Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Cognitive Loading on Pilots and Air Traffic Controller Performance: Implications for Neural Dynamics and Cognitive Flow

The digitized environment in aviation operations has seen marked growth and expansion as new technologies arrive and are implemented. The flight deck and air traffic control functions are two areas where growth is particularly robust. Previous work has identified the effects of compounded cognitive loading and SHELL interfaces in these work environments, and the potential consequences when relief or collaborative resource management is not employed effectively. This paper examines the relationship of cognitive loading in the context of cognitive flow to identify potential areas where neural metrics might aid in a better understanding of the dynamics to determine thresholds of overload. Application of the Triple-Network Model of neural regulation dynamics and Polyvagal Theory are explored for potential relationships to compromised situation awareness and working memory constraints. Conclusions indicate that when cognitive flow is disrupted, cognitive processing loads on working memory expand exponentially and rapidly reach a plateau that inhibits safe performance. Implications suggest a more focused effort in systems and training to address neural metrics and cognitive processing rates.
Topics in Trajectory Generation for Robots

A fundamental problem in robotics is generating the motion for a task. How to translate a task to motion or a series of movements is a non-trivial problem. The complexity of the task, the structure of the robot, and the desired performance determine the sequence of movements, the path, and the course of motion as a function of time, namely the trajectory. As we discuss in this thesis, a trajectory can be acquired from a human demonstration or generated by carefully designing an objective function. In the first approach, we examine a number of robotic setups which are suitable for human demonstration. More notably, admittance control as a new dimension to the robot-assisted teleoperation is investigated. We also describe a free-floating behavior which makes robust lead-through programming possible. As a way to utilize these setups, we present some ideas for developing a high-level language for an event-based programming common to assembly tasks. Since immediate reaction to variations in the target state and/or robot state is desirable, we reformulate the trajectory generation problem as a controller design problem. Using the Hamilton-Jacobi-Bellman equation, we derive a closed-loop solution to the fixed-time trajectory-generation problem with a minimum-jerk cost functional. We show that the resulting trajectory coincides with a fifth-order polynomial function of time that instantaneously updates due to changes in the reference signal and/or the robot states. A short comparison is made between kinematic and dynamic models for generating optimal trajectories. The conclusion is that given conservative kinematic constraints, both models behave in a similar way. Having this in mind, we derive an analytic solution to the problem of fixed-time trajectory generation with a quadratic cost function under velocity and acceleration constraints. The advantage of the analytic solution compared to an on-line optimization approach lies in the efficiency of the computation. To extend the idea of closed-loop trajectory generation, we adapt the Model Predictive Control (MPC) framework. MPC is traditionally applied to tracking problems, i.e., when there is an explicit reference signal. Thus, it is a common practice to have a separate layer that generates the reference signal. We propose an integrated approach by introducing a final state constraint in the formulation. Additionally, we give the interpretation that the difference between tracking and point-to-point trajectory-planning problems is in the density of the specified desired reference signal. We utilize a strategy to reduce the discretization time successively. This way, we respect the real-time constraints for computation time while the accuracy of the solution is gradually improved as the deadline approaches. We have verified our proposed MPC approach to trajectory generation in a ball-catching experiment. (Less)
Human-Robot Teams: A Discussion of the Emerging Trends

As technology advances, human robot-teams are becoming increasingly commonplace with team interactions taking place in a variety of military, industrial, service, and social settings. Consequently, as HR teams evolve, novel and unique considerations for how these teams interact and perform are rising. The purpose of this panel, therefore, is to host a discussion on state-of-theart issues and emerging trends that practitioners and researchers should scrutinize as the applications of HR teams expands. To provide a diverse set of perspectives, our panelists hail from a variety of backgrounds: military, industry, and academy. Talks will focus on moral and ethical considerations for human-agent decision-making, the influence of robots on team processes in human surgical teams, and on new considerations for trust in human-agent teams, including for covert measures of trust and for the inclusion of team cohesion in trust measurement.
Sign and indexicality: a case study of enhancing alignment of situation models in SCWT

Abstract The story continuation writing task (SCWT) is a newly emerging proficiency test of National Matriculation English Test (NMET) in three provinces in China. Nevertheless, little is known with respect to how test-takers enhance their alignment of situation models in SCWT. This paper reports on a case study of the development of situational alignment of one L2 (second language) learner in a senior high school in Jiangsu Province, China, drawing on qualitative data collected via individual face-to-face interviews, and supplemented by face-to-face conversations and email exchanges, over a period of one year. This study investigates how the test-taker enhances her alignment of situation models, and what factors contribute to her development of situational alignment under the framework of a new semiotic research finding, a pan-indexicality model. This paper concludes that test-takers possess the capacity of developing their alignment of situation models, and that a pan-indexicality model, affording opportunities for test-takers to accurately decode the meaning of a linguistic sign at the overall sense level, plays a decisive role in identifying and integrating key dimensions of situation models and eventually enhancing test-takers’ situational alignment. This study suggests that a pan-indexicality model can be employed by test-takers to understand the meaning of a linguistic sign at the overall sense level, and test-takers’ encyclopedic knowledge, contextual information, and personal emotions regarding a linguistic sign should be highlighted in classroom instruction to help test-takers construct aligned situation models in SCWT.
Age distribution of detrital monazites in the sandstones from the northern Borneo and its tectonic setting

Late Cretaceous to Late Eocene turbidite is widely distributed in the northern part of Borneo. It is known as the Rajang Group. Heavy minerals were collected from the four rivers that ﬂow through this stratum. Detrital monazites were analyzed by EPMA to obtain their ages. The monazite age distributions of the four rivers show three main peaks at 200-300 Ma, 400-500 Ma and 1850-1900 Ma, and a weak cluster at 700-1100 Ma. Such age distributions show that the detrital grains were not supplied from Southeast Asia, but from the southern part of China. There is a huge unconformity, called as Sarawak Orogeny, which occurs after deposition of the Rajang Group. The age is the Latest Eocene, close to the opening of the South China Sea which began as early as the Early Oligocene. Hence, it is probable that the Northern Borneo situated at oceanic side of the South China have been moved to its current position during the opening of the South China Sea. There is no clear tectonic event after the opening of the South China Sea between the northern Borneo and western Indonesia Archipelago. Assuming that Borneo and western Indonesia Archipelago were moved at the time of the opening of the South China Sea, the reduced connection between the Indian Ocean and the Paciﬁc Ocean at Miocene and also the Wallace line for fauna and ﬂora may be explained more simply than the recent reconstruction models.
Simulating organic aerosol in Delhi with WRF-Chem using the VBS 1 approach: Exploring model uncertainty with a Gaussian Process 2 emulator 3

The nature and origin of organic aerosol for the analysed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and identify the appropriate parameter combination for the VBS approach, and hence provide valuable information 43 to improve our understanding on SOA production. roughly simulate the manner in which VBS compounds are partitioned and aged within the WRF-Chem scheme). This processing reduces the overall volatility of the VBS compounds, first providing a spread of mass across the volatility range, before accumulating the mass in the lowest volatility bins until 90% of the VBS mass is in the Ci*=-2 volatility bin (“time” = 1). This parameter is a scalar variable (between 0-1), that indicates the dimensionless position between these two points, and has an associated volatility distribution. After examining the range of volatility distributions likely/covered by variants (as parameter space is most likely given the comparison to variants cannot rule out (are plausible) uncertainty not mean they are all
VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images

Detection of melanocytes serves as a critical prerequisite in assessing melanocytic growth patterns when diagnosing melanoma and its precursor lesions on skin biopsy specimens. However, this detection is challenging due to the visual similarity of melanocytes to other cells in routine Hematoxylin and Eosin (H&E) stained images, leading to the failure of current nuclei detection methods. Stains such as Sox10 can mark melanocytes, but they require an additional step and expense and thus are not regularly used in clinical practice. To address these limitations, we introduce VSGD-Net, a novel detection network that learns melanocyte identification through virtual staining from H&E to Sox10. The method takes only routine H&E images during inference, resulting in a promising approach to support pathologists in the diagnosis of melanoma. To the best of our knowledge, this is the first study that investigates the detection problem using image synthesis features between two distinct pathology stainings. Extensive experimental results show that our proposed model outperforms state-of-the-art nuclei detection methods for melanocyte detection. The source code and pre-trained model are available at: https://github.com/kechunl/VSGD-Net
Importance of glutamine in synaptic vesicles revealed by functional studies of SLC6A17 and its mutations pathogenic for intellectual disability

