Miracle-dz/UniFAR-FTUs huggingface dataset:
Migratory herbivorous waterfowl track multiple resource waves during spring migration

East Asian herbivorous waterfowl intensively use farmland in spring, next to their natural habitat. Accordingly, they might have expanded their migration strategy from merely tracking the green wave of newly emerging vegetation to also incorporating the availability of post-harvest agricultural seeds (here dubbed the seed wave). However, if and how waterfowl use multiple food resources to time their seasonal migration is still unknown. We test this migration strategy using 167 spring migration tracks of five East Asian herbivorous waterfowl species and mixed-effect resource selection function models. We find all study species arrive at their core stopover sites in the Northeast China Plain after agricultural seeds become available, extend their stay after spring vegetation emerges, and arrive at breeding sites around the emergence of vegetation. Moreover, the cue used in exploiting the availability of both food resources varies in different regions and among species. At the core stopover sites, larger-bodied species use the seed wave and smaller-bodied ones adopt the green wave. At the breeding sites, the former follow the green wave, and the latter track the snow-free time. Our findings suggest that waterfowl track multiple resource waves to finetune their migration, highlighting new opportunities for conservation.
A Video Game for Stimulating Children's Creativity through Writing Stories

This paper presents a video game for stimulating children’s creativity through writing stories. The video game is based on a card game called Fabula to help writers and screenwriters to build their stories. In particular, a different version of this game, Fabula For Kids, is aimed at a younger audience, i.e., kids. We decided to develop a video game counterpart of this card game since children are accustomed to digital devices like smartphones, tablets, and PCs. We conducted a usability case study involving 23 children on the video game prototype, obtaining promising results and valuable comments for improving the game.
A data-driven approach for enhancing forest productivity by accounting for indirect genetic effects

Maintaining the past decades current genetic gains for tree species is a challenging task for foresters and tree breeders due to biotic and abiotic factors. Planting a mixture of genotypes or clonal composites can be an alternative to increase the phytosanitary security and yield of forest plantations. These clonal composites are more complex than monocultures due to inter-genotypic competition and indirect genetic effects that can affect the total heritable variation. This study aims to understand how indirect genetic effects can impact the response to selection and how the stand composition can be used to explore these effects and enhance forest yield. We used a clonally trial of Eucalyptus urophylla × Eucalyptus grandis hybrids in a randomized complete block design with 24 replications, containing a single tree per plot evaluated for mean annual increment at 3 and 6 years. We focus on partitioning the genetic variation of trees into direct and indirect genetic effects based on competition intensity factors. We identified clones as aggressive, homeostatic, and sensitive based on the magnitude of indirect genetic effects. By accounting for indirect genetic effects, for mean annual increment, the total heritability decreased 39 and 44% for 3 and 6 years, respectively. We proposed a workflow that uses the direct and indirect genetic effect to predict the mean value of clonal composite combinations and to select the one with highest yield. Our methodology accounted for spatial variability and interplot competition that can contribute to the total heritable variance and response to selection in forest trials. Based on the models evaluated, the clones are easily classified according to their deviation from the indirect genetic effects mean. Also, we extract useful information to predict different clonal composites compositions, their expected average performance, and define the best recommended combination to be planted in large scale.
Deep flanking sequence engineering for efficient promoter design

Human experts are good at summarizing explicit strong patterns from small samples, while deep learning models can learn implicit weak patterns from big data. Biologists have traditionally described the sequence patterns of promoters via transcription factor binding sites (TFBSs), while the flanking sequences among TFBSs, which can also significantly influence promoter activity and function, remain largely uncharacterized. Thus, current synthetic promoters are mainly designed by the manipulation of TFBSs, while the flanking sequence is often chosen arbitrarily or by previous experience due to a lack of well-summarized optimization criteria. Here, we introduced an AI-aided promoter design framework, DeepSEED, that employs expert knowledge and deep learning methods to efficiently design synthetic promoters that have various desirable functions. DeepSEED incorporates the user-defined cis-regulatory sequences as ‘seeds’ and generates flanking sequences that match the ‘seeds’. We showed that DeepSEED can automatically capture k-mer frequencies and DNA shape features from active promoters in the training set and efficiently optimize the flanking sequences to better match desired properties in synthetic promoters. We validated the effectiveness of this framework for diverse synthetic promoter design tasks in both prokaryotic and eukaryotic cells. DeepSEED successfully designed E. coli constitutive, isopropyl-beta-D-1-thiogalactopyranoside (IPTG)-inducible, and mammalian cell doxycycline (Dox)-inducible promoters with significant performance improvements, suggesting that DeepSEED has potential as an efficient AI-aided flanking sequence optimization approach for promoter design that may be of great benefit in synthetic biology applications. Availability https://github.com/WangLabTHU/deepseed
Computation of Fractal Dimension for Digital Image in a 3-D Space

A digital image can be represented in a 3-D curved surface which is displayed by the position of pixels and corresponding color intensity of these pixels.The spatial distribution of the curved surface reflects the feature of image texture and usually appears to be a fractal.The fractal dimension was used to quantitatively describe the feature of image texture in a certain region.The results show that the counting-box dimension is suitable for analyzing larger region of images so that the adequacy of box number is assured.The iterative programming was introduced to improve the efficiency of computation.Difference covering,whole covering and some other covering methods can be adopted during the box-counting process.Brownian-motion dimension is suitable for analyzing smaller region of images so as to be limited in a non-scale range.The pixels in the concerning region are considered one by one and only the right and bottom sides of a certain boundary surrounding each pixel were used during the calculation process of Brownian-motion dimension.The more complex the image texture is,the larger the fractal dimension will be.The simpler the image texture is,the smaller the fractal dimension becomes.The similar image textures have approximately same fractal dimensions.
Polymerization force–regulated actin filament–Arp2/3 complex interaction dominates self-adaptive cell migrations

Cells migrate by adapting their leading-edge behaviours to heterogeneous extracellular microenvironments (ECMs) during cancer invasions and immune responses. Yet it remains poorly understood how such complicated dynamic behaviours emerge from millisecond-scale assembling activities of protein molecules, which are hard to probe experimentally. To address this gap, we established a spatiotemporal “resistance-adaptive propulsion” theory based on the protein interactions between Arp2/3 complexes and polymerizing actin filaments, and a multiscale dynamic modelling system spanning from molecular proteins to the cell. Combining spatiotemporal simulations with experiments, we quantitatively find that cells can accurately self-adapt propulsive forces to overcome heterogeneous ECMs via a resistance-triggered positive feedback mechanism, dominated by polymerization-induced actin filament bending and the bending-regulated actin-Arp2/3 binding. However, for high resistance regions, resistance triggered a negative feedback, hindering branched filament assembly, which adapts cellular morphologies to circumnavigate the obstacles. Strikingly, the synergy of the two opposite feedbacks not only empowers cells with both powerful and flexible migratory capabilities to deal with complex ECMs, but also endows cells to use their intracellular proteins efficiently. In addition, we identify that the nature of cell migration velocity depending on ECM history stems from the inherent temporal hysteresis of cytoskeleton remodelling. We also quantitatively show that directional cell migration is dictated by the competition between the local stiffness of ECMs and the local polymerizing rate of actin network caused by chemotactic cues. Our results reveal that it is the polymerization force-regulated actin filament-Arp2/3 complex binding interaction that dominates self-adaptive cell migrations in complex ECMs, and we provide a predictive theory and a spatiotemporal multiscale modelling system at the protein level.
Rapid protein-protein interaction network creation from multiple sequence alignments with Deep Learning

