Abstract Few studies have been conducted to investigate the association of kidney function decline with the trajectories of homocysteine (Hcy) over time, using repeated measurements. We aimed to investigate the association of kidney function with changes in plasma Hcy levels over time. Data were collected from the Rugao Longevity and Ageing Study. In detail, plasma Hcy and creatinine levels were measured in both waves (waves 2, 3 and 4) during the 3·5-year follow-up ( n 1135). Wave 2 was regarded as the baseline survey. The estimated glomerular filtration rate (eGFR) was calculated based on creatinine. Subjects were categorised into four groups according to quartiles of eGFR at baseline. Linear mixed-effect models were used to investigate the association of eGFR with subsequent plasma Hcy levels. The mean eGFR at baseline was 90·84 ( sd 11·42) ml/min per 1·73 m 2 . The mean plasma Hcy level was 14·09 ( sd 6·82) at baseline and increased to 16·28 ( sd 8·27) and 17·36 ( sd 10·39) μmol/l during follow-ups. In the crude model, the interaction between time and eGFR at baseline was significant ( β = −0·02, 95 % CI −0·02, −0·01, P = 0·002). After adjusting for confounding factors, a significant relationship remained ( β = −0·02, 95 % CI −0·02, −0·01, P = 0·003), suggesting that kidney function decline at baseline was associated with a faster increase in Hcy levels. Kidney function decline is associated with a more pronounced increase in plasma Hcy levels. Further studies with longer follow-up periods and larger sample sizes are needed to validate our findings.
Phase analysis, which classifies the set of execution intervals with similar execution behavior and resource requirements, has been widely used in a variety of systems, including dynamic cache reconfiguration, prefetching, race detection, and sampling simulation. Although phase granularity has been a major factor in the accuracy of phase analysis, it has not been well investigated, and most systems usually adopt a fine-grained scheme. However, such a scheme can only take account of recent local phase information and could be frequently interfered by temporary noise due to instant phase changes, which might notably limit the accuracy. In this article, we make the first investigation on the potential of multilevel phase analysis (MLPA), where different granularity phase analyses are combined together to improve the overall accuracy. The key observation is that the coarse-grained intervals belonging to the same phase usually consist of stably distributed fine-grained phases. Moreover, the phase of a coarse-grained interval can be accurately identified based on the fine-grained intervals at the beginning of its execution. Based on the observation, we design and implement an MLPA scheme. In such a scheme, a coarse-grained phase is first identified based on the fine-grained intervals at the beginning of its execution. The following fine-grained phases in it are then predicted based on the sequence of fine-grained phases in the coarse-grained phase. Experimental results show that such a scheme can notably improve the prediction accuracy. Using a Markov fine-grained phase predictor as the baseline, MLPA can improve prediction accuracy by 20%, 39%, and 29% for next phase, phase change, and phase length prediction for SPEC2000, respectively, yet incur only about 2% time overhead and 40% space overhead (about 360 bytes in total). To demonstrate the effectiveness of MLPA, we apply it to a dynamic cache reconfiguration system that dynamically adjusts the cache size to reduce the power consumption and access time of the data cache. Experimental results show that MLPA can further reduce the average cache size by 15% compared to the fine-grained scheme. Moreover, for MLPA, we also observe that coarse-grained phases can better capture the overall program characteristics with fewer of phases and the last representative phase could be classified in a very early program position, leading to fewer execution internals being functionally simulated. Based on this observation, we also design a multilevel sampling simulation technique that combines both fine- and coarse-grained phase analysis for sampling simulation. Such a scheme uses fine-grained simulation points to represent only the selected coarse-grained simulation points instead of the entire program execution; thus, it could further reduce both the functional and detailed simulation time. Experimental results show that MLPA for sampling simulation can achieve a speedup in simulation time of about 8.3X with similar accuracy compared to 10M SimPoint.
Abstract Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulated annealing techniques to choose an optimal label vector that minimizes nuclear norm of the pooled within cluster residual matrix. To evaluate the performance of the NNC algorithm, we compared the performance of both 15 public datasets and 2 genome-wide association studies (GWAS) on psoriasis, comparing our method with other classic methods. The results indicate that NNC method has a competitive performance in terms of F-score on 15 benchmarked public datasets and 2 psoriasis GWAS datasets. So NNC is a promising alternative method for clustering tasks.
In human medicine, the carbonic anhydrase (CA) inhibitor acetazolamide is used to treat irregular breathing disorders. Previously, we demonstrated in the rabbit that this substance stabilized closed-loop gain properties of the respiratory control system, but concomitantly weakened respiratory muscles. Among others, the highly diffusible CA-inhibitor methazolamide differs from acetazolamide in that it fails to activate Ca 2+ -dependent potassium channels in skeletal muscles. Therefore, we aimed to find out, whether or not methazolamide may exert attenuating adverse effects on respiratory muscle performance as acetazolamide. In anesthetized spontaneously breathing rabbits ( n = 7), we measured simultaneously the CO 2 responses of tidal phrenic nerve activity, tidal transpulmonary pressure changes, and tidal volume before and after intravenous application of methazolamide at two mean (± SE) cumulative doses of 3.5 ± 0.1 and 20.8 ± 0.4 mg/kg. Similar to acetazolamide, low- and high-dose methazolamide enhanced baseline ventilation by 52 ± 10% and 166 ± 30%, respectively ( P < 0.01) and lowered the base excess in a dose-dependent manner by up to 8.3 ± 0.9 mmol/l ( P < 0.001). The transmission of a CO 2 -induced rise in phrenic nerve activity into volume and/or pressure and, hence, respiratory work performance was 0.27 ± 0.05 ml·kg −1 ·kPa·unit −1 under control conditions, but remained unchanged upon low- or high-dose methazolamide, at 0.30 ± 0.06 and 0.28 ± 0.07 ml·kg −1 ·kPa·unit −1 , respectively. We conclude that methazolamide does not cause respiratory muscle weakening at elevated levels of ventilatory drive. This substance (so far not used for medication of respiratory diseases) may thus exert stabilizing influences on breathing control without adverse effects on respiratory muscle function.
Abstract The SARS-CoV-2 Omicron variant has been partitioned into four sub-lineages designated BA.1, BA.1.1, BA.2 and BA.3, with BA.2 becoming dominant worldwide recently by outcompeting BA.1 and BA.1.1. We and others have reported the striking antibody evasion of BA.1 and BA.2, but side-by-side comparison of susceptibility of all the major Omicron sub-lineages to vaccine-elicited or monoclonal antibody (mAb)-mediated neutralization are urgently needed. Using VSV-based pseudovirus, we found that sera from individuals vaccinated by two doses of inactivated whole-virion vaccines (BBIBP-CorV) showed very weak to no neutralization activity, while a homologous inactivated vaccine booster or a heterologous booster with protein subunit vaccine (ZF2001) markedly improved the neutralization titers against all Omicron variants. The comparison between sub-lineages indicated that BA.1.1, BA.2 and BA.3 had comparable or even greater antibody resistance than BA.1. We further evaluated the neutralization profile of a panel of 20 mAbs, including 10 already authorized or approved, against these Omicron sub-lineages as well as viruses with different Omicron spike single or combined mutations. Most mAbs lost their neutralizing activity completely or substantially, while some demonstrated distinct neutralization patterns among Omicron sub-lineages, reflecting their antigenic difference. Taken together, our results suggest all four Omicron sub-lineages threaten the efficacies of current vaccines and antibody therapeutics, highlighting the importance of vaccine boosters to combat the emerging SARS-CoV-2 variants.
In this work, we proposed a single-ended read disturb-free 9T SRAM cell for bit-interleaving application. A column-aware feedback-cutoff write scheme is employed in the cell to achieve higher write margin and non-intrusive bit-interleaving configuration. And a dynamic read-decoupled assist scheme is utilized by cutting loop to relax the interdependence between stability and read current, resulting in robust read operation and better read performance simultaneously. Moreover, the lower write and leakage energy consumptions are also achieved. We compared area, stability, SNM sensitivity and energy consumption between proposed 9T and standard 6T bit-cells. The write ability of 9T cell is 1.40× higher that of 6T cell at 1.0 V, and 8.16× higher at 0.3 V. The write and leakage energy dissipations are 26% and 13% lower than that of 6T at 1.0 V. In addition, robust read and better process variation tolerance are provided for proposed design with area penalty.
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