Author's personal copy Monthly streamflow forecasting using Gaussian Process Regression

Keywords: Gaussian Process Regression Machine learning theory Water/energy interactions Probabilistic streamflow forecasting Hydrologic similarity s u m m a r y Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among waterenergy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions

Flexible Micro-Nano Fiber Sensors for Tactile Sensing

Flexible tactile sensors play an important role in wearable devices, human–computer interaction devices, and advanced robotics. We propose a novel structure of bionic flexible tactile sensor. The micro-nano fibers (MNFs) are packed in a 10-μm film on a polydimethylsiloxane (PDMS) base, forming a thin film-MNF-PDMS structure. A ridge-shaped sensing region is formed on the surface of the PDMS substrate. The MNF is so close to the sensor surface that vibration and pressure signals can act directly on the MNF. Compared to existing MNF flexible sensors, this sensor has higher sensitivity and faster response time. We tested the response of the flexible sensor to vibration and temperature. This sensor can measure vibration signals from 0.1 Hz to2 kHz. The sensitivity of this sensor to temperature can reach 1.43 nm/◦C. Surfaces with different roughness or texture can be distinguished by sliding on the sensor surface. The structural and functional characteristics of this sensor are desirable in flexible bionic devices and advanced robots

Metabolomic Analysis of Biochemical Changes in the Plasma of High-Fat Diet and Streptozotocin-Induced Diabetic Rats after Treatment with Isoflavones Extract of Radix Puerariae

The main purpose of this study was to investigate the protective effects of total isoflavones from Radix Puerariae (PTIF) in diabetic rats. Diabetes was induced by a high-fat diet and intraperitoneal injection of low-dose streptozotocin (STZ; 40 mg/kg). At 26 weeks onwards, PTIF 421 mg/kg was administrated to the rats once daily consecutively for 10 weeks. Metabolic profiling changes were analyzed by Ultraperformance Liquid Chromatography-Quadrupole-Exactive Orbitrap-Mass Spectrometry (UPLC-Q-Exactive Orbitrap-MS). The principal component discriminant analysis (PCA-DA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used for multivariate analysis. Moreover, free amino acids in serum were determined by high-performance liquid chromatography with fluorescence detector (HPLC-FLD). Additionally, oxidative stress and inflammatory cytokines were evaluated. Eleven potential metabolite biomarkers, which are mainly related to the coagulation, lipid metabolism, and amino acid metabolism, have been identified. PCA-DA scores plots indicated that biochemical changes in diabetic rats were gradually restored to normal after administration of PTIF. Furthermore, the levels of BCAAs, glutamate, arginine, and tyrosine were significantly increased in diabetic rats. Treatment with PTIF could regulate the disturbed amino acid metabolism. Consequently, PTIF has great therapeutic potential in the treatment of DM by improving metabolism disorders and inhibiting oxidative damage

A Locality-based Neural Solver for Optical Motion Capture

We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network which treats markers and joints as different types of nodes, and uses graph convolution operations to extract the local features of markers and joints and transform them to clean motions. To deal with anomaly markers (e.g. occluded or with big tracking errors), the key insight is that a marker's motion shows strong correlations with the motions of its immediate neighboring markers but less so with other markers, a.k.a. locality, which enables us to efficiently fill missing markers (e.g. due to occlusion). Additionally, we also identify marker outliers due to tracking errors by investigating their acceleration profiles. Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking. The masking schemes aim to mimic the occluded and noisy markers often observed in the real data. Finally, we show that our method achieves high accuracy on multiple metrics across various datasets. Extensive comparison shows our method outperforms state-of-the-art methods in terms of prediction accuracy of occluded marker position error by approximately 20%, which leads to a further error reduction on the reconstructed joint rotations and positions by 30%. The code and data for this paper are available at https://github.com/non-void/LocalMoCap.Comment: Siggraph Asia 2023 Conference Pape

Compound Bieshe Kang’ai inhibits proliferation and induces apoptosis in HCT116 human colorectal cancer cells

