A prospective pilot clinical trial evaluating the utility of a dynamic near-infrared imaging device for characterizing suspicious breast lesions

Introduction: Characterizing and differentiating between malignant tumors, benign tumors, and normal breast tissue is increasingly important in the patient presenting with breast problems. Near-infrared diffuse optical imaging and spectroscopy is capable of measuring multiple physiologic parameters of biological tissue systems and may have clinical applications for assessing the development and progression of neoplastic processes, including breast cancer. The currently available application of near-infrared imaging technology for the breast, however, is compromised by low spatial resolution, tissue heterogeneity, and interpatient variation. Materials and methods: We tested a dynamic near-infrared imaging schema for the characterization of suspicious breast lesions identified on diagnostic clinical ultrasound. A portable handheld near-infrared tissue imaging device (P-Scan; ViOptix Inc., Fremont, CA, USA) was utilized. An external mechanical compression force was applied to breast tissue. The tissue oxygen saturation and hemoglobin concentration were recorded simultaneously by the handheld near-infrared imaging device. Twelve categories of dynamic tissue parameters were derived based on real-time measurements of the tissue hemoglobin concentration and the oxygen saturation. Results: Fifty suspicious breast lesions were evaluated in 48 patients. Statistical analyses were carried out on 36 out of 50 datasets that satisfied our inclusion criteria. Suspicious breast lesions identified on diagnostic clinical ultrasound had lower oxygenation and higher hemoglobin concentration than the surrounding normal breast tissue. Furthermore, histopathologic-proven malignant breast tumors had a lower differential hemoglobin contrast (that is, the difference of hemoglobin concentration variability between the suspicious breast lesion and the normal breast parenchyma located remotely elsewhere within the ipsilateral breast) as compared with histopathologic-proven benign breast lesions. Conclusion: The proposed dynamic near-infrared imaging schema has the potential to differentiate benign processes from those of malignant breast tumors. Further development and refinement of the dynamic imaging device and additional subsequent clinical testing are necessary for optimizing the accuracy of detection

Suppression of Phase Separation in LiFePO4 Nanoparticles During Battery Discharge

Using a novel electrochemical phase-field model, we question the common belief that LixFePO4 nanoparticles separate into Li-rich and Li-poor phases during battery discharge. For small currents, spinodal decomposition or nucleation leads to moving phase boundaries. Above a critical current density (in the Tafel regime), the spinodal disappears, and particles fill homogeneously, which may explain the superior rate capability and long cycle life of nano-LiFePO4 cathodes.Comment: 27 pages, 8 figure

Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

The Role of Genomics in the Identification, Prediction, and Prevention of Biological Threats

In all likelihood, it is only a matter of time before our public health system will face a major biological threat, whether intentionally dispersed or originating from a known or newly emerging infectious disease. It is necessary not only to increase our reactive “biodefense,” but also to be proactive and increase our preparedness. To achieve this goal, it is essential that the scientific and public health communities fully embrace the genomic revolution, and that novel bioinformatic and computing tools necessary to make great strides in our understanding of these novel and emerging threats be developed. Genomics has graduated from a specialized field of science to a research tool that soon will be routine in research laboratories and clinical settings. Because the technology is becoming more affordable, genomics can and should be used proactively to build our preparedness and responsiveness to biological threats. All pieces, including major continued funding, advances in next-generation sequencing technologies, bioinformatics infrastructures, and open access to data and metadata, are being set in place for genomics to play a central role in our public health system

A Self-Organizing Algorithm for Modeling Protein Loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies

Quantum Corrections for ABGB Black Hole

In this paper, we study quantum corrections to the temperature and entropy of a regular Ay\'{o}n-Beato-Garc\'{\i}a-Bronnikov black hole solution by using tunneling approach beyond semiclassical approximation. We use the first law of black hole thermodynamics as a differential of entropy with two parameters, mass and charge. It is found that the leading order correction to the entropy is of logarithmic form. In the absence of the charge, i.e., $e=0$, these corrections approximate the corresponding corrections for the Schwarzschild black hole.Comment: 15 pages, accepted for publication in Astrophysics and Space Scienc

A hybrid BAC physical map of potato: a framework for sequencing a heterozygous genome