AlphaFold2 (AF) can evaluate protein-protein interactions (PPIs) with high accuracy by finding evolutionary signals between proteins but comes with a high computational cost. Here, we speed up the prediction with AF for PPI network prediction 40x and reduce the disk space requirements 4000x for a set of 1000 proteins. Our protocol is easy to install and freely available from: https://github.com/patrickbryant1/SpeedPPI.
ENOD93 interacts with cytochrome c oxidase altering respiratory ATP production and root growth in plants

The early nodulin 93 (ENOD93) gene family in plants can regulate biological nitrogen fixation in legumes and nitrogen use efficiency in cereals but its molecular function is unknown. We show profile hidden Markov models define ENOD93 as a distant homolog of the N-terminal domain of RESPIRATORY SUPERCOMPLEX FACTOR 2 (RCF2). RCF2 is reported to regulate cytochrome oxidase (CIV) influencing the generation of a mitochondria proton motive force in yeast. Knockout of enod93 in Arabidopsis leads to a short root phenotype. ENOD93 is associated with a protein complex the size of CIV in isolated mitochondria but neither CIV abundance nor its activity in ruptured organelles changed in enod93. However, a progressive loss of ADP-dependent respiration rate was observed in enod93 mitochondria which could be fully recovered in complemented lines. Mitochondrial membrane potential was higher in enod93 but ATP synthesis and ADP depletion rates progressively decreased. Respiration rate of whole enod93 seedlings was elevated and root ADP content was nearly double that in WT without a change in ATP content. These altered energetic states correlated with elevated respiratory substrate levels in roots of enod93 compared to WT and complemented lines. Overexpression of ENOD93 lowered ATP content in roots and increased the abundance of a range of amino acids in both roots and leaves. We propose that two previously unconnected gene families in plants, ENOD93 and HYPOXIA INDUCED GENE DOMAIN, are the functional equivalent of yeast RCF2 but have remained undiscovered in many eukaryotic lineages because they are encoded in two separate genes. Highlight significance We identify the enigmatic early nodulin ENOD93 gene family as the plant homolog of the N-terminal regulatory domain of the yeast RESPIRATORY SUPERCOMPLEX 2 (RCF2) of the mitochondrial oxidative phosphorylation system and provide biochemical and physiological evidence of its role in plant ATP production, broadly explaining the role of ENOD93 in plants.
MELTER: A model of the thermal response of cargos transported in the Safe-Secure Trailer subject to fire environments for risk assessment applications

MELTER is an analysis of cargo responses inside a fire-threatened Safe-Secure Trailer (SST) developed for the Defense Program Transportation Risk Assessment (DPTRA). Many simplifying assumptions are required to make the subject problem tractable. MELTER incorporates modeling which balances the competing requirements of execution speed, generality, completeness of essential physics, and robustness. Input parameters affecting the analysis include those defining the fire scenario, those defining the cargo loaded in the SST, and those defining properties of the SST. For a specified fire, SST, and cargo geometry MELTER predicts the critical fire duration that will lead to a failure. The principal features of the analysis include: (a) Geometric considerations to interpret fire-scenario descriptors in terms of a thermal radiation boundary condition, (b) a simple model of the SST`s wall combining the diffusion model for radiation through optically-thick media with an endothermic reaction front to describe the charring of dimensional, rigid foam in the SST wall, (c) a transient radiation enclosure model, (d) a one-dimensional, spherical idealization of the shipped cargos providing modularity so that cargos of interest can be inserted into the model, and (e) associated numerical methods to integrate coupled, differential equations and find roots.
High-performance multi-bit adder-accumulators as components of the ALU in supercomputers

The fields of applications of multi-bit special-purpose processors for data processing in cyber-physical systems (CPS) are analyzed. Structures of multi-bit special-purpose processors (MSP) based on synchronized adders, which are used as components of arithmetic logic units (ALU) in multi-core vector and scalar supercomputers are classified. New efficient structures of MSPs, which process data given in mono binary and binary number systems, are proposed according to the criteria of maximum speed and reduced hardware complexity. The results of studies of the functional and structural, time and hardware characteristics of such MSPs are presented. Promising areas of their applications in scientific and industrial computerized systems are identified.
The Method of Joint Execution of the Basic Operations of the Rabin Cryptosystem

