Stochastic Optimization of Areas UnderPrecision-Recall Curves with Provable Convergence

Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common metrics for evaluating classification performance for imbalanced problems. Compared with AUROC, AUPRC is a more appropriate metric for highly imbalanced datasets. While stochastic optimization of AUROC has been studied extensively, principled stochastic optimization of AUPRC has been rarely explored. In this work, we propose a principled technical method to optimize AUPRC for deep learning. Our approach is based on maximizing the averaged precision (AP), which is an unbiased point estimator of AUPRC. We cast the objective into a sum of {\it dependent compositional functions} with inner functions dependent on random variables of the outer level. We propose efficient adaptive and non-adaptive stochastic algorithms named SOAP with {\it provable convergence guarantee under mild conditions} by leveraging recent advances in stochastic compositional optimization. Extensive experimental results on image and graph datasets demonstrate that our proposed method outperforms prior methods on imbalanced problems in terms of AUPRC. To the best of our knowledge, our work represents the first attempt to optimize AUPRC with provable convergence. The SOAP has been implemented in the libAUC library at~\url{https://libauc.org/}.Comment: 24 pages, 10 figure

Detecting the Thermal Properties of Bone Cement by Temperature Sensor

Polymethylmethacrylate-based (PMMA) bone cements containing functionalized carbon nanotubes (CNTs) were prepared, and the thermal properties of the resultant nanocomposite cements were characterized in accordance with the international standard for acrylic resin cements. The aim of this study was to determine the peak temperatures during the polymerization reaction in PMMA bone cement by thermocouple (temperature sensor). The CNTs were uniformly dispersed in the cement matrix. The setting time of the cement increased and the maximum temperature during exothermic polymerization reaction was effectively reduced by the incorporation of functionalized CNTs. This reduction decreased thermal necrosis of the respective nanocomposite cements, which probably could reduce the hyperthermia experienced in vivo

Highly strained, radially π-conjugated porphyrinylene nanohoops

Small π-conjugated nanohoops are difficult to prepare, but offer an excellent platform for studying the interplay between strain and optoelectronic properties, and, increasingly, these shape-persistent macrocycles find uses in host-guest chemistry and self-assembly. We report the synthesis of a new family of radially π-conjugated porphyrinylene/phenylene nanohoops. The strain energy in the smallest nanohoop [2]CPT is approximately 54 kcal mol⁻¹, which results in a narrowed HOMO-LUMO gap and a red shift in the visible part of the absorption spectrum. Because of its high degree of preorganization and a diameter of ca. 13 Å, [2]CPT was found to accommodate C₆₀ with a binding affinity exceeding 10⁸ M⁻¹ despite the fullerene not fully entering the cavity of the host (X-ray crystallography). Moreover, the ?-extended nanohoops [2]CPTN, [3]CPTN, and [3]CPTA (N for 1,4-naphthyl; A for 9,10-anthracenyl) have been prepared using the same strategy, and [2]CPTN has been shown to bind C₇₀ 5 times more strongly than [2]CPT. Our failed synthesis of [2]CPTA highlights a limitation of the experimental approach most commonly used to prepare strained nanohoops, because in this particular case the sum of aromatization energies no longer outweighs the buildup of ring strain in the final reaction step (DFT calculations). These results indicate that forcing ring strain onto organic semiconductors is a viable strategy to fundamentally influence both optoelectronic and supramolecular properties

KARR-seq reveals cellular higher-order RNA structures and RNA–RNA interactions

RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order structures and how RNA molecules may interact with each other to facilitate functions remain largely unknown. Here we present KARR-seq, which uses N3-kethoxal labeling and multifunctional chemical crosslinkers to covalently trap and determine RNA–RNA interactions and higher-order RNA structures inside cells, independent of local protein binding to RNA. KARR-seq depicts higher-order RNA structure and detects widespread intermolecular RNA–RNA interactions with high sensitivity and accuracy. Using KARR-seq, we show that translation represses mRNA compaction under native and stress conditions. We determined the higher-order RNA structures of respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) and identified RNA–RNA interactions between the viruses and the host RNAs that potentially regulate viral replication

H+-pyrophosphatases enhance low nitrogen stress tolerance in transgenic Arabidopsis and wheat by interacting with a receptor-like protein kinase

IntroductionNitrogen is a major abiotic stress that affects plant productivity. Previous studies have shown that plant H+-pyrophosphatases (H+-PPases) enhance plant resistance to low nitrogen stress. However, the molecular mechanism underlying H+-PPase-mediated regulation of plant responses to low nitrogen stress is still unknown. In this study, we aimed to investigate the regulatory mechanism of AtAVP1 in response to low nitrogen stress.Methods and ResultsAtAVP1 in Arabidopsis thaliana and EdVP1 in Elymus dahuricus belong to the H+-PPase gene family. In this study, we found that AtAVP1 overexpression was more tolerant to low nitrogen stress than was wild type (WT), whereas the avp1-1 mutant was less tolerant to low nitrogen stress than WT. Plant height, root length, aboveground fresh and dry weights, and underground fresh and dry weights of EdVP1 overexpression wheat were considerably higher than those of SHI366 under low nitrogen treatment during the seedling stage. Two consecutive years of low nitrogen tolerance experiments in the field showed that grain yield and number of grains per spike of EdVP1 overexpression wheat were increased compared to those in SHI366, which indicated that EdVP1 conferred low nitrogen stress tolerance in the field. Furthermore, we screened interaction proteins in Arabidopsis; subcellular localization analysis demonstrated that AtAVP1 and Arabidopsis thaliana receptor-like protein kinase (AtRLK) were located on the plasma membrane. Yeast two-hybrid and luciferase complementary imaging assays showed that the AtRLK interacted with AtAVP1. Under low nitrogen stress, the Arabidopsis mutants rlk and avp1-1 had the same phenotypes.DiscussionThese results indicate that AtAVP1 regulates low nitrogen stress responses by interacting with AtRLK, which provides a novel insight into the regulatory pathway related to H+-pyrophosphatase function in plants

An Experimental Study on the Establishment of Pulmonary Hypertension Model in Rats induced by Monocrotaline

Pulmonary hypertension is called PH for short. It is caused by the pulmonary artery vascular disease leading to pulmonary vascular resistance, and the increase right lung compartment load, which resulting in weakening or even collapse of the right ventricular function. The establishment of rat PH model under the action of monocrotaline is a repeatable, simple and accessible operation technique, which has been widely used in the treatment of pulmonary hypertension. This paper discusses the principle and properties of the PH model on rats under the monocrotaline action

Hedyotis diffusa Willd Inhibits Colorectal Cancer Growth in Vivo via Inhibition of STAT3 Signaling Pathway

Signal Transducer and Activator of Transcription 3 (STAT3), a common oncogenic mediator, is constitutively activated in many types of human cancers; therefore it is a major focus in the development of novel anti-cancer agents. Hedyotis diffusa Willd has been used as a major component in several Chinese medicine formulas for the clinical treatment of colorectal cancer (CRC). However, the precise mechanism of its anti-tumor activity remains largely unclear. Using a CRC mouse xenograft model, in the present study we evaluated the effect of the ethanol extract of Hedyotis diffusa Willd (EEHDW) on tumor growth in vivo and investigated the underlying molecular mechanisms. We found that EEHDW reduced tumor volume and tumor weight, but had no effect on body weight gain in CRC mice, demonstrating that EEHDW can inhibit CRC growth in vivo without apparent adverse effect. In addition, EEHDW treatment suppressed STAT3 phosphorylation in tumor tissues, which in turn resulted in the promotion of cancer cell apoptosis and inhibition of proliferation. Moreover, EEHDW treatment altered the expression pattern of several important target genes of the STAT3 signaling pathway, i.e., decreased expression of Cyclin D1, CDK4 and Bcl-2 as well as up-regulated p21 and Bax. These results suggest that suppression of the STAT3 pathway might be one of the mechanisms by which EEHDW treats colorectal cancer

A three-shell supramolecular complex enables the symmetry-mismatched chemo- and regioselective bis-functionalization of C60

Molecular Russian dolls (matryoshkas) have proven useful for testing the limits of preparative supramolecular chemistry but applications of these architectures to problems in other fields are elusive. Here we report a three-shell, matryoshka-like complex—in which C60 sits inside a cycloparaphenylene nanohoop, which in turn is encapsulated inside a self-assembled nanocapsule—that can be used to address a long-standing challenge in fullerene chemistry, namely the selective formation of a particular fullerene bis-adduct. Spectroscopic evidence indicates that the ternary complex is sufficiently stable in solution for the two outer shells to affect the addition chemistry of the fullerene guest. When the complex is subjected to Bingel cyclopropanation conditions, the exclusive formation of a single trans-3 fullerene bis-adduct was observed in a reaction that typically yields more than a dozen products. The selectivity facilitated by this matryoshka-like approach appears to be a general phenomenon and could be useful for applications where regioisomerically pure C60 bis-adducts have been shown to have superior properties compared with isomer mixtures.This work was supported by grants from MINECO-Spain (CTQ2016-77989-P and PID2019-104498GB-I00 to X.R., RTI2018-095622-B-100 to D.M. and I.I., and EUR2019-103824 to F.G.), Generalitat de Catalunya (2017SGR264 and a PhD grant to C.F.-E.) and the Severo Ochoa Center of Excellence Program (Catalan Institute of Nanoscience and Nanotechnology, grant SEV-2017-0706). X.R. is also grateful for ICREA-Acadèmia awards. M.v.D. is grateful for financial support from the Deutsche Forschungsgemeinschaft (project number 182849149-SFB953 ‘Synthetic Carbon Allotropes’), the Fonds der Chemischen Industrie (FCI), the University of Ulm and the Deutscher Akademischer Austauschdienst (PhD fellowship to O.B.). E.U. thanks Universitat de Girona for a PhD grant and we thank Serveis Tècnics de Recerca, Universitat de Girona for technical support.Peer reviewe

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4Science). Being an emerging research paradigm, AI4Science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4Science; namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4Science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4Science