Human mutations in the gene encoding the solute carrier (SLC) 6A17 caused intellectual disability (ID). The physiological role of SLC6A17 and pathogenesis of Slc6a17-based-ID were both unclear. Here we report learning deficits in SLC6A17 knockout and point mutants. Biochemistry, proteomics and electron microscopy (EM) support SLC6A17 protein localization in synaptic vesicles (SVs). Chemical analysis of SVs by liquid chromatography coupled to mass spectrometry (LC-MS) revealed glutamine (Gln) in SVs containing SLC6A17. Virally mediated overexpression of SLC6A17 increased Gln in SVs. Either genetic or virally mediated targeting of SLC6A17 reduced Gln in SVs. One ID mutation caused SLC6A17 mislocalization while the other caused defective Gln transport. Multidisciplinary approaches with 7 types of genetically modified mice have shown Gln as an endogenous substrate of SLC 6A17, uncovered Gln as a new molecule in SVs, established the necessary and sufficient roles of SLC6A17 in Gln transport into SVs, and suggested SV Gln decrease as the key pathogenetic mechanism in human ID.
Many-body localization in infinite chains

We investigate the phase transition between an ergodic and a many-body localized phase in infinite anisotropic spin-$1/2$ Heisenberg chains with binary disorder. Starting from the Neel state, we analyze the decay of antiferromagnetic order $m_s(t)$ and the growth of entanglement entropy $S_{\textrm{ent}}(t)$ during unitary time evolution. Near the phase transition we find that $m_s(t)$ decays exponentially to its asymptotic value $m_s(\infty)\neq 0$ in the localized phase while the data are consistent with a power-law decay at long times in the ergodic phase. In the localized phase, $m_s(\infty)$ shows an exponential sensitivity on disorder with a critical exponent $\nu\sim 0.9$. The entanglement entropy in the ergodic phase grows subballistically, $S_{\textrm{ent}}(t)\sim t^\alpha$, $\alpha\leq 1$, with $\alpha$ varying continuously as a function of disorder. Exact diagonalizations for small systems, on the other hand, do not show a clear scaling with system size and attempts to determine the phase boundary from these data seem to overestimate the extent of the ergodic phase.
‘Same same but different’ Exploring evolutionary and ecological causes of eye diversification in bumblebees

Bees rely heavily on vision during most of their interaction with the environment, but so far, visual abilities have not been included into functional investigations of these crucial pollinators. This is probably due to the lack of comprehensive and phylogenetically-controlled quantification of visual traits across species. In the present study, we used high-throughput micro-CT tools to quantify, compare and understand the diversity of visual traits of compound eyes in bumblebees. Visual systems of bumblebees were far from identical, with variations across sizes, castes and species. While phylogenetic proximity poorly supported interspecific variations, these were better explained by two ecological factors: social parasitism and habitat. The eye parameter – a metric that measures the relative investment of a compound eye into resolution or sensitivity – was lower in queens of social parasitic species than of non-parasitic species. Workers of species associated with forested habitat had distinct visual traits, including a higher eye parameter, than those of species living in open landscapes. These diverse visual traits are likely to provide selective advantages to bumblebees given their specific ecological requirements. We thus propose that social parasitism and forest habitat are drivers of the diversification of compound eyes in bumblebees. Finally, we discuss how the present study can inspire trait-based approaches in ecology and conservation biology.
JINXED: just in time crystallization for easy structure determination of biological macromolecules

Macromolecular crystallography is a well-established method in the field of structure biology and has led to the majority of known protein structures to date. After focusing on static structures, the method is now developing towards the investigation of protein dynamics through time-resolved methods. These experiments often require multiple handling steps of the sensitive protein crystals, e.g. for ligand soaking and cryo-protection. These handling steps can cause significant crystal damage, causing a decrease in data quality. Furthermore, in time-resolved experiments based on serial crystallography that use micron-sized crystals for short diffusion times of ligands, certain crystal morphologies with small solvent channels can prevent sufficient ligand diffusion. Described here is a method combining protein crystallization and data collection in a novel one-step-process. Corresponding experiments were successfully performed as a proof-of-principle using hen egg white lysozyme and crystallization times of only a few seconds. This method called JINXED (Just in time crystallization for easy structure determination) promises to result in high-quality data due the avoidance of crystal handling and has the potential to enable time-resolved experiments with crystals containing small solvent channels by adding potential ligands to the crystallization buffer, simulating traditional co-crystallization approaches.
Interactome Analysis Identifies MSMEI_3879 as a Substrate of Mycolicibacterium smegmatis ClpC1

