Miracle-dz/UniFAR-FTUs huggingface dataset:
Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

During both sleep and awake immobility, hippocampal place cells reactivate time-compressed versions of sequences representing recently experienced trajectories in a phenomenon known as replay. Intriguingly, spontaneous sequences can also correspond to forthcoming trajectories in novel environments experienced later, in a phenomenon known as preplay. Here, we present a model showing that sequences of spikes correlated with the place fields underlying spatial trajectories in both previously experienced and future novel environments can arise spontaneously in neural circuits with random, clustered connectivity rather than pre-configured spatial maps. Moreover, the realistic place fields themselves arise in the circuit from minimal, landmark-based inputs. We find that preplay quality depends on the network’s balance of cluster isolation and overlap, with optimal preplay occurring in small-world regimes of high clustering yet short path lengths. We validate the results of our model by applying the same place field and preplay analyses to previously published rat hippocampal place cell data. Our results show that clustered recurrent connectivity can generate spontaneous preplay and immediate replay of novel environments. These findings support a framework whereby novel sensory experiences become associated with preexisting “pluripotent” internal neural activity patterns. Impact Statement Neural circuits with small-world connectivity spontaneously emit sequences of spikes that are correlated with any of the distinct sequences of realistic place fields produced by location-modulated, monotonically varying input.
Locoweed Harm Investigation in the West Grassland of China

This paper investigated the species,distribution and harm of locoweeds and the various ways to prevent and cure locoweed poisoning in China by literature review and field survey.It shows that there are 44 species of locoweeds (including 23 species of Oxytropis and 21 species of Astragalus) which distribute beyond 11 million hm2 and 18 species of these are very harmful to the animal husbandry of grassland.Finally,several advices are given to decrease the harm of locoweed.
Cancer subtype identification using large-scale omics data analysis

Cancer is the second leading cause of death worldwide. It is characterized by abnormal cell proliferation, potentially followed by spreading into surrounding tissue and body organs. Cancer is challenging to treat since it is very heterogeneous: even tumors originating from the same organ can greatly vary in their biological mechanism, survival risk, and response to treatment. Recent years have shown the emergence of large cancer genomic projects, providing detailed multi-omic profiles together with clinical information for thousands of cancer samples. In line with the vision of precision medicine, integration of omic and clinical data using statistical and algorithmic methods allows us to computationally identify clinically distinct subgroups that may have a profound impact on diagnosis, drug discovery, and treatment. In this work, we developed a methodology for improving the classification of cancers based on high-throughput omic data and applied it to both breast and skin cancers. Our analysis of the breast cancer cohort revealed a significant heterogeneity within the luminal-A subtype and partitioned its samples into prognostic subgroups based on expression and methylation patterns. Our analysis of the skin cancer cohort identified a group of poor-prognosis melanoma samples characterized by melanogenesis genes. We also suggested a simple three-gene classifier for predicting melanoma subtypes. Lastly, we describe PROMO, an interactive software tool we developed for multi-omic cancer data analysis and subtyping that generalizes the methodology used in the breast and skin cancer projects.
SEDIMENTATION IN THE MOSUL RESERVOIR OF NORTHERN IRAQ

Mosul Dam is one of the biggest hydraulic structures in Iraq. It was constructed in 1986 on the Tigris River in the north of Iraq for multiple purposes: irrigation, flood control and power generation. The initial storage capacity and water surface area of its reservoir reached 11.11 km3 and 380 km2 respectively at the maximum operation level 330 m a.s. l. The dam was operational in 1986. Blockage of the intakes of the pump station for North Al-Jazira Irrigation Project in the Mosul Dam reservoir has highlighted the importance of sedimentation problems within the reservoir. A total of 56 samples were collected from the bottom of Mosul reservoir covering most of the reservoir area. The results of the analysis of these samples revealed that they were composed of gravel (3.8%), sand (15%), silt (55.5%) and clay (25.7%). The distribution of these sediments indicates that the silt portion represents the highest or 77% of the bottom sediments of this reservoir followed by clay (13.5%) and then sand (9.5%). However, sand percentages are the highest in the northern zone of the reservoir where the River Tigris enters the reservoir and decrease gradually toward the dam site. In the meantime, silt percentage decreases toward the dam site while the finer fraction (i.e. clay) increases. Statistically, the average median and mean sizes of the sediments are 2.81 phi (0.142 mm) and 6.1 phi (0.0146 mm) respectively. In addition, the sediments are poorly sorted, nearly symmetrical in skewness and leptokurtic, very leptokurtic, to mesocratic. Finally, it is believed that the geometry and hydrodynamics of the Mosul reservoir, the location of the River Tigris entrance together with the side tributary valleys have played the most important role in the sediments distribution and their characteristics.
RF-PSO: An Optimized Approach for Diabetes Prediction

Diabetes is a chronic disease due to a malfunction of the pancreas, which leads to high concentration of blood sugar in the blood and can affect the functioning of the body system. High blood sugar levels contribute to complications, over time as it can damage the heart, blood vessels, eyes, kidneys, and nerves.etc. Therefore, we need to develop a system capable of effectively diagnosing diabetic patients using medical details. There are various machine-learning techniques for diabetes prediction; this can help persons to prevent this disease or early detection to avoid its complications. This work is a continuation of our previous contribution for gestational diabetes prediction where we conducted a study demonstrating the effectiveness of the Random Forest algorithm in predicting gestational diabetes. In the present study, and to enhance performance, we use a swarm intelligence approach to extract best features for training Random Forest algorithm. The performance of the proposal was compared. The results demonstrate that the combination of the Random Forest algorithm with the Particle Swarm Optimization algorithm provides better accuracy.
Network-based algorithms for analysis of heterogeneous biomedical data

Technological breakthroughs in the past decade have enabled the collection of biomedical data on an unprecedented scale. Several methods are used to map diverse interactions among genes or gene products and their results can be collected into genome-wide networks. Other methods measure the activity or abundance of genes across different conditions. The data obtained by most available techniques are noisy, heterogeneous and difficult to interpret, making data integration essential. In addition, most cellular functions rely on the coordinated action of the products of multiple genes, often referred to as functional modules. A major goal of computational systems biology is to enable the delineation of these modules and to facilitate extraction of biological insights from the deduced modular structures. In this thesis we describe several methods for extraction of functional modules using heterogeneous data. We specifically focus on analysis of protein and genetic interaction networks and gene expression data, but our computational methodologies are suitable for handling additional data types. We demonstrated the effectiveness of these methods on a variety of biological systems in yeast and human. Our results include predictions of functions for genes and gene groups, delineation of novel pathways and complexes and the relationships between them, and prediction of functionally important interactions. In the context of human disease, the modules we identify provide a signature of the disease potentially useful for diagnosis, pinpoint possible pathways affected by the disease, and suggest targets for drug
Structural prediction of flexible molecular interactions

3 Acknowledgements . 8 Preface . 9 Chapter 1 : Introduction . 10 1.1 Biological Background and Motivation. 10 1.2 Challenges of flexible docking . 11 1.3 General Scheme of Flexible Docking . 12 Chapter 2 : Existing Methods for Flexible Docking . 14 2.1 Protein Flexibility Analysis . 14 2.1.1 Conformational ensemble analysis . 14 2.1.2 Molecular dynamics . 15 2.1.3 Normal modes . 16 2.1. 4 Essential dynamics . 18 2.1. 5 Rigidity theory . 19 2.2 Handling Backbone Flexibility in Docking Methods . 19 2.2.1 Soft interface . 20 2.2.2 Ensemble docking . 20 2.2.3 Modeling hinge motion . 21 2.2.4 Refinement and minimization methods for treating backbone flexibility . 22 2.3 Handling Side-Chain Flexibility in Docking Methods . 23 2.3.1 Global optimization algorithms for side-chain refinement . 24 2.3. 2 Heuristic methods for treating side-chain flexibility . 27 2.4 Handling Both Backbone and Side-Chain Flexibility in Recent Capri Challenges . 28 2.4 Discussion . 29 Chapter 3 : FiberDock Flexible induced-fit backbone refinement in molecular docking . 34 3.1 Methods . 34 3.1.1 Normal mode analysis . 36 3.1.2 Correlation measurement . 37 3.1.3 Minimization according to normal modes . 38 3.1. 4 Applying a normal mode on a protein . 39
Effect of Bank Financing and Tax Purposes on Relation Between Financial Reporting Quality and Investment Efficiency in Tehran Stock Exchange (TSE) Listed Companies

: The goal of this study is to test the impact of bank financing and tax purposes in relation between financial reporting quality (FRQ) and investment efficiency. Last studies have demonstrated that high quality financial reporting could have important economical impacts such as increasing investment efficiency. In this study discretionary accruals, discretionary revenues, accruals quality and average of these criterions are used for financial reporting quality measurement. Also under-and-over investment is considered as a criterion for investment efficiency. The statistical population of the study is 134 listed companies in Tehran Stock Exchange (TSE) for the period of 2003-2009. Results show that there is a negative relationship between financial reporting quality and under-over investment. Also there is a negative relationship between FRQ-bank financing and under-over investment. In the situation of tax pressure, there is a positive relationship between FRQ and under-over investment. Thus in this situation, financial reporting quality does not result in investment efficiency increase.
Observations of Directional Relaxation of Wind Sea Spectra

