Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of ABCDE bundle implementation on patient outcomes: A nationwide cohort study.

BACKGROUND The ABCDE bundle is a set of evidence-based practices to systematically reduce the risks of sedation, delirium, and immobility in intensive care patients. Implementing the bundle improves clinical outcome. AIMS AND OBJECTIVES To investigate the association between patient outcomes and compliance with bundle components ABC (analgosedation algorithms), D (delirium protocol), and E (early mobilization protocol). DESIGN A Spanish multicentre cohort study of adult patients receiving invasive mechanical ventilation (IMV) for ≥48 h until extubation. METHODS The primary outcome was pain level, cooperation to permit Medical Research Council Scale administration, patient days of delirium, and mobility. The secondary outcome was cumulative drug dosing by IMV days. Tertiary outcomes (ICU days, IMV days, bed rest days, ICU mortality, ICUAW) and independent variables (analgosedation, delirium, early mobilization protocols) were also studied. RESULTS Data were collected from 605 patients in 80 ICUs and 5214 patient days with IMV. Two-thirds of the ICUs studied applied no protocols. Pain was not assessed on 83.6% of patient days. Patient cooperation made scale administration feasible on 20.7% of days. Delirium and immobility were found on 4.2% and 69.9% of days, respectively. Patients had shorter stays in ICUs with bundle protocols and fewer days of IMV in ICUs with delirium and mobilization bundle components (P = 0.006 and P = 0.03, respectively). Analgosedation protocols were associated with more opioid dosing (P = 0.02), and delirium and early mobilization protocols with more propofol (P = 0.001), dexmedetomidine (P = 0.001), and lower benzodiazepine dosing (P = 0.008). CONCLUSIONS The implementation rate of ABCDE bundle components was very low in our Spanish setting, but when implemented, patients had a shorter ICU stay, more analgesia dosing, and lighter sedation. RELEVANCE TO CLINICAL PRACTICE Applying some but not all the bundle components, there is increased analgesia and light sedation drug use, decreased benzodiazepines, and increased patient cooperation and mobility, resulting in a shorter ICU stay and fewer days of IMV.
Using a Virtual Platform for Conducting Grandfamily Research

Supplemental digital content is available in the text. Background Because of the COVID-19 pandemic, our research team quickly pivoted from planned face-to-face interaction with participants to virtual interactions. During this transition, we discovered invaluable new practices for conducting research remotely, including collecting physiological data. Objective Our objective was to describe the methodological challenges we encountered when conducting this research virtually with grandparent–grandchild dyads and describe the strategies we developed to overcome those challenges. Of primary focus were procedures for finger-stick blood sample collection. Of secondary focus were procedures for conducting virtual-based research with older adults and across multiple generations. Methods During an observational study utilizing a virtual platform with 11 grandparent–grandchild dyads (mean age: 64.2 ± 5.0 years for grandparents and 9.3 ± 1.9 years for grandchildren), we documented lessons learned (based on the discussion within our team and feedback we have received from participants) that could be applicable for other, similar research endeavors. Results We found several challenges in collecting blood samples, including staff were unfamiliar with providing online instruction and participants needed to develop familiarity with the blood sample collection process (without having in-person assistance), and we had to develop methods for delivering blood sample collection kits to participants safely. We also found that it took longer than expected to run procedures—a challenge that might be encountered when conducting any type of dyadic research utilizing a virtual platform—particularly involving older adults and across multiple generations. In addition, it was challenging to keep child participants engaged in the virtual interactions. We document how we employed targeted strategies to overcome those obstacles. Recommendations for strategies from our team include to provide comprehensive and clear instruction/materials on blood sampling procedure, offer generous support throughout the blood collection process, be prepared to divide study visits into more than one session as needed, proactively anticipate potential roadblocks, and carefully consider the participants’ developmental stages and attention span. Discussion The insights we gained will help inform future research with grandparent–grandchild dyads in remote or rural populations utilizing virtual platforms.
Dexamethasone Induces Apoptosis of Embryonic Palatal Mesenchymal Cells Through the GATA-6/Bone Morphogenetic Protein-2/p38 MAPK Pathway

