There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Gold nanoparticles have been used in biomedical applications since their first colloidal syntheses more than three centuries ago. However, over the past two decades, their beautiful colors and unique electronic properties have also attracted tremendous attention due to their historical applications in art and ancient medicine and current applications in enhanced optoelectronics and photovoltaics. In spite of their modest alchemical beginnings, gold nanoparticles exhibit physical properties that are truly different from both small molecules and bulk materials, as well as from other nanoscale particles. Their unique combination of properties is just beginning to be fully realized in range of medical diagnostic and therapeutic applications. This critical review will provide insights into the design, synthesis, functionalization, and applications of these artificial molecules in biomedicine and discuss their tailored interactions with biological systems to achieve improved patient health. Further, we provide a survey of the rapidly expanding body of literature on this topic and argue that gold nanotechnology-enabled biomedicine is not simply an act of ‘gilding the (nanomedicinal) lily’, but that a new ‘Golden Age’ of biomedical nanotechnology is truly upon us. Moving forward, the most challenging nanoscience ahead of us will be to find new chemical and physical methods of functionalizing gold nanoparticles with compounds that can promote efficient binding, clearance, and biocompatibility and to assess their safety to other biological systems and their long-term term effects on human health and reproduction (472 references).

The golden age: gold nanoparticles for biomedicine

Molecular self-assembly is the spontaneous association of molecules under equilibrium conditions into stable, structurally well-defined aggregates joined by noncovalent bonds. Molecular self-assembly is ubiquitous in biological systems and underlies the formation of a wide variety of complex biological structures. Understanding self-assembly and the associated noncovalent interactions that connect complementary interacting molecular surfaces in biological aggregates is a central concern in structural biochemistry. Self-assembly is also emerging as a new strategy in chemical synthesis, with the potential of generating nonbiological structures with dimensions of 1 to 10(2) nanometers (with molecular weights of 10(4) to 10(10) daltons). Structures in the upper part of this range of sizes are presently inaccessible through chemical synthesis, and the ability to prepare them would open a route to structures comparable in size (and perhaps complementary in function) to those that can be prepared by microlithography and other techniques of microfabrication.

Molecular self-assembly and nanochemistry: A chemical strategy for the synthesis of nanostructures

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

/pdf/present-and-future-of-surface-enhanced-raman-scattering-34aqgcm385.pdf

Present and Future of Surface-Enhanced Raman Scattering

Residues of diquat (DQ), a bipyridyl herbicide, can pose a threat to human health. In this study, we designed two semi-antigens and obtained a highly specific monoclonal antibody (mAb) against...

Development of immunochromatographic strips for the rapid detection of diquat in apples and strawberries

Cell Imaging: Photoactive Hybrid AuNR‐Pt@Ag2S Core–Satellite Nanostructures for Near‐Infrared Quantitive Cell Imaging (Adv. Funct. Mater. 46/2017)

Ultrasensitive detection of four organic arsenic compounds at the same time using a five-link cardboard-based assay.

Here, we describe a sensitive and rapid gold nanoparticle-based immunochromatographic strip (GNP-ICS) for the detection of dimethachlone (DMT) in tomatoes and lettuces using GNP-labeled monoclonal antibodies (mAbs) as the probe....

Sensitive immunochromatographic assay for the detection of dimethachlone fungicide in tomatoes and lettuces

Objective This study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms. Methods A retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC). Results The malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost. Conclusions Combining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

/pdf/incorporation-of-a-machine-learning-pathological-diagnosis-25goz91w.pdf

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules

Chuanlai Xu

Papers

Development of immunochromatographic strips for the rapid detection of diquat in apples and strawberries

Cell Imaging: Photoactive Hybrid AuNR‐Pt@Ag2S Core–Satellite Nanostructures for Near‐Infrared Quantitive Cell Imaging (Adv. Funct. Mater. 46/2017)

Ultrasensitive detection of four organic arsenic compounds at the same time using a five-link cardboard-based assay.

Sensitive immunochromatographic assay for the detection of dimethachlone fungicide in tomatoes and lettuces

Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules