Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of Ecological Security Pattern and Identification of Ecological Restoration Zones in the City of Changchun, China

Identification of crucial regions in need of ecological conservation and restoration based on ecological security patterns is of utmost importance for ecological restoration across national land space with regard to China’s promotion of ecological civilization. Using Changchun, the capital of northeast China, as an illustration, the study chooses ecological sources based on the importance of ecosystem services, builds an ecological security pattern using circuit theory, and organizes critical regions for ecological conservation and restoration. The findings reveal that the 20 ecological sources chosen based on ecosystem services are more concentrated on the eastern side of the city, whereas the western side of the city has a smaller overall area; 41 ecological corridors show a network distribution, among which the southeast is relatively densely distributed; 31 ecological pinch points and 15 ecological barrier points are also identified. Prioritized restoration zones, prioritized protection zones, key conservation zones, and general conservation zones were the four different types of ecological restoration regions identified by the study. Each district’s prioritized restoration zones in the main metropolitan area are larger than the others; Gongzhuling’s priority protection zones are the largest, and Yushu’s natural substrate is the best. According to the grading, targeted solutions are suggested, offering helpful advice for the improvement of ecological patterns and ecological restoration of the aforementioned national areas.
Analysis of Tumor Microenvironment Heterogeneity among Breast Cancer Subtypes to Identify Subtype-Specific Signatures

Breast cancer is one of the most frequent malignancies in women worldwide. According to 50-gene signature, Prediction Analysis of Microarray 50 (PAM50), breast cancer can be categorized into five molecular subtypes, and these subtypes are highly heterogeneous in different molecular characteristics. However, the landscape of their tumor microenvironment (TME) heterogeneity has not been fully researched. Using the multi-omics dataset of breast cancer from the METABRIC cohort (n = 1699), we conducted extensive analyses of TME-related features to investigate TME heterogeneity in each breast cancer subtype. We then developed a cell-based subtype set enrichment analysis to identify the subtype-specific TME cells, and further evaluate their prognostic effects. Our results illustrate that different breast cancer subtypes exhibit different TME patterns. Basal-like and HER2-enriched subtypes are associated with high immune scores, expression of most immune regulatory targets, and immune cell infiltration, suggesting that these subtypes could be defined as “immune hot” tumors and suitable for immune checkpoint blockade (ICB) therapy. In contrast, Luminal A and Luminal B subtypes are associated with low immune scores and immune cell infiltration, suggesting that these subtypes could be defined as “immune cold” tumors. Additionally, the Normal-like subtype has relatively high levels of both immune and stromal features, which indicates that the Normal-like subtype may be suitable for more diverse treatment strategies. Our study reveals the breast cancer tumor microenvironment heterogeneity across subtypes. The comprehensive analysis of breast cancer TME-related characteristics may help us to adopt a tailored treatment strategy for different subtypes of patients.
Performance monitoring of a northern 3.2 kWp grid-connected photovoltaic system

This paper summarizes five years of monitoring of a facade-mounted grid-connected system in Canada's far North. The system has been in operation since July 1995 and has delivered around 2 MWh of electrical power on an annual basis since then. Climatic and solar radiation conditions at the site are reviewed, and the performance of the system is assessed from a component perspective (PV array, power conditioning unit) and from a global perspective (AC power delivered to the grid, system efficiency, system reliability).
Optical fibre laser velocimetry: a review

The applications of optical fibre technology to laser velocimetry are diverse and often critical to their successful implementation, particularly in harsh environments. Applications range from the use of optical fibres for beam delivery and scattered light collection, aiding the miniaturization of instrument probes, to the use of imaging fibre bundles for imaging the flow field in planar velocimetry systems. Optical fibre techniques have also been used in signal processing, for example fibre frequency shifters, and optical fibre devices such as amplifiers and lasers have been exploited. This paper will review the use of optical fibres in point-wise laser velocimetry techniques such as laser Doppler velocimetry and laser transit anemometry, as well as in planar measurement techniques such as particle imaging velocimetry and planar Doppler velocimetry.
A regularisation scheme for electromagnetic inverse problems: application to crack detection in civil structures

A new regularisation scheme is proposed for the solution of the inverse scattering problem arising in electromagnetic imaging. The aim is to efficiently reconstruct defects and cracks with sharp boundaries when inspecting civil structures. The approach is developed in Banach spaces and tends to alleviate the over-smoothing effects of the usually adopted regularisation schemes in Hilbert spaces. Preliminary numerical results concerning the tomographic reconstruction of defects in cylindrical models made of cement paste are reported and discussed. The improved reconstruction quality (in terms of the reduction in the smoothing effect) is proven in those cases.
Learning in Transcriptional Network Models: Computational Discovery of Pathway-Level Memory and Effective Interventions

Trainability, in any substrate, refers to the ability to change future behavior based on past experiences. An understanding of such capacity within biological cells and tissues would enable a particularly powerful set of methods for prediction and control of their behavior through specific patterns of stimuli. This top-down mode of control (as an alternative to bottom-up modification of hardware) has been extensively exploited by computer science and the behavioral sciences; in biology however, it is usually reserved for organism-level behavior in animals with brains, such as training animals towards a desired response. Exciting work in the field of basal cognition has begun to reveal degrees and forms of unconventional memory in non-neural tissues and even in subcellular biochemical dynamics. Here, we characterize biological gene regulatory circuit models and protein pathways and find them capable of several different kinds of memory. We extend prior results on learning in binary transcriptional networks to continuous models and identify specific interventions (regimes of stimulation, as opposed to network rewiring) that abolish undesirable network behavior such as drug pharmacoresistance and drug sensitization. We also explore the stability of created memories by assessing their long-term behavior and find that most memories do not decay over long time periods. Additionally, we find that the memory properties are quite robust to noise; surprisingly, in many cases noise actually increases memory potential. We examine various network properties associated with these behaviors and find that no one network property is indicative of memory. Random networks do not show similar memory behavior as models of biological processes, indicating that generic network dynamics are not solely responsible for trainability. Rational control of dynamic pathway function using stimuli derived from computational models opens the door to empirical studies of proto-cognitive capacities in unconventional embodiments and suggests numerous possible applications in biomedicine, where behavior shaping of pathway responses stand as a potential alternative to gene therapy.
The ecological drivers and consequences of wildlife trade

Wildlife trade is a key driver of extinction risk, affecting at least 24% of terrestrial vertebrates. The persistent removal of species can have profound impacts on species extinction risk and selection within populations. We draw together the first review of characteristics known to drive species use – identifying species with larger body sizes, greater abundance, increased rarity or certain morphological traits valued by consumers as being particularly prevalent in trade. We then review the ecological implications of this trade‐driven selection, revealing direct effects of trade on natural selection and populations for traded species, which includes selection against desirable traits. Additionally, there exists a positive feedback loop between rarity and trade and depleted populations tend to have easy human access points, which can result in species being harvested to extinction and has the potential to alter source–sink dynamics. Wider cascading ecosystem repercussions from trade‐induced declines include altered seed dispersal networks, trophic cascades, long‐term compositional changes in plant communities, altered forest carbon stocks, and the introduction of harmful invasive species. Because it occurs across multiple scales with diverse drivers, wildlife trade requires multi‐faceted conservation actions to maintain biodiversity and ecological function, including regulatory and enforcement approaches, bottom‐up and community‐based interventions, captive breeding or wildlife farming, and conservation translocations and trophic rewilding. We highlight three emergent research themes at the intersection of trade and community ecology: (1) functional impacts of trade; (2) altered provisioning of ecosystem services; and (3) prevalence of trade‐dispersed diseases. Outside of the primary objective that exploitation is sustainable for traded species, we must urgently incorporate consideration of the broader consequences for other species and ecosystem processes when quantifying sustainability.
Time-Varying Gaussian Markov Random Fields Learning for Multivariate Time Series Clustering

