Model selection of polynomial kernel regression

Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regularization parameter are still open in the realm of model selection. The first aim of this paper is to develop a strategy to select these parameters. On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary. In other words, the regularization parameter can decrease arbitrarily fast when the degree of the polynomial kernel is suitable tuned. On the other hand,taking account of the implementation of the algorithm, the regularization term is required. Summarily, the effect of the regularization term in polynomial kernel regression is only to circumvent the " ill-condition" of the kernel matrix. Based on this, the second purpose of this paper is to propose a new model selection strategy, and then design an efficient learning algorithm. Both theoretical and experimental analysis show that the new strategy outperforms the previous one. Theoretically, we prove that the new learning strategy is almost optimal if the regression function is smooth. Experimentally, it is shown that the new strategy can significantly reduce the computational burden without loss of generalization capability.Comment: 29 pages, 4 figure

OR-014 Chronic exercise potentiates anorectic effects of leptin in hypothalamic Pomc neurons

Objective Chronic imbalance of energy homeostasis leads to obesity and metabolic mellitus, which has developed as a major public health and economic burdens around the world. Disruption of “beige” fat mediated thermogenesis and hypothalamic neurons manipulated energy intake exaggerate this process. Multiple factors including hormonal regulation, fuel availability, and behavior contribute to energy utilization. The central nervous system (CNS) is critical for regulating energy balance and coordinating whole body metabolism. In the CNS, proopiomelanocortin (Pomc) neurons receive and integrate information about energy availability via responding to circulating hormones including insulin, leptin. Previous studies revealed that leptin receptors (LepRs) in arcuate Pomc neurons are required and sufficient for the proper regulation of energy balance and glucose homeostasis, including systemic insulin sensitivity and hepatic glucose production.  Exercise is an effective lifestyle intervention to combat obesity and metabolic diseases, which exerts many health benefits, including weight maintenance, appetite control, improved insulin sensitivity, improved mental health, and secondary prevention of chronic diseases such as obesity, type II diabetes mellitus, cancer, and hypertension. Moreover, the combined efficacy of exercise and dietary regimens on type two diabetes can surpass that of pharmacological interventions alone. Previous efforts aimed at identifying molecular mechanisms underlying the adaptive responses to exercise have mainly focused on the effects of exercise training in an organ or cell autonomous manner. However, the impact of exercise on performance, food intake after exercise, and more broadly, the healthy metabolic outcomes of exercise is not well-established. Despite the increased understanding of the importance of CNS underlying metabolic homeostasis, the specific neuronal groups and pathways that contribute to the metabolic responses during and following exercise remain largely unclear. In the current study, we aimed to investigate the role of exercise in mediating hypothalamic Pomc neuron activity, anorectic effects of leptin and glucose tolerance as well as insulin sensitivity.  Methods Animals All mice were housed under standard laboratory conditions (12 h on/off; lights on at 7:00 a.m.) and temperature-controlled environment with food and water available ad libitum. All experiments were performed in accordance with the guidelines established by the National Institute of Health Guide for the Care and Use of Laboratory Animals. Slice preparation and whole-cell recordings Male mice were deeply anesthetized with i.p. injection of 7% chloral hydrate and transcardially perfused with a modified ice-cold artificial CSF (ACSF) (described below). The mice were then decapitated, and the entire brain was removed and immediately submerged in ice-cold, carbogen-saturated (95% O2 and 5% CO2) ACSF (126 mM NaCl, 2.8 mM KCl, 1.2 mM MgCl2, 2.5 mM CaCl2, 1.25 mM NaH2PO4, 26 mM NaHCO3, and 5 mM glucose). Coronal sections (250 μm) were cut with a Leica VT1000S Vibratome and then incubated in oxygenated ACSF at room temperature for at least 1 h before recording. The slices were bathed in oxygenated ACSF (32 °C–34 °C) at a flow rate of ∼2 ml/min. All electrophysiology recordings were performed at room temperature. The