CompDP: A Framework for Simultaneous Subgraph Counting Under Connectivity Constraints

The subgraph counting problem computes the number of subgraphs of a given graph that satisfy some constraints. Among various constraints imposed on a graph, those regarding the connectivity of vertices, such as "these two vertices must be connected," have great importance since they are indispensable for determining various graph substructures, e.g., paths, Steiner trees, and rooted spanning forests. In this view, the subgraph counting problem under connectivity constraints is also important because counting such substructures often corresponds to measuring the importance of a vertex in network infrastructures. However, we must solve the subgraph counting problems multiple times to compute such an importance measure for every vertex. Conventionally, they are solved separately by constructing decision diagrams such as BDD and ZDD for each problem. However, even solving a single subgraph counting is a computationally hard task, preventing us from solving it multiple times in a reasonable time. In this paper, we propose a dynamic programming framework that simultaneously counts subgraphs for every vertex by focusing on similar connectivity constraints. Experimental results show that the proposed method solved multiple subgraph counting problems about 10-20 times faster than the existing approach for many problem settings

International Competition on Graph Counting Algorithms 2023

This paper reports on the details of the International Competition on Graph Counting Algorithms (ICGCA) held in 2023. The graph counting problem is to count the subgraphs satisfying specified constraints on a given graph. The problem belongs to #P-complete, a computationally tough class. Since many essential systems in modern society, e.g., infrastructure networks, are often represented as graphs, graph counting algorithms are a key technology to efficiently scan all the subgraphs representing the feasible states of the system. In the ICGCA, contestants were asked to count the paths on a graph under a length constraint. The benchmark set included 150 challenging instances, emphasizing graphs resembling infrastructure networks. Eleven solvers were submitted and ranked by the number of benchmarks correctly solved within a time limit. The winning solver, TLDC, was designed based on three fundamental approaches: backtracking search, dynamic programming, and model counting or #SAT (a counting version of Boolean satisfiability). Detailed analyses show that each approach has its own strengths, and one approach is unlikely to dominate the others. The codes and papers of the participating solvers are available: https://afsa.jp/icgca/.Comment: https://afsa.jp/icgca

Study on the reusability of fluorescent nuclear track detectors using optical bleaching

Fluorescent nuclear track detectors (FNTDs) based on Al${_2}$O${_3}$:C,Mg crystals are luminescent detectors that can be used for dosimetry and detection of charged particles and neutrons. These detectors can be utilised for imaging applications where a reasonably high track density, approximately of the order of 1 $\times$ $10^4$ tracks in an area of 100 $\times$ 100 $\mu$m$^2$, is required. To investigate the reusability of FNTDs for imaging applications, we present an approach to perform optical bleaching under the required track density conditions. The reusability was assessed through seven irradiation-bleaching cycles. For the irradiation, the studied FNTD was exposed to alpha-particles from an $^{241}$Am radioactive source. The optical bleaching was performed by means of ultraviolet laser light with a wavelength of 355 nm. Three dedicated regions on a single FNTD with different accumulated track densities and bleaching conditions were investigated. After every irradiation-bleaching cycle, signal-to-noise ratio was calculated to evaluate FNTD performance. It is concluded that FNTDs can be reused at least seven times for applications where accumulation of a high track density is required

Differential gene expression profiles in neurons generated from lymphoblastoid B-cell line-derived iPS cells from monozygotic twin cases with treatment-resistant schizophrenia and discordant responses to clozapine

Schizophrenia is a chronic psychiatric disorder with complex genetic and environmental origins. While many antipsychotics have been demonstrated as effective in the treatment of schizophrenia, a substantial number of schizophrenia patients are partially or fully unresponsive to the treatment. Clozapine is the most effective antipsychotic drug for treatment-resistant schizophrenia; however, clozapine has rare but serious side-effects. Furthermore, there is inter-individual variability in the drug response to clozapine treatment. Therefore, the identification of the molecular mechanisms underlying the action of clozapine and drug response predictors is imperative. In the present study, we focused on a pair of monozygotic twin cases with treatment-resistant schizophrenia, in which one twin responded well to clozapine treatment and the other twin did not. Using induced pluripotent stem (iPS) cell-based technology, we generated neurons from iPS cells derived from these patients and subsequently performed RNA-sequencing to compare the transcriptome profiles of the mock or clozapine-treated neurons. Although, these iPS cells similarly differentiated into neurons, several genes encoding homophilic cell adhesion molecules, such as protocadherin genes, showed differential expression patterns between these two patients. These results, which contribute to the current understanding of the molecular mechanisms of clozapine action, establish a new strategy for the use of monozygotic twin studies in schizophrenia research

