Miracle-dz/UniFAR-FTUs huggingface dataset:
A Four-gene Decision Tree Signature Classification of Triple-negative Breast Cancer: Implications for Targeted Therapeutics

The molecular complexity of triple-negative breast cancers (TNBCs) provides a challenge for patient management. We set out to characterize this heterogeneous disease by combining transcriptomics and genomics data, with the aim of revealing convergent pathway dependencies with the potential for treatment intervention. A Bayesian algorithm was used to integrate molecular profiles in two TNBC cohorts, followed by validation using five independent cohorts (n = 1,168), including three clinical trials. A four-gene decision tree signature was identified, which robustly classified TNBCs into six subtypes. All four genes in the signature (EXO1, TP53BP2, FOXM1, and RSU1) are associated with either genomic instability, malignant growth, or treatment response. One of the six subtypes, MC6, encompassed the largest proportion of tumors (∼50%) in early diagnosed TNBCs. In TNBC patients with metastatic disease, the MC6 proportion was reduced to 25%, and was independently associated with a higher response rate to platinum-based chemotherapy. In TNBC cell line data, platinum sensitivity was recapitulated, and a sensitivity to the inhibition of the phosphatase PPM1D was revealed. Molecularly, MC6-TNBCs displayed high levels of telomeric allelic imbalances, enrichment of CD4+ and CD8+ immune signatures, and reduced expression of genes negatively regulating the MAPK signaling pathway. These observations suggest that our integrative classification approach may identify TNBC patients with discernible and theoretically pharmacologically tractable features that merit further studies in prospective trials.
Erp-based BCI Training for children with ADHD: Motivations and Trial Design

Neurofeedback is a promising treatment for children with ADHD. However, although several studies have investigated its efficacy, the effectiveness of current approaches is still debated. This might be partly due to the biomarkers that are used and might not be enough specific of ADHD core symptoms. We here motivate the evaluation of P300-based BCI training as an alternative. We review the arguments in favor of this approach and reveal the design of an ongoing randomized and controlled clinical trial. Essentially, the P300 EEG evoked response is affected in ADHD. It does reflect selective attention and action selection. It is modulated by successful pharmacological intervention in ADHD. And it can be used in BCI for training purposes, through varied and engaging games. Interestingly, these games enable the use of precise instructions as well as multi-level feedbacks to favor learning. Finally, this new type of Neurofeedback allows for instantiating a highly specific control condition that is compatible with a double-blind design.
Optimizing and evaluating biomarker combinations as trial‐level general surrogates

We extend the method proposed in a recent work by the Authors for trial‐level general surrogate evaluation to allow combinations of biomarkers and provide a procedure for finding the “best” combination of biomarkers based on the absolute prediction error summary of surrogate quality. We use a nonparametric Bayesian model that allows us to select an optimal subset of biomarkers without having to consider a large number of explicit model specifications for that subset. This dramatically reduces the number of model comparisons needed. Given the model's flexibility, complex nonlinear relationships can be fit when enough data are available. We evaluate the operating characteristics of our proposed method in simulations designed to be similar to our motivating example. We use our method to compare and evaluate combinations of biomarkers as trial‐level general surrogates for the pentavalent rotavirus vaccine RotaTeq™ (RV5) (Merck & Co, Inc, Kenilworth, New Jersey, USA), finding that the same single biomarker identified in our previously published analysis is likely the optimal subset.
Structure of the Ambrosia Beetle (Coleoptera: Curculionidae) Mycangia Revealed Through Micro-Computed Tomography

Abstract Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae and Platypodinae) rely on a symbiosis with fungi for their nutrition. Symbiotic fungi are preserved and transported in specialized storage structures called mycangia. Although pivotal in the symbiosis, mycangia have been notoriously difficult to study, given their minute size and membranous structure. We compared the application of novel visualization methods for the study of mycangia, namely micro-computed tomography (micro-CT) and laser ablation tomography (LATscan) with traditional paraffin sectioning. Micro-CT scanning has shown the greatest promise in new organ discovery, while sectioning remains the only method with sufficient resolution for cellular visualization. All three common types of mycangia (oral, mesonotal, and pronotal) were successfully visualized and presented for different species of ambrosia beetles: Ambrosiodmus minor (Stebbing) 1909, Euplatypus compositus (Say) 1823, Premnobius cavipennis Eichhoff 1878, Scolytoplatypus raja Blandford 1893, Xylosandrus crassiusculus (Motschulsky) 1866 and X. amputatus (Blandford) 1894. A reconstruction of the mycangium and the surrounding musculature in X. amputatus is also presented. The advantages of micro-CT compared to the previously commonly used microtome sectioning include the easy visualization and recording of three-dimensional structures, their position in reference to other internal structures, the ability to distinguish natural aberrations from technical artifacts, and the unprecedented visualizations of the anatomic context of mycangia enabled by the integrated software.
Auction-based competition of hybrid small cells for dropped macrocell users

