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ABSTRACTObjectives The combination of Venetoclax (VEN) and Azacitidine (AZA) increases survival outcomes and yields excellent responses in patients with acute myeloid leukemia (AML). However, dose reduction (or discontinuation) is commonly encountered due to therapy-related toxicity. Thus, this study aimed to investigate the efficiency and safety of a lower dosage of venetoclax for the treatment of AML.Methods This observational study analyzed the characteristics and outcomes of newly diagnosed AML patients who received 100 mg VEN combined with AZA for 14 days at our institution.Results A total of 36 patients were enrolled, and the median age at diagnosis was 64 years. After a median follow-up of 15 (range 4–29) months, the median overall survival (OS) and progression-free survival (PFS) for the whole cohort were 17 (4–29) months and 12 (1–28) months, respectively. Meanwhile, the overall response rate (ORR) was 69.4%, and the CRc rate was 66.7% in the whole cohort. Subgroup analysis revealed that NPM1 mutations and FAB-M5 subtype were associated with higher response rates, whereas the adverse ELN risk group was predictive of an inferior response. Moreover, ASXL1, NPM1, and IDH1/2 mutations negatively impacted PFS.Discussion Our study optimized the administration of venetoclax plus azacytidine for the treatment of AML patients. Response rates were favorable, with median survival in agreement with the findings of earlier reports, offering valuable insights for optimizing VEN-based regimens.Conclusion In summary, the VEN combination regimen is effective for the treatment of newly diagnosed AML patients in the real world despite VEN dose reductions .

Abstract Background The cultivation of bananas encounters substantial obstacles, particularly due to the detrimental effects of cold stress on their growth and productivity. A potential remedy that has gained attention is the utilization of ethyl mesylate (EMS)-induced mutagenesis technology, which enables the creation of a genetically varied group of banana mutants. This complex procedure entails subjecting the mutants to further stress screening utilizing L-Hyp in order to identify those exhibiting improved resistance to cold. This study conducted a comprehensive optimization of the screening conditions for EMS mutagenesis and L-Hyp, resulting in the identification of the mutant cm784, which exhibited remarkable cold resistance. Subsequent investigations further elucidated the physiological and transcriptomic responses of cm784 to low-temperature stress. Results EMS mutagenesis had a substantial effect on banana seedlings, resulting in modifications in shoot and root traits, wherein a majority of seedlings exhibited delayed differentiation and limited elongation. Notably, mutant leaves displayed altered biomass composition, with starch content exhibiting the most pronounced variation. The application of L-Hyp pressure selection aided in the identification of cold-resistant mutants among seedling-lethal phenotypes. The mutant cm784 demonstrated enhanced cold resistance, as evidenced by improved survival rates and reduced symptoms of chilling injury. Physiological analyses demonstrated heightened activities of antioxidant enzymes and increased proline production in cm784 when subjected to cold stress. Transcriptome analysis unveiled 946 genes that were differentially expressed in cm784, with a notable enrichment in categories related to ‘Carbohydrate transport and metabolism’ and ‘Secondary metabolites biosynthesis, transport, and catabolism’. Conclusion The present findings provide insights into the molecular mechanisms that contribute to the heightened cold resistance observed in banana mutants. These mechanisms encompass enhanced carbohydrate metabolism and secondary metabolite biosynthesis, thereby emphasizing the adaptive strategies employed to mitigate the detrimental effects induced by cold stress.

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm’s efficiency and robustness. Ultimately, an extensive validation of MISWOA through “simulation + experimentation” approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIRS) can be used for quantitative detection of milk components. Here, milk samples of 458 Chinese Holstein cows from 11 provinces in China were collected and we established a total of 22 quantitative prediction models in milk fatty acids by FT-MIRS. The coefficient of determination of the validation set ranged from 0.59 (C18:0) to 0.76 (C4:0). The models were adopted to predict the milk fatty acids from 2138 cows and a new high-throughput computing software HiBLUP was employed to construct a multi-trait model to estimate and analyze genetic parameters in dairy cows. Finally, genome-wide association analysis was performed and seven novel SNPs significantly associated with fatty acid content were selected, investigated, and verified with the FarmCPU method, which stands for “Fixed and random model Circulating Probability Unification”. The findings of this study lay a foundation and offer technical support for the study of fatty acid trait breeding and the screening and grouping of characteristic dairy cows in China with rich, high-quality fatty acids. It is hoped that in the future, the method established in this study will be able to screen milk sources rich in high-quality fatty acids.

Although computer vision offers ample possibilities, there is currently a lack of general film measurement software. Here, we propose a software called PyCinemetrics, which is a computational film studies tool offering five functions that utilize deep learning and PySide2 to deconstruct the visual style of films. It uses TransNetV2 to divide a film into shot-frames, allowing for the exploration of Average Shot Length (ASL) and pace. The tool also extracts main colors from shot-frames using K-Means. Furthermore, it can extract movie subtitles using EasyOcr to obtain dialogue. Additionally, PyCinemetrics utilizes object detection based on VGG19 to identify metaphorical props and objects. By detecting the proportion of skeletal points occupied in the frame using OpenPose, it indirectly determines the shot scale. Finally, PyCinemetrics was integrated and implemented using PySide2. A group of classic films was analyzed using PyCinemetrics, demonstrating its accuracy and efficiency in frame analysis.

Visualization provides an important means for understanding software. Many studies focus on software call graphs or software visualization, but there is insufficient attention given to studying them as a whole. This paper presents the extracted call graphs between software packages in Python and within individual packages (modules, file directory, and classes). To avoid potential errors that may arise from directly scanning the source code, we extracts the structure data based on pip and inspect. To facilitate interactive exploration of the data extracted, we provide eight visual layouts based on D3.js and Vue3.js. Additionally, the tool allows for querying module (.py) and class source code and docs. PyVisVue3D3 essentially provides a visual PyDocs that integrates multi-granularity call graphs, aiding developers in quickly extracting knowledge from codebase and improving software comprehension efficiency (https://pyvisvue3d3.yingshinet.com).

Fatty acid is an important factor affecting the nutritional quality of milk. In this study, we collected and assessed 78,086 milk samples from 12,065 Chinese Holstein cows from 11 farms in Northern China from November 2019 to September 2022. The contents of eight fatty acid groups were predicted using FT-MIRS-based models. The contents of TFAs, SFAs, UFAs, MUFAs, PUFAs, and LCFAs in milk reached the highest at 96–125 DIM, and SCFA and MCFA contents reached the highest at 276–305 DIM. With the increase in somatic cell score, the contents of various fatty acid groups in milk gradually decreased, and the nutritional value of milk and flavor of dairy products gradually deteriorated. The contents of high-quality fatty acids in milk, particularly UFAs and MUFAs, were significantly higher in the non-pregnant state than in the pregnant state. However, SCFA and MCFA contents exhibited the opposite pattern. Our findings provided valuable information on the content and distribution range of fatty acid groups in milk from Chinese Holstein cows. Further analysis is warranted to explore the breeding of Chinese Holstein cows providing milk with abundant beneficial fatty acids.

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model based on mitochondria-related genes(MRGs) for preeclampsia using machine learning and further investigate the association of the MRGs and immune infiltration landscape in preeclampsia. In this research, we analyzed GSE75010 database and screened 552 DE-MRGs between preeclampsia samples and normal samples. Enrichment assays indicated that 552 DE-MRGs were mainly related to energy metabolism pathway and several different diseases. Then, we performed LASSO and SVM-RFE and identified three critical diagnostic genes for preeclampsia, including CPOX, DEGS1 and SH3BP5. In addition, we developed a novel diagnostic model using the above three genes and its diagnostic value was confirmed in GSE44711, GSE75010 datasets and our cohorts. Importantly, the results of RT-PCR confirmed the expressions of CPOX, DEGS1 and SH3BP5 were distinctly increased in preeclampsia samples compared with normal samples. The results of the CIBERSORT algorithm revealed a striking dissimilarity between the immune cells found in preeclampsia samples and those found in normal samples. In addition, we found that the levels of SH3BP5 were closely associated with several immune cells, highlighting its potential involved in immune microenvironment of preeclampsia. Overall, this study has provided a novel diagnostic model and diagnostic genes for preeclampsia while also revealing the association between MRGs and immune infiltration. These findings offer valuable insights for further research and treatment of preeclampsia.

