ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution

We consider the problem of efficient blackbox optimization over a large hybrid search space, consisting of a mixture of a high dimensional continuous space and a complex combinatorial space. Such examples arise commonly in evolutionary computation, but also more recently, neuroevolution and architecture search for Reinforcement Learning (RL) policies. Unfortunately however, previous mutation-based approaches suffer in high dimensional continuous spaces both theoretically and practically. We thus instead propose ES-ENAS, a simple joint optimization procedure by combining Evolutionary Strategies (ES) and combinatorial optimization techniques in a highly scalable and intuitive way, inspired by the one-shot or supernet paradigm introduced in Efficient Neural Architecture Search (ENAS). Through this relatively simple marriage between two different lines of research, we are able to gain the best of both worlds, and empirically demonstrate our approach by optimizing BBOB functions over hybrid spaces as well as combinatorial neural network architectures via edge pruning and quantization on popular RL benchmarks. Due to the modularity of the algorithm, we also are able incorporate a wide variety of popular techniques ranging from use of different continuous and combinatorial optimizers, as well as constrained optimization.Comment: 22 pages. See https://github.com/google-research/google-research/tree/master/es_enas for associated cod

Construction of a 3-year risk prediction model for developing diabetes in patients with pre-diabetes

IntroductionTo analyze the influencing factors for progression from newly diagnosed prediabetes (PreDM) to diabetes within 3 years and establish a prediction model to assess the 3-year risk of developing diabetes in patients with PreDM.MethodsSubjects who were diagnosed with new-onset PreDM at the Physical Examination Center of the First Affiliated Hospital of Soochow University from October 1, 2015 to May 31, 2023 and completed the 3-year follow-up were selected as the study population. Data on gender, age, body mass index (BMI), waist circumference, etc. were collected. After 3 years of follow-up, subjects were divided into a diabetes group and a non-diabetes group. Baseline data between the two groups were compared. A prediction model based on logistic regression was established with nomogram drawn. The calibration was also depicted.ResultsComparison between diabetes group and non-diabetes group: Differences in 24 indicators including gender, age, history of hypertension, fatty liver, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose, HbA1c, etc. were statistically significant between the two groups (P<0.05). Differences in smoking, creatinine and platelet count were not statistically significant between the two groups (P>0.05). Logistic regression analysis showed that ageing, elevated BMI, male gender, high fasting blood glucose, increased LDL-C, fatty liver, liver dysfunction were risk factors for progression from PreDM to diabetes within 3 years (P<0.05), while HDL-C was a protective factor (P<0.05). The derived formula was: In(p/1-p)=0.181×age (40-54 years old)/0.973×age (55-74 years old)/1.868×age (≥75 years old)-0.192×gender (male)+0.151×blood glucose-0.538×BMI (24-28)-0.538×BMI (≥28)-0.109×HDL-C+0.021×LDL-C+0.365×fatty liver (yes)+0.444×liver dysfunction (yes)-10.038. The AUC of the model for predicting progression from PreDM to diabetes within 3 years was 0.787, indicating good predictive ability of the model.ConclusionsThe risk prediction model for developing diabetes within 3 years in patients with PreDM constructed based on 8 influencing factors including age, BMI, gender, fasting blood glucose, LDL-C, HDL-C, fatty liver and liver dysfunction showed good discrimination and calibration

Impacts of Arbuscular Mycorrhizal Fungi on Metabolites of an Invasive Weed <i>Wedelia trilobata</i>

The invasive plant Wedelia trilobata benefits in various aspects, such as nutrient absorption and environmental adaptability, by establishing a close symbiotic relationship with arbuscular mycorrhizal fungi (AMF). However, our understanding of whether AMF can benefit W. trilobata by influencing its metabolic profile remains limited. In this study, Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was conducted to analyze the metabolites of W. trilobata under AMF inoculation. Metabolomic analysis identified 119 differentially expressed metabolites (DEMs) between the groups inoculated with AMF and those not inoculated with AMF. Compared to plants with no AMF inoculation, plants inoculated with AMF showed upregulation in the relative expression of 69 metabolites and downregulation in the relative expression of 50 metabolites. AMF significantly increased levels of various primary and secondary metabolites in plants, including amino acids, organic acids, plant hormones, flavonoids, and others, with amino acids being the most abundant among the identified substances. The identified DEMs mapped 53 metabolic pathways, with 7 pathways strongly influenced by AMF, particularly the phenylalanine metabolism pathway. Moreover, we also observed a high colonization level of AMF in the roots of W. trilobata, significantly promoting the shoot growth of this plant. These changes in metabolites and metabolic pathways significantly affect multiple physiological and biochemical processes in plants, such as free radical scavenging, osmotic regulation, cell structure stability, and material synthesis. In summary, AMF reprogrammed the metabolic pathways of W. trilobata, leading to changes in both primary and secondary metabolomes, thereby benefiting the growth of W. trilobata and enhancing its ability to respond to various biotic and abiotic stressors. These findings elucidate the molecular regulatory role of AMF in the invasive plant W. trilobata and provide new insights into the study of its competitive and stress resistance mechanisms

Growth Promotion of Yunnan Pine Early Seedlings in Response to Foliar Application of IAA and IBA

A field experiment was conducted using a 3 × 3 orthogonal regression design to explore the growth promotion of one-year-old Yunnan pine seedlings (&lt;em&gt;Pinus yunnanensis &lt;/em&gt;Franch.) in response to foliar application of IAA (indole-3-acetic acid) at rates of 0, 200 and 400 mg·L&lt;sup&gt;−1&lt;/sup&gt; and IBA (indole-3-butyric acid) at rates of 0, 200 and 400 mg·L&lt;sup&gt;−1&lt;/sup&gt; in order to promote the growth during the seedlings’ early stage. The experiment was conducted at the Lufeng Village Forest Farm of Yiliang County in Kunming, Yunnan, China. The results showed that IAA and IBA were effective in growth promotion of Yunnan pine seedlings. The response of both growth increment and biomass accumulation to the concentration of IAA and IBA can be modeled using a bivariate surface response, and each growth index had a peak value. Growth indexes increased with the increase of the dosage of photohormones before reaching a peak value, and then decreased. The different growth indexes had various responses to the concentrations and ratio of IAA and IBA. The foliar application of IAA in combination with IBA showed the largest improvement on the biomass of the needles, followed by stems and roots. The higher ratio of IAA promoted stem diameter growth, root system development and biomass accumulation in the needles, while a higher ratio of IBA contributed to height growth and biomass accumulation in the stem. Based on the auxin effect equations on the different growth indexes and surface response, the optimum concentrations and the (IAA:IBA) ratios can be obtained. The optimum concentrations of IAA and IBA were 167 and 186, 310 and 217, 193 and 159, 191 and 221, and 206 and 186 mg·L&lt;sup&gt;−1&lt;/sup&gt;, with corresponding ratios of 1:1.11, 1:0.70, 1:0.82, 1:1.15 and 1:0.90, respectively, at the maximum seedling height and collar diameter growth as well as biomass accumulation at the root, stem and needle. The above growth indexes were 22.00%, 79.80%, 48.65%, 82.20% and 107.00% higher than the control treatment