Human-machine interaction for unmanned surface systems

This research investigated the human-machine interaction (HMI) technologies for human-robot teams operating as unmanned surface systems (USS). An pilot role was found to be the most prevalent in the USS-related literature but additional human roles were determined to likely be necessary (e.g., Mission Specialist} though were not documented; interface needs have not yet been determined for any role. The human interfaces used by 67 Micro and Small X, Intermediate, Harbor, Fleet, and E,F,G-Class platforms were examined and it was determined that: i) the research literature does not well characterize the human roles present in unmanned surface systems, ii) domain complexity may necessitate increased automation of the robot platform for the human team, and iii) that unmanned surface vehicles likely lay on the human-machine interaction spectrum between unmanned ground vehicles and unmanned aerial vehicles. This work is expected to serve as a reference for future design and refinement of human interfaces for USSs and as a foundation for better understanding HMI in USSs

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future

Two New Cu(II) Coordination Polymers with 2D and 1D Frameworks that Show Reversible Structural Transformations Depending on the Present Solvents

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Myeloma Microenvironmental TIMP1 Induces the Invasive Phenotype in Fibroblasts to Modulate Disease Progression

Tissue inhibitors of metalloproteinases (TIMPs) are endogenous matrix metalloproteinase inhibitors. TIMP1 is produced by cancer cells and has pleiotropic activities. However, its role and source in multiple myeloma (MM) are unclear. Here, we evaluated TIMP1 protein and mRNA levels in bone marrow (BM) plasma cells and assessed the effects of TIMP1 expression on fibroblast invasive capacity using three-dimensional spheroid cell invasion assays. TIMP1 mRNA and protein levels were elevated when patients progressed from monoclonal gammopathy of undetermined significance or smouldering myeloma to MM. Furthermore, TIMP1 levels decreased at complete response and TIMP1 protein levels increased with higher international staging. TIMP1 mRNA levels were markedly higher in extramedullary plasmacytoma and MM with t(4;14). Overall survival and post-progression survival were significantly lower in MM patients with high TIMP1 protein. Recombinant TIMP1 did not directly affect MM cells but enhanced the invasive capacity of fibroblasts; this effect was suppressed by treatment with anti-TIMP1 antibodies. Fibroblasts supported myeloma cell invasion and expansion in extracellular matrix. Overall, these results suggested that MM-derived TIMP1 induces the invasive phenotype in fibroblasts and is involved in disease progression. Further studies are required to elucidate the specific roles of TIMP1 in MM and facilitate the development of novel therapies targeting the TIMP1 pathway

MYC Causes Multiple Myeloma Progression via Attenuating TP53-Induced MicroRNA-34 Expression

MicroRNAs (miRNAs and miRs) are small (19–25 base pairs) non-coding RNAs with the ability to modulate gene expression. Previously, we showed that the miR-34 family is downregulated in multiple myeloma (MM) as the cancer progressed. In this study, we aimed to clarify the mechanism of miRNA dysregulation in MM. We focused particularly on the interaction between MYC and the TP53-miR34 axis because there is a discrepancy between increased TP53 and decreased miR-34 expressions in MM. Using the nutlin-3 or Tet-on systems, we caused wild-type (WT) p53 protein accumulation in human MM cell lines (HMCLs) and observed upregulated miR-34 expression. Next, we found that treatment with an Myc inhibitor alone did not affect miR-34 expression levels, but when it was coupled with p53 accumulation, miR-34 expression increased. In contrast, forced MYC activation by the MYC-ER system reduced nutlin-3-induced miR-34 expression. We also observed that TP53 and MYC were negatively correlated with mature miR-34 expressions in the plasma cells of patients with MM. Our results suggest that MYC participates in the suppression of p53-dependent miRNA expressions. Because miRNA expression suppresses tumors, its inhibition leads to MM development and malignant transformation

Long Noncoding RNA PVT1 Is Regulated by Bromodomain Protein BRD4 in Multiple Myeloma and Is Associated with Disease Progression

Long noncoding RNAs (lncRNAs) are deregulated in human cancers and are associated with disease progression. Plasmacytoma Variant Translocation 1 (PVT1), a lncRNA, is located adjacent to the gene MYC, which has been linked to multiple myeloma (MM). PVT1 is expressed in MM and is associated with carcinogenesis. However, its role and regulation remain uncertain. We examined PVT1/MYC expression using real-time PCR in plasma cells purified from 59 monoclonal gammopathy of undetermined significance (MGUS) and 140 MM patients. The MM cell lines KMS11, KMS12PE, OPM2, and RPMI8226 were treated with JQ1, an MYC super-enhancer inhibitor, or MYC inhibitor 10058-F4. The expression levels of PVT1 and MYC were significantly higher in MM than in MGUS (p < 0.0001) and were positively correlated with disease progression (r = 0.394, p < 0.0001). JQ1 inhibited cell proliferation and decreased the expression levels of MYC and PVT1. However, 10054-F4 did not alter the expression level of PVT1. The positive correlation between MYC and PVT1 in patients, the synchronous downregulation of MYC and PVT1 by JQ1, and the lack of effect of the MYC inhibitor on PVT1 expression suggest that the expression of these two genes is co-regulated by a super-enhancer. Cooperative effects between these two genes may contribute to MM pathogenesis and progression