Atomistic theory of hot carrier relaxation in large plasmonic nanoparticles

Recently, there has been significant interest in harnessing hot carriers generated from the decay of localized surface plasmons in metallic nanoparticles for applications in photocatalysis, photovoltaics and sensing. In this work, we present an atomistic approach to predict the population of hot carriers under continuous wave illumination in large nanoparticles. For this, we solve the equation of motion of the density matrix taking into account both excitation of hot carriers as well as subsequent relaxation effects. We present results for spherical Au and Ag nanoparticles with up to $250,000$ atoms. We find that the population of highly energetic carriers depends both on the material and the nanoparticle size. We also study the increase in the electronic temperature upon illumination and find that Ag nanoparticles exhibit a much larger temperature increase than Au nanoparticles. Finally, we investigate the effect of using different models for the relaxation matrix but find that qualitative features of the hot-carrier population are robust

Bidirectional discrimination with application to data visualization

Linear classifiers are very popular, but can have limitations when classes have distinct subpopulations. General nonlinear kernel classifiers are very flexible, but do not give clear interpretations and may not be efficient in high dimensions. We propose the bidirectional discrimination classification method, which generalizes linear classifiers to two or more hyperplanes. This new family of classification methods gives much of the flexibility of a general nonlinear classifier while maintaining the interpretability, and much of the parsimony, of linear classifiers. They provide a new visualization tool for high-dimensional, low-sample-size data. Although the idea is generally applicable, we focus on the generalization of the support vector machine and distance-weighted discrimination methods. The performance and usefulness of the proposed method are assessed using asymptotics and demonstrated through analysis of simulated and real data. Our method leads to better classification performance in high-dimensional situations where subclusters are present in the data

Prostate cancer theranostics - An overview

Metastatic prostate cancer is incurable, and novel methods to detect the disease earlier and to direct definitive treatment are needed. Molecularly specific tools to localize diagnostic and cytotoxic radionuclide payloads to cancer cells and the surrounding microenvironment are recognized as a critical component of new approaches to combat this disease. The implementation of theranostic approaches to characterize and personalize patient management is beginning to be realized for prostate cancer patients. This review article summarized clinically translated approaches to detect, characterize, and treat disease in this rapidly expanding field

Statistical Significance of Clustering Using Soft Thresholding

Clustering methods have led to a number of important discoveries in bioinformatics and beyond. A major challenge in their use is determining which clusters represent important underlying structure, as opposed to spurious sampling artifacts. This challenge is especially serious, and very few methods are available, when the data are very high in dimension. Statistical Significance of Clustering (SigClust) is a recently developed cluster evaluation tool for high dimensional low sample size data. An important component of the SigClust approach is the very definition of a single cluster as a subset of data sampled from a multivariate Gaussian distribution. The implementation of SigClust requires the estimation of the eigenvalues of the covariance matrix for the null multivariate Gaussian distribution. We show that the original eigenvalue estimation can lead to a test that suffers from severe inflation of type-I error, in the important case where there are a few very large eigenvalues. This paper addresses this critical challenge using a novel likelihood based soft thresholding approach to estimate these eigenvalues, which leads to a much improved SigClust. Major improvements in SigClust performance are shown by both mathematical analysis, based on the new notion of Theoretical Cluster Index, and extensive simulation studies. Applications to some cancer genomic data further demonstrate the usefulness of these improvements

R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment

Summary: R/DWD is an extensible package for classification. It is built based on a recently developed powerful classification method called distance weighted discrimination (DWD). DWD is related to, and has been shown to be superior to, the support vector machine in situations that are fundamental to bioinformatics, such as very high dimensional data. DWD has proven to be very useful for several fundamental bioinformatics tasks, including classification, data visualization and removal of biases, such as batch effects. Earlier DWD implementations, however, relied on Matlab, which is not free and requires a license. The major contribution of the R/DWD package is an implementation that is completely in R and thus can be used without any requirements for licensing or software purchase. In addition, R/DWD also provides efficient solvers for second-order-cone-programming and quadratic programming

Identification of novel host-oriented targets for Human Immunodeficiency Virus type 1 using Random Homozygous Gene Perturbation

<p>Abstract</p> <p>Background</p> <p>Human Immunodeficiency Virus (HIV) is a global threat to public health. Current therapies that directly target the virus often are rendered ineffective due to the emergence of drug-resistant viral variants. An emerging concept to combat drug resistance is the idea of targeting host mechanisms that are essential for the propagation of the virus, but have a minimal cellular effect.</p> <p>Results</p> <p>Herein, using Random Homozygous Gene Perturbation (RHGP), we have identified cellular targets that allow human MT4 cells to survive otherwise lethal infection by a wild type HIV-1_NL4-3. These gene targets were validated by the reversibility of the RHGP technology, which confirmed that the RHGP itself was responsible for the resistance to HIV-1 infection. We further confirmed by siRNA knockdowns that the RHGP-identified cellular pathways are responsible for resistance to infection by either CXCR4 or CCR5 tropic HIV-1 variants. We also demonstrated that cell clones with these gene targets disrupted by RHGP were not permissible to the replication of a drug resistant HIV-1 mutant.</p> <p>Conclusion</p> <p>These studies demonstrate the power of RHGP to identify novel host targets that are essential for the viral life cycle but which can be safely perturbed without overt cytotoxicity. These findings suggest opportunities for the future development of host-oriented therapeutics with the broad spectrum potential for safe and effective inhibition of HIV infection.</p

Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager

Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients’ immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy

Large-Scale Gene Expression Differences Across Brain Regions and Inbred Strains Correlate With a Behavioral Phenotype

Behaviors are often highly heritable, polygenic traits. To investigate molecular mediators of behavior, we analyzed gene expression patterns across seven brain regions (amygdala, basal ganglia, cerebellum, frontal cortex, hippocampus, cingulate cortex, and olfactory bulb) of 10 different inbred mouse strains (129S1/SvImJ, A/J, AKR/J, BALB/cByJ, BTBR T+ tf/J, C3H/HeJ, C57BL/6J, C57L/J, DBA/2J, and FVB/NJ). Extensive variation was observed across both strain and brain region. These data provide potential transcriptional intermediates linking polygenic variation to differences in behavior. For example, mice from different strains had variable performance on the rotarod task, which correlated with the expression of >2000 transcripts in the cerebellum. Correlation with this task was also found in the amygdala and hippocampus, but not in other regions examined, indicating the potential complexity of motor coordination. Thus we can begin to identify expression profiles contributing to behavioral phenotypes through variation in gene expression

Plate tectonics of virus shell assembly and reorganization in phage φ8, a distant relative of mammalian reoviruses

The hallmark of a virus is its capsid, which harbors the viral genome and is formed from protein subunits, which assemble following precise geometric rules. dsRNA viruses use an unusual protein multiplicity (120 copies) to form their closed capsids. We have determined the atomic structure of the capsid protein (P1) from the dsRNA cystovirus Φ8. In the crystal P1 forms pentamers, very similar in shape to facets of empty procapsids, suggesting an unexpected assembly pathway that proceeds via a pentameric intermediate. Unlike the elongated proteins used by dsRNA mammalian reoviruses, P1 has a compact trapezoid-like shape and a distinct arrangement in the shell, with two near-identical conformers in nonequivalent structural environments. Nevertheless, structural similarity with the analogous protein from the mammalian viruses suggests a common ancestor. The unusual shape of the molecule may facilitate dramatic capsid expansion during phage maturation, allowing P1 to switch interaction interfaces to provide capsid plasticity