Objective To form the sampling data interval by retrospectively analyzing the sampling data of quality monitoring of fresh frozen plasma, cryoprecipitates and leukocyte-free platelets in all blood stations in Hebei Province during the past 7 years. Methods The data of blood component sampling from 12 blood station quality control laboratories in Hebei from 2015 to 2021 were collected. The FⅧ content and plasma protein content of fresh frozen plasma, the FⅧ content and fibrinogen content of cryoprecipitates, and the leukocyte residuals, red blood cell mixed and platelet content of leukocyte-free platelets were taken as the objects for discrete point and fitted curve analysis. Results The FⅧ level of fresh frozen plasma: (1.36±1.1) IU/mL, 5 blood stations showed a representative overall high or low or fluctuated characteristics; Fresh frozen plasm-plasma protein items: overall mean ±SD: (61.13±16.7) g/L, four blood stations showed scattered distribution or continuous high value scattered points; Cryoprecipitates FⅧ: the overall mean ±SD: (134.25±58.7) IU/mL, four blood stations showed the differentiation characteristics of continuous high, low or stable in the middle; Cryoprecipitates-fibrinogen items: the overall mean ±SD: (215.27±83.5) mg, five blood stations showed the overall high or low and fluctuated. Leukocyte-free apheresis platelet-to-leukocyte residual items: overall mean ±SD: 0.37±0.96 (×106/bag), two blood stations showed a relatively high representative overall characteristics, and the rest were concentrated between 0 and 1; The total mean ±SD of platelet-to-red blood cell mixture without leukocyte was 2.45±2.82 (×109/bag), with obvious segmented concentrated distribution, and scattered distribution in 3 blood centers. Platelet content: the overall mean ±SD was 3.14±1.55 (×1011/bag), many deviations were noticed in 3 blood stations, and 1 blood station showed representative overall high characteristics. Conclusion This analysis shows that the distribution status of each blood station in different items is similar. The distribution status of discrete point groups and the change trend of the concentrated part of the fitting curve show that there are some differences in the monitoring level between the quality control laboratories of each blood station, and the update of detection instruments and reagents and the selection of detection methods greatly affect the test results. The summary data presented the index interval framework formed in the past 7 years, which helped to understand the difference between the results of each laboratory, correct the accuracy of the test results, better play the guiding role of quality monitoring in the blood preparation process, and continue to enhance the standardization of the whole process of blood collection and supply in the province.

The block-scale application of photovoltaic technology in cities is becoming a viable solution for renewable energy utilization. The rapid urbanization process has provided urban buildings with a colossal development potential for solar energy in China, especially in industrial areas that provide more space for the integration of PV equipment. In developing solar energy resources, the block layout and the PV materials are two critical factors affecting the distribution of solar radiation and generation. However, few studies have analyzed how to select the most suitable PV materials for different layouts of industrial blocks to obtain the best generation. This study considered the layout of industrial blocks and PV materials simultaneously, and the generation yield was calculated when combined. A total of 40 real industrial block cases were constructed, and radiation distribution data on building surfaces of different block cases were calculated. Data on both were combined to calculate the generation of different PV materials for each block type. The findings indicated that single-story industrial blocks possessed the highest potential for solar radiation, primarily due to the higher percentage of roof area. The influence of PV materials on the installation rate of different building facades varied, with the installation rate of the west facade being the most impacted by PV performance and the roof being the least impacted. Using different PV materials in industrial blocks could lead to a 59.2% difference in solar generation capacity. For single-layer industrial blocks, mono crystalline and poly crystalline silicon were preferable to achieve higher power generation. In contrast, multi-story and high-rise industrial blocks were best suited for a-Si and CIGS to attain higher cost performance. The methods and results of this study guided the selection and installation of PV equipment in various block typologies, thereby improving the refinement of solar resource development, maximizing solar resource utilization, and promoting the development of energy conservation and carbon reduction in cities.

Background Metabolic acidosis accelerates the progression of chronic kidney disease (CKD) and increases the mortality rate. Whether oral alkali drug therapy benefits pre-dialysis CKD patients is controversial. We performed a meta-analysis of the effects of oral alkali drug therapy on major clinical outcomes in pre-dialysis CKD patients. Methods We systematically searched MEDLINE using the Ovid, EMBASE, and Cochrane Library databases without language restriction. We included all eligible clinical studies that involved pre-dialysis CKD adults and compared those who received oral alkali drug therapy with controls. Results A total of 18 eligible studies, including 14 randomized controlled trials and 4 cohort studies reported in 19 publications with 3695 participants, were included. Oral alkali drug therapy led to a 55% reduction in renal failure events (relative risk [RR]: 0.45; 95% confidence interval [CI]: 0.25–0.82), a rate of decline in the estimated glomerular filtration rate (eGFR) of 2.59 mL/min/1.73 m2 per year (95% CI, 0.88–4.31). There was no significant effect on decline in eGFR events (RR: 0.34; 95% CI: 0.09–1.23), proteinuria (standardized mean difference: −0.32; 95% CI: −1.08 to 0.43), all-cause mortality events (RR: 0.90; 95% CI: 0.40–2.02) and cardiovascular (CV) events (RR: 1.03; 95% CI: 0.32–3.37) compared with the control groups. Conclusion Based on the available and low-to-moderate certainty evidence, oral alkali drug therapy might potentially reduce the risk of kidney failure events, but no benefit in reducing all-cause mortality events, CV events, decline in eGFR and porteninuria.

