Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization

In this paper we study the personalized text search problem. The keyword based search method in conventional algorithms has a low efficiency in understanding users' intention since the semantic meaning, user profile, user interests are not always considered. Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search. Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search. According to user input, it searches the related items using keyword information, predicted user interest. Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning. Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources

Selenium nanoparticles decorated with Ulva lactuca polysaccharide potentially attenuate colitis by inhibiting NF-κB mediated hyper inflammation

Additional file 1. Supplemental information of ULP-SeNPs concerns their stability in physiological solutions, uptake by BMDMs and effect on NF-κB activation

Research on seed node mining algorithm in large-scale temporal graph

Most of the existing maximizing influence algorithms based on temporal graph were not applicable for large-scale networks due to the low time efficiency or narrow influence range.Therefore, the seed node mining algorithm named CHG combining heuristic algorithm and greedy strategy was proposed.Firstly, based on the time sequence characteristics of information diffusion in temporal graph, the concept of two-order degree of nodes was given, and the influence of nodes was heuristically evaluated.Secondly, the nodes were filtered according to the influence evaluation results, and the candidate seed node set was constructed.Finally, the marginal effect of candidate seed nodes was calculated to solve the overlap of influence ranges between nodes to ensure the optimal combination of seed nodes.The experiments were carried out on three different scale data sets, and the results show that the proposed algorithm can ensure the high influence of the seed node set even though its running time is relatively shorter.And it can achieve a better trade-off between the time efficiency and the influence range of the seed node set

Efficient Phytase Secretion and Phytate Degradation by Recombinant Bifidobacterium longum JCM 1217

Genetic engineering of probiotics, like bifidobacteria, may improve their microbial cell factory economy. This work designed a novel shuttle plasmid pBPES, which bears exogenous appA and is stable within Bifidobacterium longum JCM 1217. Cloning of three predicted promoters into pBPES proved that all of them drive appA expression in B. longum JCM 1217. Transformation of plasmids pBPES-tu and pBPES-groEL into B. longum JCM1217 resulted in much more phytase secretion suggests Ptu and PgroEL are strong promoters. Further in vitro and in vivo experiments suggested B. longum JCM 1217/pBPES-tu degrades phytate efficiently. In conclusion, the study screened two stronger promoters and constructed a recombinant live probiotic strain for effectively phytase secretion and phytate degradation in gut. The strategy used in the study provided a novel technique for improving the bioaccessibility of phytate and decreasing phosphorus excretion

Impact of the COVID-19 pandemic lockdown on Body Mass Index: a three-year follow up study in 6,156 Chinese college students

BackgroundThe novel coronavirus disease 2019 as the most pervasive and consequential pandemic in recent years, has exerted significant impacts on human health, including aspects related to body weight. Objectives: This study aims to assess the influence of the lockdown measures implemented during the COVID-19 pandemic on Chinese college students’ Body Mass Index (BMI) through a three-year cohort study.MethodsWe recruited 6156 college students (n = 4,248, 69% male, and n = 1,908, 31% female, with an average age of 18.68 ± 0.86 yr.) from a University in China to participate in this three-year cohort study. All of the subjects took the same physical fitness tests from 2019 to 2021 (pre-lockdown, during lockdown and post-lockdown). Participants’ height and weight data were objectively measured by Tongfang Health Fitness Testing Products 5000 series. A paired t-test was performed in the analysis.ResultsDuring the lockdown, there is 4.2% increase of BMI among the college student (p<0.001). Moreover, males had a greater overall mean BMI rate increase of 4.74% (p<0.001) than females (2.86%, p<0.001). After the lockdown, there is 0.94% increase of BMI among the college student (p<0.001). However, females had a greater overall mean BMI rate increase of 1.49% (p<0.001) than males (0.72%, p<0.001). During this period, the obese and overweight group’s growth rate from 2019 to 2020 was smaller than the normal and underweight group, which were 2.94% (p<0.001), 3.90% (p<0.001), 4.44% (p<0.001) and 5.25% (p<0.001), respectively.ConclusionBMI increased both during and post-lockdown periods among Chinese college students. However, during the lockdown, participants with higher BMI groups appeared to have a diminished BMI growth rate compared to those with lower BMI. After the lockdown, participants with higher BMI levels appeared to have an augmented BMI growth rate. Public policy action is needed to increase the level of physical activity of Chinese college students and take action to improve students’ physical fitness performance after the lockdown

