DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.Comment: Accepted by SIGKDD 2022 Applied Data Science Trac

Stomatal responses of terrestrial plants to global change

Quantifying the stomatal responses of plants to global change factors is crucial for modeling terrestrial carbon and water cycles. Here we synthesize worldwide experimental data to show that stomatal conductance (gs) decreases with elevated carbon dioxide (CO2), warming, decreased precipitation, and tropospheric ozone pollution, but increases with increased precipitation and nitrogen (N) deposition. These responses vary with treatment magnitude, plant attributes (ambient gs, vegetation biomes, and plant functional types), and climate. All two-factor combinations (except warming + N deposition) significantly reduce gs, and their individual effects are commonly additive but tend to be antagonistic as the effect sizes increased. We further show that rising CO2 and warming would dominate the future change of plant gs across biomes. The results of our meta-analysis provide a foundation for understanding and predicting plant gs across biomes and guiding manipulative experiment designs in a real world where global change factors do not occur in isolation

Piezoelectric Properties of Electrospun Polymer Nanofibers and Related Energy Harvesting Applications

Abstract Electrospinning (ES) methods that can produce piezoelectricity in polymer nanofibers have attracted tremendous research attention. These electrospun polymer nanofibers can be employed for sensors, energy harvesting, tissue engineering, and filtration applications. This paper reviews the performance of a variety of electrospun piezoelectric polymer nanofibers produced by different ES methods, including near‐field electrospinning and conventional far‐field electrospinning methods. Herein, it is described how the ES method can affect the piezoelectric properties of various polymer nanofibers, including poly(vinylidene difluorine), poly(vinylidene fluoride‐trifluoroethylene), nylon 11, poly(l‐lactic acid), and poly(α‐benzyl‐l‐glutamate). Due to the varied matrix structures of piezoelectric polymer nanofibers, the ES method may conduct variable effects on the piezoelectric properties of polymer nanofibers. After characterizations by X‐ray diffraction, Fourier transform infrared spectrum, dielectric spectra, and piezoelectric coefficient measurements, it is found that the piezoelectric properties of the polymer nanofibers can be significantly affected by the ES parameters. Most of previous review articles focus on the output performance of electrospun polymer nanofibers. A detailed description of how different ES methods affect the piezoelectricity of polymer nanofibers is still lacking. In this review paper, the basic principle behind ES methods and the way in which different ES methods affect the properties of polymer nanofibers are examined

A cross-sectional study of blood selenium concentration and cognitive function in elderly Americans: National Health and Nutrition Examination Survey 2011–2014

Background Cognitive decline can develop into mild cognitive impairment, a high-risk factor in the progression of Alzheimer’s disease. The antioxidant micronutrient selenium may have some effect on preventing cognitive decline, but the association between whole blood selenium concentration and cognitive function remains controversial. Aim To investigate the association between whole blood selenium concentration and cognitive function score in elderly Americans. Subjects and methods Data was obtained from the national health and nutrition survey between 2011 and 2014. A general linear model was used to adjust for possible risk factors to analyse the association between blood selenium concentration and cognitive function. Results 2068 participants were included in our study, and the average blood selenium concentration was high at 195.08 μg/L. The risk of lower cognitive scores was higher in people with lower blood selenium concentration (p < 0.05). The lower cognition may also be associated with one or more of the following characteristics: older, male, had a low poverty-income ratio, low education level, and consumed less alcohol. Related conditions such as stroke, diabetes and high blood pressure may also affect cognitive scores. Conclusions Higher blood selenium is associated with higher cognitive scores in elderly Americans

The global biogeography of soil priming effect intensity

9 páginas.- 4 figuras.- 41 referencias.- Additional supporting information may be found in the online version of the article at the publisher’s website.-Aim Fresh carbon (C) inputs to the soil can have important consequences for the decomposition rates of soil organic matter (priming effect), thereby impacting the delicate global C balance at the soil-atmosphere interface. Yet, the environmental factors that control soil priming effect intensity remain poorly understood at a global scale. Location Global. Time period 1980-2020. Major taxa studied Soil priming effect intensity. Methods We conducted a global dataset of CO2 effluxes in 711 pairwise soils with C-13 or C-14 simple C sources inputs and without C inputs from incubation experiments in which isotope-labelled C was used to quantify fresh C-induced rather than exudate-induced priming. Results Soil priming effect intensity is predominantly positive. Soil texture and C content were identified as the most important factors associated with priming effects, with sandy soils from tropical and mid-latitudes supporting the highest soil priming effect intensity, and soils with greater C content and fine textures from high latitudes maintaining the lowest soil priming effects. The negative association between C content and soil priming effect intensity was also indirectly driven by changing mean annual temperature, net primary productivity, and fungi : bacteria ratio. Using this information, we generated a global map of soil priming effect intensity, and found that the priming was lower at high latitudes and higher at lower latitudes. Main conclusions Global patterns of soil priming effect intensity can be predicted using environmental data, with soil texture and C content playing a predominant role in explaining in priming effects. These effects were also indirectly driven by climate, vegetation and soil microbial properties. We present the first global atlas of soil priming effect intensity and advance our knowledge on the potential mechanisms underlying soil priming effect intensity, which are integral to improving the climate change and soil C dynamics components of Earth System models.National Natural Science Foundation of China, Grant/Award Number: 41907031; China Postdoctoral Science Foundation, Grant/Award Number: 2021T140565; Natural Science Basic Research Plan in Shaanxi Province of China, Grant/Award Number: 2020JQ-272; Forest and Grass Technology Innovation Development and Research Projects from National Forestry and Grassland Administration, Grant/Award Number: 2020132111; China Postdoctoral Science Foundation, Grant/Award Number: 2019M650276; Chinese Academy of Sciences “Light of West China” Program for Introduced Talent in the West, Grant/Award Number: 31570440Peer reviewe

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

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