Drug-resistant tuberculosis infections are a major challenge to global public health. Much effort has been invested in identifying new drug targets in the causative pathogen, Mycobacterium tuberculosis. ABSTRACT The prevalence of drug-resistant Mycobacterium tuberculosis infections has prompted extensive efforts to exploit new drug targets in this globally important pathogen. ClpC1, the unfoldase component of the essential ClpC1P1P2 protease, has emerged as one particularly promising antibacterial target. However, efforts to identify and characterize compounds that impinge on ClpC1 activity are constrained by our limited knowledge of Clp protease function and regulation. To expand our understanding of ClpC1 physiology, we employed a coimmunoprecipitation and mass spectrometry workflow to identify proteins that interact with ClpC1 in Mycolicibacterium smegmatis, a surrogate for M. tuberculosis. We identify a diverse panel of interaction partners, many of which coimmunoprecipitate with both the regulatory N-terminal domain and the ATPase core of ClpC1. Notably, our interactome analysis establishes MSMEI_3879, a truncated gene product unique to M. smegmatis, as a novel proteolytic substrate. Degradation of MSMEI_3879 by ClpC1P1P2 in vitro requires exposure of its N-terminal sequence, reinforcing the idea that ClpC1 selectively recognizes disordered motifs on substrates. Fluorescent substrates incorporating MSMEI_3879 may be useful in screening for novel ClpC1-targeting antibiotics to help address the challenge of M. tuberculosis drug resistance. IMPORTANCE Drug-resistant tuberculosis infections are a major challenge to global public health. Much effort has been invested in identifying new drug targets in the causative pathogen, Mycobacterium tuberculosis. One such target is the ClpC1 unfoldase. Compounds have been identified that kill M. tuberculosis by disrupting ClpC1 activity, yet the physiological function of ClpC1 in cells has remained poorly defined. Here, we identify interaction partners of ClpC1 in a model mycobacterium. By building a broader understanding of the role of this prospective drug target, we can more effectively develop compounds that inhibit its essential cellular activities.
The geometry of hidden representations of protein language models

Protein language models (pLMs) transform their input into a sequence of hidden representations whose geometric behavior changes across layers. Looking at fundamental geometric properties such as the intrinsic dimension and the neighbor composition of these representations, we observe that these changes highlight a pattern characterized by three distinct phases. This phenomenon emerges across many models trained on diverse datasets, thus revealing a general computational strategy learned by pLMs to reconstruct missing parts of the data. These analyses show the existence of low-dimensional maps that encode evolutionary and biological properties such as remote homology and structural information. Our geometric approach sets the foundations for future systematic attempts to understand the space of protein sequences with representation learning techniques.
A Clade-D Auxin Response Factor is a Major Regulator of Auxin Signaling in Physcomitrium patens

Auxin Response Factors (ARFs) are a family of transcription factors that are responsible for regulating gene expression in response to changes in auxin level. The analysis of ARF sequence and activity indicates that there are two major groups-activators and repressors. One clade of ARFs, clade-D, is sister to clade-A activating ARFs, but are unique in that they lack a DNA binding domain. Clade-D ARFs are present in lycophytes and bryophytes but absent in pteridophytes and spermatophytes. The transcriptional activity of clade-D ARFs, as well as how they regulate gene expression, is not well understood. Here, we report that clade-D ARFs are transcriptional activators in the model bryophyte P. patens and have a major role in the development of this species. Δarfd1,d2 protonemata exhibit a delay in filament branching, as well as a delay in a key cell differentiation event. Additionally, leafy gametophore development in Δarfd1,d2 lines lag behind wild-type. We present evidence that ARFd1 interacts with activating, but not repressing, ARFs. An ARFd1 hypomorph, arfd1T653L, cannot multimerize. Therefore, we propose a model by which clade-D ARFs enhance gene expression by oligomerizing to DNA-bound archetypal ARFs.
Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to eradicate the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated random forest machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation. AUTHOR SUMMARY Cystic fibrosis (CF) patients are susceptible to lung infections by the opportunistic bacterial pathogen Pseudomonas aeruginosa (Pa) leading to increased morbidity and earlier mortality. Consequently, doctors use antibiotic eradication therapy (AET) to clear these new-onset Pa infections, which is successful in 60%-90% of cases. The hope is that by identifying the factors that lead to AET failure, we will improve treatment outcomes and improve the lives of CF patients. In this study, we attempted to predict AET success or failure based on the genomic sequences of the infecting Pa strains. We used machine learning models to determine the role of Pa genetics and to identify genes associated with AET failure. We found that our best model could predict treatment outcome with an accuracy of 0.87, and that genes associated with chronic infection (e.g., bacterial motility, biofilm formation, antimicrobial resistance) were also associated with AET failure.