Abstract: Exposure to dexamethasone (DEX) causes cleft palate at high rates. Our previous studies proved that GATA binding protein 6 (GATA-6)/bone morphogenetic protein-2 (BMP-2) mediated apoptosis is related to DEX-induced cleft palate, but the specific mechanism is still unclear. The goal of this research was to understand the mechanism of apoptosis in cleft palate formation induced by DEX. Palatal mesenchymal cells from mouse embryos on embryonic day 13 were isolated as the experimental group, GATA-6 was silenced by GATA-6 small interfering Ribonucleic Acid (RNA). Cell Counting Kit-8, flow cytometry and Western Blot were applied to detect cell proliferation ability, cell cycle, the proportion of apoptotic cells, and the expression of apoptosis- related proteins of GATA-6 knockdown palatal mesenchymal cells. Further proteins on the BMP-2/Mitogen-activated protein kinase (MAPK) pathways were detected using Western Blot. T results showed that knockdown of GATA-6 by siRNA significantly decreased cell proliferation and increased the expression of apoptosis-related proteins. Bone morphogenetic protein-2/P38 mitogen Activated protein kinase (P38 MARK) pathway proteins decreased significantly among the GATA-6 knockdown group, DEX-cleft palate group and control +DEX groups. The results indicated that the GATA-6/BMP-2/P38 MAPK athway was involved in the apoptosis caused by GATA-6 silencing, which may be the possible mechanism of DEX inducing cleft palate.
Theoretical and Experimental Study of the Impact of In-boiler Swirl Number on the Thermal Deviation in a Reheater

The basic conception and calculation method of an in boiler swirl number SW is described. The latter is actually characterized by a ratio of the gas flow swirling and rising speed in a boiler furnace with its magnitude exercising a direct influence on the thermal deviation in high temperature heating surfaces at a horizontal gas pass. The authors analyzed and discussed the impact of secondary air distribution mode, boiler load and the ratio of primary and secondary air on SW. Moreover, through a specific boiler an experimental research is conducted of the impact of SW variation on the thermal deviation in a reheater. The results of theoretical analyses and tests indicate that under identical loads the distribution mode of secondary air has a significant influence on SW with SW assuming a minimum value under the condition of a uniform air distribution. The maximum thermal deviation factor of the reheater will increase with the increase in SW value. Under the same secondary air distribution mode SW will increase with a reduction in boiler load. The maximum thermal deviation factor of the reheater will increase with an increase in SW value.
Flexible use of post-saccadic visual feedback in oculomotor learning

Saccadic eye movements bring objects of interest onto our fovea. These gaze shifts are essential for visual perception of our environment and the interaction with the objects within it. They precede our actions and are thus modulated by current goals. It is assumed that saccadic adaptation, a recalibration process that restores saccade accuracy in case of error, is mainly based on an implicit comparison of expected and actual post-saccadic position of the target on the retina. However, there is increasing evidence that task demands modulate saccade adaptation and that errors in task performance may be sufficient to induce changes to saccade amplitude. We investigated if human participants are able to flexibly use different information sources within the post-saccadic visual feedback in task-dependent fashion. Using intra-saccadic manipulation of the visual input, participants were either presented with congruent post-saccadic information, indicating the saccade target unambiguously, or incongruent post-saccadic information, creating conflict between two possible target objects. Using different task instructions, we found that participants were able to modify their saccade behavior such that they achieved the goal of the task. They succeeded in decreasing saccade gain or maintaining it, depending on what was necessary for the task, irrespective of whether the post-saccadic feedback was congruent or incongruent. It appears that action intentions prime task-relevant feature dimensions and thereby facilitated the selection of the relevant information within the post-saccadic image. Thus, participants use post-saccadic feedback flexibly, depending on their intentions and pending actions.
Ecuadorian Highland Quichua and the Lost Languages of the Northern Andes

In the early Spanish colonial period, the territory of Quito underwent a large-scale shift from multiple pre-Incan languages to a Quechuan language first introduced by the Incas and then adopted by the Spanish as the general language of colonial society. This article considers the evidence that these languages left linguistic effects that can still be detected in modern Ecuadorian Highland Quichua, contributing a large lexicon of non-Quechuan toponyms and influencing the development of unique morphosyntactic and phonological features that contrast with Peruvian Quechuan languages. It is concluded that there is considerable evidence that the toponymic lexicon of Quichua includes large numbers of cognate forms with modern Barbacoan languages and that many of the unique linguistic features of Ecuadorian Highland Quichua are similar to features of the Barbacoan languages, suggesting historical Barbacoan influence.
Augmented Human Intelligence for Decision Making in Maintenance Risk Taking Tasks using Reinforcement Learning

