Neural Pairwise Ranking Factorization Machine for Item Recommendation

The factorization machine models attract significant attention from academia and industry because they can model the context information and improve the performance of recommendation. However, traditional factorization machine models generally adopt the point-wise learning method to learn the model parameters as well as only model the linear interactions between features. They fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this paper, we propose a neural pairwise ranking factorization machine for item recommendation, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, the pair-wise ranking model is adopted to learn the relative preferences of users rather than predict the absolute scores. Experimental results on real world datasets show that our proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models

Relevance of PUFA-derived metabolites in seminal plasma to male infertility

AimThis study aims to investigate the biological effects of polyunsaturated fatty acid (PUFA)-derived metabolites in seminal plasma on male fertility and to evaluate the potential of PUFA as a biomarker for normozoospermic male infertility.MethodsFrom September 2011 to April 2012, We collected semen samples from 564 men aged 18 to 50 years old (mean=32.28 years old)ch., residing in the Sandu County, Guizhou Province, China. The donors included 376 men with normozoospermia (fertile: n=267; infertile: n=109) and 188 men with oligoasthenozoospermia (fertile: n=121; infertile: n=67). The samples thus obtained were then analyzed by liquid chromatography-mass spectrometry (LC-MS) to detect the levels of PUFA-derived metabolites in April 2013. Data were analyzed from December 1, 2020, to May 15, 2022.ResultsOur analysis of propensity score-matched cohorts revealed that the concentrations of 9/26 and 7/26 metabolites differed significantly between fertile and infertile men with normozoospermia and oligoasthenozoospermia, respectively (FDR < 0.05). In men with normozoospermia, higher levels of 7(R)-MaR1 (HR: 0.4 (95% CI [0.24, 0.64]) and 11,12-DHET (0.36 (95% CI [0.21, 0.58]) were significantly associated with a decreased risk of infertility, while higher levels of 17(S)-HDHA (HR: 2.32 (95% CI [1.44, 3.79]), LXA5 (HR: 8.38 (95% CI [4.81, 15.24]), 15d-PGJ2 (HR: 1.71 (95% CI [1.06, 2.76]), and PGJ2 (HR: 2.28 (95% CI [1.42, 3.7]) correlated with an increased risk of infertility. Our ROC model using the differentially expressed metabolites showed the value of the area under the curve to be 0.744.ConclusionThe PUFA-derived metabolites 7(R)-MaR1, 11,12-DHET, 17(S)-HDHA, LXA5, and PGJ2 might be considered as potential diagnostic biomarkers of infertility in normozoospermic men

Enhanced factorization machine via neural pairwise ranking and attention networks

The factorization machine models attract significant attention nowadays since they improve recommendation performance by incorporating context information into recommendation modeling. However, traditional factorization machine models often adopt the point-wise learning method for model parameter learning, as well as only model the linear interactions between features. They substantially fail to capture the complex interactions among features, which degrades the performance of factorization machine models. In this research, we propose a neural pairwise ranking factorization machine for item recommendation, namely NPRFM, which integrates the multi-layer perceptual neural networks into the pairwise ranking factorization machine model. Specifically, to capture the high-order and nonlinear interactions among features, we stack a multi-layer perceptual neural network over the bi-interaction layer, which encodes the second-order interactions between features. Moreover, instead of the prediction of the absolute scores, the pair-wise ranking model is adopted to learn the relative preferences of users. Since NPRFM does not take into account the importance of feature interactions, we propose a new variant of NPRFM, which learns the importance of feature interactions by introducing the attention mechanism. The empirical results on real-world datasets indicate that the proposed neural pairwise ranking factorization machine outperforms the traditional factorization machine models

Synthesis of Monodisperse Nanocrystals via Microreaction: Open-to-Air Synthesis with Oleylamine as a Coligand

