Deep Neural Newsvendor

We consider a data-driven newsvendor problem, where one has access to past demand data and the associated feature information. We solve the problem by estimating the target quantile function using a deep neural network (DNN). The remarkable representational power of DNN allows our framework to incorporate or approximate various extant data-driven models. We provide theoretical guarantees in terms of excess risk bounds for the DNN solution characterized by the network structure and sample size in a non-asymptotic manner, which justify the applicability of DNNs in the relevant contexts. Specifically, the convergence rate of the excess risk bound with respect to the sample size increases in the smoothness of the target quantile function but decreases in the dimension of feature variables. This rate can be further accelerated when the target function possesses a composite structure. Compared to other typical models, the nonparametric DNN method can effectively avoid or significantly reduce the model misspecification error. In particular, our theoretical framework can be extended to accommodate the data-dependent scenarios, where the data-generating process is time-dependent but not necessarily identical over time. Finally, we apply the DNN method to a real-world dataset obtained from a food supermarket. Our numerical experiments demonstrate that (1) the DNN method consistently outperforms other alternatives across a wide range of cost parameters, and (2) it also exhibits good performance when the sample size is either very large or relatively limited

Wasserstein Generative Regression

In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution. The main idea is to estimate a conditional generator that satisfies the constraint that it produces a good regression function estimator. We use deep neural networks to model the conditional generator. Our approach can handle problems with multivariate outcomes and covariates, and can be used to construct prediction intervals. We provide theoretical guarantees by deriving non-asymptotic error bounds and the distributional consistency of our approach under suitable assumptions. We also perform numerical experiments with simulated and real data to demonstrate the effectiveness and superiority of our approach over some existing approaches in various scenarios.Comment: 50 pages, including appendix. 5 figures and 6 tables in the main text. 1 figure and 7 tables in the appendi

Fast and accurate X-ray fluorescence computed tomography imaging with the ordered-subsets expectation maximization algorithm.

The ordered-subsets expectation maximization algorithm (OSEM) is introduced to X-ray fluorescence computed tomography (XFCT) and studied; here, simulations and experimental results are presented. The simulation results indicate that OSEM is more accurate than the filtered back-projection algorithm, and it can efficiently suppress the deterioration of image quality within a large range of angular sampling intervals. Experimental results of both an artificial phantom and cirrhotic liver show that with a satisfying image quality the angular sampling interval could be improved to save on the data-acquisition time when OSEM is employed. In addition, with an optimum number of subsets, the image reconstruction time of OSEM could be reduced to about half of the time required for one subset. Accordingly, it can be concluded that OSEM is a potential method for fast and accurate XFCT imaging

Multiplex LNA probe-based RAP assay for rapid and highly sensitive detection of rifampicin-resistant Mycobacterium tuberculosis

ObjectivesThe World Health Organization (WHO) Global tuberculosis Report 2021 stated that rifampicin-resistant tuberculosis (RR-TB) remains a major public health threat. However, the in-practice diagnostic techniques for RR-TB have a variety of limitations including longer time, lack of sensitivity, and undetectable low proportion of heterogeneous drug resistance.MethodsHere we developed a multiplex LNA probe-based RAP method (MLP-RAP) for more sensitive detection of multiple point mutations of the RR-TB and its heteroresistance. A total of 126 clinical isolates and 78 sputum samples collected from the National Tuberculosis Reference Laboratory, China CDC, were tested by MLP-RAP assay. In parallel, qPCR and Sanger sequencing of nested PCR product assay were also performed for comparison.ResultsThe sensitivity of the MLP-RAP assay could reach 5 copies/μl using recombinant plasmids, which is 20 times more sensitive than qPCR (100 copies/μl). In addition, the detection ability of rifampicin heteroresistance was 5%. The MLP-RAP assay had low requirements (boiling method) for nucleic acid extraction and the reaction could be completed within 1 h when placed in a fluorescent qPCR instrument. The result of the clinical evaluation showed that the MLP-RAP method could cover codons 516, 526, 531, and 533 with good specificity. 41 out of 78 boiled sputum samples were detected positive by MLP-RAP assay, which was further confirmed by Sanger sequencing of nested PCR product assay, on the contrary, qPCR was able to detect 32 samples only. Compared with Sanger sequencing of nested PCR product assay, both the specificity and sensitivity of the MLP-RAP assay were 100%.ConclusionMLP-RAP assay can detect RR-TB infection with high sensitivity and specificity, indicating that this assay has the prospect of being applied for rapid and sensitive RR-TB detection in general laboratories where fluorescent qPCR instrument is available

Phylogenomics and morphological evolution of the mega-diverse genus Artemisia (Asteraceae: Anthemideae): implications for its circumscription and infrageneric taxonomy

Background and Aims Artemisia is a mega-diverse genus consisting of ~400 species. Despite its medicinal importance and ecological significance, a well-resolved phylogeny for global Artemisia, a natural generic delimitation and infrageneric taxonomy remain missing, owing to the obstructions from limited taxon sampling and insufficient information on DNA markers. Its morphological characters, such as capitulum, life form and leaf, show marked variations and are widely used in its infrageneric taxonomy. However, their evolution within Artemisia is poorly understood. Here, we aimed to reconstruct a well-resolved phylogeny for global Artemisia via a phylogenomic approach, to infer the evolutionary patterns of its key morphological characters and to update its circumscription and infrageneric taxonomy. Methods We sampled 228 species (258 samples) of Artemisia and its allies from both fresh and herbarium collections, covering all the subgenera and its main geographical areas, and conducted a phylogenomic analysis based on nuclear single nucleotide polymorphisms (SNPs) obtained from genome skimming data. Based on the phylogenetic framework, we inferred the possible evolutionary patterns of six key morphological characters widely used in its previous taxonomy. Key Results The genus Kaschgaria was revealed to be nested in Artemisia with strong support. A well-resolved phylogeny of Artemisia consisting of eight highly supported clades was recovered, two of which were identified for the first time. Most of the previously recognized subgenera were not supported as monophyletic. Evolutionary inferences based on the six morphological characters showed that different states of these characters originated independently more than once. Conclusions The circumscription of Artemisia is enlarged to include the genus Kaschgaria. The morphological characters traditionally used for the infrageneric taxonomy of Artemisia do not match the new phylogenetic tree. They experienced a more complex evolutionary history than previously thought. We propose a revised infrageneric taxonomy of the newly circumscribed Artemisia, with eight recognized subgenera to accommodate the new results.This work was supported by the National Natural Science Foundation of China (grant nos. 31870179, 31570204, 31270237 and J1310002), the International Partnership Program (grant no. 151853KYSB20190027), Sino-Africa Joint Research Center (grant no. SAJC201614), Key technology projects of Jiangxi Province's major scientific and technological research and development project (grant no. 20223AAF01007), Survey of Wildlife Resources in Key Areas of Tibet (grant no. ZL202203601) and National Plant Specimen Resource Center (grant no. E0117G1001) of the Chinese Academy of Sciences, Key Project at Central Government Level: The Ability Establishment of Sustainable Use of Valuable Chinese Medicine Resources (grant no. 2060302) and Project of the Central Siberian Botanical Garden of the Siberian Branch of the Russian Academy of Sciences (grant no. AAAA-A21-121011290024-5).Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Conclusions SUPPLEMENTARY DATA FUNDING ACKNOWLEDGEMENTS CONFLICT OF INTEREST LITERATURE CITED Supplementary dat

Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression

We study the properties of differentiable neural networks activated by rectified power unit (RePU) functions. We show that the partial derivatives of RePU neural networks can be represented by RePUs mixed-activated networks and derive upper bounds for the complexity of the function class of derivatives of RePUs networks. We establish error bounds for simultaneously approximating $C^s$ smooth functions and their derivatives using RePU-activated deep neural networks. Furthermore, we derive improved approximation error bounds when data has an approximate low-dimensional support, demonstrating the ability of RePU networks to mitigate the curse of dimensionality. To illustrate the usefulness of our results, we consider a deep score matching estimator (DSME) and propose a penalized deep isotonic regression (PDIR) using RePU networks. We establish non-asymptotic excess risk bounds for DSME and PDIR under the assumption that the target functions belong to a class of $C^s$ smooth functions. We also show that PDIR has a robustness property in the sense it is consistent with vanishing penalty parameters even when the monotonicity assumption is not satisfied. Furthermore, if the data distribution is supported on an approximate low-dimensional manifold, we show that DSME and PDIR can mitigate the curse of dimensionality.Comment: 66 pages, 20 figures, and 6 tables. arXiv admin note: text overlap with arXiv:2207.1044

Interleukin-6 induces fat loss in cancer cachexia by promoting white adipose tissue lipolysis and browning

Abstract Background Cancer cachexia is a progressive and multi-factorial metabolic syndrome characterized by loss of adipose tissue and skeletal muscle. White adipose tissue (WAT) lipolysis and white-to-brown transdifferentiation of WAT (WAT browning) are proposed to contribute to WAT atrophy in cancer cachexia. Chronic inflammation, mediated by cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6), has been reported to promote cancer cachexia. However, whether chronic inflammation promotes cancer cachexia by regulating WAT metabolism and the underlying mechanism remains unclear. Methods In this study, we first analyzed the association between chronic inflammation and WAT metabolism in gastric and colorectal cancer cachectic patients. In cachectic mice treated with anti-IL-6 receptor antibody, we clarified whether WAT lipolysis and browning were regulated by IL-6. Results Clinical analyses showed positive significant association between serum IL-6 and free fatty acid (FFA) both in early- and late-stage cancer cachexia. However, serum TNF-α was positively associated with serum FFA in the early- but not late-stage cachexia. WAT lipolysis was increased in early- and late-stage cachexia, while WAT browning was detected only in late-stage cachexia. Anti-IL-6 receptor antibody inhibited WAT lipolysis and browning in cachectic mice. Conclusions Based on these findings, we conclude that chronic inflammation (especially that mediated by IL-6) might promote cancer cachexia by regulating WAT lipolysis in early-stage cachexia and browning in late-stage cachexia

Theaflavin pretreatment ameliorates renal ischemia/reperfusion injury by attenuating apoptosis and oxidative stress in vivo and in vitro

Oxidative stress-induced apoptosis is an important pathological process in renal ischemia/reperfusion injury (RIRI). Theaflavin (TF) is the main active pigment and polyphenol in black tea. It has been widely reported because of its biological activity that can reduce oxidative stress and protect against many diseases. Here, we explored the role of theaflavin in the pathological process of RIRI. In the present study, the RIRI model of 45 min ischemia and 24 h reperfusion was established in C57BL/6 J male mice, and theaflavin was used as an intervention. Compared with the RIRI group, the renal filtration function, renal tissue damage and antioxidant capacity of the theaflavin intervention group were significantly improved, while the level of apoptosis was reduced. TCMK-1 cells were incubated under hypoxia for 48 h and then reoxygenated for 6 h to simulate RIRI in vitro. The application of theaflavin significantly promoted the translocation of p53 from cytoplasm to nucleus, upregulated the expression of glutathione peroxidase 1 (GPx-1) in cells, and inhibited oxidative stress damage and apoptosis. Transfection with p53 siRNA can partially inhibit the effect of theaflavin. Thus, theaflavin exerted a protective effect against RIRI by inhibiting apoptosis and oxidative stress via regulating the p53/GPx-1 pathway. We conclude that theaflavin has the potential to become a candidate drug for the prevention and treatment of RIRI

Cryptic Diversity on Cliffs: Aster sanqingensis, a New Species of Asteraceae from Eastern China

It is generally believed that cliffs bear low biodiversity because of their harsh habitats. However, another reason, i.e. insufficient investigation caused by the inaccessibility of the cliffs, could not be excluded. In the genus Aster, two cliff species, Aster fanjingshanicus and Aster tianmenshanensis, respectively growing on slate and limestone cliffs, were previously described. During our extensive field investigations, a third cliff species of Aster growing on granite cliffs from eastern China was found. Based on evidence from molecular phylogeny, morphology and micro-morphology, we propose that it should be treated as a new species and named Aster sanqingensis. It is described and illustrated here. Considering its limited number of individuals, highly localized distribution, and disturbed habitat, we propose to treat it as a Critically Endangered species. Our new finding indicates there is cryptic biodiversity on the cliffs remaining to be discovered

EV-Eye : Rethinking high-frequency eye tracking through the lenses of event cameras

In this paper, we present EV-Eye, a first-of-its-kind large scale multimodal eye tracking dataset aimed at inspiring research on high-frequency eye/gaze tracking. EV-Eye utilizes an emerging bio-inspired event camera to capture independent pixel-level intensity changes induced by eye movements, achieving sub-microsecond latency. Our dataset was curated over a two-week period and collected from 48 participants encompassing diverse genders and age groups. It comprises over 1.5 million near-eye grayscale images and 2.7 billion event samples generated by two DAVIS346 event cameras. Additionally, the dataset contains 675 thousands scene images and 2.7 million gaze references captured by Tobii Pro Glasses 3 eye tracker for cross-modality validation. Compared with existing event-based high-frequency eye tracking datasets, our dataset is significantly larger in size, and the gaze references involve more natural eye movement patterns, i.e., fixation, saccade and smooth pursuit. Alongside the event data, we also present a hybrid eye tracking method as benchmark, which leverages both the near-eye grayscale images and event data for robust and high-frequency eye tracking. We show that our method achieves higher accuracy for both pupil and gaze estimation tasks compared to the existing solution