On the origin and evolution of the asteroid Ryugu: A comprehensive geochemical perspective

Presented here are the observations and interpretations from a comprehensive analysis of 16 representative particles returned from the C-type asteroid Ryugu by the Hayabusa2 mission. On average Ryugu particles consist of 50% phyllosilicate matrix, 41% porosity and 9% minor phases, including organic matter. The abundances of 70 elements from the particles are in close agreement with those of CI chondrites. Bulk Ryugu particles show higher δ18O, Δ17O, and ε54Cr values than CI chondrites. As such, Ryugu sampled the most primitive and least-thermally processed protosolar nebula reservoirs. Such a finding is consistent with multi-scale H-C-N isotopic compositions that are compatible with an origin for Ryugu organic matter within both the protosolar nebula and the interstellar medium. The analytical data obtained here, suggests that complex soluble organic matter formed during aqueous alteration on the Ryugu progenitor planetesimal (several 10’s of km), <2.6 Myr after CAI formation. Subsequently, the Ryugu progenitor planetesimal was fragmented and evolved into the current asteroid Ryugu through sublimation

Recommending Fresh URLs Using Twitter Lists

Recommender systems for social media have attracted considerable attentions due to its inherent features, such as a huge amount of information, social networks, and real-time features. In microblogs, which have been recognized as one of the most popular social media, most of URLs posted by users are considered to be fresh (i.e., shortly after creation). Hence, it is important to recommend URLs in microblogs for appropriate users because users become able to obtain such fresh URLs immediately. In this paper, we propose a URL recommender system using Twitter user lists. Twitter user list is the official functionality to group users into a list along with the name of it. Since it is expected that the members of a list (i.e., users included in the list) have similar characteristics, we utilize this feature to capture the user interests. Experimental results show that our proposed method achieves higher effectiveness than other methods based on the follow-followed network which does not offer user interests explicitly

OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation

If we know most of Smith’s friends are from Boston, what can we say about the rest of Smith’s friends? In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities. Our proposed algorithm which is referred to as OMNIProp has the following properties: (a) seamless and accurate; it works well on any label correlations (i.e., homophily, heterophily, and mixture of them) (b) fast; it is efficient and guaranteed to converge on arbitrary graphs (c) quasi-parameter free; it has just one well-interpretable parameter with heuristic default value of 1. We also prove the theoretical connections of our algorithm to the semi-supervised learning (SSL) algorithms and to random-walks. Experiments on four real, different network datasets demonstrate the benefits of the proposed algorithm, where OMNI-Prop outperforms the top competitors

Patterns in Interactive Tagging Networks

How do users behave if they can tag each other in social networks?In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems

Learning temporal data with a variational quantum recurrent neural network

We propose a method for learning temporal data using a parametrized quantum circuit. We use the circuit that has a similar structure as the recurrent neural network, which is one of the standard approaches employed for this type of machine learning task. Some of the qubits in the circuit are utilized for memorizing past data, while others are measured and initialized at each time step for obtaining predictions and encoding a new input datum. The proposed approach utilizes the tensor product structure to get nonlinearity with respect to the inputs. Fully controllable, ensemble quantum systems such as an NMR quantum computer are a suitable choice of an experimental platform for this proposal. We demonstrate its capability with simple numerical simulations, in which we test the proposed method for the task of predicting cosine and triangular waves and quantum spin dynamics. Finally, we analyze the dependency of its performance on the interaction strength among the qubits in numerical simulation and find that there is an appropriate range of the strength. This work provides a way to exploit complex quantum dynamics for learning temporal data

Reasons for Discontinuing Treatment with Sodium-Glucose Cotransporter 2 Inhibitors in Patients with Diabetes in Real-World Settings: The KAMOGAWA-A Study

Sodium-glucose cotransporter 2 inhibitors (SGLT2is) are a class of antidiabetic agents known to exert cardioprotective, renoprotective, and hypoglycemic effects. However, these agents have been associated with adverse effects, such as genital infection, volume depletion, hypoglycemia, and diabetic ketoacidosis, resulting in drug discontinuation. Herein, we aimed to determine the reasons for discontinuing treatment with SGLT2is among Japanese patients with diabetes. This retrospective cohort study enrolled 766 patients with diabetes who had initiated SGLT2is between January 2014 and September 2021. The follow-up period was 2 years from the initiation of the SGLT2is. Overall, 97 patients (12.7%) discontinued the SGLT2is during the follow-up period. The most common reasons for discontinuing the SGLT2is were frequent urination (19.6%), followed by genital infection (11.3%), improved glycemic control (10.6%), and renal dysfunction (8.2%). A comparison of the characteristics between the continuation and the discontinuation group was conducted, excluding those who discontinued the SGLT2is because of improved glycemic control. The patients in the discontinuation group (68 [55–75] years) were older than those in the continuation group (64 [53–71] years; p = 0.003). Importantly, we found no significant association between diabetes duration, diabetic control, renal function, or complications of diabetes in both groups. This real-world study revealed that frequent urination was the most common reason underlying SGLT2i discontinuation among Japanese patients with diabetes. To avoid discontinuation, precautions against various factors that may cause frequent urination must be implemented

Development of a simple and objective prognostication model for patients with advanced solid malignant tumors treated with immune checkpoint inhibitors: A pan-cancer analysis

Background Systemic therapy using immune checkpoint inhibitors (ICIs) has recently become prevalent in the treatment of patients with various types of advanced cancers; however, difficulties are still associated with predicting the outcomes of patients receiving ICIs due to heterogenous responses to these agents. Therefore, the objective of the present study was to develop a prognostic model for advanced cancer patients treated with ICIs. Patients and methods This study retrospectively analyzed the impact of clinical parameters on overall survival (OS) in 329 patients with several advanced solid malignant tumors who received systemic therapy using ICIs. Results The primary tumors of 329 patients were as follows: lung (n=89), kidney (n=70), urinary tract (n=52), skin (n=50), stomach (n=30), esophagus (n=21), and head and neck (n=17). Median OS after the introduction of ICIs was 17.3 months. Among the factors that correlated with OS in a univariate analysis, body mass index, C-reactive protein, hemoglobin, lymphocytes, and platelets were identified as independent predictors of OS in a multivariate analysis. Following the classification of patients into 3 groups based on positive numbers of these independent risk factors, median OS was not reached in the favorable risk group with 0 or 1 risk factor (n=76), 19.5 months in the intermediate-risk group with 2 or 3 risk factors (n=182), and 8 months in the poor risk group (n=71) with 4 or 5 risk factors. Conclusions Although this is a simple and objective model, it may be used as a reliable tool to predict the outcomes of advanced cancer patients receiving ICIs across multiple tumor types

Low Levels of Serum Tryptophan Underlie Skeletal Muscle Atrophy

Sarcopenia is a poor prognosis factor in some cancer patients, but little is known about the mechanisms by which malignant tumors cause skeletal muscle atrophy. Tryptophan metabolism mediated by indoleamine 2,3-dioxygenase is one of the most important amino acid changes associated with cancer progression. Herein, we demonstrate the relationship between skeletal muscles and low levels of tryptophan. A positive correlation was observed between the volume of skeletal muscles and serum tryptophan levels in patients with diffuse large B-cell lymphoma. Low levels of tryptophan reduced C2C12 myoblast cell proliferation and differentiation. Fiber diameters in the tibialis anterior of C57BL/6 mice fed a tryptophan-deficient diet were smaller than those in mice fed a standard diet. Metabolomics analysis revealed that tryptophan-deficient diet downregulated glycolysis in the gastrocnemius and upregulated the concentrations of amino acids associated with the tricarboxylic acid cycle. The weights and muscle fiber diameters of mice fed the tryptophan-deficient diet recovered after switching to the standard diet. Our data showed a critical role for tryptophan in regulating skeletal muscle mass. Thus, the tryptophan metabolism pathway may be a promising target for preventing or treating skeletal muscle atrophies