Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the reliability of media reports for gathering information about illegal wildlife trade seizures

Illegal wildlife trade (IWT) is threatening many species across the world. It is important to better understand the scale and characteristics of IWT to inform conservation priorities and actions. However, IWT usually takes place covertly, meaning that the data on species, trade routes and volumes is limited. This means that conservationists often have to rely on publicly available law enforcement reports of seizures as potential indicators of the magnitude and characteristics of IWT. Still, even these data may be difficult to access, leading conservationists to use media reports of seizures instead. This is the case in countries like Nepal, which have limited capacity in data keeping and reporting, and no centralized data management system. Yet reliance on media reports risks introducing further biases, which are rarely acknowledged or discussed. Here we characterize IWT in Nepal by comparing data from three sources of information on IWT between January 2005 and July 2017: seizure reports from three Nepali national daily newspapers, official seizure records for Kathmandu district, and data on additional enforcement efforts against IWT in Nepal. We found a strong positive correlation between the number of official and media-reported seizures over time, but media under-reported seizure numbers, with 78% of seizures going unreported. Seizures of charismatic, protected species were reported more often and seizure reports involving tigers were most likely to be reported (57%). Media reports appeared to be a good indicator of trends and the species being seized but not overall seizure number, with the media largely underestimating total seizure numbers. Therefore, media reports cannot be solely relied upon when it comes to informing conservation decision-making. We recommend that conservationists triangulate different data sources when using seizure data reported in the media to more rigorously characterise IWT.
Colorectal Cancer Diagnosis: The Obstacles We Face in Determining a Non-Invasive Test and Current Advances in Biomarker Detection

Simple Summary Colorectal cancer (CRC) is one of the most common cancers in the western world. CRC originates from precursor adenomatous polyps, which may over time develop into cancer. Endoscopic evaluation remains the gold-standard investigation for the disease. In the absence of molecular tools for early detection, the removal of neoplastic adenomas via polypectomy remains an important measure to prevent dysplastic adenomas from evolving into invasive carcinoma. Colonoscopy is an intrusive procedure that provides an uncomfortable experience for patients. Kits for testing for the presence of blood hemoglobin in the stool are now widely used, and DNA methylation-based detection kits have been approved in the USA for testing the stool and plasma, but few other molecular biomarkers have found their way into medical practice. This review summarizes current trends in the detection and screening of CRC and provides a definitive review of emerging molecular biomarkers for CRC. Abstract Globally, colorectal cancer (CRC) is the third most common cancer, with 1.4 million new cases and over 700,000 deaths per annum. Despite being one of the most common cancers, few molecular approaches to detect CRC exist. Carcinoembryonic antigen (CEA) is a known serum biomarker that is used in CRC for monitoring disease recurrence or response to treatment. However, it can also be raised in multiple benign conditions, thus having no value in early detection or screening for CRC. Molecular biomarkers play an ever-increasing role in the diagnosis, prognosis, and outcome prediction of disease, however, only a limited number of biomarkers are available and none are suitable for early detection and screening of CRC. A PCR-based Epi proColon® blood plasma test for the detection of methylated SEPT9 has been approved by the USFDA for CRC screening in the USA, alongside a stool test for methylated DNA from CRC cells. However, these are reserved for patients who decline traditional screening methods. There remains an urgent need for the development of non-invasive molecular biomarkers that are highly specific and sensitive to CRC and that can be used routinely for early detection and screening. A molecular approach to the discovery of CRC biomarkers focuses on the analysis of the transcriptome of cancer cells to identify differentially expressed genes and proteins. A systematic search of the literature yielded over 100 differentially expressed CRC molecular markers, of which the vast majority are overexpressed in CRC. In terms of function, they largely belong to biological pathways involved in cell division, regulation of gene expression, or cell proliferation, to name a few. This review evaluates the current methods used for CRC screening, current availability of biomarkers, and new advances within the field of biomarker detection for screening and early diagnosis of CRC.
The Use of AlphaFold for In Silico Exploration of Drug Targets in the Parasite Trypanosoma cruzi