Multivariate time series (MTS) clustering is an important technique for discovering co-evolving patterns and interpreting group characteristics in many areas including economics, bioinformatics, data science, etc. Although time series clustering has been widely studied in the past decades, no enough attention has been paid to capture time-varying correlation patterns in MTS. In this article, we propose a novel clustering approach for MTS data based on time-varying features. We introduce a time-varying Gaussian Markov Random Fields (T-GMRF) model to describe the correlation structure between MTS variables, and formulate the time-varying feature extraction problem as a convex optimization problem, which can be solved by a T-GMRF learning algorithm based on random block coordinate descent. We further apply a principal component analysis (PCA) based method on GMRF sequences to obtain low-dimensional feature vectors, and adopt a multi-density based clustering approach to form the cluster assignments. We conduct extensive experiments to compare the proposed T-GMRF method with 11 clustering algorithms based on 33 open MTS datasets, which show that T-GMRF significantly outperforms the state-of-the-arts with performance improvement up to 16%-64.5% on a variety of clustering performance metrics. The source codes of T-GMRF are publicly available at GitHub.
Ligand-Based Drug Design of Novel Antimicrobials against Staphylococcus aureus by Targeting Bacterial Transcription

Staphylococcus aureus is a common human commensal pathogen that causes a wide range of infectious diseases. Due to the generation of antimicrobial resistance, the pathogen becomes resistant to more and more antibiotics, resulting in methicillin-resistant S. aureus (MRSA) and even multidrug-resistant S. aureus (MDRSA), namely ‘superbugs’. This situation highlights the urgent need for novel antimicrobials. Bacterial transcription, which is responsible for bacterial RNA synthesis, is a valid but underutilized target for developing antimicrobials. Previously, we reported a novel class of antimicrobials, coined nusbiarylins, that inhibited bacterial transcription by interrupting the protein–protein interaction (PPI) between two transcription factors NusB and NusE. In this work, we developed a ligand-based workflow based on the chemical structures of nusbiarylins and their activity against S. aureus. The ligand-based models—including the pharmacophore model, 3D QSAR, AutoQSAR, and ADME/T calculation—were integrated and used in the following virtual screening of the ChemDiv PPI database. As a result, four compounds, including J098-0498, 1067-0401, M013-0558, and F186-026, were identified as potential antimicrobials against S. aureus, with predicted pMIC values ranging from 3.8 to 4.2. The docking study showed that these molecules bound to NusB tightly with the binding free energy ranging from −58 to −66 kcal/mol.
Semi-Definite Matrix Constraints in Optimization

Positive semi-definite matrix constraints arise in a number of optimization problems in which some or all of the elements of a matrix are variables, such as the educational testing and matrix modification problems. The structure of such constraints is developed, including expressions for the normal cone, feasible directions and their application to optimality conditions. A computational framework is given within which these concepts can be exploited and which permits the quantification of second order effects. The matrix of Lagrange multipliers in this formulation is shown to have an important relationship to the characterization of the normal cone. Modifications of various iterative schemes in nonlinear programming are considered in order to develop an effective algorithm for the educational testing problem, and comparative numerical experiments are described. It is shown that a particular choice of the penalty parameter for an $l_1 $ exact penalty function is appropriate for this type of problem. The be.
Intraocular pressure and cardiovascular effects of dexmedetomidine premedication and tiletamine-zolazepam for anesthetic induction in dogs