Maternal septic shock due to Acinetobacter lwoffii infection: a case report

The incidence of Acinetobacter infections has increased in recent years. Acinetobacter infections are resistant to most antibiotics and can be found in hospitalized patients. Pregnancies complicated by severe sepsis or septic shock are associated with a higher rate of preterm labor and delivery, fetal infection, and operative delivery. This case report describes septic shock due to Acinetobacter lwoffii infection in the 31st week of gestation. A 47-year-old woman, with a gestation of 31 weeks and one day, presented with a fever, and signs of bacterial infection on laboratory tests. Although the patient was started on tazobactam/piperacillin, she went into septic shock, and was transferred to our hospital. Cesarean section was performed at a gestation of 31 weeks and 4 days because of severe maternal pneumonia and non-reassuring fetal status. A. lwoffii was detected in blood cultures collected at the previous hospital, and susceptibility to piperacillin and meropenem to A. lwoffii was confirmed. The pneumonia responded to antibiotic treatment and there were no findings of infection in the neonate. Maternal sepsis is an infrequent but important complication, causing significant maternal and fetal morbidity and fetal and neonatal mortality; therefore, early antibiotic therapy is required to improve the clinical outcome

Performance of anti-SARS-CoV-2 antibody testing in asymptomatic or mild COVID-19 patients: A retrospective study in outbreak on a cruise ship

Objectives: A few studies on antibody testing have focused on asymptomatic or mild coronavirus disease 2019 (COVID-19) patients with low initial anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody responses. Anti-SARS-CoV-2 antibody-testing performance was evaluated using blood samples from asymptomatic or mild COVID-19 patients.Methods: Blood samples were collected from 143 COVID-19 patients during an outbreak on a cruise ship 3 weeks after diagnosis. Simultaneously, a follow-up SARS-CoV-2 genetic test was performed. Samples stored before the COVID-19 pandemic were also used to evaluate the lateral flow immunochromatographic assay (LFA) and electrochemiluminescence immunoassay (ECLIA). Titers of anti-SARS-CoV-2 IgM and IgG antibodies against the nucleocapsid and spike proteins were measured using the enzyme-linked immunosorbent assay to confirm which antibodies were influenced on LFA- and ECLIA- false-negative result in crew-member samples.Results: Sensitivity, specificity, positive-predictive, and negative-predictive values of LFA-detected IgM antibodies were 0.231, 1.000, 1.000, and 0.613, respectively; those of LFA-detected IgG antibodies were 0.483, 0.989, 0.972, and 0.601, respectively; and those of ECLIA-detected total antibodies were 0.783, 1.000, 1.000, and 0.848, respectively. All antibody titers measured using ELISA were significantly lower in blood samples with negative results than in those with positive results in both LFA and ECLIA. In the patients with negative results from the follow-up genetic testing, IgM-, IgG-, and total-antibody positivity rates were 22.9%, 47.6%, and 72.4%, respectively.Conclusions: These findings suggest that anti-SARS-CoV-2 antibody testing has lower performance in asymptomatic or mild COVID-19 patients than required in the guidelines

Detection of SARS-CoV-2 using qRT-PCR in saliva obtained from asymptomatic or mild COVID-19 patients, comparative analysis with matched nasopharyngeal samples

Objectives: The accurate detection of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) is essential for the diagnosis of coronavirus disease 2019 (COVID-19). We compared the quantitative RT-PCR results between nasopharyngeal swabs and saliva specimens.Methods: A COVID-19 outbreak occurred on a cruise ship at Nagasaki port, Japan. We obtained 123 nasopharyngeal swabs and saliva each from asymptomatic or mild patients in the late phase of infection.Results: The intervals from the diagnosis to the sampling were 25.5 days for nasopharyngeal swabs and 28.9 days for saliva. The positive rate was 19.5% (24/123) for nasopharyngeal swabs and 38.2% (47/123) for saliva (P = 0.48). The quantified viral copies (mean ± SEM copies/5 μl) were 9.3±2.6 in nasopharyngeal swabs and 920±850 in saliva (P = 0.0006).Conclusions: The advantages of saliva specimens include positive rate improvement and accurate viral load detection. Saliva may be used as a reliable sample for SARS-CoV-2 detection

Practical Frank–Wolfe Method with Decision Diagrams for Computing Wardrop Equilibrium of Combinatorial Congestion Games

Computation of equilibria for congestion games has been an important research subject. In many realistic scenarios, each strategy of congestion games is given by a combination of elements that satisfies certain constraints; such games are called combinatorial congestion games. For example, given a road network with some toll roads, each strategy of routing games is a path (a combination of edges) whose total toll satisfies a certain budget constraint. Generally, given a ground set of n elements, the set of all such strategies, called the strategy set, can be large exponentially in n, and it often has complicated structures; these issues make equilibrium computation very hard. In this paper, we propose a practical algorithm for such hard equilibrium computation problems. We use data structures, called zero-suppressed binary decision diagrams (ZDDs), to compactly represent strategy sets, and we develop a Frank–Wolfe-style iterative equilibrium computation algorithm whose per-iteration complexity is linear in the size of the ZDD representation. We prove that an ϵ-approximate Wardrop equilibrium can be computed in O(poly(n)/ϵ) iterations, and we improve the result to O(poly(n) log ϵ−1) for some special cases. Experiments confirm the practical utility of our method