The process of performing arithmetic operations of addition, multiplication, and exponentiation, where the operands are multi-bit numbers, is extremely common in applied problems of number theory, computational and discrete mathematics. This applies to methods of pattern recognition, digital signal processing, tamper-proof coding, statistical data processing, etc. However, their most important application is in asymmetric cryptography. In this field, for example, in the Rabin cryptosystem, it is often necessary to simultaneously perform the Euclidean algorithm and multiply two multi-bit numbers. The work presents new methods of finding the greatest common divisor based on the formation of appropriate matrices, which allows parallelization of calculations. Their advantages over the existing ones are indicated, in particular, the possibility of increasing speed. It was established that these methods, in addition to finding the greatest common divisor, make it possible to factorize it simultaneously. In the C+ high-level programming language, a program implementation of the simultaneous execution of the Euclidean algorithm and the multiplication of two multi-bit numbers by the classical Euclidean algorithm and the proposed methods was carried out. Corresponding experimental studies of the time characteristics of this software implementation have been conducted. The proposed method involves the use of intermediate results of the Euclidean algorithm and access to the table of squares stored in the computer's memory. The Rabin encryption scheme based on the proposed method of multiplying multi-bit numbers is given. Numbers of different digits were used for the experiment. It is shown that in the vast majority of cases considered, the proposed method is characterized by a higher speed of processing multi-bit numbers. The average time of operations is reduced by approximately 1.43 times. All calculations were repeated 5000 times to eliminate random influences on the time characteristics of the computer. The proposed method can be effectively used when simultaneous execution of the Euclidean algorithm and multiplication of two multi-bit numbers is required.
EpoMemory: Multi-state Shape Memory for Programmable Morphing Interfaces

Smart shape-changing materials can be adapted to different usages, which have been leveraged for dynamic affordances and on-demand haptic feedback in HCI. However, the applicability of these materials is often bottlenecked by their complex fabrication and the challenge of programming localized and individually addressable responses. In this work, we propose a toolkit for designing and fabricating programmable morphing objects using off-the-shelf epoxies. Our method involves varying the crosslinker to epoxy resin ratio to control morphing temperatures from 40 ℃ to 90 ℃, either across different regions of a shape memory device or across devices. Functional components (e.g., conductive fabric, magnetic particles) are also incorporated with the epoxy for sensing and active reconfiguration. A toolbox of fabrication methods and a primitive design library are introduced to support design ideation and programmable morphing. Finally, we demonstrate application examples, including morphing toys, a shape-changing input device, and an active window shutter.
What is Sensitive About (Sensitive) Data? Characterizing Sensitivity and Intimacy with Google Assistant Users

Digital technologies have increasingly integrated into people’s lives, continuously capturing their behavior through potentially sensitive data. In the context of voice assistants, there is a misalignment between experts, regulators, and users on whether and what data is ‘sensitive’, partly due to how data is presented to users; as single interactions. We investigate users’ perspectives on the sensitivity and intimacy of their Google Assistant speech records, introduced comprehensively as single interactions, patterns, and inferences. We collect speech records through data donation and explore them in collaboration with 17 users during interviews based on predefined data-sharing scenarios. Our results indicate a tipping point in perceived sensitivity and intimacy as participants delve deeper into their data and the information derived from it. We propose a conceptualization of sensitivity and intimacy that accounts for the fuzzy nature of data and must disentangle from it. We discuss the implications of our findings and provide recommendations.
InfinitePaint: Painting in Virtual Reality with Passive Haptics Using Wet Brushes and a Physical Proxy Canvas