Background and Aim: The effect of anesthetic drugs on intraocular pressure (IOP) is an important concern in ophthalmic surgery. The impact of dexmedetomidine (DEX) combined with tiletamine-zolazepam on IOP is scarcely studied. This study aimed to evaluate IOP and cardiovascular effects in dogs after premedication with 5 μg/kg (DEX5) or 10 μg/kg (DEX10) of intramuscular DEX followed by intravenous tiletamine-zolazepam administration for induction of anesthesia in healthy dogs. Materials and Methods: Eighteen dogs, American Society of Anesthesiologists I or II, without ocular abnormality were investigated. All dogs were randomly divided into the DEX5 (n = 9) and DEX10 groups (n = 9). The IOP, heart rate (HR), systolic blood pressure (SBP), oxygen saturation, and sedation scale were measured before premedication (baseline), after premedication at 5, 10, 15, and 20 min, after tiletamine-zolazepam administration, after endotracheal intubation, and post-operative. Results: There were no significant differences between the groups at any time point. The DEX5 and DEX10 groups had significantly decreased HR values at 10 min compared with baseline. The IOP at 20 min was significantly lower compared to the baseline in the DEX10 group. Moreover, the DEX10 group showed increased IOP, HR, SBP, and sedation scale values after induction and intubation compared with 20 min, but these values did not differ significantly from baseline. All parameters of both groups did not change significantly between post-operative and baseline. Conclusion: Intramuscular DEX (10 μg/kg) is an appropriate premedication in ophthalmic examination or surgical procedures. Moreover, it could be combined with tiletamine-zolazepam for generalized anesthesia in dogs with an ophthalmic problem, as it had no clinically significant effects on IOP or cardiovascular values.
A Novel In Vitro Wound Healing Assay Using Free-Standing, Ultra-Thin PDMS Membranes

Advances in polymer science have significantly increased polymer applications in life sciences. We report the use of free-standing, ultra-thin polydimethylsiloxane (PDMS) membranes, called CellDrum, as cell culture substrates for an in vitro wound model. Dermal fibroblast monolayers from 28- and 88-year-old donors were cultured on CellDrums. By using stainless steel balls, circular cell-free areas were created in the cell layer (wounding). Sinusoidal strain of 1 Hz, 5% strain, was applied to membranes for 30 min in 4 sessions. The gap circumference and closure rate of un-stretched samples (controls) and stretched samples were monitored over 4 days to investigate the effects of donor age and mechanical strain on wound closure. A significant decrease in gap circumference and an increase in gap closure rate were observed in trained samples from younger donors and control samples from older donors. In contrast, a significant decrease in gap closure rate and an increase in wound circumference were observed in the trained samples from older donors. Through these results, we propose the model of a cell monolayer on stretchable CellDrums as a practical tool for wound healing research. The combination of biomechanical cell loading in conjunction with analyses such as gene/protein expression seems promising beyond the scope published here.
Mealworm Ethanol Extract Enhances Myogenic Differentiation and Alleviates Dexamethasone-Induced Muscle Atrophy in C2C12 Cells

Aging, and other disease-related muscle disorders are serious health problems. Dexamethasone (DEX), a synthetic glucocorticoid, can trigger skeletal muscle atrophy. This study examined the effects of mealworm (Tenebrio molitor larva) ethanol extract (TME) on C2C12 myoblast differentiation and DEX-induced myotube atrophy. TME induced myotube formation compared to the differentiation medium (DM) group. TME also significantly increased the mRNA expression of muscle creatine kinase (CKm) and myogenic regulatory factors (MRFs), such as myogenin (MyoG), myogenic factor (Myf)5, and MRF4 (Myf6). TME dramatically increased the muscle-specific protein, MyoG, compared to the control, whereas the expression of myogenic differentiation 1 (MyoD) remained unchanged. It also activated the mammalian target of rapamycin (mTOR) signaling pathway. In the DEX-induced muscle atrophy C2C12 model, TME reduced the gene expression of atrogin-1, muscle RING finger protein-1 (MuRF-1), and myostatin, which are involved in protein degradation in skeletal muscles. Furthermore, TME elevated the phosphorylation of forkhead box O3 (FoxO3α) and protein kinase B (Akt). These findings suggest that TME can enhance myotube hypertrophy by regulating the mTOR signaling pathway, and can rescue DEX-induced muscle atrophy by alleviating atrophic muscle markers mediated by Akt activation. Thus, TME can be a potential therapeutic agent for treating muscle weakness and atrophy.
OMC-SLIO: Online Multiple Calibrations Spinning LiDAR Inertial Odometry