We propose an auction-based beamforming and user association algorithm for a wireless network consisting of a macrocell and multiple small cell access points (SCAs). The SCAs compete for serving the macrocell base station (MBS) users (MUs). The corresponding user association problem is solved by the proposed bid-wait auction method. The authors considered two scenarios. In the first scenario, the MBS initially admits the largest possible set of MUs that it can serve simultaneously and then auctions off the remaining MUs to the SCAs, who are willing to admit guest users in addition to their commitments to serve their own host users. This problem is solved by the proposed forward bid-wait auction. In the second scenario, the MBS aims to offload as many MUs as possible to the SCAs and then admits the largest possible set of remaining MUs. This is solved by the proposed backward bid-wait auction. The proposed algorithms provide a solution that is very close to the optimum solution obtained by using a centralised global optimisation.
An Occupational Health Education Program for Thai Farmers Exposed to Chlorpyrifos

The majority of the farmers in this sample of Thai farmers did not use sufficient self-protective behaviors when using the organophosphate insecticide chlorpyrifos. Most were directly exposed to chlorpyrifos, potentially leading to illnesses. The aim of this study was to improve safety behaviors when using chlorpyrifos by an occupational health education program. A controlled trial (n = 70) of an occupational health education program was undertaken in rural Thailand prior to the occupational health education program. There were no differences in behavior between experimental and control groups. Completion of the program led to a significant improvement in safe working practices and in the amount of the metabolite. However, significant differences were noticed after participating in the occupational health education program on both safety behaviors and the amount of the metabolite 3,5,6-trichloro-2-pyridinol present in urine.
Cutting Edge: IL-1α and Not IL-1β Drives IL-1R1–Dependent Neonatal Murine Sepsis Lethality

Sepsis disproportionately affects the very old and the very young. IL-1 signaling is important in innate host defense but may also play a deleterious role in acute inflammatory conditions (including sepsis) by promulgating life-threatening inflammation. IL-1 signaling is mediated by two distinct ligands: IL-1α and IL-1β, both acting on a common receptor (IL-1R1). IL-1R1 targeting has not reduced adult human sepsis mortality despite biologic plausibility. Because the specific role of IL-1α or IL-1β in sepsis survival is unknown in any age group and the role of IL-1 signaling remains unknown in neonates, we studied the role of IL-1 signaling, including the impact of IL-1α and IL-1β, on neonatal murine sepsis survival. IL-1 signaling augments the late plasma inflammatory response to sepsis. IL-1α and not IL-1β is the critical mediator of sepsis mortality, likely because of paracrine actions within the tissue. These data do not support targeting IL-1 signaling in neonates.
Viroid research and its significance for RNA technology and basic biochemistry

Abstract Viroids were described 47 years ago as the smallest RNA molecules capable of infecting plants and autonomously self-replicating without an encoded protein. Work on viroids initiated the development of a number of innovative methods. Novel chromatographic and gelelectrophoretic methods were developed for the purification and characterization of viroids; these methods were later used in molecular biology, gene technology and in prion research. Theoretical and experimental studies of RNA folding demonstrated the general biological importance of metastable structures, and nuclear magnetic resonance spectroscopy of viroid RNA showed the partially covalent nature of hydrogen bonds in biological macromolecules. RNA biochemistry and molecular biology profited from viroid research, such as in the detection of RNA as template of DNA-dependent polymerases and in mechanisms of gene silencing. Viroids, the first circular RNA detected in nature, are important for studies on the much wider spectrum of circular RNAs and other non-coding RNAs.
An integrated framework for identification of effective and synergistic anti-cancer drug combinations

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The K -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.
Agility in crisis management information systems requires an iterative and flexible approach to assessing ethical, legal and social issues

