The impact of healthy nutrition education based on traffic light labels on food selection, preference, and consumption in patients with acute coronary syndrome: a randomized clinical trial.

BACKGROUND: Acute Coronary Syndrome is the most common heart disease and the most significant cause of death and disability-adjusted life years worldwide. Teaching a healthy eating style is one preventive measure to prevent the disease's recurrence. This study aimed to determine the effect of healthy nutrition education with the help of traffic light labels on food selection, preference, and consumption in patients with acute coronary syndrome. METHODS: This randomized, single-blinded clinical trial was conducted with 139 participants (66 in the intervention group and 73 in the control group) from January 2021 to August 2021 in Shaheed Rajaie Hospital, Tehran, Iran. The control group received standard training. The intervention group, besides this, received additional bedside training with an educational poster on traffic light labels from the research team during their final hospitalization days. Data were collected using a researcher-made questionnaire on food selection, preference, and consumption. RESULTS: The Brunner-Munzel test showed no significant difference between the two groups in terms of selection (P = 0.127), preference (P = 0.852), and food consumption (P = 0.846) in the baseline, while after the intervention, there were significant differences in selection (P > 0.001), preference (P > 0.001), and consumption (p < 0.004). Comparing the difference between the two groups in the difference between the before and after scores for selection (p < 0.001), preference (p < 0.001), and food consumption (p = 0.011) with the Brunner-Munzel test indicated a significant difference in all outcome variables. CONCLUSIONS: Teaching healthy eating styles with the help of traffic light labels affected food selection, preference, and consumption and led to healthier diets in these patients. CLINICAL TRIAL REGISTRATION NUMBER: Clinical trial registration: It was prospectively registered in the Iran Clinical Trials Registration Center on this date 30/10/2020 (IRCT20200927048857N1).

Learning agile soccer skills for a bipedal robot with deep reinforcement learning.

We investigated whether deep reinforcement learning (deep RL) is able to synthesize sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be composed into complex behavioral strategies. We used deep RL to train a humanoid robot to play a simplified one-versus-one soccer game. The resulting agent exhibits robust and dynamic movement skills, such as rapid fall recovery, walking, turning, and kicking, and it transitions between them in a smooth and efficient manner. It also learned to anticipate ball movements and block opponent shots. The agent's tactical behavior adapts to specific game contexts in a way that would be impractical to manually design. Our agent was trained in simulation and transferred to real robots zero-shot. A combination of sufficiently high-frequency control, targeted dynamics randomization, and perturbations during training enabled good-quality transfer. In experiments, the agent walked 181% faster, turned 302% faster, took 63% less time to get up, and kicked a ball 34% faster than a scripted baseline.

Investigation of the effect of the N-oxidation process on the interaction of selected pyridine compounds with biomacromolecules: structural, spectral, theoretical and docking studies.

Two new N-oxide compounds, namely glycinium 2-carboxy-1-(λ1-oxidaneyl)-1λ4-pyridine-6-carboxylate-glycine-water (1/1/1), C2H6NO2+·C7H4NO5-·C2H5NO2·H2O or [(2,6-HpydcO)(HGLY)(GLY)(H2O)], 1, and methyl 6-carboxy-1-(λ1-oxidaneyl)-1λ4-pyridine-2-carboxylate, C8H7NO5 or 2,6-HMepydcO, 2, were prepared and identified by elemental analysis, FT-IR, Raman spectroscopy and single-crystal X-ray diffraction. The X-ray analysis of 1 revealed an ionic compound containing a 2,6-HpydcO- anion, a glycinium cation, a neutral glycine molecule and a water molecule. Compound 2 is a neutral compound with two independent units in its crystal structure. In addition to the hydrogen bonds, the crystal network is stabilized by π-π stacking interactions of the types pyridine-carboxylate and carboxylate-carboxylate. The thermodynamic stability and charge-distribution patterns for isolated molecules of 2,6-H2pydcO and 2,6-HMepydcO, and their two similar derivatives, pyridine-2,6-dicarboxylic acid (2,6-H2pydc) and dimethyl 1-(λ1-oxidaneyl)-1λ4-pyridine-2,6-dicarboxylate (2,6-Me2pydcO), were studied by density functional theory (DFT) and natural bond orbital (NBO) analysis, respectively. The ability of these compounds and their analogues to interact with nine selected biomacromolecules (BRAF kinase, CatB, DNA gyrase, HDAC7, rHA, RNR, TrxR, TS and Top II) was investigated using docking calculations.

A novel multi-epitopic immune network model hybridized with neural theory and fuzzy concept.

The natural immune system provides an effective defense mechanism against foreign substances via complex interactions among various cells and molecules. Jerne introduced the immune network theory to model the relation between immune cells and molecules. The immune system like the neural system is able to learn from experience. In this paper, a multi-epitopic immune network model is proposed. The proposed model is hybridized with Learning Vector Quantization (LVQ) and fuzzy set theory to present a new supervised learning method. The new method is called Hybrid Fuzzy Neuro-Immune Network based on Multi-Epitope approach (HFNINME). To evaluate the performance of the proposed method several experiments on benchmark classification problems are carried out and the results are compared with two prominent immune-based classifiers as well as several versions of the LVQ algorithm. The results of the experiments reveal that the proposed method yields a parsimonious classifier that can classify data more accurately and more efficiently.