Abstract Two-dimensional wave spectra were acquired through a NOAA Experimental Research Buoy in 34 m of water off the North Carolina coast (Atlantic Ocean). These are analyzed in ideal wave-growth situations and under rapidly turning winds. The relationship between variance and peak frequency for growing seas, determined from the buoy data, agrees well with relationships based on data from other methods. The response of mean wave direction as a function of frequency is documented graphically and by simple regression analyses for several cases of rapidly turning wind fields. The relaxation rates for wave direction are similar to limited previous estimates. The present results can be used in evaluating wave prediction models.
DEG/ENaC/ASIC channels vary in their sensitivity to anti-hypertensive and non-steroidal anti-inflammatory drugs

Animal physiology depends on degenerin, epithelial sodium, and acid-sensing ion channels (DEG/ENaC/ASICs). By measuring the sensitivity of three C. elegans DEG/ENaC/ASICs to amiloride analogs and NSAIDs, Fechner et al. show that individual channels have distinct pharmacological footprints.
“It’s Not Only Our Task” Administrative Barriers of Enabling Urban Gardening in Tampere, Finland

An ongoing academic debate shows that urban community gardening (CG) has diverse governance models with differing roles of city administration and citizens. This article uses an empirical case study conducted in the city of Tampere, Finland, to explore what I call the “operational space” of urban CG seen from the viewpoint of city officials. Two rounds of interviews were conducted with eight city officials, and a discourse analysis was applied for the data. As an analytic term developed in this article, the operational space emerges by administrative policies and practices that enable or constrain urban gardening under two general trends of urban governance: institutional ambiguity
Interorganizational Data Sharing in Health Ecosystems - A Case Interorganizational Data Sharing in Health Ecosystems - A Case Sudy Sudy

The integration of external data offers enormous potential for new and expanded value propositions for companies. Despite this potential, organizations often hesitate to share data due to the anticipated negative consequences. This study delves into the decision-making process of organizations regarding data sharing within an ecosystem and explores strategies to motivate other organizations to share data. By combining privacy calculus and ecosystems theory, we employ a qualitative case study approach to examine the German orthopedic market. Our findings reveal three risks (weakening one's position in the ecosystem, IT alignment investments, and penalties for data protection violations) and three benefits (increased value creation for the ecosystem, competitive advantage over other ecosystems, and gaining additional transactions) that organizations perceive when contemplating data sharing. Moreover, we identify three strategies for obtaining data from other organizations: mitigating the risks, emphasizing the benefits, and bypassing the calculus.
Effects of boron application on the physiological potential of stored sunflower seeds

The objective of this study was to assess the physiological quality of sunflower seeds as a function of boron rates and seed storage times. The experiment was conducted at the Seed Laboratory of the State University of Goiás, Ipameri campus, Goias, Brazil. A completely randomized experimental design was used, in a 6×6 factorial arrangement, with four replications. The factors consisted of six boron rates (0, 1, 2, 3, 4, and 5 kg ha -1 ) manually applied to the soil using boric acid (17%), and six seed storage times (0, 2, 4, 6, 8, and 10 months). The seeds used were from plants grown in 2017 as a second crop, which was fertilized according to the boron treatments. The following evaluations were conducted after harvesting for each seed storage time: germination test, first germination count, accelerated aging test, total germination, seed moisture content, seedling length and dry weight, 1,000-seed weight, and tetrazolium test. Boron rates and storage times had positive effects on most analyzed variables. The most adequate boron rate found was 2 to 3 kg ha -1 , with no effect of seed storage times on the physiological quality of sunflower seeds.
GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver

This paper presents a novel method for GUI testing in web applications that largely automates the process by integrating the advanced language model GPT-4 with Selenium, a popular web application testing framework. Unlike traditional deep learning approaches, which require extensive training data, GPT-4 is pre-trained on a large corpus, giving it significant generalisation and inference capabilities. These capabilities allow testing without the need for recorded data from human testers, significantly reducing the time and effort required for the testing process. We also compare the efficiency of our integrated GPT-4 approach with monkey testing, a widely used technique for automated GUI testing where user input is randomly generated. To evaluate our approach, we implemented a web calculator with an integrated code coverage system. The results show that our integrated GPT-4 approach provides significantly better branch coverage compared to monkey testing. These results highlight the significant potential of integrating specific AI models such as GPT-4 and automated testing tools to improve the accuracy and efficiency of GUI testing in web applications.
2.—Palynological Correlations within the Lower Carboniferous of Scotland and Northern England

Synopsis This paper provides a comprehensive account of the distribution of miospore species which are considered to be stratigraphically significant in the Dinantian (Calciferous Sandstone Measures and Lower Limestone Group) of the Midland Valley of Scotland. Five miospore concurrent range zones are recognised in these beds: Tripartites vetustus—Rotaspora fracta (VF) Zone Raistrickia nigra—Triquitrites marginatus (NM) Zone Perotrilites tessellatus—Schulzospora campyloptera (TC) Zone Lycospora pusilla (Pu) Zone Schopfites claviger—Auroraspora macra (CM) Zone The distribution of these zones in the sections studied provides a method of correlation in a series of rocks, which are largely of non-marine origin and notable for their lack of stratigraphically useful macrofossils and for marked variations in facies and thickness. The zones have also been proved in sections of Lower Carboniferous rocks in the Northumbrian trough, thereby providing a potential method of correlation between the two areas. Two new genera, Colatisporites and Biannulatisphaerites, are described and one Tricidarisporites emended. Fifteen new species and one new type are described, three species recombined and three emended. Representative assemblages of the miospore zones are illustrated.
Fuzzing with Sequence Diversity Inference for Sequential Decision-making Model Testing

Nowadays increasing AI techniques, e.g., reinforcement learning, imitation learning, etc., are applied to solve sequential decision-making problems by modeling them as Markov Decision Process (MDP), and achieve superior performance in areas, such as video games, robotics and autonomous driving etc. The reliability of such models is facing severe challenges especially in some safety-critical areas, where failures would bring intolerable disasters. Existing works testing episodic decision-making models are not workable, since they neglect the nature of sequentiality and interactivity in MDP. While other works testing sequential decision-making models are challenged by low testing efficiency because the interaction of MDP is time-consuming. In this paper, we propose an optimized fuzzing framework SeqDivFuzz which infers the sequence diversity during the MDP interaction process to effectively and efficiently test sequential decision-making models in blackbox settings. It adapts the existing fuzzing framework, including Seed Selection, Seed Mutation, Feedback Analysis and integrating a module of Diversity Inference to accelerate the fuzzing procedure. The module learns historical in-process information to check the diversity of test cases when running up to checkPoint in the course of MDP, and early terminating those non-diverse ones. We conduct experimental evaluation with four models involving three simulation environments. The results reflect that SeqDivFuzz exposes 12.3%~49.1% more crashes during a 12-hour testing procedure in four pairs of models and environments compared with the state-of-the-art fuzzing framework. The idea of in-process terminating can potentially boost other techniques for testing sequential decision-making models.
How to Manage Change-Induced Incidents? Lessons from the Study of Incident Life Cycle

In online service systems, software changes cause a majority of incidents (i.e., unplanned interruptions and outages). Managing change-induced incidents efficiently is crucial for ensuring the reliability and availability of online service systems. Understanding the incidents can help improve change-induced incident management. The task is challenging because the life cycle of change-induced incidents is complicated due to diverse change deployment and incident resolution procedures. Detailed records of the incidents and changes, together with a comprehensive analysis, are needed to gain an in-depth understanding. In this paper, we conduct a qualitative and quantitative study on 231 change-induced incidents in a real-world, large-scale online service system. Detailed change tickets and incident timeline in the post-mortems provides extensive information about the incident life cycle, enabling us to understand each incident in depth. Based on the data, we give a generic model of the complicated life cycle of change-induced incidents. Following the model, we systematically study the whole life cycle of the incident, including the introduction and resolution stages, and answer what affects the efficiency of resolution. We obtain 9 major findings from our study. Based on the findings, we discuss existing techniques and promising future directions for improving change-induced incident management.
An improved SCE-UA algorithm and its application in parameter calibration of the Xin'anjiang model

The Shuffled Complex Evolution-developed at University of Arizona (SCE-UA) algorithm has been widely applied in the field of hydrological model parameter calibration. This algorithm exhibits strong global optimization capability and good robustness. The reflection and contraction steps in the downhill simplex search (DSS) of the SCE-UA algorithm adopt a fixed scaling factor, and we find that the potential to further improve the optimization efficiency of DSS has been revealed by numerical experiments. In order to enhance the optimization efficiency of the algorithm, numerical experiments and analysis were conducted, leading to the identification of an optimal range for the scaling factor. Multiple sampling points were uniformly selected within the optimal range, and an optimal selection method was used to dynamically determine the most suitable scaling factor during the process of optimization. This resulted in the development of the SCE-UA algorithm considering adaptive dynamic scaling factor (SCE-UA-ADSF). Studies on model parameter calibration were carried on by using artificial rainfall-runoff data based on SCE-UA-ADSF. The results of the numerical experiments demonstrate that the improved algorithm, which incorporates adaptive dynamic scaling factor, significantly improves the optimization efficiency compared to using a fixed scaling factor. This improvement holds valuable potential for future general purpose real-world applications.
Urban Effects on Regional Climate: A Case Study in the Phoenix and Tucson “Sun Corridor”

AbstractLand-use and land-cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise because of projected urbanization in the Phoenix–Tucson corridor, Arizona, this study applied the coupled WRF Model–Noah–Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreas.