(6,6′-Dimeth­oxy­biphenyl-2,2′-di­yl)bis(diphenyl­phosphane) P,P′-dioxide dihydrate

In the title compound, C38H32O4P2·2H2O, the dihedral angle between the meth­oxy­phenol rings is 84.11 (7)°. O—H⋯O hydrogen bonds connect the water mol­ecules of crystallization with the main mol­ecule

Exploring anterion capsular contraction syndrome in cataract surgery: insights into pathogenesis, clinical course, influencing factors, and intervention approaches

Anterior capsular contraction syndrome (ACCS) is a challenging complication that can occur following phacoemulsification cataract surgery. Characterized by capsular bag wrinkling, intraocular lens (IOL) decentration and tilt, ACCS can have negative effects on visual outcomes and patient satisfaction. This review aims to investigate the pathogenesis, clinical course, influencing factors, and intervention approaches for ACCS after cataract surgery. By understanding the underlying mechanisms and identifying factors that contribute to ACCS, surgeons can enhance their ability to predict and manage this complication. Various intervention strategies are discussed, highlighting their importance in reducing complications and improving surgical outcomes. However, further research is needed to determine optimal prevention and management strategies through long-term follow-up and comparative analyses. Advancements in this field will ultimately lead to improved visual outcomes and optimized cataract surgery for patients

Insights into the rotational stability of toric intraocular lens implantation: diagnostic approaches, influencing factors and intervention strategies

Toric intraocular lenses (IOLs) have been developed to enhance visual acuity impaired by cataracts and correct corneal astigmatism. However, residual astigmatism caused by postoperative rotation of the toric IOL is an important factor affecting visual quality after implantation. To decrease the rotation of the toric IOL, significant advancements have been made in understanding the characteristics of toric IOL rotation, the factors influencing its postoperative rotation, as well as the development of various measurement techniques and interventions to address this issue. It has been established that factors such as the patient’s preoperative refractive status, biological parameters, surgical techniques, postoperative care, and long-term management significantly impact the rotational stability of the toric IOL. Clinicians should adopt a personalized approach that considers these factors to minimize the risk of toric IOL rotation and ensure optimal outcomes for each patient. This article reviews the influence of various factors on toric IOL rotational stability. It discusses new challenges that may be encountered to reduce and intervene with rotation after toric IOL implantation in the foreseeable future

Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors

[EN] Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.This research was funded by the National Key R&D Program, grant number 2019YFC0119700, and the National Natural Science Foundation of China, grant number U20A20388.Zhang, Y.; Hao, D.; Yang, L.; Zhou, X.; Ye Lin, Y.; Yang, Y. (2022). Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors. Sensors. 22(9):1-18. https://doi.org/10.3390/s2209335211822

Detecting local processing unit in drosophila brain by using network theory

Community detection method in network theory was applied to the neuron network constructed from the image overlapping between neuron pairs to detect the Local Processing Unit (LPU) automatically in Drosophila brain. 26 communities consistent with the known LPUs, and 13 subdivisions were found. Besides, 45 tracts were detected and could be discriminated from the LPUs by analyzing the distribution of participation coefficient P. Furthermore, layer structures in fan-shaped body (FB) were observed which coincided with the images shot by the optical devices, and a total of 13 communities were proven closely related to FB. The method proposed in this work was proven effective to identify the LPU structure in Drosophila brain irrespectively of any subjective aspect, and could be applied to the relevant areas extensively

Chronic constant light exposure aggravates high fat diet-induced renal injury in rats

Obesity-related kidney disease is now recognized as a global health issue, with a substantial number of patients developing progressive renal failure and end-stage renal disease. Interestingly, recent studies indicate light pollution is a novel environmental risk factor for chronic kidney disease. However, the impact of light pollution on obesity-related kidney disease remains largely unknown, with its underlying mechanism insufficiently explained. Renal hypoxia induced factor 1α (HIF1α) is critical in the development of glomerulosclerosis and renal fibrosis. The present study explored effects of constant light exposure on high fat diet (HFD) -induced renal injury and its association with HIF1α signal pathway. Thirty-two male Sprague Dawley rats were divided into four groups according to diet (HFD or normal chow diet) and light cycles (light/dark or constant light). After 16 weeks treatment, rats were sacrificed and pathophysiological assessments were performed. In normal chow fed rats, constant light exposure led to glucose abnormalities and dyslipidemia. In HFD fed rats, constant light exposure exacerbated obesity, glucose abnormalities, insulin resistance, dyslipidemia, renal functional decline, proteinuria, glomerulomegaly, renal inflammation and fibrosis. And, constant light exposure caused an increase in HIF1α and a decrease in prolyl hydroxylase domain 1 (PHD1) and PHD2 expression in kidneys of HFD-fed rats. Then, we demonstrated that BMAL1 bound directly to the promoters of PHD1 in mouse podocyte clone 5 cell line (MPC5) by ChIP assays. In conclusion, chronic constant light exposure aggravates HFD-induced renal injuries in rats, and it is associated with activation of HIF1α signal pathway

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule-guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis. Second, logic rules of different lengths are mined from knowledge graph to learn new entity representations. Finally, knowledge graph inference is performed based on representing entities that converge multi-media features. Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference, demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features

Conservative State Value Estimation for Offline Reinforcement Learning

Offline reinforcement learning faces a significant challenge of value over-estimation due to the distributional drift between the dataset and the current learned policy, leading to learning failure in practice. The common approach is to incorporate a penalty term to reward or value estimation in the Bellman iterations. Meanwhile, to avoid extrapolation on out-of-distribution (OOD) states and actions, existing methods focus on conservative Q-function estimation. In this paper, we propose Conservative State Value Estimation (CSVE), a new approach that learns conservative V-function via directly imposing penalty on OOD states. Compared to prior work, CSVE allows more effective in-data policy optimization with conservative value guarantees. Further, we apply CSVE and develop a practical actor-critic algorithm in which the critic does the conservative value estimation by additionally sampling and penalizing the states \emph{around} the dataset, and the actor applies advantage weighted updates extended with state exploration to improve the policy. We evaluate in classic continual control tasks of D4RL, showing that our method performs better than the conservative Q-function learning methods and is strongly competitive among recent SOTA methods