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BOOK
Quantum simulations of materials on near-term quantum computers
Quantum computers hold promise to enable efficient simulations of the properties of molecules and materials; however, at present they only permit ab initio calculations of a few atoms, due to a limited number of qubits. In order to harness the power of near-term quantum computers for simulations of larger systems, it is desirable to develop hybrid quantum-classical methods where the quantum computation is restricted to a small portion of the system. This is of particular relevance for molecules and solids where an active region requires a higher level of theoretical accuracy than its environment. Here, we present a quantum embedding theory for the calculation of strongly-correlated electronic states of active regions, with the rest of the system described within density functional theory. We demonstrate the accuracy and effectiveness of the approach by investigating several defect quantum bits in semiconductors that are of great interest for quantum information technologies. We perform calculations on quantum computers and show that they yield results in agreement with those obtained with exact diagonalization on classical architectures, paving the way to simulations of realistic materials on near-term quantum computers.
Journal Article
An Optimized Framework for Breast Cancer Classification Using Machine Learning
Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.
Journal Article
Breast Cancer Segmentation Methods: Current Status and Future Potentials
Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Segmentation plays a significant role in image analysis and includes detection, feature extraction, classification, and treatment. Segmentation helps physicians quantify the volume of tissue in the breast for treatment planning. In this work, we have grouped segmentation methods into three groups: classical segmentation that includes region-, threshold-, and edge-based segmentation; machine learning segmentation; and supervised and unsupervised and deep learning segmentation. The findings of our study revealed that region-based segmentation is frequently used for classical methods, and the most frequently used techniques are region growing. Further, a median filter is a robust tool for removing noise. Moreover, the MIAS database is frequently used in classical segmentation methods. Meanwhile, in machine learning segmentation, unsupervised machine learning methods are more frequently used, and U-Net is frequently used for mammogram image segmentation because it does not require many annotated images compared with other deep learning models. Furthermore, reviewed papers revealed that it is possible to train a deep learning model without performing any preprocessing or postprocessing and also showed that the U-Net model is frequently used for mammogram segmentation. The U-Net model is frequently used because it does not require many annotated images and also because of the presence of high-performance GPU computing, which makes it easy to train networks with more layers. Additionally, we identified mammograms and utilised widely used databases, wherein 3 and 28 are public and private databases, respectively.
Journal Article
Vessel Navigation Behavior Analysis and Multiple-Trajectory Prediction Model Based on AIS Data
With the increasing application and utility of automatic identification systems (AISs), large volumes of AIS data are collected to record vessel navigation. In recent years, the prediction of vessel trajectories has become one of the hottest research issues. In contrast to existing studies, most researchers have focused on the single-trajectory prediction of vessels. This article proposes a multiple-trajectory prediction model and makes two main contributions. First, we propose a novel method of trajectory feature representation that uses a hierarchical clustering algorithm to analyze and extract the vessel navigation behavior for multiple trajectories. Compared with the classic methods, e.g., Douglas–Peucker (DP) and least-squares cubic spline curve approximation (LCSCA) algorithms, the mean loss of trajectory features extracted by our method is approximately 0.005, and it is reduced by 50% and 30% compared to the DP and LCSCA algorithms, respectively. Second, we design an integrated model for simultaneous prediction of multiple trajectories using the proposed features and employ the long short-term memory (LSTM)-based neural network and recurrent neural network (RNN) to pursue this time series task. Furthermore, the comparative experiments prove that the mean value and standard deviation of root mean squared error (RMSE) using the LSTM are 4% and 14% lower than those using the RNN, respectively.
Journal Article
Advances in the identification of novel cell signatures in benign prostatic hyperplasia and prostate cancer using single-cell RNA sequencing
Nowadays, chronic benign and malignant prostatic diseases are prevalent, costly, and impose a significant burden. Benign prostatic hyperplasia (BPH), a common condition in the aging population, often coexists with localized prostate cancer (PCa). These diseases likely share underlying molecular mechanisms, which remain poorly understood. The exploration of novel cell subpopulations and specific biomarkers for accurate diagnosis and treatment of prostatic diseases is ongoing and holds great clinical promise. Prostate cell proliferation and immune inflammation are key contributors to the progression of BPH and PCa, involving various prostate and immune cell subpopulations. This raises important questions about how specific cell types drive phenotypic heterogeneity. Advanced single-cell RNA sequencing (scRNA-seq), a cutting-edge technology, offers unparalleled insights at the single-cell level. Similar to a microscope that identifies cell types within tissue samples, scRNA-seq elucidates cellular heterogeneity and diversity within single cell populations, positioning itself as a future-leading sequencing technology. Considering that BPH and PCa share androgen-dependent growth, chronic inflammation and specific microenvironmental changes, this review discusses recent discoveries of novel cell subpopulations and molecular signatures in BPH and PCa that can be dissected by scRNA-seq. It aims to help researchers better understand the molecular pathogenesis of these conditions while offering new therapeutic possibilities for clinical management of benign and malignant prostatic disorders.
