As the soaring demand for energy storage continues to grow, batteries that can cope with extreme conditions are highly desired. Yet, existing battery materials are limited by weak mechanical ...properties and freeze‐vulnerability, prohibiting safe energy storage in devices that are exposed to low temperature and unusual mechanical impacts. Herein, a fabrication method harnessing the synergistic effect of co‐nonsolvency and “salting‐out” that can produce poly(vinyl alcohol) hydrogel electrolytes with unique open‐cell porous structures, composed of strongly aggregated polymer chains, and containing disrupted hydrogen bonds among free water molecules, is introduced. The hydrogel electrolyte simultaneously combines high strength (tensile strength 15.6 MPa), freeze‐tolerance (< −77 °C), high mass transport (10× lower overpotential), and dendrite and parasitic reactions suppression for stable performance (30 000 cycles). The high generality of this method is further demonstrated with poly(N‐isopropylacrylamide) and poly(N‐tertbutylacrylamide‐co‐acrylamide) hydrogels. This work takes a further step toward flexible battery development for harsh environments.
Co‐nonsolvency and “salting‐out” are synergistically utilized to create a flexible electrolyte with superior strength, freezing tolerance, high mass transport (10× lower overpotential over semi‐closed‐pore counterparts), and stable cycling, benefitting from the suppressed dendritic growth and side reactions (30 000 cycles), which pushes the limit of flexible batteries toward higher stability in harsh environments.
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.
Electromyograms (EMG signals) may be contaminated by electrocardiographic (ECG) signals that cannot be easily separated with traditional filters, because both signals have some overlapping spectral ...components. Therefore, the first challenge encountered in signal processing is to extract the ECG noise from the EMG signal. In this study, the EMG, mixed with different degrees of noise (ECG), is simulated to investigate the variations of the EMG features. Simulated data were derived from the MIT-BIH Noise Stress Test (NSTD) Database. Two EMG and four ECG data were composed with four EMG/ECG SNR to 32 simulated signals. Following Pan-Tompkins R-peak detection, four ECG removal methods were used to remove ECG with different compensation algorithms to obtain the denoised EMG signal. A total of 13 time-domain and four frequency-domain EMG features were calculated from the denoised EMG. In addition, the similarity of denoised EMG features compared to clean EMG was also evaluated. Our results showed that with the ratio EMG/ECG SNR = 10 and 20, the ECG can be almost ignored, and the similarity of EMG features is close to 1. When EMG/ECG SNR = 1 and 2, there is a large variation of EMG features. The results of our simulation study would be beneficial for understanding the variations of EMG features upon the different EMG/ECG SNR.
Computing-in-memory (CIM) based on SRAM is a promising approach to achieving energy-efficient multiply-and-accumulate (MAC) operations in artificial intelligence (AI) edge devices; however, existing ...SRAM-CIM chips support only DNN inference. The flow of training data requires that CIM arrays perform convolutional computation using transposed weight matrices. This article presents a two-way transpose (TWT) multiply cell with high resistance to process variation and a novel read scheme that uses input-aware zone prediction of maximum partial MAC values to enhance the signal margin for robust readout. A 28-nm 64-kb TWT CIM macro fabricated using foundry-provided compact 6T-SRAM cells achieved <inline-formula> <tex-math notation="LaTeX">T_{\text {AC}} </tex-math></inline-formula> of 3.8-21 ns and energy efficiency of 7-61.1 TOPS/W in performing MAC operations using 2-8-b inputs, 4-8-b weights, and 10-20-b outputs.
This article presents a computing-in-memory (CIM) structure aimed at improving the energy efficiency of edge devices running multi-bit multiply-and-accumulate (MAC) operations. The proposed scheme ...includes a 6T SRAM-based CIM (SRAM-CIM) macro capable of: 1) weight-bitwise MAC (WbwMAC) operations to expand the sensing margin and improve the readout accuracy for high-precision MAC operations; 2) a compact 6T local computing cell to perform multiplication with suppressed sensitivity to process variation; 3) an algorithm-adaptive low MAC-aware readout scheme to improve energy efficiency; 4) a bitline header selection scheme to enlarge signal margin; and 5) a small-offset margin-enhanced sense amplifier for robust read operations against process variation. A fabricated 28-nm 64-kb SRAM-CIM macro achieved access times of 4.1-8.4 ns with energy efficiency of 11.5-68.4 TOPS/W, while performing MAC operations with 4- or 8-b input and weight precision.
Nucleocytoplasmic glycosylation of proteins with O-linked N-acetylglucosamine residues (O-GlcNAc) is recognized as a conserved post-translational modification found in all metazoans. O-GlcNAc has ...been proposed to regulate diverse cellular processes. Impaired cellular O-GlcNAcylation has been found to lead to decreases in the levels of various proteins, which is one mechanism by which O-GlcNAc seems to exert its varied physiological effects. Here we show that O-GlcNAcylation also occurs cotranslationally. This process protects nascent polypeptide chains from premature degradation by decreasing cotranslational ubiquitylation. Given that hundreds of proteins are O-GlcNAcylated within cells, our findings suggest that cotranslational O-GlcNAcylation may be a phenomenon regulating proteostasis of an array of nucleocytoplasmic proteins. These findings set the stage to assess whether O-GlcNAcylation has a role in protein quality control in a manner that bears similarity with the role played by N-glycosylation within the secretory pathway.
In the context of behavior recognition, the emerging bed-exit monitoring system demands a rapid deployment in the ward to support mobility and personalization. Mobility means the system can be ...installed and removed as required without construction; personalization indicates human body tracking is limited to the bed region so that only the target is monitored. To satisfy the above-mentioned requirements, the behavior recognition system aims to: (1) operate in a small-size device, typically an embedded system; (2) process a series of images with narrow fields of view (NFV) to detect bed-related behaviors. In general, wide-range images are preferred to obtain a good recognition performance for diverse behaviors, while NFV images are used with abrupt activities and therefore fit single-purpose applications. This paper develops an NFV-based behavior recognition system with low complexity to realize a bed-exit monitoring application on embedded systems. To achieve effectiveness and low complexity, a queueing-based behavior classification is proposed to keep memories of object tracking information and a specific behavior can be identified from continuous object movement. The experimental results show that the developed system can recognize three bed behaviors, namely off bed, on bed and return, for NFV images with accuracy rates of 95~100%.
Hayata is a unique plant species found in Taiwan. Previous studies have identified its anti-hypertensive, anti-oxidative, and anti-inflammatory effects. In this study, a bioactivity-guided approach ...was employed to extract 20 compounds from the ethyl acetate fraction of the ethanol extract of
Hayata's pine needles. The anti-aging effects of these compounds were investigated using HT-1080 cells. The structures of the purified compounds were confirmed through NMR and LC-MS analysis, revealing the presence of nine flavonoids, two lignans, one coumarin, one benzofuran, one phenylic acid, and six diterpenoids. Among them, PML18, PML19, and PML20 were identified as novel diterpene. Compounds
,
, and
exhibited remarkable inhibitory effects against MMP-2 and showed no significant cell toxicity at 25 μM. Although the purified compounds showed lower activity against Pro MMP-2 and Pro MMP-9 compared to the ethyl acetate fraction, we speculate that this is the result of synergistic effects.
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