<p>Abstract</p> <p>Background</p> <p>Potato is the world's third most important food crop, yet cultivar improvement and genomic research in general remain difficult because of the heterozygous and tetraploid nature of its genome. The development of physical map resources that can facilitate genomic analyses in potato has so far been very limited. Here we present the methods of construction and the general statistics of the first two genome-wide BAC physical maps of potato, which were made from the heterozygous diploid clone RH89-039-16 (RH).</p> <p>Results</p> <p>First, a gel electrophoresis-based physical map was made by AFLP fingerprinting of 64478 BAC clones, which were aligned into 4150 contigs with an estimated total length of 1361 Mb. Screening of BAC pools, followed by the KeyMaps <it>in silico </it>anchoring procedure, identified 1725 AFLP markers in the physical map, and 1252 BAC contigs were anchored the ultradense potato genetic map. A second, sequence-tag-based physical map was constructed from 65919 whole genome profiling (WGP) BAC fingerprints and these were aligned into 3601 BAC contigs spanning 1396 Mb. The 39733 BAC clones that overlap between both physical maps provided anchors to 1127 contigs in the WGP physical map, and reduced the number of contigs to around 2800 in each map separately. Both physical maps were 1.64 times longer than the 850 Mb potato genome. Genome heterozygosity and incomplete merging of BAC contigs are two factors that can explain this map inflation. The contig information of both physical maps was united in a single table that describes hybrid potato physical map.</p> <p>Conclusions</p> <p>The AFLP physical map has already been used by the Potato Genome Sequencing Consortium for sequencing 10% of the heterozygous genome of clone RH on a BAC-by-BAC basis. By layering a new WGP physical map on top of the AFLP physical map, a genetically anchored genome-wide framework of 322434 sequence tags has been created. This reference framework can be used for anchoring and ordering of genomic sequences of clone RH (and other potato genotypes), and opens the possibility to finish sequencing of the RH genome in a more efficient way via high throughput next generation approaches.</p

Advances in research on the use of biochar in soil for remediation: a review

Purpose: Soil contamination mainly from human activities remains a major environmental problem in the contemporary world. Significant work has been undertaken to position biochar as a readily-available material useful for the management of contaminants in various environmental media notably soil. Here, we review the increasing research on the use of biochar in soil for the remediation of some organic and inorganic contaminants.  Materials and methods: Bibliometric analysis was carried out within the past 10 years to determine the increasing trend in research related to biochar in soil for contaminant remediation. Five exemplar contaminants were reviewed in both laboratory and field-based studies. These included two inorganic (i.e., As and Pb) and three organic classes (i.e., sulfamethoxazole, atrazine, and PAHs). The contaminants were selected based on bibliometric data and as representatives of their various contaminant classes. For example, As and Pb are potentially toxic elements (anionic and cationic, respectively), while sulfamethoxazole, atrazine, and PAHs represent antibiotics, herbicides, and hydrocarbons, respectively.  Results and discussion: The interaction between biochar and contaminants in soil is largely driven by biochar precursor material and pyrolysis temperature as well as some characteristics of the contaminants such as octanol-water partition coefficient (KOW) and polarity. The structural and chemical characteristics of biochar in turn determine the major sorption mechanisms and define biochar’s suitability for contaminant sorption. Based on the reviewed literature, a soil treatment plan is suggested to guide the application of biochar in various soil types (paddy soils, brownfield, and mine soils) at different pH levels (4–5.5) and contaminant concentrations ( 50 mg kg−1).  Conclusions: Research on biochar has grown over the years with significant focus on its properties, and how these affect biochar’s ability to immobilize organic and inorganic contaminants in soil. Few of these studies have been field-based. More studies with greater focus on field-based soil remediation are therefore required to fully understand the behavior of biochar under natural circumstances. Other recommendations are made aimed at stimulating future research in areas where significant knowledge gaps exist

The Transcriptional Cofactor Nab2 Is Induced by TGF-β and Suppresses Fibroblast Activation: Physiological Roles and Impaired Expression in Scleroderma

By stimulating collagen synthesis and myofibroblasts differentiation, transforming growth factor-β (TGF- β) plays a pivotal role in tissue repair and fibrosis. The early growth response-1 (Egr-1) transcription factor mediates profibrotic TGF-β responses, and its expression is elevated in biopsies from patients with scleroderma. NGF1-A-binding protein 2 (Nab2) is a conserved transcriptional cofactor that directly binds to Egr-1 and positively or negatively modulates Egr-1 target gene transcription. Despite the recognized importance of Nab2 in governing the intensity of Egr-1-dependent responses, the regulation and function of Nab2 in the context of fibrotic TGF-β signaling is unknown. Here we show that TGF-β caused a time-dependent stimulation of Nab2 protein and mRNA in normal fibroblasts. Ectopic expression of Nab2 in these cells blocked Egr-1-dependent transcriptional responses, and abrogated TGF-β-induced stimulation of collagen synthesis and myofibroblasts differentiation. These inhibitory effects of Nab2 involved recruitment of the NuRD chromatin remodeling complex to the COL1A2 promoter and were accompanied by reduced histone H4 acetylation. Mice with targeted deletion of Nab2 displayed increased collagen accumulation in the dermis, and genetic or siRNA-mediated loss of Nab2 in fibroblasts was associated with constitutively elevated collagen synthesis and accentuation of Egr-1-dependent TGF-β responses in vitro. Expression of Nab2 was markedly up-regulated in skin biopsies from patients with scleroderma, and was localized primarily to epidermal keratinocytes. In contrast, little Nab2 could be detected in dermal fibroblasts. These results identify Nab2 as a novel endogenous negative regulator of Egr-1-dependent TGF-β signaling responsible for setting the intensity of fibrotic responses. Defective Nab2 expression or function in dermal fibroblasts might play a role in persistent fibrotic responses in scleroderma