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There is little evidence in the literature about the relationship between frailty and falls in older adults. Our objective was to explore the relationship between frailty and falls, and to analyze the effect factors (e.g., gender, different frailty assessment tools, areas, level of national economic development, and year of publication) of the association between frailty and falls among older adults. Systematic review and meta-analysis. Cohort studies that evaluated the association between frailty and falls in the older adults were included. We excluded any literature outside of cohort studies. We did a systematic literature search of English databases PubMed, Scopus, Web of Science, EBSCOhost, and SciElO, as well as the Chinese databases CNKI, WANFANG, and VIP from 2001 until October 2022. The eligible studies were evaluated for potential bias using the Newcastle-Ottawa Scale (NOS). Study selection, data extraction and assessment of study quality were each conducted by two investigators. In Stata/MP 17.0 software, we calculated pooled estimates of the prevalence of falls by using a random-effects model, Subgroup analysis was conducted based on gender, different frailty assessment tools, areas, level of economic development, and year of publication. The results are presented using a forest plot. Twenty-nine studies were included in this meta-analysis and a total of 1,093,270 participants aged 65 years and above were enrolled. Among the older adults, frailty was significantly associated with a higher risk for falls, compared with those without frailty (combined RR-relative risk = 1.48, 95% CI-confidence interval: 1.27–1.73, I2=98.9%). In addition, the results of subgroup analysis indicated that men had a higher risk for falls than women among the older adults with frailty (RR 1.94, 95% CI: 1.18–3.2 versus RR 1.44, 95% CI: 1.24–1.67). Subgroup analysis by different frailty assessment tools revealed an increased risk of falls in older adults with frailty when assessed using the Frailty Phenotype (combined RR 1.32, 95%CI: 1.17–1.48), FRAIL score (combined RR 1.82, 95%CI: 1.36–2.43), and Study of Osteoporotic Fractures index (combined RR 1.54, 95%CI: 1.10–2.16). Furthermore, subgroup analysis by areas and level of national economic development found the highest fall risk in Oceania (combined RR 2.35, 95%CI: 2.28–2.43) and the lowest in Europe (combined RR 1.20, 95%CI: 1.05–1.38). Developed countries exhibited a lower fall risk compared to developing countries (combined RR 1.44, 95%CI: 1.21–1.71). Analysis by year of publication showed the highest fall risk between 2013–2019 (combined RR 1.79, 95%CI: 1.45–2.20) and the lowest between 2001–2013 (combined RR 1.21, 95%CI: 1.13–1.29). Frailty represents a significant risk factor for falls in older adults, with the degree of risk varying according to the different frailty assessment tools employed, and notably highest when using the FRAIL scale. Additionally, factors such as gender, areas, level of national economic development, and healthcare managers' understanding of frailty may all impact the correlation between frailty and falls. Thus, it's imperative to select suitable frailty diagnostic tools tailored to the specific characteristics of the population in question. This, in turn, facilitates the accurate identification of frailty in older adults and informs the development of appropriate preventive and therapeutic strategies to mitigate fall risk.

Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Particle trapping and binding in optical potential wells provide a versatile platform for various biomedical applications. However, implementation systems to study multi-particle contact interactions in an optical lattice remain rare. By configuring an optofluidic lattice, we demonstrate the precise control of particle interactions and functions such as controlling aggregation and multi-hopping. The mean residence time of a single particle is found considerably reduced from 7 s, as predicted by Kramer’s theory, to 0.6 s, owing to the mechanical interactions among aggregated particles. The optofluidic lattice also enables single-bacteria-level screening of biological binding agents such as antibodies through particle-enabled bacteria hopping. The binding efficiency of antibodies could be determined directly, selectively, quantitatively and efficiently. This work enriches the fundamental mechanisms of particle kinetics and offers new possibilities for probing and utilising unprecedented biomolecule interactions at single-bacteria level. Optical trapping is a versatile tool for biomedical applications. Here, the authors use an optofluidic lattice to achieve controllable multi-particle hopping and demonstrate single-bacteria-level screening and measurement of binding efficiency of biological binding agents through particle-enabled bacteria hopping.

Plastic crystal neopentylglycol (NPG) exhibits colossal barocaloric effects (BCEs) with record-high entropy changes, offering exciting prospects for the field of solid-state cooling through the application of moderate pressures. Here, we show that the intermolecular hydrogen bond plays a key role in the orientational order of NPG molecules, while its broken due to thermal perturbation prominently weakens the activation barrier of orientational disorder. The analysis of hydrogen bond strength, rotational entropy free energy and entropy changes provides insightful understanding of BCEs in order-disorder transition. External pressure reduce the hydsrogen bond length and enhance the activation barrier of orientational disorder, which serves as a route of varying intermolecular interaction to tune the order-disorder transition. Our work provides atomic-scale insights on the orientational order-disorder transition of NPG as the prototypical plastic crystal with BCEs, which is helpful to achieve superior caloric materials by molecular designing in the near future. Colossal barocaloric effects with high entropy changes is reported in plastic crystal neopentylglycol, while microscopic mechanism needs to be further explored. Here, the authors show hydrogen bond related reorientational dynamics of neopentylglycol and provide insights in order-disorder transition.

