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Falls are the leading cause of injury in stroke patients. However, the cause of a fall is complicated, and several types of risk factors are involved. Therefore, a comprehensive model to predict falls with high sensitivity and specificity is needed. This study was a prospective study of 112 inpatients in a rehabilitation ward with follow-up interviews in patients' homes. Evaluations were performed 1 month after stroke and included the following factors: (1) status of cognition, depression, fear of fall and limb spasticity; (2) functional assessments [walking velocity and the Functional Independence Measure (FIM)]; and (3) objective, computerized gait and balance analyses. The outcome variable was the number of accidental falls during the 6-month follow-up period after baseline measurements. The non-faller group exhibited significantly better walking velocity and FIM scale compared to the faller group (P < .001). The faller group exhibited higher levels of spasticity in the affected limbs, asymmetry of gait parameters in single support (P < .001), double support (P = .027), and step time (P = .003), and lower stability of center of gravity in the medial-lateral direction (P = .008). Psychological assessments revealed that the faller group exhibited more severe depression and lower confidence without falling. A multivariate logistic regression model identified three independent predictors of falls with high sensitivity (82.6%) and specificity (86.5%): the asymmetry ratio of single support [adjusted odds ratio, aOR = 2.2, 95% CI (1.2-3.8)], the level of spasticity in the gastrocnemius [aOR = 3.2 (1.4-7.3)], and the degree of depression [aOR = 1.4 (1.2-1.8)]. This study revealed depression, in additional to gait asymmetry and spasticity, as another independent factor for predicting falls. These results suggest that appropriate gait training, reduction of ankle spasticity, and aggressive management of depression may be critical to prevent falls in stroke patients.

To address the issue of low accuracy in existing remaining useful life (RUL) prediction algorithms for rolling bearings, this paper proposes a novel RUL prediction method based on the Beluga Whale Optimization (BWO) algorithm, Variational Mode Decomposition (VMD), an improved Convolutional Block Attention Module (CBAM*), and a Bidirectional Long Short-Term Memory (BiLSTM) network. First, BWO is utilized to optimize the parameters of VMD, which is then applied to decompose and reconstruct the original vibration signals. Subsequently, time-domain and frequency-domain features are extracted from the reconstructed data to construct a degradation feature set. Finally, the degradation feature set is input into a BiLSTM network integrated with the CBAM* for RUL prediction of bearings. Comparative and ablation experiments are conducted on the IEEE PHM 2012 data set to evaluate the proposed method. The experimental results demonstrate that the method achieves lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) in bearing RUL prediction compared to the other method models and ablation methods, highlighting the superiority and effectiveness of the proposed approach. This study not only ensures the safe operation of rotating machinery but also provides a valuable reference for RUL prediction of other types of equipment.

CRISPR/Cas9 is a programmable genome editing tool widely used for biological applications and engineered Cas9s have increased discrimination against off-target cleavage compared with wild-type Streptococcus pyogenes (SpCas9) in vivo. To understand the basis for improved discrimination against off-target DNA containing important mismatches at the distal end of the guide RNA, we performed kinetic analyses on the high-fidelity (Cas9-HF1) and hyper-accurate (HypaCas9) engineered Cas9 variants. We show that DNA cleavage is impaired by more than 100- fold for the high-fidelity variants. The high-fidelity variants improve discrimination by slowing the observed rate of cleavage without increasing the rate of DNA rewinding and release. The kinetic partitioning favors release rather than cleavage of a bound off-target substrate only because the cleavage rate is so low. Further improvement in discrimination may require engineering increased rates of dissociation of off-target DNA. Engineered high-fidelity Cas9s have increased discrimination against off-targets. Kinetic analyses of Cas9-HF1 and HypaCas9 engineered Cas9 variants show that their DNA cleavage is impaired by more than 100- fold, which leads to release rather than cleavage of a bound off-target substrate.

Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf 0.2 Zr 0.8 O 2 anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf 0.2 Zr 0.8 O 2 films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>10 12 ), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware. The scalability of neuromorphic devices depends on the dismissal of capacitors and additional circuits. Here Liu et al. report an artificial neuron based on the polarization and depolarization of an anti-ferroelectric film, avoiding additional elements and reaching 37 fJ/spike of power consumption.

SEC reserves assessment is an important indicator to measure the development potential of oil companies. Based on the evaluation of SEC reserves, this paper summarizes the dynamic evaluation methods of SEC reserves, including analogy method, volume method, decline method, material balance method and reservoir simulation. Among them, the decline method is the most commonly used method, which has the characteristics of easy prediction of production and economic life cycle. Therefore, this paper discusses how to choose the decline method, the initial output and the determination method of the decline rate.

