Income inequality and carbon dioxide emissions: The case of Chinese urban households Energy

This paper draws on Chinese survey data to investigate variations in carbon dioxide emissions across households with different income levels. Rich households generate more emissions per capita than poor households via both their direct energy consumptio

Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression

Knowledge distillation (KD) has been actively studied for image classification tasks in deep learning, aiming to improve the performance of a student model based on the knowledge from a teacher model. However, there have been very few efforts for applying KD in image regression with a scalar response, and there is no KD method applicable to both tasks. Moreover, existing KD methods often require a practitioner to carefully choose or adjust the teacher and student architectures, making these methods less scalable in practice. Furthermore, although KD is usually conducted in scenarios with limited labeled data, very few techniques are developed to alleviate such data insufficiency. To solve the above problems in an all-in-one manner, we propose in this paper a unified KD framework based on conditional generative adversarial networks (cGANs), termed cGAN-KD. Fundamentally different from existing KD methods, cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. This unique mechanism makes cGAN-KD suitable for both classification and regression tasks, compatible with other KD methods, and insensitive to the teacher and student architectures. Also, benefiting from the recent advances in cGAN methodology and our specially designed subsampling and filtering procedures, cGAN-KD also performs well when labeled data are scarce. An error bound of a student model trained in the cGAN-KD framework is derived in this work, which theoretically explains why cGAN-KD takes effect and guides the implementation of cGAN-KD in practice. Extensive experiments on CIFAR-10 and Tiny-ImageNet show that we can incorporate state-of-the-art KD methods into the cGAN-KD framework to reach a new state of the art. Also, experiments on RC-49 and UTKFace demonstrate the effectiveness of cGAN-KD in image regression tasks, where existing KD methods are inapplicable

Continuous Conditional Generative Adversarial Networks for Image Generation: Novel Losses and Label Input Mechanisms

This work proposes the continuous conditional generative adversarial network (CcGAN), the first generative model for image generation conditional on continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (eg, class labels); conditioning on regression labels is mathematically distinct and raises two fundamental problems:(P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (aka empirical cGAN losses) often fails in practice;(P2) Since regression labels are scalar and infinitely many, conventional label input methods are not applicable. The proposed CcGAN solves the above problems, respectively, by (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a naive label input (NLI) method and an improved label input (ILI) method to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. The error bounds of a discriminator trained with HVDL and SVDL are derived under mild assumptions in this work. Two new benchmark datasets (RC-49 and Cell-200) and a novel evaluation metric (Sliding Fr\'echet Inception Distance) are also proposed for this continuous scenario. Our experiments on the Circular 2-D Gaussians, RC-49, UTKFace, Cell-200, and Steering Angle datasets show that CcGAN is able to generate diverse, high-quality samples from the image distribution conditional on a given regression label. Moreover, in these experiments, CcGAN substantially outperforms cGAN both visually and quantitatively

Variation in Basal Body Localisation and Targeting of Trypanosome RP2 and FOR20 Proteins

TOF-LisH-PLL motifs defines FOP family proteins; some members are involved in flagellum assembly. The critical role of FOP family protein FOR20 is poorly understood. Here, we report relative localisations of the four FOP family proteins in parasitic Trypanosoma brucei: TbRP2, TbOFD1 and TbFOP/FOP1-like are mature basal body proteins whereas TbFOR20 is present on pro- and mature basal bodies – on the latter it localises distal to TbRP2. We discuss how the data, together with published work for another protist Giardia intestinalis, informs on likely FOR20 function. Moreover, our localisation study provides convincing evidence that the antigen recognised by monoclonal antibody YL1/2 at trypanosome mature basal bodies is FOP family protein TbRP2, not tyrosinated α-tubulin as widely stated in the literature. Curiously, FOR20 proteins from T. brucei and closely related African trypanosomes possess short, negatively-charged N-terminal extensions absent from FOR20 in other trypanosomatids and other eukaryotes. The extension is necessary for protein targeting, but insufficient to re-direct TbRP2 to probasal bodies. Yet, FOR20 from the American trypanosome T. cruzi, which lacks any extension, localises to pro- and mature basal bodies when expressed in T. brucei. This identifies unexpected variation in FOR20 architecture that is presently unique to one clade of trypanosomatids

