Search

A highly anticipated application for quantum computers is as a universalsimulator of quantum many-body systems, as was conjectured by Richard Feynmanin the 1980s. The last decade has witnessed the growing success of quantumcomputing for simulating static properties of quantum systems, i.e., the groundstate energy of small molecules. However, it remains a challenge to simulatequantum many-body dynamics on current-to-near-future noisy intermediate-scalequantum computers. Here, we demonstrate successful simulation of nontrivialquantum dynamics on IBM's Q16 Melbourne quantum processor and Rigetti's Aspenquantum processor; namely, ultrafast control of emergent magnetism by THzradiation in an atomically-thin two-dimensional material. The full code andstep-by-step tutorials for performing such simulations are included to lowerthe barrier to access for future research on these two quantum computers. Assuch, this work lays a foundation for the promising study of a wide variety ofquantum dynamics on near-future quantum computers, including dynamiclocalization of Floquet states and topological protection of qubits in noisyenvironments.

A highly anticipated application for quantum computers is as a universalsimulator of quantum many-body systems, as was conjectured by Richard Feynmanin the 1980s. The last decade has witnessed the growing success of quantumcomputing for simulating static properties of quantum systems, i.e., the groundstate energy of small molecules. However, it remains a challenge to simulatequantum many-body dynamics on current-to-near-future noisy intermediate-scalequantum computers. Here, we demonstrate successful simulation of nontrivialquantum dynamics on IBM's Q16 Melbourne quantum processor and Rigetti's Aspenquantum processor; namely, ultrafast control of emergent magnetism by THzradiation in an atomically-thin two-dimensional material. The full code andstep-by-step tutorials for performing such simulations are included to lowerthe barrier to access for future research on these two quantum computers. Assuch, this work lays a foundation for the promising study of a wide variety ofquantum dynamics on near-future quantum computers, including dynamiclocalization of Floquet states and topological protection of qubits in noisyenvironments.

Background: Falls are the leading cause of hip fracture in older women, with important public health implications. Fall risk increases with age and other clinical factors, and varies by race/ethnicity. International studies suggest that fall risk is lower in Asians, although data are limited in U.S.Populations: This study examines racial/ethnic differences in fall prevalence among older U.S. women within a large integrated healthcare delivery system.Methods: This cross-sectional study used data from 6277 women ages 65-90 who responded to the 2008 or 2011 Kaiser Permanente Northern California Member Health Survey (KPNC-MHS). The KPNC-MHS is a mailed questionnaire sent to a random sample of adult members stratified by age, gender, and geographic location, representing a population estimate of >200,000 women age ≥65 years. Age, race/ethnicity, self-reported health status, presence of diabetes, arthritis or prior stroke, mobility limitations and number of falls in the past year were obtained from the KPNC-MHS. The independent association of race/ethnicity and recent falls was examined, adjusting for known risk factors.Results: The weighted sample was 76.7% non-Hispanic white, 6.2% Hispanic, 6.8% black and 10.3% Asian. Over 20% reported having fallen during the past year (28.5% non-Hispanic white, 27.8% Hispanic, 23.4% black and 20.1% Asian). Older age was associated with greater fall risk, as was having diabetes (OR 1.24, CI 1.03-1.48), prior stroke (OR 1.51, CI 1.09-2.07), arthritis (OR 1.61, CI 1.39-1.85) and mobility limitations (OR 2.82, CI 2.34-3.39), adjusted for age. Compared to whites, Asian (OR 0.64, CI 0.50-0.81) and black (OR 0.73, CI 0.55-0.95) women were much less likely to have ≥1 fall in the past year, adjusting for age, comorbidities, mobility limitation and poor health status. Asians were also less likely to have ≥2 falls (OR 0.62, CI 0.43-0.88).Conclusions: Among older women, the risk of having a recent fall was substantially lower for black and Asian women when compared to white women. This may contribute to their lower rates of hip fracture. Future studies should examine cultural and behavioral factors that contribute to these observed racial/ethnic differences in fall risk among U.S. women. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP., Methods: In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value., Results: Among the seven ML models, the light gradient boosting machine (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation., Conclusions: This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Wei, Tao, Geng, Ning, Li and Bi.)

Azo dyes are widely used in textile, paper and packing industries, and have become one of the research hot spots in dye wastewater treatment because of their carcinogenicity, teratogenic mutagenicity, stable structure and degradation difficulty. In this study, the biodecolorization of acid orange 7 (AO7), an azo dye, by different white rot fungi was investigated, and the effect of different conditions on the decolorization rate of the dye was analyzed. At the same time, the degradation liquor was analyzed and the phytotoxicity experiment was performed to deduce the possible degradation pathway of AO7 and assess the toxicity of its degradation products. The results showed that the decolorization rate reached 93.46% in 24 h at pH 4.5, 28 ℃ by Pleurotus eryngii and Trametes versicolor when AO7 concentration was 100 mg/L. The biodegradation pathway of AO7 was initiated by the cleavage of the azo bond of AO7, generating p-aminobenzenesulfonic acid and 1-amino-2-naphthol. Subsequently, the sulfonic acid group of p-aminobenzene sulfonic acid was removed to generate hydroquinone. Moreover, the 1-amino-2-naphthol was de-ringed to generate phthalic acid and p-hydroxybenzaldehyde, and then further degraded into benzoic acid. Finally, hydroquinone and benzoic acid may be further oxidized into other small molecules, carbon dioxide and water. Phytotoxicity experiment showed that the toxicity of AO7 could be reduced by P . eryngii and T . versicolor .

Vascular calcification is a predictor of cardiovascular mortality and is prevalent in patients with atherosclerosis and chronic renal disease. It resembles skeletal osteogenesis, and many bone cells as well as bone-related factors involved in both formation and resorption have been localized in calcified arteries. Previously, we showed that aortic medial cells undergo osteoblastic differentiation and matrix calcification both spontaneously and in response to PKA agonists. The PKA signaling pathway is also involved in regulating bone resorption in skeletal tissue by stimulating osteoblast-production of osteoclast regulating cytokines, including receptor-activator of nuclear κB ligand (RANKL) and interleukins. Therefore, we investigated whether PKA activators regulate osteoclastogenesis in aortic smooth muscle cells (SMC). Treatment of murine SMC with the PKA agonist forskolin stimulated RANKL expression at both mRNA and protein levels. Forskolin also stimulated expression of interleukin-6 but not osteoprotegerin (OPG), an inhibitor of RANKL. Consistent with these results, osteoclastic differentiation was induced when monocytic preosteoclasts (RAW264.7) were cocultured with forskolin-treated aortic SMC. Oxidized phospholipids also slightly induced RANKL expression in T lymphocytes, another potential source of RANKL in the vasculature. Because previous studies have shown that RANKL treatment alone induces matrix calcification of valvular and vascular cells, we next examined whether RANKL mediates forskolin-induced matrix calcification by aortic SMC. RANKL inhibition with OPG had little or no effect on osteoblastic differentiation and matrix calcification of aortic SMC. These findings suggest that, as in skeletal tissues, PKA activation induces bone resorptive factors in the vasculature and that aortic SMC calcification specifically induced by PKA, is not mediated by RANKL.