Chagas disease is a devastating neglected disease caused by the parasite Trypanosoma cruzi, which affects millions of people worldwide. The two anti-parasitic drugs available, nifurtimox and benznidazole, have a good efficacy against the acute stage of the infection. But this is short, usually asymptomatic and often goes undiagnosed. Access to treatment is mostly achieved during the chronic stage, when the cardiac and/or digestive life-threatening symptoms manifest. Then, the efficacy of both drugs is diminished, and their long administration regimens involve frequently associated adverse effects that compromise treatment compliance. Therefore, the discovery of safer and more effective drugs is an urgent need. Despite its advantages over lately used phenotypic screening, target-based identification of new anti-parasitic molecules has been hampered by incomplete annotation and lack of structures of the parasite protein space. Presently, the AlphaFold Protein Structure Database is home to 19,036 protein models from T. cruzi, which could hold the key to not only describe new therapeutic approaches, but also shed light on molecular mechanisms of action for known compounds. In this proof-of-concept study, we screened the AlphaFold T. cruzi set of predicted protein models to find prospective targets for a pre-selected list of compounds with known anti-trypanosomal activity using docking-based inverse virtual screening. The best receptors (targets) for the most promising ligands were analyzed in detail to address molecular interactions and potential drugs’ mode of action. The results provide insight into the mechanisms of action of the compounds and their targets, and pave the way for new strategies to finding novel compounds or optimize already existing ones.
Effectiveness of Human-in-the-Loop Sentiment Polarization with Few Corrected Labels

—In this work, we investigated the effectiveness of adopting Human-in-the-Loop (HITL) aimed to correct automatically generated labels from existing scoring models, e.g. SentiWordNet and Vader to enhance prediction accuracy. Recently, many proposals showed a trend in utilizing these models to label data by assuming that the labels produced are near to ground truth. However, none investigated the correctness of this notion. Therefore, this paper fills this gap. Bad labels result in bad predictions, hence hypothetically, by positioning a human in the computing loop to correct inaccurate labels accuracy performance can be improved. As it is infeasible to expect a human to correct a multitude of labels, we set out to answer the questions of “What is the smallest percentage of corrected labels needed to improve prediction quality against a baseline? ” and “Would randomly selecting automatic labels for correction produce better prediction than specifically choosing labels with distinct data points? ”. Naïve Bayes (NB) and Decision Tree (DT) were employed on AirBnB and Vaccines public datasets. We could conclude from our results that not all ML algorithms are suited to be used in a HITL environment. NB fared better than DT at producing improved accuracy with small percentages of corrected labels, as low as 1%, exceeding the baseline. When selected for human correction, labels with distinct data points assisted in enhancing the accuracy better than random selection for NB across both datasets, yet partially for DT.
Microwave Transistor Amplifiers: Analysis and Design

Preface. 1. Representations of Two-Port Networks. 2. Matching Networks and Signal Flow Graphs. 3. Microwave Transistor Amplifier Design. 4. Noise, Broadband, and High-Power Design Methods. 5. Microwave Transistor Oscillator Design. Appendix: Computer-Aided Designs. Appendix A: A. 1. Circle Equations: Bilinear Transformation, A. 2. Derivation of the Input and Output Stability Circles. Appendix B: Stability Conditions. Appendix C: Unconditional Stability Conditions. Appendix D: Derivation of the Unilateral Constant- Gain Circles. Appendix E: E. 1. Analysis of (3.65) and (3.6.6) for . , E. 2. Condition for a Simultaneous Conjugate Match. Appendix F: Derivation of Gt, max. Appendix G: Derivation of the Constant Operating Power-Gain Circles. Appendix H: Expressions for ml. Appendix I: Constant VSWR Circles. Appendix J: Mapping of Circles. Appendix K: Noise Concepts. Appendix L: Noise Figure of an Amplifier, Appendix M: Conditions for a Stable Oscilllation. Index.
DDoS Attack Detection in IoT Using Machine Learning Based Intrusion Detection System (IDS)

The market for Internet of things (IoT) has expanded tremendously. This needs the creation of an ecosystem that is user-friendly and secure. Internet connected IoT devices provide rich user experience and add-on services. Eventually, the IoT devices are interconnected to a management system. Attacking the IoT ecosystem is rising at the same rate as those of the physical attacks and vandalism happening in the IT world. The cyberattack is more severe than the physical attack as it may affect thousands of the IoT devices at the same time. Intrusion Detection is vital in defending diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to find out each traffic stream is legitimate or malicious. Furthermore, the IDS can identify the type of the malicious traffic. This study is a benchmark of different machine learning classifier algorithms for finding fraudulent traffic specifically in the IoT network environment. A typical IoT dataset obtained from actual IoT traffic is used in the study. We compare various classification algorithms that is placed inline to the traffic and contain different types of attacks targeting the IoT network. The obtained results from the different algorithms would improve the stability and significantly reduce the amount of cyberattacks that could disrupt daily living activities in the IoT Environment.
Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings

In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system’s efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining.
Fixed points and completeness on partial metric spaces

Recently, Suzuki [T. Suzuki, A generalized Banach contraction principle that characterizes metric completeness, Proc. Amer. Math. Soc. 136 (2008), 1861-1869] proved a ﬁxed point theorem that is a generalization of the Banach contraction principle and characterizes the metric completeness. Paesano and Vetro [D. Paesano and P. Vetro, Suzuki’s type characterizations of completeness for partial metric spaces and ﬁxed points for partially ordered metric spaces, Topology Appl. , 159 (2012), 911-920] proved an analogous ﬁxed point result for a self-mapping on a partial metric space that characterizes the partial metric 0-completeness. In this paper we prove a ﬁxed point result for a new class of contractions of Berinde-Suzuki type on a partial metric space. Moreover, using our results, as application we obtain a new characterization of partial metric 0-completeness. Finally, we give a typical application of ﬁxed point methods to integral equation, by using our results.
A microsphere-based assay for multiplexed single nucleotide polymorphism analysis using single base chain extension.

A rapid, high throughput readout for single-nucleotide polymorphism (SNP) analysis was developed employing single base chain extension and cytometric analysis of an array of fluorescent microspheres. An array of fluorescent microspheres was coupled with uniquely identifying sequences, termed complementary ZipCodes (cZipCodes), which allowed for multiplexing possibilities. For a given assay, querying a polymorphic base involved extending an oligonucleotide containing both a ZipCode and a SNP-specific sequence with a DNA polymerase and a pair of fluoresceinated dideoxynucleotides. To capture the reaction products for analysis, the ZipCode portion of the oligonucleotide was hybridized with its cZipCodes on the microsphere. Flow cytometry was used for microsphere decoding and SNP typing by detecting the fluorescein label captured on the microspheres. In addition to multiplexing capability, the ZipCode system allows multiple sets of SNPs to be analyzed by a limited set of cZipCode-attached microspheres. A standard set of non-cross reactive ZipCodes was established experimentally and the accuracy of the system was validated by comparison with genotypes determined by other technologies. From a total of 58 SNPs, 55 SNPs were successfully analyzed in the first pass using this assay format and all 181 genotypes across the 55 SNPs were correct. These data demonstrate that the microsphere-based single base chain extension (SBCE) method is a sensitive and reliable assay. It can be readily adapted to an automated, high-throughput genotyping system. [Primer sequences used in this study are available as online supplementary materials at www.genome. org. ]
Benchmarking Evolutionary Community Detection Algorithms in Dynamic Networks

In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Louvain, or Greedy Modularity Algorithm (GMA), is widely employed in real-world applications due to its intuitiveness and scalability. Nevertheless, addressing CD in dynamic graphs remains an open problem, since the evolution of the network connections may poison the identification of communities, which may be evolving at a slower pace. Hence, naively applying GMA to successive network snapshots may lead to temporal inconsistencies in the communities. Two evolutionary adaptations of GMA, sGMA and $\alpha$GMA, have been proposed to tackle this problem. Yet, evaluating the performance of these methods and understanding to which scenarios each one is better suited is challenging because of the lack of a comprehensive set of metrics and a consistent ground truth. To address these challenges, we propose (i) a benchmarking framework for evolutionary CD algorithms in dynamic networks and (ii) a generalised modularity-based approach (NeGMA). Our framework allows us to generate synthetic community-structured graphs and design evolving scenarios with nine basic graph transformations occurring at different rates. We evaluate performance through three metrics we define, i.e. Correctness, Delay, and Stability. Our findings reveal that $\alpha$GMA is well-suited for detecting intermittent transformations, but struggles with abrupt changes; sGMA achieves superior stability, but fails to detect emerging communities; and NeGMA appears a well-balanced solution, excelling in responsiveness and instantaneous transformations detection.
Seminar Paper: Operating System Support for Embedded Devices

Living in a digital era, the terms "Internet of Things" and "embedded devices" are well known. An increasing number of products contain electronics and are internet-capable, making them part of the Internet of Things. Nowadays, these devices are capable of collecting and computing data. Furthermore, they can communicate via general-purpose protocols. Thus, operating systems are required to execute corresponding applications. While many general-purpose operating systems already exist for such devices, they may not be the optimal solution due to limited resources on these devices. This raises the question of how to save resources while still performing tasks performantly and reliably. Wang and Seltzer suggest that building a use-case-specific operating system represents a better alternative to general-purpose operating systems. Thereby they present Tinkertoy, a set of tools from which one can assemble a custom operating system from building blocks. Consequently, an operating system can be built providing only the necessary functionality to run a specific application, omitting unrequired parts of the system. Furthermore, Wang and Seltzer show that such an operating system can be assembled in only a few lines of code and provides a significant improvement in memory usage compared to established operating systems for embedded devices while performing virtually the same in terms of runtime.
Damage tolerance of composite stiffened structures

Composite stiffened panels have been extensively used in primary aerospace applications, due to their efficient stiffness/weight ratio and the ability to tailor properties. Despite their apparent superiority to metals, the susceptibility of composites to damage has proved to be a critical factor. A common example in practice is the barely visible impact damage caused by a low velocity impact event. Such events can generate inter-laminar defects (delaminations), which under compressive loading can reduce the strength and stability of the structure. There is a need to establish the influence of such damage on composite stiffened structures. Current practice allows for delamination induced strength reduction by applying strain limits to the material that ensure delamination does not produce failure before ultimate levels of loading. However, these limits are established via coupon tests, which focus on the scale of the delamination only and do not account for the interaction at the structural scale. The objective of the current work is to investigate the behaviour of the delaminated region in the presence of structural effects and coupling phenomena, and subsequently, identify the effect of the delamination itself in the preand post-buckling response of the structure. Therefore, efficiency and safety requirements in composite stiffened structures may be enhanced. In accordance to simplified plate and strut models, delaminations may have little or significant debilitating effect according to their position through the depth of the laminate. Thus, the current work investigates experimentally and theoretically the effect of delamination damage, in a variety of locations and through the thickness positions, on the strength and stability of a laminated thin-walled channel-section strut. Both damaged and undamaged channel-section struts are designed according to novel design methods. The effectiveness of the methods is established by comparisons to
Recommendations for the management of patients with unruptured intracranial aneurysms: A statement for healthcare professionals from the Stroke Council of the American Heart Association.

A neurysmal subarachnoid hemorrhage (SAH) has a 30-day mortality rate of 45%, with approximately half the survivors sustaining irreversible brain damage. 1 On the basis of an annual incidence of 6 per 100 000, ’ 15 000 Americans will have an aneurysmal SAH each year. Population-based incidence rates vary considerably from 6 to 16 per 100 000, with the highest rates reported from Japan and Finland. 2–5 Approximately 5% to 15% of stroke cases are secondary to ruptured saccular aneurysms. Although the prevention of hemorrhage has been advocated as the most effective strategy aimed at lowering mortality rates, 6 the optimal management of patients with unruptured intracranial aneurysms (UIAs) remains controversial. Management decisions require an accurate assessment of the risks of various treatment options compared with the natural history of the condition. The natural history of UIAs and treatment outcomes are influenced by (1) patient factors, such as previous aneurysmal SAH, age, and coexisting medical conditions; (2) aneurysm characteristics, such as size, location, and morphology; and (3) factors in management, such as the experience of the surgical team and the treating hospital. These many influences have contributed to considerable variability in the reported risks for aneurysmal SAH and the treatment of UIAs. There are no prospective randomized trials of treatment interventions versus conservative management to date, and it is possible that no such studies will be carried out in the future. According to a classification system suggested by Cook et al, 7 randomized clinical trials with low likelihoods of false-positive and false-negative errors