu/ml were cultured, isolated, identified, and tested for susceptibility to select antimicrobials.Results226 of the 234 urines were polymicrobial (97%), with an average of 4.7 isolates per weekly specimen. 228 urines (97%) exhibited ≥106 CFU/ml, 220 (94%) exhibited abnormal urinalysis, 126 (54%) were associated with at least one possible sign or symptom of infection, 82 (35%) would potentially meet a standardized definition of CAUTI, but only 3 had a caregiver diagnosis of CAUTI. 286 (30%) of bacterial isolates were resistant to a tested antimicrobial agent, and bacteriuria composition was remarkably stable despite a combined total of 54 catheter changes and 23 weeks of antimicrobial use.ConclusionsBacteriuria composition was largely polymicrobial, including persistent colonization by organisms previously considered to be urine culture contaminants. Neither antimicrobial use nor catheter changes sterilized the urine, at most resulting in transient reductions in bacterial burden followed by new acquisition of resistant isolates. Thus, this patient population exhibits a high prevalence of bacteriuria coupled with potential indicators of infection, necessitating further exploration to identify sensitive markers of true infection.FundingThis work was supported by the NIH (R00 DK105205, R01 DK123158, UL1 TR001412)

It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3-5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations observed in any new tumor are very rarely occurring mutations, and indeed the preponderance of these may never have been observed in any previous recorded tumor. To create a viable diagnostic tool we need to harness the information content in this "hidden genome" of variants for which no direct information is available. To accomplish this we propose a multi-level meta-feature regression to extract the critical information from rare variants in the training data in a way that permits us to also extract diagnostic information from any previously unobserved variants in the new tumor sample. A scalable implementation of the model is obtained by combining a high-dimensional feature screening approach with a group-lasso penalized maximum likelihood approach based on an equivalent mixed-effect representation of the multilevel model. We apply the method to the Cancer Genome Atlas whole-exome sequencing data set including 3702 tumor samples across 7 common cancer sites. Results show that our multi-level approach can harness substantial diagnostic information from the hidden genome., Comment: 24 pages, 4 figures, 2 tables

This paper proposes a novel shot boundary detection method which combines the Advantages of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to optimize the weights of the Feed-Forward Neural Network (FNN). To increase the performance of the system, the output of the hybrid technique is again analyzed by forming a Continuity matrix (ϕ). Then an Outlier along with a Continuity matrix is used for extracting a possible set of transition frames. A set of thresholds δ1 and δ2 is selected for classifying abrupt and gradual transitions from the available set of possible transition frames. Experimental results using TRECVid 2001 depicts that PSOGSA outperforms GSA and PSO in-terms of training the feed forward neural network and generating a higher overall F1 score. The proposed system also gives better performance when compared with other latest techniques in-terms of F1 score.

The vast preponderance of somatic mutations in a typical cancer are either extremely rare or have never been previously recorded in available databases that track somatic mutations. These constitute a hidden genome that contrasts the relatively small number of mutations that occur frequently, the properties of which have been studied in depth. Here we demonstrate that this hidden genome contains much more accurate information than common mutations for the purpose of identifying the site of origin of primary cancers in settings where this is unknown. We accomplish this using a projection-based statistical method that achieves a highly effective signal condensation, by leveraging DNA sequence and epigenetic contexts using a set of meta-features that embody the mutation contexts of rare variants throughout the genome., The vast majority of somatic mutations observed in tumors are rare. Here, the authors show that these large numbers of rare mutations are more predictive of the tissue of origin of a tumor than the information from a few common driver mutations.

Copper doping in II-VI semiconductor nanocrystals (NCs) has sparked enormous debate regarding the oxidation state of Cu ions and their hugely differing consequences in optoelectronic applications. The identity of a magnetically active Cu

Resident performance on the American Board of Surgery In-Training Examination (ABSITE) is used for evaluation of surgical knowledge and guides resident selection for institutional remediation programs. Remediation thresholds have historically been based on ABSITE percentile scores; however, this does not account for predictors that can impact a resident's exam performance. We sought to identify predictors of yearly ABSITE performance to help identify residents "at-risk" for performing below their expected growth trajectory.The knowledge of the residents, as measured by standardized ABSITE scores, was modeled as a function of the corresponding postgraduate year via a linear mixed effects regression model. Additional model covariates included written USMLE-1-3 examination scores, gender, number of practice questions completed, and percentage correct of practice questions. For each resident, the predicted ABSITE standard score along with a 95% bootstrap prediction interval was obtained. Both resident-specific and population-level predictions for ABSITE standard scores were also estimated.The study was conducted at a single, large academic medical center (Massachusetts General Hospital, Boston, MA).Six years of general surgery resident score reports at a single institution between 2014 and 2019 were deidentified and analyzed.A total of 376 score reports from 130 residents were analyzed. Covariates that had a significant effect on the model included USMLE-1 score (PGY1: p = 0.013; PGY2: p = 0.007; PGY3: p = 0.011), USMLE-2 score (PGY1: p0.001; PGY2: p0.001; PGY3: p0.001; PGY4: p0.001; PGY5: p = 0.032), male gender (PGY1: p = 0.003; PGY2: p0.001; PGY3: p0.001; PGY4: p = 0.008), and number of practice questions completed (p=0.003). Five residents were identified as having "fallen off" their predicted knowledge curve, including a single resident on 2 occasions. Population prediction curves were obtained at 7 different covariate percentile levels (5%, 10%, 25%, 50%, 75%, 90%, and 95%) that could be used to plot predicted resident knowledge progress.Performance on USMLE-1 and -2 examinations, male gender, and number of practice questions completed were positive predictors of ABSITE performance. Creating residency-wide knowledge growth curves as well as individualized predictive ABSITE performance models allows for more efficient identification of residents potentially at risk for poor ABSITE performance and structured monitoring of surgical knowledge progression.

The contribution of agroforestry systems (AFS) to enhance soil organic carbon (SOC) storage in soil layers due to the presence of deep tree roots are of interest in the context of promoting carbon sinks and greenhouse gas mitigation. To quantify the relative soil C contribution from trees in agroforestry systems (AFS), this study assessed the reported differences in SOC stocks under agroforestry systems in comparison with other land-use systems (Agriculture, Forestry, Pasture, or Uncultivated Land) in various soil-depth classes in four agroecological regions (arid and semiarid, ASA; lowland humid tropics, LHT; Mediterranean, MED; and temperate, TEM) around the world. Using mixed-effect models and a meta-analytical approach, we synthesized data from 78 peer-reviewed studies that generated 858 data points (sites) on SOC stock under various AFS practiced globally. Comparing Agroforest vs. Agriculture or Agroforest vs. Pasture, SOC stocks under AFS were higher by +27% in the ASA region, +26% in LHT, and +5.8% in TEM, but –5.3% in the TEM in the 0–100 cm soil depth. The Agroforest aged between 10–20 years had higher SOC stock than newly established, as well as