Purpose: To study the effect of Compound Bieshe Kang’ai (CBK) on proliferation and apoptosis in colorectal cancer cells.Methods: HCT116 colorectal cancer cells and FHs 74 Int intestinal cells were treated with CBK, followed by determination of cell proliferation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Caspase-9 and caspase-3 activities as well as protein expressions of Bcl-2 and BAX, and mRNA levels of caspase-9, caspase-3, Bcl-2 and BAX in HCT116 cells were evaluated, followed by examination of the morphological alterations of HCT116 cells with Hoechst 33342 staining.Results: CBK suppressed proliferation of HCT116 cells in a concentration- and time-dependent pattern, without cytotoxicity to FHs 74 Int cells. CBK also elevated caspase-9 and caspase-3 activities, mitigated protein translation of Bcl-2 and augmented that of BAX. It also enhanced mRNA transcriptions of caspase-9, caspase-3 and BAX, but decreased that of Bcl-2 in HCT116 cells in a  concentrationdependent manner, as well as induced cancer cell shrinkage, nuclear fragmentation and chromatin condensation.Conclusion: The findings highlight CBK as a promising therapeutic agent for colorectal cancers, by retarding proliferation and inducing apoptosis in cancer cells.Keywords: Apoptosis, BAX, Bcl-2, Cancer, Caspase, Compound Bieshe Kang’ai, Chromatin condensation, Nuclear fragmentatio

Discriminant Analysis of Jiang-Flavor Baijiu of Different Grades by Gas Chromatography-Mass Spectrometry and Electronic Tongue

Gas chromatography-mass spectrometry (GC-MS) and electronic tongue were used to quantitatively determine the volatile compounds and taste indices of 21 Jiang-flavor baijiu samples of different grades. These samples were differentiated by chemometrics, and key differential compounds among grades were identified. Finally, a discriminant model was established by machine learning. The results showed that there were differences in the contents of volatile compounds in Jiang-flavor baijiu of three grades, indicating the feasibility of further discriminant analysis. The total content of flavor compounds in second-grade baijiu (4 908 mg/L) was significantly lower than that in premium-grade (6 583 mg/L) and first-grade baijiu (8 254 mg/L), while the proportion of several esters responsible for floral and fruity aromas in total esters showed a decreasing trend as the grade decreased. Partial least squares-discriminant analysis (PLS-DA) identified 16 key differential compounds represented by ethyl palmitate and acetic acid. The results of electronic tongue showed that the taste indexes of premium-grade baijiu were more consistent, with lower bitterness and astringency aftertaste. The taste indexes of second-grade baijiu showed significant intersample differences. Principal component analysis (PCA) showed clear discrimination of Jiang-flavor baijiu of different grades according to their taste indexes. The above results provide a basis for the establishment of Jiang-flavor baijiu quality system. Four discriminant models were established based on 25 differential compounds and taste indexes identified. The accuracy of all models was higher than 90%, and the support vector machine (SVM) model performed best, with an accuracy of 100%

Function of TRP channels in monocytes/macrophages

The transient receptor potential channel (TRP channel) family is a kind of non- specific cation channel widely distributed in various tissues and organs of the human body, including the respiratory system, cardiovascular system, immune system, etc. It has been reported that various TRP channels are expressed in mammalian macrophages. TRP channels may be involved in various signaling pathways in the development of various systemic diseases through changes in intracellular concentrations of cations such as calcium and magnesium. These TRP channels may also intermingle with macrophage activation signals to jointly regulate the occurrence and development of diseases. Here, we summarize recent findings on the expression and function of TRP channels in macrophages and discuss their role as modulators of macrophage activation and function. As research on TRP channels in health and disease progresses, it is anticipated that positive or negative modulators of TRP channels for treating specific diseases may be promising therapeutic options for the prevention and/or treatment of disease

Exploring Participants’ Behavior on Online Weight Loss Community：A Data Mining Perspective

This study aimed to classify community user according the characteristics of their participation behavior and understand the issues discussed in OWLCs (online weight loss communities)

Monthly Streamflow Forecasting Using Gaussian Process Regression

Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among water-energy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions. © 2014 Elsevier B.V