Neuro-Symbolic Recommendation Model based on Logic Query

A recommendation system assists users in finding items that are relevant to them. Existing recommendation models are primarily based on predicting relationships between users and items and use complex matching models or incorporate extensive external information to capture association patterns in data. However, recommendation is not only a problem of inductive statistics using data; it is also a cognitive task of reasoning decisions based on knowledge extracted from information. Hence, a logic system could naturally be incorporated for the reasoning in a recommendation task. However, although hard-rule approaches based on logic systems can provide powerful reasoning ability, they struggle to cope with inconsistent and incomplete knowledge in real-world tasks, especially for complex tasks such as recommendation. Therefore, in this paper, we propose a neuro-symbolic recommendation model, which transforms the user history interactions into a logic expression and then transforms the recommendation prediction into a query task based on this logic expression. The logic expressions are then computed based on the modular logic operations of the neural network. We also construct an implicit logic encoder to reasonably reduce the complexity of the logic computation. Finally, a user's interest items can be queried in the vector space based on the computation results. Experiments on three well-known datasets verified that our method performs better compared to state of the art shallow, deep, session, and reasoning models.Comment: 17 pages, 6 figure

Nanoplanktonic diatom rapidly alters sinking velocity via regulating lipid content and composition in response to changing nutrient concentrations

Diatom sinking plays a crucial role in the global carbon cycle, accounting for approximately 40% of marine particulate organic carbon export. While oceanic models typically represent diatoms as microphytoplankton (> 20 μm), it is important to recognize that many diatoms fall into the categories of nanophytoplankton (2-20 μm) and picophytoplankton (< 2 μm). These smaller diatoms have also been found to significantly contribute to carbon export. However, our understanding of their sinking behavior and buoyancy regulation mechanisms remains limited. In this study, we investigate the sinking behavior of a nanoplanktonic diatom, Phaeodactylum tricornutum (P. tricornutum), which exhibits rapid changes in sinking behavior in response to varying nutrient concentrations. Our results demonstrate that a higher sinking rate is observed under phosphate limitation and depletion. Notably, in phosphate depletion, the sinking rate of P. tricornutum was 0.79 ± 0.03 m d-1, nearly three times that of the previously reported sinking rates for Skeletonema costatum, Ditylum brightwellii, and Chaetoceros gracile. Furthermore, during the first 6 h of phosphate spike, the sinking rate of P. tricornutum remained consistently high. After 12 h of phosphate spike, the sinking rate decreased to match that of the phosphate repletion phase, only to increase again over the next 12 hours due to phosphate depletion. This rapid sinking behavior contributes to carbon export and potentially allows diatoms to exploit nutrient-rich patches when encountering increased nutrient concentrations. We also observed a significant positive correlation (P< 0.001) between sinking rate and lipid content (R = 0.91) during the phosphate depletion and spike experiment. It appears that P. tricornutum regulates its sinking rate by increasing intracellular lipid content, particularly digalactosyldiacylglycerol, hexosyl ceramide, monogalactosyldiacylglycerol, and triglycerides. Additionally, P. tricornutum replaces phospholipids with more dense membrane sulfolipids, such as sulfoquinovosyldiacylglycerol under phosphate shortage. These findings shed light on the intricate relationship between nutrient availability, sinking behavior, and lipid composition in diatoms, providing insights into their adaptive strategies for carbon export and nutrient utilization

Protective Roles of Gadd45 and MDM2 in Blueberry Anthocyanins Mediated DNA Repair of Fragmented and Non-Fragmented DNA Damage in UV-Irradiated HepG2 Cells

Peer reviewe

Protective Roles of Gadd45 and MDM2 in Blueberry Anthocyanins Mediated DNA Repair of Fragmented and Non-Fragmented DNA Damage in UV-Irradiated HepG2 Cells

Peer reviewe

The Morphological Features and Mitochondrial Oxidative Stress Mechanism of the Retinal Neurons Apoptosis in Early Diabetic Rats

This paper aims to explore the relationship of retinal neuron apoptosis and manganese superoxidase dismutase (MnSOD) at early phase of diabetic retinopathy. Sprague-Dawley rats were grouped into normal controls and diabetics. Data were collected after 4, 8, and 12 weeks (n = 12). The pathological changes and ultrastructure of the retina, the apoptosis rate of retinal neurons by TdT-mediated dUTP nick end label (TUNEL), mRNA expressions of MnSOD and copper-zinc superoxide dismutase (Cu–Zn SOD), and the activities of total SOD (T-SOD) and subtypes of SOD were tested. For the controls, there was no abnormal structure or apoptosis of retinal neurons at any time. There was no change of structure for rats with diabetes at 4 or 8 weeks, but there was a decrease of retinal ganglion cells (RGCs) number and thinner inner nuclear layer (INL) at 12 weeks. The apoptosis ratio of RGCs was higher than that of the controls at 8 and 12 weeks (P < 0.001). The activity and mRNA levels of MnSOD were lower in diabetics at 4, 8, and 12 weeks (P < 0.05). In summary, the apoptosis of the retinal neurons occurred at 8 weeks after the onset of diabetes. Retinal neuron apoptosis in early diabetic rats may be associated with the decreased activity and mRNA of MnSOD

Language splitting and relevance-based belief change in Horn logic

This paper presents a framework for relevance-based belief change in propositional Horn logic.We firstly establish a parallel interpolation theorem for Horn logic and show that Parikh’s Finest Splitting Theorem holds with Horn formulae. By reformulating Parikh’s relevance criterion in the setting of Horn belief change, we construct a relevance-based partial meet Horn contraction operator and provide a representation theorem for the operator. Interestingly, we find that this contraction operator can be fully characterised by Delgrande and Wassermann’s postulates for partial meet Horn contraction as well as Parikh’s relevance postulate without requiring any change on the postulates, which is qualitatively different from the case in classical propositional logic

A sequential model of bargaining in logic programming

This paper proposes a sequential model of bargaining specifying reasoning processes of an agent behind bargaining procedures. We encode agents’ background knowledge, demands, and bargaining constraints in logic programs and represent bargaining outcomes in answer sets. We assume that in each bargaining situation, each agent has a set of goals to achieve, which are normally unachievable without an agreement among all the agents who are involved in the bargaining. Through an alternating-offers procedure, an agreement among bargaining agents may be reached by abductive reasoning.We show that the procedure converges to a Nash equilibrium if each agent makes rational offers/counter-offers in each round. In addition, the sequential model also has a number of desirable properties, such as mutual commitments, individual rationality, satisfactoriness, and honesty