Microreaction provides a controllable tool to synthesize CdSe nanocrystals (NCs) in an accelerated fashion. However, the surface traps created during the fast growth usually result in low photoluminescence (PL) efficiency for the formed products. Herein, the reproducible synthesis of highly luminescent CdSe NCs directly in open air was reported, with a microreactor as the controllable reaction tool. Spectra investigation elucidated that applying OLA both in Se and Cd stock solutions could advantageously promote the diffusion between the two precursors, resulting in narrow full-width-at-half maximum (FWHM) of PL (26 nm). Meanwhile, the addition of OLA in the source solution was demonstrated helpful to improve the reactivity of Cd monomer. In this case, the focus of size distribution was accomplished during the early reaction stage. Furthermore, if the volume percentage (vol.%) of OLA in the precursors exceeded a threshold of 37.5%, the resulted CdSe NCs demonstrated long-term fixing of size distribution up to 300 s. The observed phenomena facilitated the preparation of a size series of monodisperse CdSe NCs merely by the variation of residence time. With the volume percentage of OLA as 37.5% in the source solution, a 78 nm tuning of PL spectra (from 507 to 585) was obtained through the variation of residence time from 2 s to 160 s, while maintaining narrow FMWH of PL (26–31 nm) and high QY of PL (35–55%)

A new genus and two new species of nematodes (Nematoda: Thelastomatoidea) from Ceracupes fronticornis (Westwood) (Insecta: Passalidae) in China

Zhang, Ningning, Yin, Shi, Zhang, Luping (2022): A new genus and two new species of nematodes (Nematoda: Thelastomatoidea) from Ceracupes fronticornis (Westwood) (Insecta: Passalidae) in China. Zoological Systematics 47 (2): 109-116, DOI: 10.11865/zs.2022202, URL: http://zoobank.org/d0cbfcdd-9b13-4dae-937e-0db625b297b

Figure 1 in A new genus and two new species of nematodes (Nematoda: Thelastomatoidea) from Ceracupes fronticornis (Westwood) (Insecta: Passalidae) in China

Figure 1. Hystriganthus baihualingensis sp. nov., female. A. Whole worm, lateral view; B. Anterior part of body, lateral view; C–D. Anterior end of body, lateral view; E. Posterior part of body, lateral view; F. Egg; G. Transverse section just posterior to first cephalic annule, showing first row of spines. Scale bars = 100 μm.Published as part of Zhang, Ningning, Yin, Shi & Zhang, Luping, 2022, A new genus and two new species of nematodes (Nematoda: Thelastomatoidea) from Ceracupes fronticornis (Westwood) (Insecta: Passalidae) in China, pp. 109-116 in Zoological Systematics 47 (2) on page 111, DOI: 10.11865/zs.2022202, http://zenodo.org/record/717575

Computational and experimental research on mechanism of cis/trans isomerization of oleic acid

The harm of trans-fatty acids to health has aroused public concern. It is believed that the main source of trans-fatty acids in diets is the isomerization of unsaturated fatty acids in edible oils during cooking. However, the information on the isomerization mechanism is very limited. In this paper, we used oleic acid, an unsaturated fatty acid, as a simplified model for edible oil and investigated the mechanism of cis/trans isomerization by computation and experiments. The computational results show that Rc-O-O-H is a very important intermediate, and the cleavage of O-O bond in Rc-O-O-H is the rate-controlling step during the cis/trans isomerization. Using the ATR-FTIR measurements, the contents of elaidic acid were measured quantitatively in sites. The experimental results indicate that the cis/trans isomerization of oleic acid can occur obviously only under oxidizing condition when the temperature is higher than 120 °C

Estimation of Spring Maize Evapotranspiration in Semi-Arid Regions of Northeast China Using Machine Learning: An Improved SVR Model Based on PSO and RF Algorithms

Accurate estimation of crop evapotranspiration (ETc) is crucial for effective irrigation and water management. To achieve this, support vector regression (SVR) was applied to estimate the daily ETc of spring maize. Random forest (RF) as a data pre-processing technique was utilized to determine the optimal input variables for the SVR model. Particle swarm optimization (PSO) was employed to optimize the SVR model. This study used data obtained from field experiments conducted between 2017 and 2019, including crop coefficient and daily meteorological data. The performance of the innovative hybrid RF–SVR–PSO model was evaluated against a standalone SVR model, a back-propagation neural network (BPNN) model and a RF model, using different input meteorological variables. The ETc values were calculated using the Penman–Monteith equation, which is recommended by the FAO, and used as a reference for the models’ estimated values. The results showed that the hybrid RF–SVR–PSO model performed better than all three standalone models for ETc estimation of spring maize. The Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) ranges were 0.956–0.958, 0.275–0.282 mm d−1, 0.221–0.231 mm d−1 and 0.957–0.961, respectively. It is proved that the hybrid RF–SVR–PSO model is appropriate for estimation of daily spring maize ETc in semi-arid regions