pipette solution for whole-cell recording was modified to include an intracellular dye (Alexa Fluor350 hydrazide dye) for whole-cell recording: 120 mM K-gluconate, 10 mM KCl, 10 mM HEPES, 5 mM EGTA, 1 mM CaCl2, 1 mM MgCl2, and 2 mM MgATP, 0.03 mM Alexa Fluor 350 hydrazide dye (pH 7.3). Epifluorescence was briefly used to target fluorescent cells, at which time the light source was switched to infrared differential interference contrast imaging to obtain the whole-cell recording (Zeiss Axioskop FS2 Plus equipped with a fixed stage and a QuantEM:512SC electron-multiplying charge-coupled device camera). Electrophysiological signals were recorded using an Axopatch 700B amplifier (Molecular Devices), low-pass filtered at 2–5 kHz, and analyzed offline on a PC with pCLAMP programs (Molecular Devices). Membrane potential and firing rate were measured by whole-cell current clamp recordings from Pomc neurons in brain slices. Recording electrodes had resistances of 2.5–5 MΩ when filled with the K-gluconate internal solution. Input resistance was assessed by measuring voltage deflection at the end of the response to a hyperpolarizing rectangular current pulse steps (500 ms of −10 to −50 pA). Leptin (100 nM) was added to the ACSF for specific experiments. Solutions containing drug were typically perfused for 5 min. A drug effect was required to be associated temporally with peptide application, and the response had to be stable within a few minutes. A neuron was considered depolarized or hyperpolarized if a change in membrane potential was at least 2 mV in amplitude. Neurons were voltage-clamped at −75 mV (for excitatory postsynaptic currents) and −15 mV (for inhibitory postsynaptic currents). Frequency and peak amplitude were measured by using the Mini Analysis program (Synaptosoft, Inc.) Exercise protocols Motorized treadmills (Exer-6; Columbus Instruments, Columbus, OH) were used for exercise experiments. All mice were familiarized to the treadmills for 7 days prior to the exercise bout [Day 1: 5 min rest on the treadmill followed by running for 5 min at the speed of 8 m/min and then for 5 min at the speed of 10 m/min; Day 2-3: 5 min rest on the treadmill followed by running for 5 min at the speed of 10 m/min and then for 5 min at the speed of 12 m/min; Day 4-7: 5 min rest on the treadmill followed by running for 60 min at the speed of 12 m/min]. On Day 8, mice were subjected to a high intensity interval exercise (HIIE) bout to assess exercise-induced changes in plasma leptin, blood glucose, and food intake. Briefly, food was removed from all the mice at the start of the light cycle (7 AM) for a duration of 6 h, so as to eliminate any differences in food intake on the measured parameters. Mice were rested on the treadmill for 5 min prior to performing the 1 h of exercise consisting of 3 × 20 min intervals (5 min at the speed of 12 m/min, followed by 10 min at the speed of 17 m/min, and then 5 min at the speed of 22 m/min), without rest between intervals. Tolerance test and food intake For GTT, mice fasted for 16 h received an intraperitoneal injection of glucose (1 g/kg). For ITT, mice fasted for 6 h received an intraperitoneal injection of human insulin (0.75 IU/kg). Blood glucose concentrations were measured from tail blood at the indicated times using a One-Touch Ultra® glucometer (LifeScan Inc., Milpitas, CA). Food intake was measured hourly for 6 hours and then a single measurement at 24 hours. Results To assess the predominant role of exercise on the neuronal activation of hypothalamic Pomc neuron, electrophysiology studies was conducted on transgenic mice after treadmill habitation for 7 days. And we found that exercise significantly reduced food intake and enhanced glucose tolerance as well as insulin sensitivity. Notably, chronic exercise dramatically potentiates leptin-induced depolarization of Pomc neurons and exerts leptin induced anorectic effects in vivo and in vitro. Furthermore, gene assay demonstrated an upregulation of sirtin1 after exercise, suggest­­­­­ing a link between the exercise and key proteins involved in epigenetics, providing potential targets for the treatment of metabolic disease. Conclusions Our results demonstrated chronic exercise potentiates anorectic effects of leptin in hypothalamic Pomc neurons. Moreover, these data provide evidence for sirtin1 as a substrate of exercise to regulate food intake and glucose tolerance as well as leptin sensitivity via activating Pomc neurons

Precursor Information Recognition of Rockburst in the Coal-Rock Mass of Meizoseismal Area Based on Multiplex Microseismic Information Fusion and Its Application: A Case Study of Wudong Coal Mine