Light detection and ranging (LiDAR) is often combined with an inertial measurement unit (IMU) to get the LiDAR inertial odometry (LIO) for robot localization and mapping. In order to apply LIO efficiently and non-specialistically, self-calibration LIO is a hot research topic in the related community. Spinning LiDAR (SLiDAR), which uses an additional rotating mechanism to spin a common LiDAR and scan the surrounding environment, achieves a large field of view (FoV) with low cost. Unlike common LiDAR, in addition to the calibration between the IMU and the LiDAR, the self-calibration odometer for SLiDAR must also consider the mechanism calibration between the rotating mechanism and the LiDAR. However, existing self-calibration LIO methods require the LiDAR to be rigidly attached to the IMU and do not take the mechanism calibration into account, which cannot be applied to the SLiDAR. In this paper, we propose firstly a novel self-calibration odometry scheme for SLiDAR, named the online multiple calibration inertial odometer (OMC-SLIO) method, which allows online estimation of multiple extrinsic parameters among the LiDAR, rotating mechanism and IMU, as well as the odometer state. Specially, considering that the rotating and static parts of the motor encoder inside the SLiDAR are rigidly connected to the LiDAR and IMU respectively, we formulate the calibration within the SLiDAR as two separate sets of calibrations: the mechanism calibration between the LiDAR and the rotating part of the motor encoder and the sensor calibration between the static part of the motor encoder and the IMU. Based on such a SLiDAR calibration formulation, we can construct a well-defined kinematic model from the LiDAR to the IMU with the angular information from the motor encoder. Based on the kinematic model, a two-stage motion compensation method is presented to eliminate the point cloud distortion resulting from LiDAR spinning and platform motion. Furthermore, the mechanism and sensor calibration as well as the odometer state are wrapped in a measurement model and estimated via an error-state iterative extended Kalman filter (ESIEKF). Experimental results show that our OMC-SLIO is effective and attains excellent performance.
Diversity, Evenness, and Species Richness of Aerial Insects in Dry Land of Kefamenanu, North Central Timor, East Nusa Tenggara

This research was conducted to determine the diversity, richness, and evenness of aerial insects based on habitat characteristics in the dry land of Kefamenanu city, North Central Timor, East Nusa Tenggara. The research was conducted on three types of habitats, namely dry land, rivers, and lakes by purposive sampling method. Sampling was done by documenting specimens in each habitat, identifying, inventorying, and analyzing quantitatively to obtain data on species diversity, species richness, and species evenness. Results revealed that aerial insects in Kefamenanu City were found in 9 orders, 25 families, 44 genera, 54 species, and 1998 individuals in three habitat types with diversity index (H'=3,068), evenness index (E=0.774), richness index (R). = 6.974), and dominance index (0.074). Aerial insects in dryland habitats are found in 8 orders, 18 families, 32 genera, 32 species, 514 individuals with diversity index (H'=2.735), evenness index (E=0.789), richness index (R=4.966), and dominance index. (0.092). Aerial insects in river habitat found 7 orders, 20 families, 40 genera, 47 species, and 792 individuals with diversity index (H'=3.205), evenness index (E=0.833), richness index (R=6.892), and dominance index (0.075). Aerial insects in the lake habitat found 8 orders, 18 families, 35 genera, 39 species, 692 individuals with diversity index (H'=2,906), evenness index (E=0.793), richness index (R=5.811), and dominance index (0.083).
Algorithm for automatic image registration on Harris-Laplace features

In order to overcome the difficulty of automatic registration between the images of different rotations and resolutions, this paper presents a new image registration algorithm. In the algorithm, Harris-Laplace detector is applied to extract image features; a method named scale-space projection based on scale-invariance of Harris-Laplace is proposed; and a feature space on the projection information is constructed in the direction of the main feature orientation, which makes feature space rotation-invariant and resolution-invariant. In addition, the two steps are taken to accomplish feature matching: Euclidean distance is used to match two features coarsely, and then Random Sample Consensus (RANSAC) is applied to identify the correct matches, which implements feature matching accurately and efficiently. Experiments show that the algorithm in this study can accomplish image registration automatically and accurately between images even with large differences in resolutions and rotations.