Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving crop models with respect to yield variability and climate extremes as a precondition for food security assessments

Agriculture is a cornerstone of food security, producing eighty percent of the calories that we consume either directly as food or indirectly as animal feed. Production increases in agriculture have contributed to achieve food security for the large majority of people in recent decades, with ’food security’ defined as a situation where all people at all times have nutritious and sufficient food. Nevertheless, there are still more than one billion people who are malnourished, and multiple threats are looming for the future of food security. A growing population and dietary shifts towards more animal-based food will increase the demand for agricultural production. At the same time, production capacity is under pressure, in particular by climate change. Even though the impact of climate change is uncertain, climate variability and extremes are likely to increase with it and may diminish harvests. Hence it is decisive to quantify the influences of climate on production. An important tool to quantify climatic influences on crop yields are crop models, which are mathematical descriptions of plant growth and yield. These crop models have matured over decades, but deficiencies remain. Therefore this thesis aims at improving crop models by addressing deficiencies with respect to yield variability and climate extremes. It consists of four parts. The first part is a meta-study, presenting a new appraisal and structurization of the abundant literature knowledge on crop physiology. A novel method is used to build a network-based encyclopedia, which enables a consistent and systematic classification of diverse physiological influences on crop growth. The network then allows for deducing improvement suggestions for crop models. Two of these suggestions, namely crop damage from ozone pollution and extreme temperatures, are treated in the remaining chapters. The second part presents a study of ozone damages on historical crop yields. A newly developed ozone module is implemented in the global crop model LPJmL. The enhanced model is used to simulate global historical wheat and soybean yield losses from ozone pollution. Crop water status, temperature and CO2 are considered as modulators of ozone damage, which constitutes an improvement over previous global assessments that were based on linear correlations between ozone and yield. The analysis indicates that ozone is a major problem for crop production, causing yield losses up to occasional 50%. The third part contains an analysis of the effects of high temperatures on yield losses in the US, a major crop producing country. Heat waves are likely to occur more frequently
Classifying Gastric Cancer Carcinoma Stages with Deep Semantic Features and GLCM Texture Features

Gastric cancer is one of the leading health issues that contributes to cancer related deaths. The tricky thing about cancer is that it often goes undetected until at higher stages, which makes treatment less effective. The significant death rate from gastric cancer highlights the importance of a precise and prompt diagnosis. This paper aims to tackle this problem by proposing an approach to classify the early and advanced stages of gastric cancer. This importance of this study stems from its two-pronged strategy, which provides a deeper understanding of stomach cancer stages using texture analysis and deep learning. We take advantage of the strengths of deep learning features, Gray Level Co-occurrence Matrix (GLCM) features, and machine learning algorithm to create a diagnostic tool that is more precise and accurate. Medical images from gastric cancer dataset showing early and advanced stages of gastric cancers carcinoma are included to develop this model. Our method combines the effectiveness of texture features extracted from GLCM combined with deep semantic features and classify the stages with machine learning model. We carefully evaluated Machine learning classifiers namely Support Vector Machine (SVM), Decision Tree (DT), and K-nearest neighbour (KNN) to classify the early and advanced stages. Each classifier was evaluated with different performance measures. The Support Vector Machine (SVM) classifier demonstrated the best performance with an accuracy of 96.93%. This highlights the potential of SVM for diagnosing different cancer stages, which could have positive implications, for clinical practice.
Screening magnetic fields by superconductors: A simple model

We introduce a simple approach to evaluate the magnetic field distribution around superconducting samples, based on the London equations; the elementary variable is the vector potential. This procedure has no adjustable parameters, only the sample geometry and the London length, λ, determine the solution. This approach was validated by comparing the induction field calculated to the one measured above MgB2 disks of different diameters, at 20 K and for applied fields lower than 0.4 T. The model can be applied if the flux line penetration inside the sample can be neglected when calculating the induction field distribution outside the superconductor. We conclude by showing on a cup-shape geometry how one can design a magnetic shield satisfying a specific constraint.
DIMA 3.0: Domain Interaction Map

Domain Interaction MAp (DIMA, available at http://webclu.bio.wzw.tum.de/dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46 900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software.
Diaphragmatic Surface Reconstruction from Massive Temporal Registration of Orthogonal MRI Sequences

Abstract The diaphragm is an important component of the respiratory pumping process. Orthogonal temporal sequences of MR images are acquired in free breathing without use of contrast. The vertical intersection between two orthogonal temporal sequences is determined, the diaphragmatic level in the vertical segment is determined and used to perform the temporal registration. Previous works used similar temporal registration algorithms to reconstruct the diaphragmatic surface. However, it is shown that the available information was not efficiently used. The proposed method massively performs temporal registrations, and then validates the determined temporal registrations. The images to be used in the diaphragmatic surface reconstruction are determined and its associated diaphragmatic levels are collected. Positions with inconsistencies have their diaphragmatic level determined by interpolation. The proposed algorithm is tested with MR image sequences obtained from a healthy subject.
Validating the MYSTIC three-dimensional radiative transfer model with observations from the complex topography of Arizona's Meteor Crater

Abstract. The MYSTIC three-dimensional Monte-Carlo radiative transfer model has been extended to simulate solar and thermal irradiances with a rigorous consideration of topography. Forward as well as backward Monte Carlo simulations are possible for arbitrarily oriented surfaces and we demonstrate that the backward Monte Carlo technique is superior to the forward method for applications involving topography, by greatly reducing the computational demands. MYSTIC is used to simulate the short- and longwave radiation fields during a clear day and night in and around Arizona's Meteor Crater, a bowl-shaped, 165-m-deep basin with a diameter of 1200 m. The simulations are made over a 4 by 4 km2 domain using a 10-m horizontal resolution digital elevation model and meteorological input data collected during the METCRAX (Meteor Crater Experiment) field experiment in 2006. Irradiance (or radiative flux) measurements at multiple locations inside the crater are then used to evaluate the simulations. MYSTIC is shown to realistically model the complex interactions between topography and the radiative field, resolving the effects of terrain shading, terrain exposure, and longwave surface emissions. The effects of surface temperature variations and of temperature stratification within the crater atmosphere on the near-surface longwave irradiance are then evaluated with additional simulations.
Causal Inference and Genome-Wide Perturb-Seq Reveals Tau as Downstream Biomarker but not Driver of Diseases

Tau is a protein created by alternative splicing of the gene MAPT, which plays an important role in stabilizing the internal structure of neurons. While Tau has been strongly associated with neurodegenerative diseases like Multiple Sclerosis (MS) and Alzheimer's disease, it remains unclear whether Tau is the cause of these diseases or merely a downstream biomarker. To investigate the causal association between MS and Tau levels, a Mendelian randomization (MR) analysis was performed and the results indicated that MS can causally elevate blood Tau expression. Specifically, for every one log-odds increase in MS risk, blood MAPT expression increases by 0.063 standard deviations. However, the reverse MR analysis did not show any evidence suggesting that increased blood Tau levels doesn’t affect the risk of developing MS. To gain further insights into the regulatory mechanisms of Tau and its relationship with neurodegenerative disease, we utilized Perturb-seq, a single-cell sequencing method that enables the mapping of transcriptional effects resulting from genetic perturbations. Though analyzing the transcriptomic data from genome-wide knockouts in a human cell line, it turned out that MAPT knock-out has relatively small transcriptomic impact. We also identified that genes regulating Tau, but not genes regulated by Tau, are enriched with neurodegenerative disease-related pathways. These findings suggest that Tau may serve as a downstream biomarker rather than an initiator of neurodegenerative disease, implying that future gene-based interventions should focus primarily on the upstream regulators of Tau instead of targeting Tau itself.
Non-intrusive, out-of-band and out-of-the-box systems monitoring in the cloud

The dramatic proliferation of virtual machines (VMs) in datacenters and the highly-dynamic and transient nature of VM provisioning has revolutionized datacenter operations. However, the management of these environments is still carried out using re-purposed versions of traditional agents, originally developed for managing physical systems, or most recently via newer virtualization-aware alternatives that require guest cooperation and accessibility. We show that these existing approaches are a poor match for monitoring and managing (virtual) systems in the cloud due to their dependence on guest cooperation and operational health, and their growing lifecycle management overheads in the cloud. In this work, we first present Near Field Monitoring (NFM), our non-intrusive, out-of-band cloud monitoring and analytics approach that is designed based on cloud operation principles and to address the limitations of existing techniques. NFM decouples system execution from monitoring and analytics functions by pushing monitoring out of the targets systems' scope. By leveraging and extending VM introspection techniques, our framework provides simple, standard interfaces to monitor running systems in the cloud that require no guest cooperation or modification, and have minimal effect on guest execution. By decoupling monitoring and analytics from target system context, NFM provides ``always-on'' monitoring, even when the target system is unresponsive. NFM also works ``out-of-the-box'' for any cloud instance as it eliminates any need for installing and maintaining agents or hooks in the monitored systems. We describe the end-to-end implementation of our framework with two real-system prototypes based on two virtualization platforms. We discuss the new cloud analytics opportunities enabled by our decoupled execution, monitoring and analytics architecture. We present four applications that are built on top of our framework and show their use for across-time and across-system analytics.
Systematic Review of ED-based Intimate Partner Violence Intervention Research