AbstractIn recent years, the rockburst induced by steeply inclined coal seam mining in the Urumqi mining area has become serious. In this paper, the evolution law of multiplex microseismic information before and after the rockburst is obtained through in-depth mining of the field microseismic data. In addition, the evolution characteristics of microseismic activities before and after the rockburst of steeply inclined coal-rock mass in the meizoseismal area are revealed from three important scales: time, space, and strength. The results show the following: (1) The microseismic activity of the Wudong Coal Mine is mainly of stress migration type. The sandwiched rock pillar is the primary inducement of rockburst, and the b value decreases greatly with the mining progresses (by 23.9%). It indicates that the risk of rockburst induced by the local failure of rock mass in this area is increasing. (2) From the time scale and strength index, the precursory indexes of rockburst are put forward, respectively: ① the daily total energy and the frequency of microseisms suddenly rise and fall rapidly at the same time in the shock start-up period (5 days before rockburst), and the daily total energy of microseisms decreases to the abnormal valley value within 30 days. ② The abnormal growth rate of microseismic events exceeded 60% in a certain stage, and “induced shock events” appeared. (3) The shock risk is positively correlated with the decline rate of energy index, the growth rate of cumulative apparent volume, and Schmidt. It is determined that the rockburst will occur within 19 days after entering the shock early warning period. The results of prediction examples show that this method has a good prediction effect on rockburst in strong meizoseismal areas, which can provide a reference for rockburst prevention in the mining process in strong meizoseismal areas

An analytical methodology of rock burst with fully mechanized top-coal caving mining in steeply inclined thick coal seam

Rock burst disaster is still one of the most serious dynamic disasters in coal mining, seriously restricting the safety of coal mining. The b value is the main parameter for monitoring rock burst, and by analyzing its changing characteristics, it can effectively predict the dangerous period of rock burst. This article proposes a method based on deep learning that can predict rock burst using data generated from microseismic monitoring in underground mining. The method first calculates the b value from microseismic monitoring data and constructs a time series dataset, and uses the dynamic time warping algorithm (DTW) to reconstruct the established b value time series. A bidirectional short-term and short-term memory network (BiLSTM) loaded with differential evolution algorithm and attention mechanism was used for training, and a prediction model for the dangerous period of rock burst based on differential algorithm optimization was constructed. The study used microseismic monitoring data from the B1+2 fully mechanized mining face and B3+6 working face in the southern mining area of Wudong Coal Mine for engineering case analysis. The commonly used residual sum of squares, mean square error, root mean square error, and correlation coefficient R2 for time series prediction were introduced, which have significant advantages compared to basic LSTM algorithms. This verifies that the prediction method proposed in this article has good prediction results and certain feasibility, and can provide technical support for the prediction and prevention of rock burst in steeply inclined thick coal seams in strong earthquake areas

Dysbiosis of vaginal and cervical microbiome is associated with uterine fibroids

Dysbiosis of the female reproductive tract is closely associated with gynecologic diseases. Here, we aim to explore the association between dysbiosis in the genital tract and uterine fibroids (UFs) to further provide new insights into UF etiology. We present an observational study to profile vaginal and cervical microbiome from 29 women with UFs and 38 healthy women, and 125 samples were obtained and sequenced. By comparing the microbial profiles between different parts of the reproductive tract, there is no significant difference in microbial diversity between healthy subjects and UF patients. However, alpha diversity of UF patients was negatively correlated with the number of fibroids. Increased Firmicutes were observed in both the cervical and vaginal microbiome of UF patients at the phylum level. In differential analysis of relative abundance, some genera were shown to be significantly enriched (e.g., Erysipelatoclostridium, Mucispirillum, and Finegoldia) and depleted (e.g., Erysipelotrichaceae UCG-003 and Sporolactobacillus) in UF patients. Furthermore, the microbial co-occurrence networks of UF patients showed lower connectivity and complexity, suggesting reduced interactions and stability of the cervical and vaginal microbiota in UF patients. In summary, our findings revealed the perturbation of microbiome in the presence of UFs and a distinct pattern of characteristic vaginal and cervical microbiome involved in UFs, offering new options to further improve prevention and management strategies

Precursory characteristics and disaster prevention of rock burst in roadway excavation in steeply inclined extra-thick coal seam

