Wei Qian

Bio: Wei Qian is an academic researcher from University of Texas at El Paso. The author has contributed to research in topics: Physics & Medicine. The author has an hindex of 50, co-authored 411 publications receiving 15575 citations. Previous affiliations of Wei Qian include University of Notre Dame & Northeastern University (China).

Cancer Cell Imaging and Photothermal Therapy in the Near-Infrared Region by Using Gold Nanorods

Abstract: Due to strong electric fields at the surface, the absorption and scattering of electromagnetic radiation by noble metal nanoparticles are strongly enhanced. These unique properties provide the potential of designing novel optically active reagents for simultaneous molecular imaging and photothermal cancer therapy. It is desirable to use agents that are active in the near-infrared (NIR) region of the radiation spectrum to minimize the light extinction by intrinsic chromophores in native tissue. Gold nanorods with suitable aspect ratios (length divided by width) can absorb and scatter strongly in the NIR region (650−900 nm). In the present work, we provide an in vitro demonstration of gold nanorods as novel contrast agents for both molecular imaging and photothermal cancer therapy. Nanorods are synthesized and conjugated to anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibodies and incubated in cell cultures with a nonmalignant epithelial cell line (HaCat) and two malignant oral epithelial ...

Cancer cells assemble and align gold nanorods conjugated to antibodies to produce highly enhanced, sharp, and polarized surface Raman spectra: a potential cancer diagnostic marker.

Abstract: Human oral cancer cells are found to assemble and align gold nanorods conjugated to anti-epidermal growth factor receptor (anti-EGFR) antibodies. Immnoconjugated gold nanorods and nanospheres were shown previously to exhibit strong Rayleigh (Mie) scattering useful for imaging. In the present letter, molecules near the nanorods on the cancer cells are found to give a Raman spectrum that is greatly enhanced (due to the high surface plasmon field of the nanorod assembly in which their extended surface plasmon fields overlap), sharp (due to a homogeneous environment), and polarized (due to anisotropic alignments). These observed properties can be used as diagnostic signatures for cancer cells.

Amorphous silica nanowires: Intensive blue light emitters

Abstract: We report large-scale synthesis of silica nanowires (SiONWs) using an excimer laser ablation method. Silica was produced in the form of amorphous nanowires at a diameter of ∼15 nm and a length up to hundreds micrometers. The SiONWs emit stable and high brightness blue light at energies of 2.65 and 3.0 eV. The intensity of the emission is two orders of magnitude higher than that of porous silicon. The SiONWs may have potential applications in high-resolution optical heads of scanning near-field optical microscope or nanointerconnections in future integrated optical devices.

Comparative and functional genomic analyses of the pathogenicity of phytopathogen Xanthomonas campestris pv. campestris

Abstract: Xanthomonas campestris pathovar campestris (Xcc) is the causative agent of crucifer black rot disease, which causes severe losses in agricultural yield world-wide. This bacterium is a model organism for studying plant-bacteria interactions. We sequenced the complete genome of Xcc 8004 (5,148,708 bp), which is highly conserved relative to that of Xcc ATCC 33913. Comparative genomics analysis indicated that, in addition to a significant genomic-scale rearrangement cross the replication axis between two IS1478 elements, loss and acquisition of blocks of genes, rather than point mutations, constitute the main genetic variation between the two Xcc strains. Screening of a high-density transposon insertional mutant library (16,512 clones) of Xcc 8004 against a host plant (Brassica oleraceae) identified 75 nonredundant, single-copy insertions in protein-coding sequences (CDSs) and intergenic regions. In addition to known virulence factors, full virulence was found to require several additional metabolic pathways and regulatory systems, such as fatty acid degradation, type IV secretion system, cell signaling, and amino acids and nucleotide metabolism. Among the identified pathogenicity-related genes, three of unknown function were found in Xcc 8004-specific chromosomal segments, revealing a direct correlation between genomic dynamics and Xcc virulence. The present combination of comparative and functional genomic analyses provides valuable information about the genetic basis of Xcc pathogenicity, which may offer novel insight toward the development of efficient methods for prevention of this important plant disease.

Nanoscale silicon wires synthesized using simple physical evaporation

Abstract: We report the large-scale synthesis of silicon nanowires (SiNWs) using a simple but effective approach. High purity SiNWs of uniform diameters around 15 nm were obtained by sublimating a hot-pressed silicon powder target at 1200 °C in a flowing carrier gas environment. The SiNWs emit stable blue light which seems unrelated to quantum confinement, but related to an amorphous overcoating layer of silicon oxide. Our approach can be used, in principle, as a general method for synthesis of other one-dimensional semiconducting, or conducting nanowires.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Gold nanoparticles: assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology.

Abstract: Although gold is the subject of one of the most ancient themes of investigation in science, its renaissance now leads to an exponentially increasing number of publications, especially in the context of emerging nanoscience and nanotechnology with nanoparticles and self-assembled monolayers (SAMs). We will limit the present review to gold nanoparticles (AuNPs), also called gold colloids. AuNPs are the most stable metal nanoparticles, and they present fascinating aspects such as their assembly of multiple types involving materials science, the behavior of the individual particles, size-related electronic, magnetic and optical properties (quantum size effect), and their applications to catalysis and biology. Their promises are in these fields as well as in the bottom-up approach of nanotechnology, and they will be key materials and building block in the 21st century. Whereas the extraction of gold started in the 5th millennium B.C. near Varna (Bulgaria) and reached 10 tons per year in Egypt around 1200-1300 B.C. when the marvelous statue of Touthankamon was constructed, it is probable that “soluble” gold appeared around the 5th or 4th century B.C. in Egypt and China. In antiquity, materials were used in an ecological sense for both aesthetic and curative purposes. Colloidal gold was used to make ruby glass 293 Chem. Rev. 2004, 104, 293−346

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.