Development and external validation of a nomogram for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage

BackgroundPostoperative pneumonia (POP) is a common complication after aneurysmal subarachnoid hemorrhage (aSAH) associated with increased mortality rates, prolonged hospitalization, and high medical costs. It is currently understood that identifying pneumonia early and implementing aggressive treatment can significantly improve patients' outcomes. The primary objective of this study was to explore risk factors and develop a logistic regression model that assesses the risks of POP.MethodsAn internal cohort of 613 inpatients with aSAH who underwent surgery at the Neurosurgical Department of First Affiliated Hospital of Wenzhou Medical University was retrospectively analyzed to develop a nomogram for predicting POP. We assessed the discriminative power, accuracy, and clinical validity of the predictions by using the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA). The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database.ResultsAmong patients in our internal cohort, 15.66% (n = 96/613) of patients had POP. The least absolute shrinkage and selection operator (LASSO) regression analysis identified the Glasgow Coma Scale (GCS), mechanical ventilation time (MVT), albumin, C-reactive protein (CRP), smoking, and delayed cerebral ischemia (DCI) as potential predictors of POP. We then used multivariable logistic regression analysis to evaluate the effects of these predictors and create a final model. Eighty percentage of patients in the internal cohort were randomly assigned to the training set for model development, while the remaining 20% of patients were allocated to the internal validation set. The AUC values for the training, internal, and external validation sets were 0.914, 0.856, and 0.851, and the corresponding Brier scores were 0.084, 0.098, and 0.143, respectively.ConclusionWe found that GCS, MVT, albumin, CRP, smoking, and DCI are independent predictors for the development of POP in patients with aSAH. Overall, our nomogram represents a reliable and convenient approach to predict POP in the patient population

Increasing Cytosine Base Editing Scope and Efficiency With Engineered Cas9-PmCDA1 Fusions and the Modified sgRNA in Rice

Base editors that do not require double-stranded DNA cleavage or homology-directed repair enable higher efficiency and cleaner substitution of targeted single nucleotides in genomic DNA than conventional approaches. However, their broad applications are limited within the editing window of several base pairs from the canonical NGG protospacer adjacent motif (PAM) sequence. In this study, we fused the D10A nickase of several Streptococcus pyogenes Cas9 (SpCas9) variants with Petromyzon marinus cytidine deaminase 1 (PmCDA1) and uracil DNA glycosylase inhibitor (UGI) and developed two new effective PmCDA1-based cytosine base editors (pBEs), SpCas9 nickase (SpCas9n)-pBE and VQR nickase (VQRn)-pBE, which expanded the scope of genome targeting for cytosine-to-thymine (C-to-T) substitutions in rice. Four of six and 12 of 18 target sites selected randomly in SpCas9n-pBE and VQRn-pBE, respectively were base edited with frequencies of 4–90% in T0 plants. The effective deaminase window typically spanned positions 1–7 within the protospacer and the single target C showed the maximum C-to-T frequency at or near position 3, counting the end distal to PAM as position 1. In addition, the modified single guide RNA (sgRNA) improved the base editing efficiencies of VQRn-pBE with 1.3- to 7.6-fold increases compared with the native sgRNA, and targets that could not be mutated using the native sgRNA were edited successfully using the modified sgRNA. These newly developed base editors can be used to realize C-to-T substitutions and may become powerful tools for both basic scientific research and crop breeding in rice