With the gradual coal mining of deep rock burst mine, the impact accompanying roadway excavation becomes more and more intense. Aiming at the problem of effective prevention and control of rock burst in roadway excavation, taking the steep seam mine in the Wudong Coal Mine as an example, the temporal and spatial precursor characteristics of rock burst in roadway excavation were analyzed by microseismic monitoring. Combined with the numerical simulation analysis of stress and energy changes in roadway excavation, the mechanism of rock burst in roadway excavation was revealed, and the prevention and control strategy of rock burst in steeply inclined extra-thick coal seam roadway was put forward, which was verified by field engineering practice. The results show that the total energy of microseisms is extremely low for 2−5 days or there is an energy latency of at least 4 days before the rock burst occurs due to roadway excavation in steeply inclined extra-thick coal seam. Within 5 days before rock burst occurs, there is a high-frequency fluctuation period of maximum energy ratio for more than 3 days. There is an obvious lack of earthquake before the rock burst occurs, and the occurrence position is concentrated in the range of minimum value of microseismic energy near the heading face, or in the range of minimum value of microseismic frequency near the extreme value of microseismic energy, and the rock burst event is located in the area with high impact deformation energy index. The hard overburden structure of horizontal sublevel fully mechanized caving mining in steeply inclined extra-thick coal seam is not easy to break, which makes the stress concentration on both sides of upper horizontal goaf exist in roadway excavation. The stress between the front of the heading face and the bottom of the roadway squeezed by the roof and floor strata is concentrated and the energy accumulation is remarkable. With the increase of the heading depth of the roadway, the stress concentration and energy accumulation are further enhanced, which is easy to induce dynamic disasters such as rock burst. The prevention and control strategies of rock burst was established through comprehensive analysis, which consist of face blasting pressure relief, roadway drilling pressure relief and reinforcement support, and scaffolding in complex areas. Combined with the temporal and spatial precursory anomalies of rock burst, it provides an opportunity to strengthen the unloading pressure in time. Through the pressure relief of working face and roadway, the accumulated microseismic energy of more than 1×105 J per day did not occur during the excavation. After the support was optimized and the complex area was protected, the daily average microseismic energy of roadway excavation decreased to 2.2 kJ, and the proportion of microseismic events above 1 kJ decreased, and the overall section of roadway was flat

LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and more than 600K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 730K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. To evaluate a tracker on different attributes, we define 4 scenario attributes and 12 challenge attributes in the evaluation dataset. By releasing LSOTB-TIR, we encourage the community to develop deep learning based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance. Furthermore, we re-train several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.Comment: accepted by ACM Mutlimedia Conference, 202

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support

Genetic diversity and population structure of core watermelon (Citrullus lanatus) genotypes using DArTseq-based SNPs

Watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai var. lanatus] is an economically important vegetable belonging to the Cucurbitaceae family. Genotypes that exhibit agronomically important traits are selected for the development of elite cultivars. Understanding the genetic diversity and the genotype population structure based on molecular markers at the genome level can speed up the utilization of diverse genetic resources for varietal improvement. In the present study, we carried out an analysis of genetic diversity based on 3882 SNP markers across 37 core watermelon genotypes, including the most widely used watermelon varieties and wild watermelon. Based on the SNP genotyping data of the 37 watermelon genotypes screened, gene diversity and polymorphism information content values across chromosomes varied between 0.03–0.5 and 0.02–0.38, with averages of 0.14 and 0.13, respectively. The two wild watermelon genotypes were distinct from cultivated varieties and the remaining 35 cultivated genotypes were differentiated into three major clusters: 20 genotypes were grouped in cluster I; 11 genotypes were grouped in cluster II; three advanced breeding lines of yellow fruit flesh and genotype SW043 were grouped in cluster III. The results from neighbour-joining dendrogram, principal coordinate analysis and STRUCTURE analysis approaches were consistent, and the grouping of genotypes was generally in agreement with their origins. Here we reveal the genetic relationships among the core watermelon genotypes maintained at the Jiangsu Academy of Agricultural Sciences, China. The molecular and phenotypic characterization of the existing core watermelon genotypes, together with specific agronomic characteristics, can be utilized by researchers and breeders for future watermelon improvement

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery