Keterkaitan pola pangan harapan (PPH) dengan kejadian stunting pada balita

Background: Food security from the consumption pillar is reflected by the loyal ability of citizens to consume food that is sufficient in quantity and nutritional quality, safe, diverse and affordable. Consumption of adequate and nutritionally balanced food is a form that must be met to minimize nutritional problems including stunting.Objectives: This study aims to determine the relationship between the Expected Food Pattern (PPH) and the incidence of stunting in toddlers.Methods: The type of research used is observational research, using a cross sectional design. The research sample is 90 samples. The research location is in North Kluet District, Aceh Regency. Data analysis using SPSS Software Independent Test t-test. Research data will be presented in the form of univariate and bivariate analysis.Results: The results showed that there was no difference in the Expected Food Pattern (PPH) score between stunting toddlers and the PPH score for normal toddlers, the p value = 0.553 (p > 0.553).Conclusion: In conclusion, the PPH score in stunting toddlers with normal toddlers does not show a difference on average in North Kluet District, South Aceh.

Differential Peripheral Blood Glycoprotein Profiles in Symptomatic and Asymptomatic COVID-19

Glycosylation is the most common form of post-translational modification of proteins, critically affecting their structure and function. Using liquid chromatography and mass spectrometry for high-resolution site-specific quantification of glycopeptides coupled with high-throughput artificial intelligence-powered data processing, we analyzed differential protein glyco-isoform distributions of 597 abundant serum glycopeptides and non-glycosylated peptides in 50 individuals who had been seriously ill with COVID-19 and in 22 individuals who had recovered after an asymptomatic course of COVID-19. As additional comparison reference phenotypes, we included 12 individuals with a history of infection with a common cold coronavirus, 16 patients with bacterial sepsis, and 15 healthy subjects without history of coronavirus exposure. We found statistically significant differences, at FDR<0.05, for normalized abundances of 374 of the 597 peptides and glycopeptides interrogated, between symptomatic and asymptomatic COVID-19 patients. Similar statistically significant differences were seen when comparing symptomatic COVID-19 patients to healthy controls (350 differentially abundant peptides and glycopeptides) and common cold coronavirus seropositive subjects (353 differentially abundant peptides and glycopeptides). Among healthy controls and sepsis patients, 326 peptides and glycopeptides were found to be differentially abundant, of which 277 overlapped with biomarkers that showed differential expression between symptomatic COVID-19 cases and healthy controls. Among symptomatic COVID-19 cases and sepsis patients, 101 glycopeptide and peptide biomarkers were found to be statistically significantly abundant. Using both supervised and unsupervised machine learning techniques, we found specific glycoprotein profiles to be strongly predictive of symptomatic COVID-19 infection. LASSO-regularized multivariable logistic regression and K-means clustering yielded accuracies of 100% in an independent test set and of 96% overall, respectively. Our findings are consistent with the interpretation that a majority of glycoprotein modifications observed which are shared among symptomatic COVID-19 and sepsis patients likely represent a generic consequence of a severe systemic immune and inflammatory state. However, there are glyco-isoform changes that are specific and particular to severe COVID-19 infection. These may be representative of either COVID-19-specific consequences or of susceptibility to or predisposition for a severe course of the disease. Our findings support the potential value of glycoproteomic biomarkers in the biomedical understanding, and, potentially, the clinical management of serious acute infectious conditions.

Polyketide Derivatives in the Resistance of Gerbera hybrida to Powdery Mildew

Powdery mildew is a common disease affecting the commercial production of gerbera flowers (Gerbera hybrida, Asteraceae). Some varieties show a certain degree of resistance to it. Our objective was to identify biomarkers of resistance to powdery mildew using an 1H nuclear magnetic resonance spectroscopy and chemometrics approach in a complex, fully factorial experiment to suggest a target for selection and breeding. Resistant varieties were found to differ from those that were susceptible in the metabolites of the polyketide pathway, such as gerberin, parasorboside, and gerberinside. A new compound probably involved in resistance, 5-hydroxyhexanoic acid 3-O-β-D-glucoside, was described for the first time. A decision tree model was built to distinguish resistant varieties, with an accuracy of 57.7%, sensitivity of 72%, and specificity of 44.44% in an independent test. Our results suggest the mechanism of resistance to powdery mildew in gerbera and provide a potential tool for resistance screening in breeding programs.

Quality Evaluation of the 0.01° Multi-Source Fusion Precipitation Product and Its Application in Extreme Precipitation Event

High-resolution and high-quality precipitation data play an important role in Numerical Weather Prediction Model testing, mountain flood geological disaster monitoring, hydrological monitoring and prediction and have become an urgent need for the development of modern meteorological business. The 0.01° multi-source fusion precipitation product is the latest precipitation product developed by the National Meteorological Information Center to meet the above needs. Taking the hourly precipitation observation data of 2400 national automatic stations as the evaluation base, independent and non-independent test methods are used to evaluate the 0.01° multi-source fusion precipitation product in 2020. The product quality differences between the 0.01° precipitation product and the 0.05° precipitation product are compared, and their application in extreme precipitation events are analyzed. The results show that, in the independent test, the product quality of the 0.01° precipitation product and the 0.05° precipitation product are basically the same, which is better than that of each single input data source, and the product quality in winter and spring is slightly lower than that in summer, and both products have better quality in the east in China. The evaluation results of the 0.01° precipitation product in the non-independent test are far better than that of the 0.05° product. The root mean square error and the correlation coefficient of the 0.01° multi-source fusion precipitation product are 0.169 mm/h and 0.995, respectively. In the extreme precipitation case analysis, the 0.01° precipitation product, which is more consistent with the station observation values, effectively improves the problem that the extreme value of the 0.05° product is lower than that of station observation values and greatly improves the accuracy of the precipitation extreme value in the product. The 0.01° multi-source fusion precipitation product has better spatial continuity, a more detailed description of precipitation spatial distribution and a more accurate reflection of precipitation extreme values, which will better provide precipitation data support for refined meteorological services, major activity support, disaster prevention and reduction, etc.