This paper focuses on the assessment of ethical, legal and social issues (ELSI) in relation to agile information systems in the domain of crisis management. The authors analyse the differing needs of a move from a traditional approach to the development of information systems to an agile approach, which offers flexibility, adaptability and responds to the needs of users as the system develops. In turn, the authors argue that this development requires greater flexibility and an iterative approach to assessing ELSI. The authors provide an example from the Horizon 2020 EU-funded project iTRACK (Integrated system for real-time TRACKing and collective intelligence in civilian humanitarian missions) to exemplify this move to an iterative approach in practice, drawing on the process of undertaking an ethical and privacy impact assessment for the purpose of this project.
Beliefs About the Causes of Hypertension and Associations With Pro-Health Behaviors

Objective: To describe behavioral and genetic beliefs about developing hypertension (HTN) by sociodemographic factors and self-reported HTN status, and among those with a history of HTN, evaluate associations between HTN-related causal beliefs and behavior change attempts. Method: Data from the 2014 Health Information National Trends Survey were evaluated. HTN causal beliefs questions included (a) “How much do you think health behaviors like diet, exercise, and smoking determine whether or not a person will develop high blood pressure/HTN? ”; and (b) “How much do you think genetics, that is characteristics passed from one generation to the next, determine whether or not a person will develop high blood pressure/HTN? ” Multivariate logistic regressions evaluated associations between HTN causal beliefs and behavior change attempts including diet, exercise, and weight management. Results: Approximately 1,602 out of 3,555 respondents with nonmissing data (33% weighted) reported ever having HTN. In logistic regression models, results show that the more strongly people believed in the impact of behavior on developing HTN, the higher their odds for behavior change attempts. Beliefs about genetic causes of HTN were not associated with behavior change attempts. Women had higher odds of attempts to increase fruit and vegetable intake, reduce soda intake, and lose weight compared to men. Blacks and Hispanics were significantly more likely than Whites to report attempts to lose weight. Conclusions: Beliefs about behavioral causes of HTN, but not genetic, were associated with behavior change attempts. Health messages that incorporate behavioral beliefs and sociodemographic factors may enhance future prohealth behavior changes.
Assessing command and control teams' performance and agility

Crisis response organizations and military units must be agile and able to adapt to dynamic situations. The ability to adapt includes command and control agility, organizational adaptability and individual adaptability. An exploratory study of these adaptability traits were undertaken during a naval exercise. The exercise scenario was designed to progressively become more challenging, suggesting decreased performance and adaptability over time. The study objectives were to develop a data collection approach for adaptability traits and to investigate association between these traits and performance. Data collected from four command teams were evaluated in terms of response rate and item variation. Principal component analysis was used to explore latent structures and relationships. The results indicate acceptable survey response rates and trends showing a decrease in organizational adaptability and C2 agility over time while individual adaptability increased. The analysis also identified five partially independent components in the latent structure.
“Cutting Down on Sugar” by Non-Dieting Young Women: An Impact on Diet Quality on Weekdays and the Weekend

Restricted sugar intake is an important part of a healthy lifestyle and may contribute to the prevention of diet-related diseases. The aim of the study was to investigate whether declared sugar restriction is reflected in actual sucrose intake and diet quality of young non-dieting women, with differentiation between the weekend and weekdays. A convenient sample of 192 non-dieting women aged 20–30 years old was recruited. The sample was divided into two groups based on each woman’s declaration: “restricting sugar” (RS, n = 76) and “not restricting sugar” (nRS, n = 116). Comparisons between groups were made separately for seven days of the week, five weekdays and two weekend days without and with an adjustment for 2000 kcal of energy. Relative differences (RD, in %) in mean diet nutritional values between groups were calculated, where RD (%) = (RS − nRS) × 100/nRS, and were verified with a two-tailed t-test. In the seven-day diet of the RS group, in comparison to the nRS group, a lower daily intake of sucrose (by 22%) and MUFA (by 8%) and a higher content of most nutrients (by 7–38%) was found. No significant differences were observed in energy intake between RS and nRS women over the course of seven days, including weekdays and the weekend. Daily intake for the weekend, in comparison to weekdays, in RC and nRC women was higher with respect to energy (by 530 and 512 kcal, i.e., 37% and 34%, respectively), sucrose (44% and 23%, respectively), and most nutrients (by 17–98% and 16–42%, respectively). However, after the adjustment for 2000 kcal of energy, the daily intake of most nutrients in the weekend was lower (by 6–30% and 3–27%, in RS and nRS groups, respectively), than during weekdays. The intention to restrict sucrose has improved diet quality by decreasing sucrose intake and increasing the content of most nutrients, but had no effect on energy intake throughout the week. The quality of the women’s diet during the weekend was compromised, regardless of restricting or not restricting sugar. Encouragement to restrict sugar intake throughout the week and control the food intake during the weekend may be an effective strategy for young women to maintain a healthy diet.
Hybrid Thermodynamic Control Systems with Phase Transitions