Introduction Assessment reactivity may be a factor in the modest results of brief interventions for substance use in the emergency department (ED). The presence of assessment reactivity in studies of interventions for intimate partner violence (IPV) has not been studied. Our objectives were to identify ED IPV intervention studies and evaluate the presence of a consistently positive effect on the control groups. Methods We performed a systematic search of electronic databases for English=language intervention studies addressing IPV in the ED published since 1990. Study selection and assessment of methodologic quality were performed by two independent reviewers. Data extraction was performed by one reviewer and then independently checked for completeness and accuracy by a second reviewer. Results Of 3,620 unique manuscripts identified by database search, 667 underwent abstract review and 12 underwent full-text review. Only three met full eligibility criteria; data on the control arm were available for two studies. In these two studies, IPV-related outcomes improved for both the experimental and control condition. Conclusion The paucity of controlled trials of IPV precluded a robust evaluation for assessment reactivity. This study highlighted a critical gap in ED research on IPV.
Visualizing Clone Cohesion and Coupling

Coupling and cohesion between subsystems are commonly studied metrics when analyzing the architecture of software systems. It is usually desirable for subsystems to have high cohesion within the subsystem and to have low coupling to other subsystems. High cohesion implies cohesive concerns and low coupling implies localized changes. We extend the ideas of coupling and cohesion to code cloning. A code clone is a segment of code that has been created through duplication of another piece of code. Previous research has shown that in some instances code cloning is desirable, whereas in other cases it is not. We believe that it is justifiable to have code cloning within subsystems (high cohesion), whereas it is not justifiable and likely not desirable to have it across subsystems (high coupling). We present an approach, which consists of a framework that generates and filters cloning data and a visualization technique which graphically highlights clone cohesion and coupling between architectural subsystems. Our approach can be used by developers to locate undesirable cloning in their software system. We demonstrate our approach through a case study on the SCSI drivers in the Linux kernel.
Optimizing treatment of desmoid tumors.

PURPOSE This study compared a large series of desmoid patients treated at a single institution to a previously published series from the same institution to determine if patient population characteristics, treatment approaches, and clinical outcomes had undergone change over the two study periods. MATERIALS AND METHODS Data from a prospective soft tissue tumor database was used to analyze clinical courses of 189 desmoid patients treated at The University of Texas M.D. Anderson Cancer Center (UTMDACC) from 1995 to 2005 as compared with 189 UTMDACC desmoid patients treated between 1965 and 1994. RESULTS A nearly three-fold increase in annualized UTMDACC desmoid referral volume with significantly higher percentages and numbers of primary desmoid tumor referrals to UTMDACC was observed in the most recent study period. Significantly increased systemic therapy use and decreased reliance on surgery alone was observed more recently. While the recent series patients had higher rates of macroscopic residual disease and equivalent rates of positive microscopic margins after definitive surgery, the estimated 5-year local recurrence rate of 20% was improved compared with the 30% rate observed in the earlier series. CONCLUSION Increased awareness of the complex multidisciplinary management needed for desmoid tumor control may underlie significantly increased numbers of referrals to UTMDACC, especially primary untreated desmoids. Increased neoadjuvant treatments may be associated with improved desmoid patient outcomes. These trends should be supported, particularly if personalized molecular-based therapies are to be rapidly and effectively deployed for the benefit of those afflicted by this rare and potentially debilitating disease.
Exploring Selfish Trends of Malicious Mobile Devices in MANET

The research effort on mobile computing has focused mainly on routing and usually assumes that all mobile devices (MDs) are cooperative. These assumptions hold on military or search and rescue operations, where all hosts are from the same authority and their users have common goals. The application of mobile ad hoc networks (MANETs) as open networks has emerged recently but proliferated exponentially. Energy is a valuable commodity in MANETs due to the limited battery of the portable devices. Batteries typically cannot be replaced in MANETs, making their lifetime limited. Diverse users, with unlike goals, share the resources of their devices and ensuring global connectivity comes very low in their priority. This sort of communities can already be found in wired networks, namely on peer-to-peer networks. In this scenario, open MANETs will likely resemble social environments. A group of persons can provide benefits to each of its members as long as everyone provides his contribution. For our particular case, each element of a MANET will be called to forward messages and to participate on routing protocols. A selfish behavior threatens the entire community and also this behavior is infectious as, other MDs may also start to perform in the same way. In the extreme, this can take to the complete sabotage of the network. This paper investigates the prevalent malicious attacks in MANET and analyzes recent selfish trends in MANET. We analyzed the respective strengths and vulnerabilities of the existing selfish behaviour prevention scheme.
Convolutional Spectral Kernel Learning with Generalization Guarantees (Abstract Reprint)