A comparison of deep learning models for end-to-end face-based video retrieval in unconstrained videos

Face-based video retrieval (FBVR) is the task of retrieving videos that containing the same face shown in the query image. In this article, we present the first end-to-end FBVR pipeline that is able to operate on large datasets of unconstrained, multi-shot, multi-person videos. We adapt an existing audiovisual recognition dataset to the task of FBVR and use it to evaluate our proposed pipeline. We compare a number of deep learning models for shot detection, face detection, and face feature extraction as part of our pipeline on a validation dataset made of more than 4000 videos. We obtain 97.25% mean average precision on an independent test set, composed of more than 1000 videos. The pipeline is able to extract features from videos at $$\sim $$ ∼ 7 times the real-time speed, and it is able to perform a query on thousands of videos in less than 0.5 s.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

Penerapan Atraumatic Care dengan Kecemasan Anak Pra-Sekolah Saat Proses Hospitalisasi

Pendahuluan: Hospitalisasi merupakan suatu keadaan krisis pada anak, saat anak sakit dan dirawat di rumah sakit. Sakit dan dirawat di rumah sakit merupakan pengalaman yang tidak menyenangkan dan sebagian besar proses keperawatan menjadikan anak takut bahkan trauma. Pelayanan Atraumatic care merupakan suatu pelayanan perawatan terapeutik dalam tatanan pelayanan kesehatan anak melalui penggunaan tindakan yang mengurangi distres fisik maupun distres psikologis yang dialami anak maupun orang tua. Penerapan atraumatic care dengan audiovisual yaitu memberikan video kartun, terapi ini sangat efektif mengurangi kecemasan pada anak yang mengalami hospitalisasi. Tujuan menurunkan angka kecemasan akibat hospitalisasi dengan Atraumatic care. Metode penelitian yaitu quasi eksprimen dengan rancangan pre and posttest control group. Analisa data menggunakan uji wilcoxon dan untuk menguji perbedaan dua kelompok menggunakan uji independent test atau Mann Whitney test. Sampel 56 anak prasekolah. Hasil: Ada perbedaan kecemasan anak prasekolah saat hospitalisasi pada kelompok eksperimen dan kelompok kontrol (p value 0,001). Kesimpulan ada pengaruh penerapan atraumatik audio-visual pada kecemasan anak prasekolah. Saran sebaiknya menerapkan atraumatic care audio visual untuk mengurangi kecemasan anak pra-sekolah.

LE-MDCAP: A Computational Model to Prioritize Causal miRNA–Disease Associations

MicroRNAs (miRNAs) are associated with various complex human diseases and some miRNAs can be directly involved in the mechanisms of disease. Identifying disease-causative miRNAs can provide novel insight in disease pathogenesis from a miRNA perspective and facilitate disease treatment. To date, various computational models have been developed to predict general miRNA–disease associations, but few models are available to further prioritize causal miRNA–disease associations from non-causal associations. Therefore, in this study, we constructed a Levenshtein-Distance-Enhanced miRNA–Disease Causal Association Predictor (LE-MDCAP), to predict potential causal miRNA–disease associations. Specifically, Levenshtein distance matrixes covering the sequence, expression and functional miRNA similarities were introduced to enhance the previous Gaussian interaction profile kernel-based similarity matrix. LE-MDCAP integrated miRNA similarity matrices, disease semantic similarity matrix and known causal miRNA–disease associations to make predictions. For regular causal vs. non-disease association discrimination task, LF-MDCAP achieved area under the receiver operating characteristic curve (AUROC) of 0.911 and 0.906 in 10-fold cross-validation and independent test, respectively. More importantly, LE-MDCAP prominently outperformed the previous MDCAP model in distinguishing causal versus non-causal miRNA–disease associations (AUROC 0.820 vs. 0.695). Case studies performed on diabetic retinopathy and hsa-mir-361 also validated the accuracy of our model. In summary, LE-MDCAP could be useful for screening causal miRNA–disease associations from general miRNA–disease associations.

Multimodality MRI-Based Radiomics For Aggressiveness Prediction In Papillary Thyroid Cancer

Objective: To investigate the ability of a multimodality MRI-based radiomics model in predicting the aggressiveness of papillary thyroid carcinoma (PTC).Methods: This study included consecutive patients who underwent neck magnetic resonance (MR) scans and subsequent thyroidectomy during the study period. The pathological diagnosis of thyroidectomy specimens was the gold standard to determine the aggressiveness. Thyroid nodules were manually segmented on three modal MR images, and then radiomics features were extracted. A machine learning model was established to evaluate the prediction of PTC aggressiveness.Results: The study cohort included 107 patients with PTC confirmed by pathology (training cohort: n = 71; test cohort: n = 36). A total of 1584 features were extracted from contrast-enhanced T1-weighted (CE-T1 WI), T2-weighted (T2 WI) and diffusion weighted (DWI) images of each patient. Sparse representation method is used for radiation feature selection and classification model establishment. The accuracy of the independent test set that using only one mode, like CE-T1WI, T2WI or DWI was not particularly satisfactory. In contrast, the result of these three modes combined achieved 0.917.Conclusion: Our study shows that multimodality MR image based on radiomics model can accurately distinguish aggressiveness in PTC from non-aggressiveness PTC before operation. This method may be helpful to inform the treatment strategy and prognosis of patients with aggressiveness PTC.

ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.