Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals.Comment: 8 pages, 7 figure

Morphological effect of oscillating magnetic nanoparticles in killing tumor cells

Abstract Forced oscillation of spherical and rod-shaped iron oxide magnetic nanoparticles (MNPs) via low-power and low-frequency alternating magnetic field (AMF) was firstly used to kill cancer cells in vitro. After being loaded by human cervical cancer cells line (HeLa) and then exposed to a 35-kHz AMF, MNPs mechanically damaged cell membranes and cytoplasm, decreasing the cell viability. It was found that the concentration and morphology of the MNPs significantly influenced the cell-killing efficiency of oscillating MNPs. In this preliminary study, when HeLa cells were pre-incubated with 100 μg/mL rod-shaped MNPs (rMNP, length of 200 ± 50 nm and diameter of 50 to 120 nm) for 20 h, MTT assay proved that the cell viability decreased by 30.9% after being exposed to AMF for 2 h, while the cell viability decreased by 11.7% if spherical MNPs (sMNP, diameter of 200 ± 50 nm) were used for investigation. Furthermore, the morphological effect of MNPs on cell viability was confirmed by trypan blue assay: 39.5% rMNP-loaded cells and 15.1% sMNP-loaded cells were stained after being exposed to AMF for 2 h. It was also interesting to find that killing tumor cells at either higher (500 μg/mL) or lower (20 μg/mL) concentration of MNPs was less efficient than that achieved at 100 μg/mL concentration. In conclusion, the relatively asymmetric morphological rod-shaped MNPs can kill cancer cells more effectively than spherical MNPs when being exposed to AMF by virtue of their mechanical oscillations.</jats:p

Maltohexaose-based probes for bacteria-specific imaging: Great sensitivity, specificity and translational potential

Abstract(#br)Infectious diseases have always been a major cause of mobility and mortality, early and accurate diagnosis is important for their management. However, current clinical diagnosis for bacterial infection still remains troublesome. Recently, many attempts on molecular imaging have been made for prompt bacteria detection, especially for early and precise disease diagnosis. Among them, maltohexaose-based probes serve as a superb candidate due to the bacteria-specific maltodextrin transport pathway. These probes can visualize bacterial foci with unparalleled sensitivity and specificity. Such metabolism-based targeting strategy offers a powerful delivery platform for imaging and theranostic agents, providing good translational potential for developing antibacterial agents

Impact of ENSO events on meteorological drought in the Weihe River basin, China

El Niño–Southern Oscillation (ENSO) events influence elements of the terrestrial water cycle such as precipitation and temperature, which in turn have a significant impact on drought. This work assessed the impact of El Niño and La Niña on droughts from 1970 to 2020 in the Weihe River basin (WRB) in China. This study used a standardized precipitation index (SPI) to characterize meteorological drought. The regional drought response to extreme events in El Niño/La Niña was analyzed using principal component analysis (PCA), Wilcoxon and Mann–Whitney tests, and other methods. The results showed that, based on PCA, the WRB is divided into two regions, with the northwest region (67%) comprising more area than the southeast region (33%). El Niño/La Niña significantly impacted drought in the WRB. Droughts mainly occurred in the El Niño year and the year following La Niña. El Niño had the highest number of drought years (44%), followed by the year following La Niña (43%). The number of droughts was lowest in the year following El Niño (22%). At 1-, 3-, and 6-month timescales, significant droughts mainly occurred from July to December in El Niño years and the summer following La Niña. On a 12-month timescale, significant droughts mainly occurred from January to April in El Niño years, while no droughts occurred in La Niña years. The longer the timescale of the SPI, the more months of significant drought in El Niño years; however, the intensity of drought in the basin was reduced. In the year following La Niña, summer droughts intensified on a 6-month timescale compared to a 3-month timescale. El Niño and La Niña had greater impacts on the drought index in the northwest region of the WRB. In the northwest region, 60% of the months showed significant drought, compared to only 2% of the months in the southeast region. The drought intensity was higher in the northwest region. The results of this study provide a reference for drought management and early warning systems in the WRB and support solutions to water shortage

FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving

This is the first paper in a series of work we have accomplished over the past three years. In this paper, we have constructed a consistent formal plane geometry system. This will serve as a crucial bridge between IMO-level plane geometry challenges and readable AI automated reasoning. Within this formal framework, we have been able to seamlessly integrate modern AI models with our formal system. AI is now capable of providing deductive reasoning solutions to IMO-level plane geometry problems, just like handling other natural languages, and these proofs are readable, traceable, and verifiable. We propose the geometry formalization theory (GFT) to guide the development of the geometry formal system. Based on the GFT, we have established the FormalGeo, which consists of 88 geometric predicates and 196 theorems. It can represent, validate, and solve IMO-level geometry problems. we also have crafted the FGPS (formal geometry problem solver) in Python. It serves as both an interactive assistant for verifying problem-solving processes and an automated problem solver. We've annotated the formalgeo7k and formalgeo-imo datasets. The former contains 6,981 (expand to 133,818 through data augmentation) geometry problems, while the latter includes 18 (expand to 2,627 and continuously increasing) IMO-level challenging geometry problems. All annotated problems include detailed formal language descriptions and solutions. Implementation of the formal system and experiments validate the correctness and utility of the GFT. The backward depth-first search method only yields a 2.42% problem-solving failure rate, and we can incorporate deep learning techniques to achieve lower one. The source code of FGPS and datasets are available at https://github.com/BitSecret/FGPS.Comment: 44 page

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Impact of CRAMP-34 on Pseudomonas aeruginosa biofilms and extracellular metabolites

Biofilm is a structured community of bacteria encased within a self-produced extracellular matrix. When bacteria form biofilms, they undergo a phenotypic shift that enhances their resistance to antimicrobial agents. Consequently, inducing the transition of biofilm bacteria to the planktonic state may offer a viable approach for addressing infections associated with biofilms. Our previous study has shown that the mouse antimicrobial peptide CRAMP-34 can disperse Pseudomonas aeruginosa (P. aeruginosa) biofilm, and the potential mechanism of CRAMP-34 eradicate P. aeruginosa biofilms was also investigated by combined omics. However, changes in bacterial extracellular metabolism have not been identified. To further explore the mechanism by which CRAMP-34 disperses biofilm, this study analyzed its effects on the extracellular metabolites of biofilm cells via metabolomics. The results demonstrated that a total of 258 significantly different metabolites were detected in the untargeted metabolomics, of which 73 were downregulated and 185 were upregulated. Pathway enrichment analysis of differential metabolites revealed that metabolic pathways are mainly related to the biosynthesis and metabolism of amino acids, and it also suggested that CRAMP-34 may alter the sensitivity of biofilm bacteria to antibiotics. Subsequently, it was confirmed that the combination of CRAMP-34 with vancomycin and colistin had a synergistic effect on dispersed cells. These results, along with our previous findings, suggest that CRAMP-34 may promote the transition of PAO1 bacteria from the biofilm state to the planktonic state by upregulating the extracellular glutamate and succinate metabolism and eventually leading to the dispersal of biofilm. In addition, increased extracellular metabolites of myoinositol, palmitic acid and oleic acid may enhance the susceptibility of the dispersed bacteria to the antibiotics colistin and vancomycin. CRAMP-34 also delayed the development of bacterial resistance to colistin and ciprofloxacin. These results suggest the promising development of CRAMP-34 in combination with antibiotics as a potential candidate to provide a novel therapeutic approach for the prevention and treatment of biofilm-associated infections