Digital painting interfaces require an input fidelity that preserves the artistic expression of the user. Drawing tablets allow for precise and low-latency sensing of pen motions and other parameters like pressure to convert them to fully digitized strokes. A drawback is that those interfaces are rigid. While soft brushes can be simulated in software, the haptic sensation of the rigid pen input device is different compared to using a soft wet brush on paper. We present InfinitePaint, a system that supports digital painting in Virtual Reality on real paper with a real wet brush. We use special paper that turns black wherever it comes into contact with water and turns blank again upon drying. A single camera captures those temporary strokes and digitizes them while applying properties like color or other digital effects. We tested our system with artists and compared the subjective experience with a drawing tablet.
Decorative, Evocative, and Uncanny: Reactions on Ambient-to-Disruptive Health Notifications via Plant-Mimicking Shape-Changing Interfaces

Ambient Information Systems (AIS) have shown some success when used as a notification towards users’ health-related activities. But in the actual busy lives of users, ambient notifications might be forgotten or even missed, nullifying the original notification. Could a system use multiple levels of noticeability to ensure its message is received, and how could this concept be effectively portrayed? To examine these questions, we took a Research through Design approach and created plant-mimicking Shape-Changing Interface (S-CI) artifacts, then conducted interviews with 10 participants who currently used a reminder system for health-related activities. We report findings on acceptable scenarios to disrupting people for health-related activities, and participants’ reactions to our design choices, including how using naturalistic aesthetics led to interpretations of the uncanny and morose, and which ways system physicality affected imagined uses. We offer design suggestions in health-related notification systems and S-CIs, and discuss future work in ambient-to-disruptive technology.
Bean Academy: A Music Composition Game for Beginners with Vocal Query Transcription

Bean Academy is a music composition game designed for musically-unskilled learners to lower entry barriers to music composition learning such as music theory comprehension, literacy and proficiency in utilizing music composition software. As a solution, Bean Academy’s Studio Mode was designed with the adaptation of an auditory-based ‘Vocal Query Transcription(VQT)’ model to enhance learners’ satisfaction and enjoyment towards music composition learning. Through the VQT model, players can experience a simple and efficient music composition process by experiencing their recorded voice input being transcripted into an actual musical piece. Based on our playtest, thematic analysis was conducted in two separate experiment groups. Here, we noticed that although Bean Academy does not outperform the current-level Digital Audio Workstation(DAW) in terms of performance or functionality, it can be highly considered as suitable learning material for musically-unskilled learners.
TOFI: Designing Intraoral Computer Interfaces for Gamified Myofunctional Therapy

In the last two decades, there have been several proposals for Intraoral Computer Interfaces (ICI), with demonstrations of novel technologies mostly aimed at assistive devices or in hand-free scenarios. This paper demonstrates a case study for wearable ICIs that allows users to train their tongue muscles with gamified myofunctional therapy: a method that can reduce snoring and mild sleep apnea. This demonstrates a novel application of ICIs together with new design and technological solutions for fabricating such devices. We also discuss the various challenges associated with the design of ICIs and ICI based interactions to lay the groundwork for future research, prototypes and a broader exploration of ICI application areas.
Towards Developing a Virtual Guitar Instructor through Biometrics Informed Human-Computer Interaction

Within the last few years, wearable sensor technologies have allowed us to access novel biometrics that give us the ability to connect musical gesture to computing systems. Doing this affords us to study how we perform musically and understand the process at data level. However, biometric information is complex and cannot be directly mapped to digital systems. In this work, we study how guitar performance techniques can be captured/analysed towards developing an AI which can provide real-time feedback to guitar students. We do this by performing musical exercises on the guitar whilst acquiring and processing biometric (plus audiovisual) information during their performance. Our results show: there are notable differences within biometrics when playing a guitar scale in two different ways (legato and staccato) and this outcome can be used to motivate our intention to build an AI guitar tutor.