Daily decision-making circumstances often include risk and uncertainty, and the decision-maker’s risk preference constantly influences the course of action taken. Modeling how individuals make decisions in real-world situations remains a key challenge, and the use of machine learning techniques to model and augment individuals’ decision-making processes has garnered little attention. In this paper, we propose a new framework to evaluate the feasibility of inferring risk attitudes from human behavior modeled using the Reinforcement Learning (RL) model. The RL-based model framework is then generalized by including a situational or contextual variable that is indicative of cognitive biases observed in human decision-making, using an equipment maintenance scenario as a case study. Interestingly, when compared to human participants with working experience, the mean performance of a trained RL agent bears some semblance to the surveyed human participants. Riding on such strong empirical results, we also discover that the risk-seeking group of human participants are 9 times more likely to engage in risk-seeking associated actions. Besides, our exploratory simulations show that near optimal performance can be consistently achieved with several enhancements to state-of-the-art advantage actor-critic RL algorithms under the situational context of BUSY and IDLE. Overall, our proposed RL-based model framework can categorically infer the user-defined human risk attitude, and the trained RL agent demonstrates its rational decision-making prowess by outperforming the majority of human participants.
EEG activity under different speeds and prompt types in motor imagery and motor execution

Motor imagery has been widely employed in noninvasive brain-computer interface (BCI) systems. Maintaining and manipulating specific motor imagery is essential to these systems, but motor imagery is usually performed using individual strategies and thus can be quite challenging for some healthy and disabled users. Kinematic movement parameters, such as the clenching speed, are important factors affecting the modulation strength of brain rhythm. Intuitively, prompts containing more detailed instruction related to the task could inform the users more precisely and vividly and hopefully could alleviate the challenge of motor imagination. In this sense, we explored the electroencephalography (EEG) characteristics of performing motor imagery and motor execution under different motion speeds and different prompt types. Ten subjects were invited to participate in motor imagery (MI) and motor execution (ME) tasks of the left hand and right hand at seven different speeds with either audio prompt or GIF prompt. A hypothesis of a linear relationship between the modulation of alpha (8-12 Hz) and beta band (18-28 Hz) ERD/ERS and motion speeds were tested. The experimental results showed that motion speeds were not linearly correlated to relative EEG changes at the alpha and beta bands in the sensorimotor cortex. Different prompts are found to have an effect on EEG activities in alpha and beta bands across the scalp, especially at occipital and frontal areas. Compared with audio prompts, subjects performed tasks with significantly improved accuracy with the assistance of GIF instructions. The current results could provide meaningful guidance for a better MI-based BCI system design as well as the MI classification enhancement.
Is Timing Critical to Trace Reconstruction?

Dynamic analysis of real-world software systems is challenging due to imperfections, noise and data loss. Moreover, these systems evolve with time and their requirements are usually either not clearly specified or unknown, which makes it hard to analyze them. Therefore, it is important to create models that can learn to behave similarly to these systems to enable us to predict their actions, recover missing data, or detect potential failures ahead of time.Several models have been proposed to model sequential data, but the vast majority of them only have a qualitative notion of time or no notion of it at all. In this paper, we extend the work on incorporating a quantitative notion of time to RNN and introduce Time GRU. This modified GRU can learn the behaviour of complex software systems to a very high degree of accuracy. Our approach is scalable and has shown state-of-the-art performance on industry-strength software with real operating logs from Blackberry’s QNX real-time operating system. The proposed model can predict upcoming sequences of events more than 100 timesteps ahead in time with more than 90% accuracy. This allows for significant improvement in trace reconstruction and failure explainability.
Boosted Dense Segmentation Networks For Constrained Distributed Systems

Deployed AI applications are often heavily con-strained in computational resources; to this end, a method of producing miniature, well-performing neural networks for 2D image segmentation is devised and applied to the Severstal Steel Defect Detection and Kidney Tumor Segmentation (KiTS19) Challenges. By limiting the width of U-Net and employing a full-domain activation function, a network of aggregated weak learners is able to achieve a mean F1 score within 93% of the EfficientNetB0 baseline using only 0.5% of the trainable parameters. A similar network of aggregated strong learners matches the mean F1 score of the baseline on the Severstal dataset using only 10% of the trainable parameters. Gradient boosting is then applied to the weak learners, achieving a mean F1 score within 98% of the strong learner network on the Severstal dataset with approximately 20% of the FLOPS; the key insight is in constraining the O(n2) relationship between network width and FLOPS. The same approach is applied to the KiTS19 dataset with good success in kidney detection. Interestingly, the method does not perform as well on the much harder to isolate tumor class, and the authors explore some possible reasons. In analyzing the impact of the full-domain activation function, the authors show that density of information is promoted by significantly reduced peaks in layer outputs and a wider range of output values. The method has significant implications in constrained deployments, as many small devices could be used to compute the overall network.
A Novel Power-Aware Task Scheduling for Energy Harvesting-Based Wearable Biomedical Devices Using FPA