Efficient reproductive management of dairy cows depends primarily upon accurate estrus identification. However, the currently available estrus detection methods, such as visual observation, are poor. Hence, there is an urgent need to discover novel biomarkers in non-invasive bodily fluids such as milk to reliably detect estrus status. Proteomics is an emerging and promising tool to identify biomarkers. In this study, the proteomics approach was performed on milk sampled from estrus and non-estrus dairy cows to identify potential biomarkers of estrus. Dairy cows were synchronized and timed for artificial insemination, and the cows with insemination leading to conception were considered to be in estrus at the day of insemination (day 0). Milk samples of day 0 (estrus group) and day −3 (non-estrus group) from dairy cows confirming to be pregnant were collected for proteomic analysis using the tandem mass tags (TMT) proteomics approach. A total of 89 differentially expressed proteins were identified, of which 33 were upregulated and 56 were downregulated in the estrus milk compared with the non-estrus milk. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that acetyl coenzyme A carboxylase α (ACACA), apolipoprotein B (APOB), NAD(P)H steroid dehydrogenase-like (NSDHL), perilipin 2 (PLIN2), and paraoxonase 1 (PON1) participated in lipid binding, lipid storage, lipid localization, and lipid metabolic process, as well as fatty acid binding, fatty acid biosynthesis, and fatty acid metabolism, and these processes are well documented to be related to estrus regulation. These milk proteins are proposed as possible biomarkers of estrus in dairy cows. Further validation studies are required in a large population to determine their potential as estrus biomarkers.

Previous studies have suggested that microwave (MW) radiation with certain parameters can induce spatial memory deficits. However, the effect of MW on the topological organization of the brain network is still unknown. This work aimed to investigate the topological organization of the brain network in rats with spatial memory impairments induced by acute microwave (MW) radiation. The Morris water maze (MWM) test and resting-state functional magnetic resonance imaging were performed to estimate the spatial memory ability and brain network topological organization of the rats after MW exposure. Compared with the sham group, the rats exposed to 30 mW/cm2 1.5 GHz MW radiation exhibited a significantly decreased normalized clustering coefficient (γ) (p = 0.002) 1 d after the exposure and a prolonged average escape latency (AEL) (p = 0.014) 3 d after the exposure. Moreover, after 10 mW/cm2 1.5 GHz MW radiation, a significantly decreased γ (p = 0.003) was also observed in the rats, without any changes in AEL. In contrast, no adverse effects on AEL or topological parameters were observed after 9.375 GHz MW radiation. In conclusion, the rats with spatial memory deficits induced by MW radiation exhibited disruptions in the topological organization of the brain network. Moreover, these topological organization disruptions emerged earlier than behavioral symptom onset and could even be found in the rats without a decline in the performance of the spatial memory task. Therefore, it is possible to use the topological parameters of the brain network as early and sensitive indicators of the spatial memory impairments induced by acute MW radiation.

The probable nanotoxicity to human health and the environment is a significant challenge for the sustainable application of nanomaterials in medicine. The cytototoxical effect of succimer (meso-2,3-dimercaptosuccinic acid-DMSA) coated titanium dioxide (DMSA-TiO2) with cultured human aortic endothelial cells (HAoECs) was assessed in this investigation. Our findings have shown that DMSA-TiO2 can be accumulated in HAoECs and dispersed in a cytoplasm on the culture medium. DMSA-cytotoxicity TiO2 effects were dose-responsive, and the concentrations were of little toxicity, and MTT stain testing showed that they had only 0.02 mg ml−1. Meanwhile, the lactate dehydrogenase biomarker was not considerably more remarkable than the biomarker from untreated (control) cells (free DMSA-TiO2). Though, also without any apparent signs of cell damage, the endocrine functions for prostacyclin I-2 and endothelin-1 and the urea transporter functions were modified. In addition, in vitro endothelial tube development has been shown that HAoECs could induce angiogenesis even with small amounts of DMSA-TiO2 (0.01 and 0.02 mg ml−1). Further, we have examined the in vivo toxicity and biochemical parameter by animal model. Furthermore, in vivo assessments designated that the resulting DMSA-TiO2 presented synergistic activities of angiogenesis activity. Overall, these findings show the cytotoxicity of DMSA-TiO2 and could induce adverse effects on normal endothelial cells.

Purple tea plant cultivars, enrich with flavonoids and anthocyanins, are valuable materials for manufacturing tea with unique color and flavor. Researchers found that ‘Zijuan’ leaves changed from purple to green mainly caused by the decreased flavonoids and anthocyanins concentrations. The mechanism of flavonoids and anthocyanin biosynthesis has been studied in many purple tea plant cultivars and the key genes which regulated the biosynthesis of flavonoid and anthocyanins in different purple tea plant cultivars were quite different. Also, the molecular regulation mechanism underlying the flavonoids and anthocyanins biosynthesis during leaves development and color changes is less-thoroughly understood. In this study, an integrative analysis of transcriptome and metabolome was performed on the purple leaves and green leaves of ‘Zijuan’ tea plant to reveal the regulatory networks correlated to flavonoid biosynthesis and to identify key regulatory genes. Our results indicated that the ‘Zijuan’ new shoots leaves were purple might be due to the copigmentation of quercetin and kaempferol derivatives. In ‘Zijuan’ tea plant cultivar, flavonoids metabolites concentrations in purple leaves and green leaves were significantly influenced by the genes involved in flavonoid biosynthesis, transcriptional regulation, transport, and hormone response. Transcription factors including NAC008, MYB23, and bHLH96 and transporters such as ABC transporter I might be responsible for the flavonoid and anthocyanins accumulation in purple leaves. This study provides a new insight into the metabolism and molecular mechanisms underlying flavonoid and anthocyanin biosynthesis in tea plant.

Objective To investigate the biomarker(s) that could affect the decision for immediate or delayed delivery in severe preeclampsia. Methods Data on serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), creatinine, blood urea nitrogen (BUN), uric acid (UA) and platelet counts from 134 cases were collected and analysed. Results Higher UA levels were seen in case with immediate delivery. Higher stillbirth was seen in cases with delayed delivery. Conclusion UA levels could be a potential management biomarker for immediate or delayed delivery in severe preeclampsia. However, the higher risk of stillbirth must be considered in delayed delivery.

Visible and near-infrared reflectance (Vis-NIR) spectroscopy can provide low-cost and high-density data for mapping various soil properties. However, a weak correlation between the spectra and measurements of soil heavy metals makes spectroscopy difficult to use in predicting incipient risk areas. In this study, we introduce a new spectral index (SI) based on Vis-NIR spectra and use it as a covariate in ordinary cokriging (OCK) to improve the mapping of soil heavy metals. The SI was defined from the highest covariance between spectra and heavy metal content in the partial least squares regression (PLSR) model. The proposed mapping approach was compared with an ordinary kriging (OK) predictor that uses only soil heavy metal data and an OCK predictor that uses soil organic matter (SOM) and Fe as covariates. To this end, a total of 100 topsoil (0-20 cm) samples were collected in an agricultural area near Longkou City, and the contents of As, Pb and Zn in the soil were determined. The results showed that OCK with the SI provided better results in terms of unbiasedness and accuracy compared to other comparative methods. Additionally, we explored the SI through simple strategies based on spectral analysis and correlation statistics and found that the SI synthesized most of the soil properties affected by heavy metals and was not limited to Fe and SOM. In summary, the SI method is cost-effective for improving soil heavy metal mapping and can be applied to other areas.

Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.

Partially hydrolyzed polyacrylamide (HPAM) was widely implemented to improve the rheological properties of displacing fluids, but the high temperature and salinity of the reservoir brine limited their applications. Herein, copolymers including HPAM, zwitterion-modified HPAM (z-HPAM), PEG-modified HPAM (p-HPAM), and zwitterion/PEG-modified HPAM (zp-HPAM) were prepared by free radical polymerization in an aqueous solution. The viscosity of these copolymers under different temperature and salinity was measured in aqueous solution. It is found that the viscosity of the HPAM under the harsh condition (90oC, 20 × 104 mg/L salinity) is only 9.6% of that value under the normal condition (25oC, pure water), while the z-HPAM can significantly improve salt resistance by the effects of salting-in effect and intermolecular electrostatic crosslinking, showing a viscosity retention of 22.9% under the harsh condition. The addition of PEG-containing monomer can strengthen hydrogen bonding between the polymer chains and form a sterically ordered structure with improved salinity and temperature resistance. The synergistic effect of zwitterion units and PEG units endows the zp-HPAM with good salinity and temperature resistance; thus, the sample viscosity under the harsh condition remains 170 mPa s, which retains 29% of the value under the normal condition. The enhanced rheology properties of the zp-HPAM under the harsh condition are significant for the enhanced oil recovery of water-soluble polymer flooding.

Shale contains distributed directional bedding planes, which make the shale transverse isotropic. To model shale with consideration of the bedding planes, a cohesive finite element method (CFEM) is developed based on the randomized triangular mesh. The interface orientation generated from such mesh tends to be uniformly distributed with the element number increasing. To represent the bedding plane, the interfaces aligned with the bedding plane are assigned the cohesive law that characterizes the bedding plane while the other interfaces are assigned the cohesive law that characterizes the matrix. By this means, the anisotropy characteristics of the stiffness and the strength of shale are well represented. The simulation examples demonstrate that the bedding plane has a significant influence on the fracture trajectory, which is consistent with the observation in the experiment. It is suggested that this modeling method of shale is feasible. It provides an alternative approach to fracture simulation in shale. Keywords: Shale, Bedding plane, Cohesive finite element, Transverse isotropy, Fracture simulation

Objective This study aimed to investigate the optimal strategy for myomectomy for removing giant uterine fibroids when necessary in women undergoing cesarean section. Methods This study was retrospective in design, and assessed outcomes in 26 patients who underwent myomectomy using a “base purse-string suture” during cesarean section. The operative duration, blood loss, uterine involution, and duration of postpartum lochia were analyzed. Results This suture was associated with a mean operative duration of 11.17 ± 5.36 minutes and the mean estimated blood loss was 11.15 ± 6.05 mL. The mean postpartum duration of lochia was 34.92 ± 7.55 days and there were no cases of postpartum hemorrhage. Uterine size returned to normal within 6 weeks of delivery, without any apparent defects or abnormalities in the uterine wall as shown by an ultrasonic examination. Conclusion This novel implementation of a base purse-string suture during cesarean myomectomy for removal of giant fibroids is a simple, safe, and effective intervention that should be considered for implementation in appropriate patients.

A new type of semi-submersible drilling platform is designed. The numerical simulation software is used to analyze the heave response of the new platform in the frequency domain and time domain, and the semi-submersible drilling platforms with double-floating four-column structure and heave plate structure are compared with the new platform. This paper introduces the design principles of the new platform and the theoretical basis, mathematical model and boundary conditions during correlation analysis. The numerical simulation results show that the maximum and mean values of the heave response of the new platform are significantly reduced in the frequency domain analysis compared with the double-floating four-column and the heave-plate structure platform; and the new platform has a significant increase in the natural heaving period of the new platform, which can effectively prevent the occurrence of resonance. In the mooring time domain coupling analysis, the surge, sway and roll response of the new platform is small, and the heave response is greatly reduced. In the spectral analysis, the new platform has a smaller peak response and better wave frequency characteristics. The new platform has excellent anti-heave performance, reasonable structure and feasibility, and can provide reference for the design and selection of new generation semi-submersible drilling platform. Key words: drilling platform, semi-submersible drilling platform, heave prevention, frequency domain analysis, time domain analysis

With the sustained development of social networks, increasing attention has been paid to social recommender systems. Current studies usually focus on indirect factors such as the similarity between users, but multiple direct interactions, such as mentions, reposts, and comments, are seldom considered. This paper addresses direct connections between users in social recommender systems. We analyze direct interactions to investigate the connection strength between users, and then, user preferences and item characteristics can be better described. Based on the analysis of social influence between users and users' influence over the whole social network, we propose a recommendation method with social influence, which makes full use of information among users in social networks and introduces the mechanisms of macroscopic and microscopic influences. Direct interactions between users are incorporated into a matrix factorization objective function. Real-world microblog data are applied to verify our model, and the results show that the proposed recommendation method outperforms other state-of-the-art recommendation algorithms.

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm−1, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

The use of abnormal milk mid-infrared (MIR) spectrum strongly affects prediction quality, even if the prediction equations used are accurate. So, this record must be detected after or before the prediction process to avoid erroneous spectral extrapolation or the use of poor-quality spectral data by dairy herd improvement (DHI) organizations. For financial or practical reasons, adapting the quality protocol currently used to improve the accuracy of fat and protein contents is unfeasible. This study proposed three different statistical methods that would be easy to implement by DHI organizations to solve this issue: the deletion of 1% of the extreme high and low predictive values (M1), the deletion of records based on the Global-H (GH) distance (M2), and the deletion of records based on the absolute fat residual value (M3). Additionally, the combinations of these three methods were investigated. A total of 346,818 milk samples were analyzed by MIR spectrometry to predict the contents of fat, protein, and fatty acids. Then, the same traits were also predicted externally using their corresponded standardized MIR spectra. The interest in cleaning procedures was assessed by estimating the root mean square differences (RMSDs) between those internal and external predicted phenotypes. All methods allowed for a decrease in the RMSD, with a gain ranging from 0.32% to 41.39%. Based on the obtained results, the “M1 and M2” combination should be preferred to be more parsimonious in the data loss, as it had the higher ratio of RMSD gain to data loss. This method deleted the records based on the 2% extreme predictions and a GH threshold set at 5. However, to ensure the lowest RMSD, the “M2 or M3” combination, considering a GH threshold of 5 and an absolute fat residual difference set at 0.30 g/dL of milk, was the most relevant. Both combinations involved M2 confirming the high interest of calculating the GH distance for all samples to predict. However, if it is impossible to estimate the GH distance due to a lack of relevant information to compute this statistical parameter, the obtained results recommended the use of M1 combined with M3. The limitation used in M3 must be adapted by the DHI, as this will depend on the spectral data and the equation used. The methodology proposed in this study can be generalized for other MIR-based phenotypes.

In this paper we report a facile method for preparing co-immobilized enzyme and magnetic nanoparticles (MNPs) using metal coordinated hydrogel nanofibers. Candida rugosa lipase (CRL) was selected as guest protein. For good aqueous dispersity, low price and other unique properties, citric acid-modified magnetic iron oxide nanoparticles (CA-Fe3O4 NPs) have been widely used for immobilizing enzymes. As a result, the relative activity of CA-Fe3O4@Zn/AMP nanofiber-immobilized CRL increased by 8-fold at pH 10.0 and nearly 1-fold in a 50 °C water bath after 30 min, compared to free CRL. Moreover, the immobilized CRL had excellent long-term storage stability (nearly 80% releative activity after storage for 13 days). This work indicated that metal-nucleotide nanofibers could efficiently co-immobilize enzymes and MNPs simultaneously, and improve the stability of biocatalysts.

BACKGROUND: Lonicera japonica Thunb. is a plant used in traditional Chinese medicine known for its anti-inflammatory, anti-oxidative, anti-carcinogenic, and antiviral pharmacological properties. The major active secondary metabolites of this plant are chlorogenic acid (CGA) and luteoloside. While the biosynthetic pathways of these metabolites are relatively well known, the genetic information available for this species, especially the biosynthetic pathways of its active ingredients, is limited. METHODOLOGY/PRINCIPAL FINDINGS: We obtained one million reads (average length of 400 bp) in a whole sequence run using a Roche/454 GS FLX titanium platform. Altogether, 85.69% of the unigenes covering the entire life cycle of the plant were annotated and 325 unigenes were assigned to secondary metabolic pathways. Moreover, 2039 unigenes were predicted as transcription factors. Nearly all of the possible enzymes involved in the biosynthesis of CGA and luteoloside were discovered in L. japonica. Three hydroxycinnamoyl transferase genes, including two hydroxycinnamoyl-CoA quinate hydroxycinnamoyl transferase genes and one hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (HCT) gene featuring high similarity to known genes from other species, were cloned. The HCT gene was discovered for the first time in L. japonica. In addition, 188 candidate cytochrome P450 unigenes and 245 glycosyltransferase unigenes were found in the expressed sequence tag (EST) dataset. CONCLUSION: This study provides a high quality EST database for L. japonica by 454 pyrosequencing. Based on the EST annotation, a set of putative genes involved in CGA and luteoloside biosynthetic pathways were discovered. The database serves as an important source of public information on genetic markers, gene expression, genomics, and functional genomics in L. japonica.