Drug Repositioning and Pharmacophore Identification in the Discovery of Hookworm MIF Inhibitors

SummaryThe screening of bioactive compound libraries can be an effective approach for repositioning FDA-approved drugs or discovering new pharmacophores. Hookworms are blood-feeding, intestinal nematode parasites that infect up to 600 million people worldwide. Vaccination with recombinant Ancylostoma ceylanicum macrophage migration inhibitory factor (rAceMIF) provided partial protection from disease, thus establishing a “proof-of-concept” for targeting AceMIF to prevent or treat infection. A high-throughput screen (HTS) against rAceMIF identified six AceMIF-specific inhibitors. A nonsteroidal anti-inflammatory drug (NSAID), sodium meclofenamate, could be tested in an animal model to assess the therapeutic efficacy in treating hookworm disease. Furosemide, an FDA-approved diuretic, exhibited submicromolar inhibition of rAceMIF tautomerase activity. Structure-activity relationships of a pharmacophore based on furosemide included one analog that binds similarly to the active site, yet does not inhibit the Na-K-Cl symporter (NKCC1) responsible for diuretic activity

The impact of a native dominant plant, Euphorbia jolkinii, on plant–flower visitor networks and pollen deposition on stigmas of co-flowering species in subalpine meadows of Shangri-La, SW China

Anthropogenic activity can modify the distribution of species abundance in a community leading to the appearance of new dominant species. While many studies report that an alien plant species which becomes increasingly dominant can change species composition, plant–pollinator network structure and the reproductive output of native plant species, much less is known about native plant species which become dominant in their communities. Euphorbia jolkinii Boissier (Euphorbia, hereafter) has become a dominant native plant in the over-grazed meadows of Shangri-La, SW China. During the flowering season of Euphorbia and over 2 years, we quantified the impact of Euphorbia on plant richness and flower visitor richness in 12 subalpine meadows along a gradient of Euphorbia dominance. We also evaluated the floral preferences of flower visitors, interaction evenness of plant–flower visitor networks and the deposition of pollen on the stigmas of two co-flowering plant species (Gentiana chungtienensis and Anemone rupestris) in each meadow. The species richness of flower visitors to non-Euphorbia plants was negatively correlated with Euphorbia dominance. As the proportion of Euphorbia increases, flower visitors to Euphorbia decreased, while flower visitors to other co-flowering plants increased. Interaction evenness decreased as the proportion of Euphorbia increased. Furthermore, the conspecific pollen deposition of one of the two co-flowering plant species studied, G. chungtienensis, decreased as the proportion of Euphorbia increased. Synthesis. There appears to be little substantive difference between the impact of a newly dominant native plant and the impacts reported for many alien plants on native plant–pollinator communities. This lack of difference suggests that dominance, in addition to plant origin (alien vs. native), could play an important role in influencing the structure and functioning of native communities. This finding has considerable implications for restoration ecology. Thus, communities where natural dominance order has been changed due to anthropogenic activity may not be considered a problem as all the species are native—in reality though, they may be as damaged as communities invaded by alien species.H.W. was supported by Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31020000), Yunlin Scholarship of Yunnan Province (YLXL20170001) and Joint Fund of the National Natural Science Foundation of China-Yunnan Province (U1502261). Y.-H.Z. was supported by National Natural Science Foundation of China (31700361), and Chinese Academy of Sciences ‘Light of West China’ Program. A.L. was supported by a Ramóny Cajal (RYC-2015-19034) contract from the Spanish Ministry of Science, Innovation and Universities, the Spanish State Research Agency, European Social Funds (ESF invests in your future) and the University of the Balearic Islands, and by the project CGL2017-89254-R financed by the Spanish Ministry of Economy and Competitiveness, Feder founds and the Spanish Research Agency (Call 2017)