Pretransplant BKV-IgG serostatus and BKV-specific ELISPOT assays to predict BKV infection after kidney transplantation

IntroductionPolyomavirus (BKV) infection can lead to major complications and damage to the graft in kidney transplant recipients (KTRs). We investigated whether pretransplant BK serostatus and BK-specific cell-mediated immunity (CMI) predicts post-transplant BK infection.MethodsA total of 93 donor-recipient pairs who underwent kidney transplantation (KT) and 44 healthy controls were examined. Assessment of donor and recipient BKV serostatus and BKV-CMI in recipients was performed prior to transplantation using BKV-IgG ELISA and BKV-specific IFN-g ELISPOT assays against five BK viral antigens (LT, St, VP1, VP2, and VP3). BK viremia was diagnosed when blood BKV-DNA of 104 copies/mL or more was detected during follow-up periods. ResultsAnti-BKV IgG antibody was detected in 74 (79.6%) of 93 KTRs and in 68 (73.1%) of 93 KT donors. A greater percentage of KTRs who received allograft from donors with high levels of anti-BKV IgG had posttransplant BK viremia (+) than KTRs from donors with low anti-BKV IgG (25.5% [12/47] vs. 4.3% [2/46], respectively; P = 0.007). Pretransplant total BKV-ELISPOT results were lower in BK viremia (+) patients than in patients without viremia (-) 20.5 [range 9.9−63.6] vs. 72.0 [43.2 - 110.8]; P = 0. 027). The sensitivity and specificity of the total BKV-ELISPOT assay (cut-off ≤ 53 spots/3×105 cells) for prediction of posttransplant BK viremia were 71.4 (95% CI: 41.9–91.6) and 54.4 (42.8–65.7), respectively. The combination of high donor BKV-IgG, low recipient BKV-IgG, and low total BKV-ELISPOT results improved specificity to 91.1%.DiscussionOur study highlights the importance of pretransplant BKV-IgG serostatus and BKV-specific CMI in predicting posttransplant BKV infection in KTRs. The combination of high donor BKV-IgG, low recipient BKV-IgG, and low total BKV-ELISPOT results predicted BK viremia after KT. Pretransplant identification of patients at highrisk for BK viremia could enable timely interventions and improve clinical outcomes of KTRs

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Neutral Gauge Boson Contributions to the Dimuon Charge Asymmetry in B Decays

Recently, the D0 Collaboration measured the CP-violating like-sign dimuon charge asymmetry in neutral B decays, finding a 3.2sigma difference from the standard-model (SM) prediction. A non-SM charge asymmetry a_sl^s suggests a new-physics (NP) contribution to Bs-Bsbar mixing. In this case, in order to explain the measured value of a_sl^s within its 1sigma range, NP must be present in Gamma_12^s, the absorptive part of the mixing. In this paper, we examine whether such an explanation is possible in models with flavor-changing Z (ZFCNC) or Z' (Z'FCNC) gauge bosons. The models must also reproduce the measured values of the indirect CP asymmetry S_psi-phi in Bs -> J/psi phi, and Delta Gamma_s, the Bs-Bsbar width difference. We find that the ZFCNC model cannot reproduce the present measured values of S_psi-phi and a_sl^s within their 1sigma ranges. On the other hand, in the Z'FCNC model, the values of all three observables can be simultaneously reproduced.Comment: 18 pages, 7 figures, JHEP format. Some ZFCNC equations corrected, ZFCNC analysis redone, references added, conclusions unchange

i-Schools as a Natural Home for Digital Libraries Education

Given that digital libraries bring together technology, information, and the people using the information, it can easily be argued that i-schools should play a central role in educating DL professionals. This study examines the existing roles that i-schools play in DL education from two different vantage points: their offering of DL courses and their participation in a DL curriculum development project. In addition, we explore the potential to expand the iSchools Caucus by recruiting those schools that are active in DL education efforts (i.e., those that offer courses or participate in curriculum development) but are not yet members of the Caucus. based on the seven course syllabi available on the open Web, DL courses in the i-schools are further analyzed, in terms of the topics covered, the textbooks used, and the types of assignments used. From this analysis, we conclude that there is not yet a consensus on the topics covered or the assignment used in DL courses

Novel Structure for 2-Dimensional Liquid Crystal Phase Grating with a Vertically Aligned Fringe-Field Switching Mode

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Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients

Abstract Background Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH), which is the standard method for survival analysis, has several limitations. The purpose of our study was to improve survival prediction in patients with NSCLC by incorporating prognostic information from F-18 fluorodeoxyglucose positron emission tomography (FDG PET) images into a traditional survival prediction model using clinical data. Results The multimodal deep learning model showed the best performance, with a C-index and mean absolute error of 0.756 and 399 days under a five-fold cross-validation, respectively, followed by ResNet3D for PET (0.749 and 405 days) and CPH for clinical data (0.747 and 583 days). Conclusion The proposed deep learning-based integrative model combining the two modalities improved the survival prediction in patients with NSCLC

Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning

Unlike previous dialogue-based question-answering (QA) datasets, DREAM, multiple-choice Dialogue-based REAding comprehension exaMination dataset, requires a deep understanding of dialogue. Many problems require multi-sentence reasoning, whereas some require commonsense reasoning. However, most pre-trained language models (PTLMs) do not consider commonsense. In addition, because the maximum number of tokens that a language model (LM) can deal with is limited, the entire dialogue history cannot be included. The resulting information loss has an adverse effect on performance. To address these problems, we propose a Dialogue-based QA model with Common-sense Reasoning (DQACR), a language model that exploits Semantic Search and continual learning. We used Semantic Search to complement information loss from truncated dialogue. In addition, we used Semantic Search and continual learning to improve the PTLM’s commonsense reasoning. Our model achieves an improvement of approximately 1.5% over the baseline method and can thus facilitate QA-related tasks. It contributes toward not only dialogue-based QA tasks but also another form of QA datasets for future tasks