Power management and saving in energy harvesting-based biomedical wearable devices are mandatory to ensure prolonged and stable operation under a stringent power budget. Thus, power-aware task scheduling can play a key role in minimizing energy consumption to improve system durability while maintaining device functionality. This paper proposes a novel biosensor task scheduling for optimizing energy consumption through wearable biomedical devices. The proposed approach is based on Flower Pollination Algorithm (FPA). The biomedical functionality constraints are enforced with a Hamming-based Tikhonov regularization. We proposed a greedy approach to compute the Tikhonov regularization term efficiently. The algorithm has been tested for scheduling the tasks of two biosensors: a heart rate sensor and a temperature sensor on a lab-based biomedical device.
Watch-and-Learn-Net: Self-supervised Online Learning for Probabilistic Vehicle Trajectory Prediction

The prediction of other road users is an essential task in autonomous driving for preventing collisions and enabling dynamic trajectory planning. This task becomes even more complex because different road users have different driving behaviors. There are underlying intentions that cannot be predicted with certainty without direct communication. In the current state of the art, most promising pattern-based models are trained on a dataset and then applied in the real world. In this paper we present an algorithm for vehicle trajectory prediction that is using online learning. The algorithm uses observations during the inference to optimize the underlying neural network at runtime. We show that our model can adapt to an observed behavior and thus improve the predicted uncertainty of trajectory predictions. Furthermore, we emphasize that our online learning approach can be transferred to many problems in self-supervised learning. The code used in this research is available as open-source software: https://github.com/TUMFTM/Wale-Net
A Dynamic Stock Trading System using GNQTS and RSI in the U.S. Stock Market

Investing in stocks is one of the most common options of financial management, and many people use technical indicators to decide when to buy and sell stocks. This research proposes a novel method of utilizing the commonly used technical indicator, Relative Strength Index (RSI), to the extreme. First, we remove the restriction of the traditional RSI’s default parameters and expand all the possibilities in RSI to find the best strategy and to maximize profit. Second, we employ a modified metaheuristic algorithm, the global best-guide quantum-inspired tabu search algorithm with quantum not gate (GNQTS), to efficiently search for the optimized parameters for RSI. Third, our approach also applies a sliding window to flexibly adjust training periods and avoid over fitting at the same time. The experimental environment covers popular indices and companies in the United States stock market such as DJIA, AAPL, etc. By removing the restriction of RSI, the experiment result shows that GNQTS can find optimized parameters of RSI to get more profit than traditional RSI and buy-and-hold strategies.
One-sided Versus Two-sided: A Novel Opinion Dynamics Information-Type Education-Based Hegselmann–Krause Model

The classic Hegselmann–Krause opinion dynamics model plays an important role in analyzing opinion evolution among people. However, it is not in line with the reality of interpersonal communication during the modern online social times, in which people are easy to exchange opinions with another one who agrees or disagrees with themselves on certain issues by social networks. In this paper, considering the effects of the one-sided versus two-sided presentation of a controversial issue, we improve the classic HK model and propose a novel information-type education-based Hegselmann–Krause model. In our work, we formulate rules of how agents’ opinions change and carry out simulation experiments on networks with different proportions of high-educated agents. Extensive experiments on artificially generated networks show that it is more difficult for someone to reach a consensus with another one who is within a group with a higher proportion of low-educated people than with a higher proportion of high-educated people, which verify the effectiveness of the proposed model.
Fine-Grained VR Sketching: Dataset and Insights.

We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the sketch community on fine-grained data analysis, and extends it to an actively developing 3D domain. We argue for the most convenient sketching scenario where the sketch consists of sparse lines and does not require any sketching skills, prior training or time-consuming accurate drawing. We then, for the first time, study the scenario of fine-grained 3D VR sketch to 3D shape retrieval, as a novel VR sketching application and a proving ground to drive out generic insights to inform future research. By experimenting with carefully selected combinations of design factors on this new problem, we draw important conclusions to help follow-on work. We hope our dataset will enable other novel applications, especially those that require a fine-grained angle such as fine-grained 3D shape reconstruction. The dataset is available at tinyurl.com/ VRSketch3DV21.
Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

Learning through a point cloud is attractive be-cause a point cloud contains geometric data and can help robots understand environments in a robust manner. However, a point cloud is sparse, unstructured and unordered, and its accurate recognition by a traditional convolutional neural network (CNN) or a recurrent neural network (RNN) is difficult. Hence, the present paper proposes a linked dynamic graph CNN (LDGCNN) to directly classify and segment a point cloud. The present work removes the transformation network, links hierarchical features from dynamic graphs, freezes the feature extractor, and retrains the classifier in order to increase the performance of LDGCNN. Also, the paper describes LDGCNN using theoretical analysis and visualization. The LDGCNN is able to classify a point cloud consisting of 1024 three dimensional points without normal vectors in the ModelNet40 dataset, achieving 92.9% accuracy, which is better than the state-of-art. The LDGCNN also achieves state-of-the-art segmentation performance in the ShapeNet dataset.