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There is an ongoing debate over uniformity versus flexibility in accounting regulation. This study examines the financial reporting consequences of a rigid accounting rule in China under which the fiscal year-end is uniform for all companies. Using extensive interviews together with large-sample archival analyses, we find that “mismatched” firms—those whose mandated financial reporting cycles are not aligned with their business cycles—exhibit higher levels of absolute abnormal accruals than their nonmismatched counterparts. Further analyses suggest that the negative association between mismatching and financial reporting quality is mainly driven by unintentional estimation errors rather than intentional earnings manipulation. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M41; M48; K22.

The present study aims to investigate the role of AKT2 in the pathogenesis of hepatic and cardiac lipotoxicity induced by lipid overload-induced obesity and identify its downstream targets. WT and Akt2 KO mice were fed either normal diet, or high-fat diet (HFD) to induce obesity model in vivo. Human hepatic cell line (L02 cells) and neonatal rat cardiomyocytes (NRCMs) were used as in vitro models. We observed that during HFD-induced obesity, Akt2 loss-of-function mitigated lipid accumulation and oxidative stress in the liver and heart tissue. Mechanistically, down-regulation of Akt2 promotes SIRT6 expression in L02 cells and NRCMs, the latter deacetylates SOD2, which promotes SOD2 activity and therefore alleviates oxidative stress-induced injury of hepatocytes and cardiomyocytes. Furthermore, we also proved that AKT2 inhibitor protects hepatocytes and cardiomyocytes from HFD-induced oxidative stress. Therefore, our work prove that AKT2 plays an important role in the regulation of obesity-induced lipid metabolic disorder in the liver and heart. Our study also indicates AKT2 inhibitor as a potential therapy for obesity-induced hepatic and cardiac injury.

As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approaches. We describe a “top-down” reinforcement learning–based design approach that solves this problem using Monte Carlo tree search to sample protein conformers in the context of an overall architecture and specified functional constraints. Cryo–electron microscopy structures of the designed disk-shaped nanopores and ultracompact icosahedra are very close to the computational models. The icosohedra enable very-high-density display of immunogens and signaling molecules, which potentiates vaccine response and angiogenesis induction. Our approach enables the top-down design of complex protein nanomaterials with desired system properties and demonstrates the power of reinforcement learning in protein design.

In the area of artificial intelligence, deep reinforcement learning has grown in significance. It has accomplished extraordinary feats and offers a fresh approach to previously challenging challenges, such as controlling a robotic arm and discovering game strategies. The two primary categories of deep reinforcement learning methods—deep reinforcement learning based on value function and deep reinforcement learning based on policy gradient—are initially explained in this study. The limitations of current approaches and the difficulties faced by deep reinforcement learning methods in related domains are further sorted out, and then the future application directions of deep reinforcement learning methods in the military sphere are examined. Finally, a growing trend for deep reinforcement learning techniques is anticipated in military applications.

The poor operational stability of non-fullerene electron acceptor (NFA) organic solar cells (OSCs) currently limits their commercial application. While previous studies have primarily focused on the degradation of the NFA component, we also consider here the electron donor material. We examine the stability of three representative donor polymers, PM6, D18, and PTQ10, paired with the benchmark NFA, Y6. After light soaking PM6 and D18 in air, we find an enhanced conversion of singlet excitons into trapped interchain polaron pairs on sub-100 femtosecond timescales. This process outcompetes electron transfer to Y6, significantly reducing the charge generation yield. However, this pathway is absent in PTQ10. We identify twisting in the benzo[1,2-b:4,5-b′]dithiophene (BDT)-thiophene motif shared by PM6 and D18 as the cause. By contrast, PTQ10 does not contain this structural motif and has improved stability. Thus, we show that the donor polymer can be a weak link for OSC stability, which must be addressed collectively with the NFA.

Objective: To explore the risk factors of diabetic nephropathy and its correlation with blood pressure control. Methods: A retrospective analysis of 80 patients with diabetic nephropathy (diabetic nephropathy group) and another 80 patients with diabetes (diabetic group) who were admitted to the Department of Nephrology and Endocrinology at our hospital from October 2021 to October 2022 was conducted. The general data of the two groups were compared, the influencing factors associated with the two groups were analyzed unilaterally, and unconditional dichotomous logistic regression was performed to analyze the influencing factors in patients with diabetic nephropathy. Results: There were no significant differences in high-density lipoprotein, systolic blood pressure, diastolic blood pressure, and creatinine between the two groups (P > 0.05); however, compared with the diabetic group, the DN group had significantly elevated glycated hemoglobin, low-density lipoprotein, 24-h urine protein, insulin resistance, and diabetes duration ? 10 years (P < 0.05). Conclusion: The clinical research on the correlation between the incidence of hypertension and the control of blood pressure in patients with diabetic nephropathy should be strengthened in order to formulate reasonable and feasible treatment plans.

Manganese sulfate is an important base manganese salt; nearly 80% of the world's manganese products are produced using manganese sulfate or manganese sulfate solution. Furthermore, manganese sulfate has many applications in industry and agriculture; thus, manganese sulfate solution impurity removal technology is important. This study aims to remove impurities from manganese sulfate solution-using complex low-grade manganese ore and manganese-rich fumes after pressure acid leaching to obtain a manganese sulfate solution, which is then purified through iron removal by oxidation neutralization; finally, the purified liquid is treated using extraction-back-extraction. We investigated the effects of various extraction parameters on the extraction rate of manganese, as well as the effects of various back-extraction parameters on the manganese back-extraction rate and manganese ion concentration in the back-extraction solution, and studied the extraction-back-extraction process. We found that the extraction and back-extraction rates of manganese could reach 95% and 96.9%, respectively, under optimal conditions..

Objective: To investigate the relationship between postprandial blood glucose (PBG), fasting insulin (FINS), and glycated hemoglobin (HbA1c) levels and early diabetic nephropathy in patients with type 2 diabetes. Methods: 96 cases of type 2 diabetes mellitus treated in our hospital from May 2021 to May 2022 were selected as the research subjects. The patients were divided into two groups according to the urinary albumin excretion rate (UAER), with 53 cases in the type 2 diabetes group (UAER < 30 ?g/min) and 43 cases in the early diabetic nephropathy group (30 ?g/min ? UAER < 300 ?g/min). PBG, FINS, and HbA1c levels were detected in 87 healthy patients. Results: The levels of PBG, FINS, and HbA1c in the early diabetic nephropathy group were higher than those in the control group (P < 0.01) and the type 2 diabetes group (P < 0.01). Conclusion: PBG, FINS, and HbA1c are factors affecting the occurrence of diabetic nephropathy in patients with type 2 diabetes; thus, controlling the levels of PBG, FINS, and HbA1c can effectively prevent the occurrence of diabetic nephropathy in type 2 diabetes mellitus.

We develop a Hybrid Bidirectional Long and Short-Term Memory based on attention mechanism (HBiLSTM-Attention) model and a BiLSTM-Attention model for multiclass flare forecasting within 24 h. We construct a new data base containing 10 separate data sets with magnetogram images and magnetic field parameters. Based on the same data base, for the first time we compare the multiclass forecasting performance of our proposed HBiLSTM-Attention model, BiLSTM-Attention model, and three other deep-learning models based on Convolutional Neural Network (CNN-based) from two aspects of categorical performance with the true skill statistic (TSS) and probabilistic performance with the Brier skill score (BSS). The major results are as follows. (1) The TSS values of our proposed model are 0.692 ± 0.042, 0.475 ± 0.038, 0.642 ± 0.043, 0.754 ± 0.062, 0.692 ± 0.042, and 0.708 ± 0.052 for No-flare, C, M, X, ≥C, and ≥M class, respectively, which are better than those of the BiLSTM-Attention model, and much better than those of the three other CNN-based models. (2) Our proposed model achieves the scores of BSS = 0.498 ± 0.061, 0.202 ± 0.037, 0.209 ± 0.050, −0.271 ± 0.180, 0.498 ± 0.061, 0.268 ± 0.056 for No-flare, C, M, X, ≥C, and ≥M class, respectively, outperforming the other four models in every class except for X class. (3) To our knowledge, HBiLSTM-Attention is the first multiclass flare forecasting model based on magnetic field parameters and deep learning, and achieves promising prediction performance. Moreover, this is the first attempt to investigate the reliability of probabilistic prediction for multiclass flares.

For the problem of difficulty in measuring the surface roughness of large-pitch internal threads, predictions are used instead of measurements, whereas the common predictions are not highly accurate and narrow in scope of use. In this paper, the homologous isomerism data of vibration signal were utilized to establish a predictive model, which predicted the surface roughness of large-pitch internal threads. The corresponding homologous isomerism data were acquired by turning the large-pitch internal thread, and the data were processed using Relief-F algorithm to obtain the weights, which are the effects of different features on surface roughness. Additionally, influenced by the structural characteristics of the workpiece with a large pitch and a small number of teeth, support vector machine (SVM) and RBF neural network, which are suitable for researches with small sample sizes, from the machine learning algorithm were used to establish the predictive model with the homologous isomerism data of vibration signal as the input parameters. Eventually, the SVM model with higher accuracy of prediction and better ability of generalization was more appropriate for the research of this paper through comparison and analysis. The SVM model was verified to have absolute errors within 0.05μm and relative errors within 4% for turning both left and right threaded surface, demonstrating that the predictive model could take the place of measuring the surface roughness in the mass production of large-pitch internal threads. The method proposed in this paper can also be extended to other parts for the prediction of surface roughness, especially those whose surface roughness are difficult to measure due to structural characteristics.

Triple negative breast cancer (TNBC) is the most aggressive breast cancer subtype and has the highest rate of recurrence. The predominant standard of care for advanced TNBC is systemic chemotherapy with or without immunotherapy, however responses are typically short-lived. Thus, there is an urgent need to develop more effective treatments. PI3K pathway components represent plausible therapeutic targets, as approximately 40% of TNBCs have PIK3CA/AKT1/PTEN alterations. However, unlike hormone receptor-positive tumors, it is still unclear if or how PI3K pathway inhibitors will be effective in triple-negative disease. Here we identify a promising AKT inhibitor-based therapeutic combination for TNBC. Specifically, we show that AKT inhibitors potently synergize with agents that suppress the histone methyltransferase, EZH2, and promote robust tumor regression in multiple TNBC models in vivo. AKT and EZH2 inhibitors exert these effects by first cooperatively driving basal-like TNBC cells into a more differentiated, luminal-like state, which cannot be effectively induced by either agent alone. More importantly, once differentiated, these agents kill TNBCs by hijacking signals that normally drive mammary gland involution. Together these findings identify a promising therapeutic strategy for this highly aggressive tumor type and illustrate how deregulated epigenetic enzymes can insulate tumors from oncogenic vulnerabilities. These studies also reveal how developmental tissue-specific cell death pathways may be co-opted for therapeutic benefit. Citation Format: Amy Schade, Naiara Perurena, Marina Watanabe, Carrie L. Rodriguez, Patrick Loi, Natalie Pilla, Rachel A. Davis, Kaia Mattioli, Dongxi Xiang, Jason J. Zoeller, Zhe Li, Ana C. Garrido-Castro, Sara Tolaney, Karen Cichowski. AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-08-11.