The evolutions of MHD instability behaviors and enhancement of both electrostatic and electromagnetic turbulence towards the plasma disruption have been clearly observed in the HL-2A plasmas. Two types of plasma disruptive discharges have been investigated for similar equilibrium parameters: one with a distinct stage of a small central temperature collapse ( ∼ 5–10%) around 1 millisecond before the thermal quench (TQ), while the other without. For both types, the TQ phase is preceded by a rotating 2/1 tearing mode, and it is the development of the cold bubble from the inner region of the 2/1 island O-point along with its inward convection that causes the massive energy loss. In addition, the micro-scale turbulence, including magnetic fluctuations and density fluctuations, increases before the small collapse, and more significantly towards the TQ. Also, temperature fluctuations measured by electron cyclotron emission imaging enhances dramatically at the reconnection site and expand into the island when approaching the small collapse and TQ, and the expansion is more significant close to the TQ. The observed turbulence enhancement near the X-point cannot be fully interpreted by the linear stability analysis by GENE. Evidences suggest that nonlinear effects, such as the reduction of local E r × B shear and turbulence spreading, may play an important role in governing turbulence enhancement and expansion. These results imply that the turbulence and its interaction with the island facilitate the stochasticity of the magnetic flux and formation of the cold bubble, and hence, the plasma disruption.

Mappings of classical computation onto statistical mechanics models have led to remarkable successes in addressing some complex computational problems. However, such mappings display thermodynamic phase transitions that may prevent reaching solution even for easy problems known to be solvable in polynomial time. Here we map universal reversible classical computations onto a planar vertex model that exhibits no bulk classical thermodynamic phase transition, independent of the computational circuit. Within our approach the solution of the computation is encoded in the ground state of the vertex model and its complexity is reflected in the dynamics of the relaxation of the system to its ground state. We use thermal annealing with and without ‘learning’ to explore typical computational problems. We also construct a mapping of the vertex model into the Chimera architecture of the D-Wave machine, initiating an approach to reversible classical computation based on state-of-the-art implementations of quantum annealing. Solutions of computations can be encoded in the ground state of many-body spin models. Here the authors show that solutions to generic reversible classical computations can be encoded in the ground state of a vertex model, which can be reached without finite temperature phase transitions.

Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections ( P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.

The original version of this Article omitted the author Kuan Wang, who is from the ‘College of Biomedical Engineering, Taipei Medical University, Taipei 11031, Taiwan’ and ‘Nanyang Environment & Water Research Institute, Nanyang Technological University, Singapore 637141, Singapore.’ Also, the author S.H. Lim was incorrectly given as L.S. Hoi and A. Larsson was incorrectly given as A. Larson. The “Author contributions” was amended to reflect the authorship changes. It previously read ‘Y.Z.S., C.-W.Q., and A.Q.L. jointly conceived the idea. Y.Z.S., S.X., Y.Z., J.B.Z., W.S., J.H.W., T.N.C., Z.C.Y., Y.L.H., B.L., P.H.Y., D.P.T., and C.-W.Q. performed the numerical simulations and theoretical analysis. Y.Z.S., S.X., and L.K.C. did the fabrication and experiments of particle hopping, biomolecule binding and flow cytometry. A.L. and L.S.H. did the SPR experiments. S.X., Y.Z.S., Y.Z., C.-W.Q., Y.-Y.C., L.K.C., T.H.Z., and A.Q.L. prepared the manuscript. S.X., Y.Z., C.-W.Q., and A.Q.L. supervised and coordinated all the work. All authors commented on the manuscript.’ The correct version states ‘B.L., K. W., P.H.Y.’ instead of ‘B.L., P.H.Y.’ and ‘S.H.L.’ in place of ‘L.S.H.’ This has been corrected in both the PDF and HTML versions of the Article.

In order to study the damage law of cuboid explosives to reinforced concrete beams under contact explosion, the damage effect of reinforced concrete beams under the action of different length-to-width ratios, explosive heights and explosive equivalents of cuboid explosives is studied by LS-DYNA numerical simulation.The results show that when the amount of explosives and the height of explosives remain unchanged, and the ratio of length to width is 1:1, the volume of concrete damage is the largest. When the amount of explosives and the length-to-width ratio remain unchanged, the volume of concrete damage decreases with the increase in height. When the length-width-height ratio of the explosive is constant, the damage volume increases linearly with the increase of the explosive equivalent. The damage degree of beam is divided into three grades: Reinforced concrete beam is slightly damaged when the damage volume ratio is η <3%. Reinforced concrete beam is moderately damaged when the damage volume ratio is 3%< η <7%. Reinforced concrete beam is serious damaged when the damage volume ratio is η>7%.