Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Second Quinone Binding Site in Proton Pumping by Respiratory Complex I

Respiratory complex I performs the reduction of quinone (Q) to quinol (QH2) and pumps protons across the membrane. Structural data on complex I have provided spectacular insights into the electron and proton transfer paths, as well as into the long (~30 Å) and unique substrate binding channel. However, due to missing structural information on Q binding modes, it remains unclear how Q reduction drives long range (~20 nm) redox-coupled proton pumping in complex I. Here we applied multiscale computational approaches to study the dynamics and redox chemistry of Q and QH2. Based on tens of microseconds of atomistic molecular dynamics (MD) simulations of bacterial and mitochondrial complex I, we find that the dynamics of Q is remarkably rapid and it diffuses from the N2 binding site to another stable site near the entrance of the Q channel in microseconds. Analysis of simulation trajectories also reveal the presence of yet another Q binding site 25–30 Å from the N2 center, which is in remarkable agreement with the electron density observed in recent cryo electron microscopy structure of complex I from Yarrowia lipolytica. Quantum chemical computations on the two Q binding sites closer to the entrance of the Q tunnel reveal redox-coupled protonation reactions that may be important in driving the proton pump of complex I.
Can we Predict Performance Events with Time Series Data from Monitoring Multiple Systems?

Predicting performance-related events is an important part of proactive fault management. As a result, many approaches exist for the context of single systems. Surprisingly, despite its potential benefits, multi-system event prediction, i.e., using data from multiple, independent systems, has received less attention. We present ongoing work towards an approach for multi-system event prediction that works with limited data and can predict events for new systems. We present initial results showing the feasibility of our approach. Our preliminary evaluation is based on 20 days of continuous, preprocessed monitoring time series data of 90 independent systems. We created five multi-system machine learning models and compared them to the performance of single-system machine learning models. The results show promising prediction capabilities with accuracies and F1-scores over 90% and false-positive-rates below 10%.
Effect of Monosaccharides and Disaccharides Type on Ionic Conductivity of Liquid Electrolyte Based Lithium Iodide

Liquid electrolyte was prepared by dissolving glucose, fructose, sucrose and lactose separately with different percentage of lithium iodide (10 – 35%) in aqueous solution of 1% acetic acid. Liquid electrolyte is characterized using conductivity meter to determine ionic conductivity. Computer simulations of Density Functional Theory (DFT) was used to identify the dominant functional groups on monomers such as glucose, sucrose, fructose and lactose when interact with the lithium salt by using B3LYP/6-31G ++ (d, p) basis set. The highest ionic conductivity for monosaccharide is glucose at 28.20 mS/cm while for disaccharide is lactose at 28.00 mS/cm with percentage of salt at 35 wt. %. Ionic conductivity increases when concentration of salt increase because there is an interaction between salt with functional groups of compounds. Based on computer simulations of DFT, interaction between lithium with compounds can be occurred due to negative electrostatic potential on the molecule. Electronegativity value of oxygen atom in glucose (-0.562e) and lactose (-0.567e) higher than fructose (-0.559e) and sucrose (-0.515e). Functional groups that are dominant to interact when interact with lithium salt are O-15 for glucose and O-17 for lactose due to the shorter bond length, the stronger energy attraction between functional groups with lithium.
Plant gross primary production, plant respiration and carbonyl sulfide emissions over the globe inferred by atmospheric inverse modelling

Abstract. Carbonyl Sulphide (COS), a trace gas showing striking similarity to CO2 in terms of biochemical diffusion pathway into leaves, has been recognized as a promising indicator of the plant gross primary production (GPP), the amount of carbon dioxide that is absorbed through photosynthesis by terrestrial ecosystems. However, large uncertainties about the other components of its atmospheric budget prevent us from directly relating the atmospheric COS measurements to GPP. The largest uncertainty comes from the closure of its atmospheric budget, with a source component missing. Here, we explore the benefit of assimilating both COS and CO2 measurements into the LMDz atmospheric transport model to obtain consistent information on GPP, plant respiration and COS budget. To this end, we develop an analytical inverse system that optimizes biospheric fluxes for the 15 plant functional types (PFTs) defined in the ORCHIDEE global land surface model. Plant uptake of COS is parameterized as a linear function of GPP of the leaf relative uptake (LRU), which is the ratio of COS to CO2 deposition velocities in plants. A possible scenario for the period 2008–2019 leads to a global biospheric sink of 800 GgS.yr−1, with higher absorption in the high latitudes and higher oceanic emissions between 400 and 600 GgS.yr−1 most of which is located in the tropics. As for the CO2 budget, the inverse system increases GPP in the high latitudes by a few GtC.yr−1 without modifying the respiration compared to the ORCHIDEE fluxes used as a prior. In contrast, in the tropics the system tends to weaken both respiration and GPP. The optimized components of the COS and CO2 have been evaluated against independent measurements over Northern America, the Pacific Ocean, at three sites in Japan and at one site in France. Overall, the posterior COS concentrations are in better agreement with the COS retrievals at 250 hPa from the MIPAS satellite and with airborne measurements made over North America and the Pacific Ocean. The system seems to have rightly corrected the underestimated GPP over the high latitudes. However, the change in seasonality of GPP in the tropics disagrees with Solar Induced Fluorescence (SIF) data. The decline in biospheric sink in the Amazon driven by the inversion also disagrees with MIPAS COS retrievals at 250 hPa, highlighting the lack of observational constraints in this region. Moreover, the comparison with the surface measurements in Japan and France suggests misplaced sources in the prior anthropogenic inventory, emphasizing the need for an improved inventory to better partition oceanic and continental sources in Asia and Europe.
Empowering leadership: balancing self-determination and accountability for motivation

PurposeThis study set out to investigate the role of self-determination and accountability in the relationship between empowering leadership, motivation and performance.Design/methodology/approachData were collected from 172 participants working in an international software development organization. Hypotheses were tested in PROCESS using the Preacher and Hayes (2008) bootstrapping method.FindingsResults indicate that empowering leadership played a significant role satisfying basic psychological needs. As expected, the satisfaction of needs is related to autonomous motivation. Crucially, perceived accountability was also positively related to autonomous motivation.Research limitations/implicationsThe cross-sectional design of this study limits the ability to rule out the possibility of reverse causation.Practical implicationsThe results suggest that traditional management practices such as accountability can be successfully utilized in conjunction with self-determination theory without undermining autonomous motivation. This study highlights two key opportunities for human resources (HR) professionals. Specifically, by equipping managers with the skills to display empowering leadership behaviors, and by communicating organizational values and individual job meaning to enhance identified forms of motivation, HR can fully realize the benefits of autonomous employee motivation.Originality/valueThis paper offers valuable insight into how leaders can balance the satisfaction of basic psychological needs with accountability to influence autonomous motivation in employees. The model presented demonstrates the potential of empowering leadership in achieving this balance and highlights the importance of identified motivation as a powerful correlate of work performance.
PerfVis: Pervasive Visualization in Immersive Augmented Reality for Performance Awareness

Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. In this paper, we propose the use of a city visualization that dynamically provides developers with a pervasive view of the continuous performance of a system. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool, and discuss early feedback that we collected of its usability. Our investigation explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be adequate to analyze dynamic performance data on a large and non-trivial software system.
A Design-flow for implementing, validating and evaluating Machine-learning Classifiers on FPGAs

The emergence of artificial intelligence (AI) applications have shown great benefits in saving costs and at the same time in executing efficiently many of our daily life tasks. Yet the application of AI in the safety-critical embedded systems domain is still limited due to the high functional and extra-functional (real-time, power, memory) requirements these systems must fulfill. To tap the full potential of AI for embedded systems, a design-flow which systematizes the process of refining AI implementations from the software implementation level to hardware design level accompanied with a validation flow of functional and extra-functional requirements on each refinement level, is inevitable. In this work, we propose such a design-flow for implementing and validating pre-trained machine-learning (ML) models on Field-Programmable Gate Arrays (FPGAs). Our design-flow helps automating parts of the implementation refinement and validation by providing uniform interfaces for testing and integrating the ML hardware components to the target design. The refinement process is divided into four layers, each having an implementation and a verification step. In the first layer, the ML model is implemented and tested in software with mature libraries support. On the second layer, a hardware component is auto-generated out of the software model and is tested within a hardware simulator. The third layer consists out of refining and testing the ML HW component in a real FPGA test-design also evaluating it according to various criteria (timing, power, accuracy and resource utilization). In the final integration layer the component is integrated to the final target design and is tested with the real application environment. Our design-flow was demonstrated using a multirotor use-case, evaluating the integration of three ML implementations for ships identification on camera images into an existing FPGA design. Thanks to our design-flow, the Random Forests [8] based ML implementation was found best choice in terms of above criteria compared to Decision Trees[12] and Multi Layer Perceptrons (MLP) [15] implementations.
Rejoinder to Discussions on: Model confidence bounds for variable selection

School of Statistics, Renmin University of China Center for Applied Statistics, Renmin University of China Department of Statistics, University of Wisconsin, Madison Faculty of Economics and Management, Free University of Bozen-Bolzano Department of Biostatistics, Yale University School of Mathematical Sciences, University of Chinese Academy of Sciences Department of Operations, Business Analytics, and Information Systems, University of Cincinnati
A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity

Cover crops play an increasingly important role in improving soil quality, reducing agricultural inputs and improving environmental sustainability. The main objectives of this critical global review and systematic analysis were to assess cover crop practices in the context of their impacts on nitrogen leaching, net greenhouse gas balances (NGHGB) and crop productivity. Only studies that investigated the impacts of cover crops and measured one or a combination of nitrogen leaching, soil organic carbon (SOC), nitrous oxide (N2O), grain yield and nitrogen in grain of primary crop, and had a control treatment were included in the analysis. Long‐term studies were uncommon, with most data coming from studies lasting 2–3 years. The literature search resulted in 106 studies carried out at 372 sites and covering different countries, climatic zones and management. Our analysis demonstrates that cover crops significantly (p < 0.001) decreased N leaching and significantly (p < 0.001) increased SOC sequestration without having significant (p > 0.05) effects on direct N2O emissions. Cover crops could mitigate the NGHGB by 2.06 ± 2.10 Mg CO2‐eq ha−1 year−1. One of the potential disadvantages of cover crops identified was the reduction in grain yield of the primary crop by ≈4%, compared to the control treatment. This drawback could be avoided by selecting mixed cover crops with a range of legumes and non‐legumes, which increased the yield by ≈13%. These advantages of cover crops justify their widespread adoption. However, management practices in relation to cover crops will need to be adapted to specific soil, management and regional climatic conditions.
A Novel Dynamic Morphed Stimuli Set to Assess Sensitivity to Identity and Emotion Attributes in Faces

Face-based tasks are used ubiquitously in the study of human perception and cognition. Video-based (dynamic) face stimuli are increasingly utilized by researchers because they have higher ecological validity than static images. However, there are few ready-to-use dynamic stimulus sets currently available to researchers that include non-emotional and non-face control stimuli. This paper outlines the development of three original dynamic stimulus sets: a set of emotional faces (fear and disgust), a set of non-emotional faces, and a set of car animations. Morphing software was employed to vary the intensity of the expression shown and to vary the similarity between actors. Manipulating these dimensions permits us to create tasks of varying difficulty that can be optimized to detect more subtle differences in face-processing ability. Using these new stimuli, two preliminary experiments were conducted to evaluate different aspects of facial identity recognition, emotion recognition, and non-face object discrimination. Results suggest that these five different tasks successfully avoided floor and ceiling effects in a healthy sample. A second experiment found that dynamic versions of the emotional stimuli were recognized more accurately than static versions, both for labeling, and discrimination paradigms. This indicates that, like previous emotion-only stimuli sets, the use of dynamic stimuli confers an advantage over image-based stimuli. These stimuli therefore provide a useful resource for researchers looking to investigate both emotional and non-emotional face-processing using dynamic stimuli. Moreover, these stimuli vary across crucial dimensions (i.e., face similarity and intensity of emotion) which allows researchers to modify task difficulty as required.
Bioproduction of the Recombinant Sweet Protein Thaumatin: Current State of the Art and Perspectives

There is currently a worldwide trend to reduce sugar consumption. This trend is mostly met by the use of artificial non-nutritive sweeteners. However, these sweeteners have also been proven to have adverse health effects such as dizziness, headaches, gastrointestinal issues, and mood changes for aspartame. One of the solutions lies in the commercialization of sweet proteins, which are not associated with adverse health effects. Of these proteins, thaumatin is one of the most studied and most promising alternatives for sugars and artificial sweeteners. Since the natural production of these proteins is often too expensive, biochemical production methods are currently under investigation. With these methods, recombinant DNA technology is used for the production of sweet proteins in a host organism. The most promising host known today is the methylotrophic yeast, Pichia pastoris. This yeast has a tightly regulated methanol-induced promotor, allowing a good control over the recombinant protein production. Great efforts have been undertaken for improving the yields and purities of thaumatin productions, but a further optimization is still desired. This review focuses on (i) the motivation for using and producing sweet proteins, (ii) the properties and history of thaumatin, (iii) the production of recombinant sweet proteins, and (iv) future possibilities for process optimization based on a systems biology approach.
A Network Perspective for Community Assembly

Species interactions are responsible for many key mechanisms that govern the dynamics of ecological communities. Variation in the way interactions are organized among species results in different network structures, which translates into a community's ability to resist collapse and change. To better understand the factors involved in dictating ongoing dynamics in a community at a given time, we must unravel how interactions affect the assembly process. Here, we build a novel, integrative conceptual model for understanding how ecological communities assemble that combines ecological networks and island biogeography theory, as well as the principles of niche theory. Through our conceptual model, we show how the rate of species turnover and gene flow within communities will influence the structure of ecological networks. We conduct a preliminary test of our predictions using plant-herbivore networks from differently-aged sites in the Hawaiian archipelago. Our approach will allow future modeling and empirical studies to develop a better understanding of the role of the assembly process in shaping patterns of biodiversity.
A pilot and feasibility study of the effectiveness of care mapping on person-centred care in neurorehabilitation settings

ABSTRACT Dementia care mapping for neurorehabilitation (DCM-NR) is a tool designed to increase person-centred care (PCC) in neurorehabilitation settings. This paper reports pilot and feasibility testing of a cluster-randomized controlled trial assessing the effectiveness of DCM-NR at increasing PCC. Forty-one staff members, from four neurorehabilitation wards, were cluster-randomized to receive DCM-NR (experimental group) or care as usual (control). Measures of PCC, attitudes to people with brain injury, and PCC self-efficacy were taken at baseline and follow-up. The experimental group were also interviewed about their experiences of the trial. Twenty-nine participants completed the study. Participants generally found the intervention and trial to be acceptable. PCC was significantly higher in the experimental than the control group at follow-up, despite levels of exposure to the intervention being low. PCC self-efficacy increased in the control group, but not in the experimental group, perhaps reflecting an initial reduction in perceived competence following feedback. Neither group showed a change in attitudes to people with brain injury. The findings suggest that this cluster-randomized trial design is acceptable; that DCM-NR shows promise as a tool for increasing PCC; and highlight modifications to the protocol that could increase the success and theoretical value of a future large-scale study.
MPT0G413, A Novel HDAC6-Selective Inhibitor, and Bortezomib Synergistically Exert Anti-tumor Activity in Multiple Myeloma Cells

In multiple myeloma (MM), homeostasis is largely maintained by misfolded protein clearance via the proteasomal and aggresomal pathways. Histone deacetylase 6 (HDAC6) binds polyubiquitinated proteins and dynein motors and transports this protein cargo to the aggresome for further degradation. Accordingly, a combination of an HDAC6 inhibitor and bortezomib (BTZ) could increase ubiquitinated protein accumulation, leading to further apoptosis. Here we evaluated the anti-MM activity of MPT0G413, a novel specific HDAC6 inhibitor, using in vitro and in vivo models. MPT0G413 treatment more significantly inhibited cell growth in MM cells than in normal bone marrow cells. Furthermore, the combination of MPT0G413 and BTZ enhanced polyubiquitinated protein accumulation and synergistically reduced MM viability, increased caspase-3, caspase-8, caspase-9 levels, and cleaved poly (ADP) ribosome polymerase and also inhibited adherence of MM cells to bone marrow stromal cells (BMSC) and reduced VEGF and IL-6 levels and cell growth in a co-culture system. The combination treatment disturbed the bone marrow microenvironment and induced synergic, caspase-dependent apoptosis. Xenograft tumor growth significantly decreased in combination-treated SCID mice. In conclusion, MPT0G413 and BTZ synergistically inhibit MM viability, providing a framework for the clinical evaluation of combined therapies for MM.
Effects of Learning Attitudes and COVID-19 Risk Perception on Poor Academic Performance among Middle School Students

The purpose of this study is to examine the effects of middle school students’ learning attitudes and risk perception toward COVID-19 on their poor academic performance since the COVID-19 pandemic began. This study limited the sample to middle school students who responded that their academic performance was an A or B grade during the last academic year in 2019. For this study, 268 respondents were selected and logistic regression was employed. Self-motivated studying time and positive attitudes toward online learning predicted consistent academic performance since the COVID-19 pandemic began. Middle school students’ preference toward an in-person classroom format was related to poor academic performance since the COVID-19 pandemic began. A risk perception toward COVID-19 was related to poor academic performance since the COVID-19 pandemic began. It is imperative to provide educational programs which help students develop self-motivated studying habits to maintain their academic performance during COVID-19. Policymakers in schools should consider providing in-person options for students who are more academically successful in such an environment.