Kernel methods are powerful tools to capture nonlinear patterns behind given data but often lead to poor performance on complicated tasks compared to convolutional neural networks. The reason is that kernel methods are still shallow and fully connected models, failing to reveal hierarchical features and local interdependencies. In this paper, to acquire hierarchical and local knowledge, we incorporate kernel methods with deep architectures and convolutional operators in a spectral kernel learning framework. Based on the inverse Fourier transform and Rademacher complexity theory, we provide the generalization error bounds for the proposed model and prove that under suitable initialization, deeper networks lead to tighter error bounds. Inspired by theoretical findings, we finally completed the convolutional spectral kernel network (CSKN) with two additional regularizers and an initialization strategy. Extensive ablation results validate the effectiveness of non-stationary spectral kernel, multiple layers, additional regularizers, and the convolutional filters, which coincide with our theoretical findings. We further devise a VGG-type 8-layers CSKN, and it outperforms the existing kernel-based networks and popular CNN models on the medium-sized image classification tasks.
Extreme Learning Machine for Biomedical Image Classification: A Multi-Case Study

In the current realm of biomedical image classification, the predominant choice remains deep learning networks, particularly convolutional neural network (CNN) models. However, deep learning suffers from a notable drawback in terms of its high training cost, mainly due to intricate data models. A recent alternative, known as the Extreme Learning Machine (ELM), has emerged as a promising solution. Empirical investigations have indicated that ELM can offer satisfactory predictive performance for a wide array of classification tasks, while significantly reducing training costs when compared to deep learning networks trained using back propagation.This research paper introduces a methodology designed to evaluate the suitability of employing the Extreme Learning Machine for biomedical classification tasks. Our study encompasses binary and multiclass classification across four distinct scenarios, involving the analysis of biomedical images obtained from both dermatoscopes and blood cell microscopes. The findings underscore the effectiveness of the Extreme Learning Machine, showcasing its successful utilization in the classification of biomedical images.
Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

This study aims to assess the effectiveness of several decision tree machine techniques for identifying formation lithology of complex carbonate reservoir rocks in Gamal oil field. A total of 20966 log data points from four wells were used to create the study's data. Lithology is determined using seven log parameters. The seven log parameters are the density log, neutron log, sonic log, gamma ray log, deep lateral log, shallow lateral log, and resistivity log. Different decision tree-based algorithms for classification approaches were applied. Several typical machine learning models, namely the, Random Forest. Random trees, J48, reduced-error pruning decision trees, logistic model trees, Hoeffding Tree were assessed using well logging data for formation lithology prediction. The obtained results show that the random forest model, out of the proposed decision tree models, performed best at lithology identification, with precession, recall, and F-score values of 0.913, 0.914, and 0.913, respectively. Random trees came next. with average precision, recall, and F1-score of 0.837, 0.84, and 0.837, respectively, the J48 model came in third place. The Hoeffding Tree classification model, however, showed the worst performance. We conclude that boosting strategies enhance the performance of tree-based models. Evaluation of prediction capability of models is also carried out using different datasets
Electronic cigarettes and nicotine clinical pharmacology

Objective To review the available literature evaluating electronic cigarette (e-cigarette) nicotine clinical pharmacology in order to understand the potential impact of e-cigarettes on individual users, nicotine dependence and public health. Methods Literature searches were conducted between 1 October 2012 and 30 September 2013 using key terms in five electronic databases. Studies were included in the review if they were in English and publicly available; non-clinical studies, conference abstracts and studies exclusively measuring nicotine content in e-cigarette cartridges were excluded from the review. Results Nicotine yields from automated smoking machines suggest that e-cigarettes deliver less nicotine per puff than traditional cigarettes, and clinical studies indicate that e-cigarettes deliver only modest nicotine concentrations to the inexperienced e-cigarette user. However, current e-cigarette smokers are able to achieve systemic nicotine and/or cotinine concentrations similar to those produced from traditional cigarettes. Therefore, user experience is critically important for nicotine exposure, and may contribute to the products’ ability to support and maintain nicotine dependence. Conclusions Knowledge about e-cigarette nicotine pharmacology remains limited. Because a user's e-cigarette experience may significantly impact nicotine delivery, future nicotine pharmacokinetic and pharmacodynamic studies should be conducted in experienced users to accurately assess the products’ impact on public health.