In this paper, an integrated production and distribution problem for perishable products is presented, which is an NP hard problem where a single machine, multi-customers, and homogenous vehicles with capacity constraints are considered. The objective is to minimize the total order weighted delivery time to measure the customer service level, by making two interacted decisions, production scheduling and vehicle routing, simultaneously. An integrated mathematical model is built, and the validity is measured by the linear programming software CPLEX by solving the small-size instances. An improved large neighborhood search algorithm is designed to address the problem. Firstly, a two-stage algorithm is constructed to generate the initial solution, which determines the order production sequence according to the given vehicle routing. Secondly, several removal/insertion heuristics are applied to enlarge the search space of neighbor solutions. Then, a local search algorithm is designed to improve the neighbor solutions, which further generates more chances to find the optimal solution. For comparison purposes, a genetic algorithm developed in a related problem is employed to solve this problem. The computational results show that the proposed improved large neighborhood search algorithm can provide higher quality solutions than the genetic algorithm.

Background Alzheimer's disease (AD) is a neurodegenerative disease characterized by a progressive decline in cognitive ability. Exosomes derived from bone-marrow mesenchymal stem cells (BMSC-exos) are extracellular vesicles that can execute the function of bone-marrow mesenchymal stem cells (BMSCs). Given the versatile therapeutic potential of BMSC and BMSC-exos, especially their neuroprotective effect, the aim of this study was to investigate the potential effect of BMSC-exos on AD-like behavioral dysfunction in mice and explore the possible molecular mechanism. Methods BMSC-exos were extracted from the supernatant of cultured mouse BMSCs, which were isolated from the femur and tibia of adult C57BL/6 mice, purified and sorted via flow cytometry, and cultured in vitro. BMSC-exos were identified via transmission electron microscopy, and typical marker proteins of exosomes were also detected via Western blot. A sporadic AD mouse model was established by intracerebroventricular injection of streptozotocin (STZ). Six weeks later, BMSC-exos were administered via lateral ventricle injection or caudal vein injection lasting five consecutive days, and the control mice were intracerebroventricularly administered an equal volume of solvent. Behavioral performance was observed via the open field test (OFT), elevated plus maze test (EPM), novel object recognition test (NOR), Y maze test (Y-maze), and tail suspension test (TST). The mRNA and protein expression levels of IL-1β, IL-6, and TNF-α in the hippocampus were measured via quantitative polymerase chain reaction (qPCR) and Western blot, respectively. Moreover, the protein expression of Aβ 1-42 , BACE, IL-1β, IL-6, TNF-α, GFAP, p-Tau (Ser396), Tau5, synaptotagmin-1 (Syt-1), synapsin-1, and brain-derived neurotrophic factor (BDNF) in the hippocampus was detected using Western blot, and the expression of GFAP, IBA1, Aβ 1−42 and DCX in the hippocampus was measured via immunofluorescence staining. Results Lateral ventricle administration, but not caudal vein injection of BMSC-exos improved AD-like behaviors in the STZ-injected mouse model, as indicated by the increased number of rearing, increased frequency to the central area, and increased duration and distance traveled in the central area in the OFT, and improved preference index of the novel object in the NOR. Moreover, the hyperactivation of microglia and astrocytes in the hippocampus of the model mice was inhibited after treatment with BMSC-exos via lateral ventricle administration, accompanied by the reduced expression of IL-1β, IL-6, TNF-α, Aβ 1-42, and p-Tau and upregulated protein expression of synapse-related proteins and BDNF. Furthermore, the results of the Pearson test showed that the preference index of the novel object in the NOR was positively correlated with the hippocampal expression of BDNF, but negatively correlated with the expression of GFAP, IBA1, and IL-1β. Apart from a positive correlation between the hippocampal expression of BDNF and Syt-1, BDNF abundance was found to be negatively correlated with markers of glial activation and the expression of the inflammatory cytokines, Aβ 1-42 , and p-Tau, which are characteristic neuropathological features of AD. Conclusions Lateral ventricle administration, but not caudal vein injection of BMSC-exos, can improve AD-like behavioral performance in STZ-injected mice, the mechanism of which might be involved in the regulation of glial activation and its associated neuroinflammation and BDNF-related neuropathological changes in the hippocampus.

The dynamic regulation of supramolecular chirality in non-equilibrium systems can provide valuable insights into molecular self-assembly in living systems. Herein, we demonstrate the use of chemical fuels for regulating self-assembly pathway, which thereby controls the supramolecular chirality of assembly in non-equilibrium systems. Depending on the nature of different fuel acids, the system shows pathway-dependent non-equilibrium self-assembly, resulting in either dynamic self-assembly with transient supramolecular chirality or kinetically trapped self-assembly with inverse supramolecular chirality. More importantly, successive conducting of chemical-fueled process and thermal annealing process allows for the sequential programmability of the supramolecular chirality between four different chiral hydrogels, affording a new example of a multistate supramolecular chiroptical switch that can be recycled multiple times. The current finding sheds new light on the design of future supramolecular chiral materials, offering access to alternative self-assembly pathways and kinetically controlled non-equilibrium states. The dynamic regulation of supramolecular chirality in non-equilibrium systems can provide valuable insights into molecular self-assembly in living systems. Here, the authors demonstrate the use of chemical fuels for regulating self-assembly pathway, which thereby controls the supramolecular chirality of a non-equilibrium assembled system.

Interest in RNA editing has emerged in molecular medicine due to its widespread dysregulation and therapeutic potential. Its regulatory mechanisms in governing non-coding RNAs, especially microRNAs (miRNAs) remain largely unresolved. Emerging evidence in diseases reveals a functional convergence between miRNAs and polyamine metabolism, two systems traditionally studied separately. miRNAs serve as primary substrates for adenosine deaminase acting on RNA (ADAR) which could regulate polyamine metabolism via the sirtuin (SIRT1)-p53 axis, forming a disease-relevant loop. Indeed, in many proliferative malignancies, hyper-editing of miRNAs coincides with high polyamine levels and promotes SIRT1-mediated p53 deacetylation. Conversely, in many age-related diseases, hypo-editing and polyamine loss blunt this pathway. This review dissects this emerging ADAR-editing-miRNA-polyamine circuit anchored on the SIRT1-p53 axis. We propose this as a unifying working model to integrate disparate correlative observations, providing a roadmap for future validation studies to confirm its potential for combinatorial therapeutic targets and diagnostic biomarkers. Graphical abstract The scheme merges correlative data: cancers display high ADAR activity, hyper-edited miRNAs and elevated polyamines, whereas aging shows the opposite trend. Whether these edited miRNAs causally shape polyamine levels via the SIRT1-p53 axis remains experimentally untested. The diagram therefore depicts a working model linking ADAR editing, SIRT1-p53 signaling and polyamine regulators of biosynthesis/autophagy. The solid red arrows trace the primary regulatory axis synthesized from the reviewed evidence (from ADAR to p53, to polyamine), while the dashed green arrows indicate a potential feedback loop from polyamines back to the editing machinery that is currently supported by more limited data

Sustainability, as a trend of social development and the embodiment of corporate social responsibility, has begun to receive more attention. To achieve this goal, sustainable supplier selection (SSS) and order allocation (OA) are seen as the crucial activities in corporate management. In the process of SSS, the psychological behavior of decision-makers (DMs) could play a critical role in the evaluation results. Therefore, introducing it into the decision-making process may lead to decision in line with the actual situation. In the uncertain multi-criteria group decision-making (MCGDM) problem described by probability linguistic term sets (PLTS), the DMs can evaluate the criteria of each supplier based on his own preference and hesitation, which is useful to avoid the loss of information. For this reason, this study develops a novel multi-criteria group decision-making combined with fuzzy multi-objective optimization (MCGDM-FMOO) model for SSS/OA problems by considering the triple bottom line (TBL) in which includes economic, environmental and social factors. The proposed method includes four stages. (1) the best-worst method (BWM) and entropy weight method are utilized to assign the weights of criteria to obtain the comprehensive weight. According to the output weights, the an acronym for interactive and multi-criteria decision-making in Portugese (TODIM) approach is applied to rank the suppliers under PLTS environment; (2) a FMOO model that can effectively deal with uncertainties and dynamic nature of parameter is formulated for allocating optimal order quantities; (3) two novel approaches are utilized to solve the FMOO model in order to obtain the richer Pareto frontier; and (4) the final OA solution is achieved by technique for order preference by similarity to ideal solution (TOPSIS) method. Finally, the validity and practicability of proposed MCGDM-FMOO model are verified by an example and comparative analysis with other classical MCGDM methods.