Journal Article
Connections between Various Disorders: Combination Pattern Mining Using Apriori Algorithm Based on Diagnosis Information from Electronic Medical Records
Objective. Short-term or long-term connections between different diseases have not been fully acknowledged. This study was aimed at exploring the network association pattern between disorders that occurred in the same individual by using the association rule mining technique. Methods. Raw data were extracted from the large-scale electronic medical record database of the affiliated hospital of Xuzhou Medical University. 1551732 pieces of diagnosis information from 144207 patients were collected from 2015 to 2020. Clinic diagnoses were categorized according to “International Classification of Diseases, 10th revision”. The Apriori algorithm was used to explore the association patterns among those diagnoses. Results. 12889 rules were generated after running the algorithm at first. After threshold filtering and manual examination, 110 disease combinations (support≥0.001, confidence≥60%, lift>1) with strong association strength were obtained eventually. Association rules about the circulatory system and metabolic diseases accounted for a significant part of the results. Conclusion. This research elucidated the network associations between disorders from different body systems in the same individual and demonstrated the usefulness of the Apriori algorithm in comorbidity or multimorbidity studies. The mined combinations will be helpful in improving prevention strategies, early identification of high-risk populations, and reducing mortality.
Journal Article
Dynamic cross-lagged effects between healthy lifestyles and multimorbidity among middle-aged and older adults in China
Background
While healthy lifestyles mitigate the risk of multimorbidity (≥ 2 chronic diseases), their temporal dynamics in aging populations, particularly in low- and middle-income countries undergoing rapid demographic structure transition, remain understudied.
Methods
Using longitudinal data (2014–2020) from 6,852 Chinese adults (aged ≥ 45 years) in the China Family Panel Studies, we used the subgroup analysis to investigate high risk groups in the chronic diseases status, employed alluvial diagrams to visualize diseases status transition and random intercept cross-lagged panel model to quantify the lagged effect between healthy lifestyles (sleep, physical exercise, smoking, drinking) and chronic diseases status (without diseases, single, multimorbidity).
Results
Compared to male, urban and middle-aged individuals, female, rural and older adults demonstrated more severe chronic diseases status (
P
< 0.05). The proportion of people with multimorbidity increased over time, from 9.2% in 2014 to 29.1% in 2020. A total of 37.8% of participants experienced diseases status transition, and more than half of whom progressed to multimorbidity. Disease trajectories disproportionately progressed toward multimorbidity. The direction and size of the cross-lagged effects are dynamic. Healthier lifestyles predicted reduced disease severity from 2014 to 2018 (β
1
=-0.106,
P
1
< 0.001; β
2
=-0.111,
P
2
< 0.001), but this protective effect reversed post-2018, with multimorbidity predicting lower probability of choosing healthy lifestyles (β
3
=-0.160,
P
3
< 0.001).
Conclusions
Our study demonstrates dynamic cross-lagged effect exists between healthy lifestyles and chronic diseases status in middle-aged and older Chinese. Disease trajectories and lifestyle-disease interplay reveal critical time-sensitive windows for intervention. Early-stage lifestyle promotion could delay progression, whereas later-stage disease management requires system-level strategies addressing urban-rural healthcare disparities and self-efficacy barriers. These findings directly inform China’s Healthy Aging 2030 priorities.
Journal Article
Complete structural characterization of single carbon nanotubes by Rayleigh scattering circular dichroism
Non-invasive, high-throughput spectroscopic techniques can identify chiral indices (
n
,
m
) of carbon nanotubes down to the single-tube level
1
–
6
. Yet, for complete characterization and to unlock full functionality, the handedness, the structural property associated with mirror symmetry breaking, also needs to be identified accurately and efficiently
7
–
14
. So far, optical methods fail in the handedness characterization of single nanotubes because of the extremely weak chiroptical signals (roughly 10
−7
) compared with the excitation light
15
,
16
. Here we demonstrate the complete structure identification of single nanotubes in terms of both chiral indices and handedness by Rayleigh scattering circular dichroism. Our method is based on the background-free feature of Rayleigh scattering collected at an oblique angle, which enhances the nanotube’s chiroptical signal by three to four orders of magnitude compared with conventional absorption circular dichroism. We measured a total of 30 single-walled carbon nanotubes including both semiconducting and metallic nanotubes and found that their absolute chiroptical signals show a distinct structure dependence, which can be qualitatively understood through tight-binding calculations. Our strategy enables the exploration of handedness-related functionality of single nanotubes and provides a facile platform for chiral discrimination and chiral device exploration at the level of individual nanomaterials.
Optical spectroscopy can identify chiral indices of individual carbon nanotubes, but has so far been unable to determine their handedness because of the weak chiroptical signal. Rayleigh scattering circular dichroism now enables the identification of both chiral indices and handedness of individual nanotubes.
Journal Article
Entanglement and control of single nuclear spins in isotopically engineered silicon carbide
Nuclear spins in the solid state are both a cause of decoherence and a valuable resource for spin qubits. In this work, we demonstrate control of isolated
29
Si nuclear spins in silicon carbide (SiC) to create an entangled state between an optically active divacancy spin and a strongly coupled nuclear register. We then show how isotopic engineering of SiC unlocks control of single weakly coupled nuclear spins and present an ab initio method to predict the optimal isotopic fraction that maximizes the number of usable nuclear memories. We bolster these results by reporting high-fidelity electron spin control (
F
= 99.984(1)%), alongside extended coherence times (Hahn-echo
T
2
= 2.3 ms, dynamical decoupling
T
2
DD
> 14.5 ms), and a >40-fold increase in Ramsey spin dephasing time (
T
2
*) from isotopic purification. Overall, this work underlines the importance of controlling the nuclear environment in solid-state systems and links single photon emitters with nuclear registers in an industrially scalable material.
Isotope engineering of silicon carbide leads to control of nuclear spins associated with single divacancy centres and extended electron spin coherence.
Journal Article