Abstract This paper describes a modelling framework for the description of thermodynamic systems with phase transitions. It is shown that these systems can be conveniently described within the framework of (regional) hybrid systems. A hybrid optimal control problem for a particular class of van der Waals type systems with the liquid-gas phase transition is analized in detail.
Sensing Time Optimisation and Sensor Selection in Multi-channel Multi-antenna Wireless Cognitive Sensor Networks

: Energy efficiency of cooperative spectrum sensing is a critical issue in wireless cognitive sensor networks (CSNs). However, sensor nodes consume high energy to support cognitive radio (CR) functionalities such as channel sensing. In this study, the energy-efficient cooperative spectrum sensing in multi-channel multi-antenna CSNs is investigated. First, a multi-channel CSN is considered to obtain sensing sensors and sensing time for each band in a CR system. The criteria is to minimise energy consumption of cooperative spectrum sensing under the global probabilities of detection and false alarm constraints. The authors employ a convex optimisation method to obtain optimal sensing time and sensing nodes for each band. Next, their analysis is extended into multi-antenna sensors, which is a promising solution instead of using more sensors. In multi-antenna cooperative spectrum sensing, diversity helps sensors behave virtually the same as multi-sensors systems. Then, the problem of sensor selection and also setting the sensing time for each band is solved based on the convex optimisation framework. Simulation results highlight that in their proposed algorithms a significant energy is saved compared with the baseline methods with equal sensing time in all bands.
IMU-Based Virtual Road Profile Sensor for Vehicle Localization

A road profile can be a good reference feature for vehicle localization when a Global Positioning System signal is unavailable. However, cost effective and compact devices measuring road profiles are not available for production vehicles. This paper presents a longitudinal road profile estimation method as a virtual sensor for vehicle localization without using bulky and expensive sensor systems. An inertial measurement unit installed in the vehicle provides filtered signals of the vehicle’s responses to the longitudinal road profile. A disturbance observer was designed to extract the characteristic features of the road profile from the signals measured by the inertial measurement unit. Design synthesis based on a Kalman filter was used for the observer design. A nonlinear damper is explicitly considered to improve the estimation accuracy. Virtual measurement signals are introduced for observability. The suggested methodology estimates the road profile that is sufficiently accurate for localization. Based on the estimated longitudinal road profile, we generated spectrogram plots as the features for localization. The localization is realized by matching the spectrogram plot with pre-indexed plots. The localization using the estimated road profile shows a few meters accuracy, suggesting a possible road profile estimation method as an alternative sensor for vehicle localization.
Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction

In apprenticeship learning (AL), agents learn by watching or acquiring human demonstrations on some tasks of interest. However, the lack of human demonstrations in novel tasks where they may not be a human expert yet, or when it is too expensive and/or time consuming to acquire human demonstrations motivated a new algorithm: Apprenticeship bootstrapping (ABS). The basic idea is to learn from demonstrations on sub-tasks then autonomously bootstrap a model on the main, more complex, task. The original ABS used inverse reinforcement learning (ABS-IRL). However, the approach is not suitable for continuous action spaces. In this paper, we propose ABS via Deep learning (ABS-DL). It is first validated in a simulation environment on an aerial and ground coordination scenario, where an Unmanned Aerial Vehicle (UAV) is required to maintain three Unmanned Ground Vehicles (UGVs) within a field of view of the UAV 's camera (FoV). Moving a machine learning algorithm from a simulation environment to an actual physical platform is challenging because `mistakes' made by the algorithm while learning could lead to the damage of the platform. We then take this extra step to test the algorithm in a physical environment. We propose a safety-net as a protection layer to ensure that the autonomy of the algorithm in learning does not compromise the safety of the platform. The tests of ABS-DL in the real environment can guarantee a damage-free, collision avoidance behaviour of autonomous bodies. The results show that performance of the proposed approach is comparable to that of a human, and competitive to the traditional approach using expert demonstrations performed on the composite task. The proposed safety-net approach demonstrates its advantages when it enables the UAV to operate more safely under the control of the ABS-DL algorithm.