Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk factors of pancreatic cancer and their possible uses in diagnostics.

Pancreatic cancer (PC) is a form of malignancy of increasing incidence and poor prognosis, with an average of less than 10% of patients surviving 5 years after being diagnosed. The main reason for this unfavorable situation is the long asymptomatic course of the disease, and the absence of a simple screening method, typically leading to the late discovery of the disease. The development of the malignancy from the initial carcinogenesis into invasive pancreatic carcinoma takes approximately 10 years. However, the progression of pancreatic cancer from early into advanced stages can be, according to the latest studies, incredibly fast, just a few months. Early stages of pancreatic malignancy can be detected only by expensive, and sometimes invasive, diagnostic methods (CT, MR/MRCP, or EUS). Due to the current absence of a reliable non-invasive screening method, it is necessary to define a group of patients who have the highest risk of PC development, five to ten times higher risk compared to the regular population at a minimum. Risk factors combine in their effect; therefore, relative risks of PC development need to be summarized to obtain a total relative risk for each person. The main and non-influenceable risk factor in the development of PC is the increasing age. The other non-influenceable risk factor of PC is a genetic predisposition - family incidence of the disease can be detected in 4-16% of patients. Some specific genes and mutations which can play a role in PC development have already been identified (for example mutation of the PRSS-1 gene). Among the influenceable risk factors of PC is primarily smoking; obesity can play a part in PC development as well. A higher risk of PC is observed in patients with chronic pancreatitis. Nowadays, the relationship between PC and diabetes mellitus (DM) is hotly discussed. In the case of long-standing DM, the risk of pancreatic cancer is two times higher compared to the healthy population. However, new-onset DM can be the first sign of still asymptomatic PC. These patients, with paraneoplastic DM caused by pancreatic cancer cells, represent approximately 1% of recently diagnosed patients. However, this group of patients is still too large for screening. Because of that, it is necessary to find specific criteria to distinguish classic DM from the paraneoplastic form. The application of these criteria can help with the better stratification of risk in patients with new-onset diabetes and hence, it can help to discover PC in its early stages.
Natural Convection Heat Transfer in Horizontal Cylindrical Cavities: A Computational Fluid Dynamics (CFD) Investigation

Many processes in power plants involve the storage and transfer of fluids including water in outdoor pipelines. Under extreme cold weather conditions, water can freeze if allowed to cool down to the freezing temperature. Installing insulation and maintaining adequate flow rate can sometimes prevent problems. However, during extended non-processing times, there are circumstances where cool down cannot be avoided and heat tracing along the piping becomes a necessity.In many instances, the need for the installation of heat tracing is simply determined based on pipe size. However, by performing accurate calculations, it is possible to determine if the need for heat tracing is real or not, thus saving on installation and maintenance costs. Correlations for the estimation of the heat transfer coefficient in horizontal cavities are not sufficiently documented in literature. In the present work, two-dimensional CFD models are used to investigate the natural convection in water-filled horizontal pipes of different diameters. The analysis has been carried out based on the assumption of a uniform pipe surface temperature. The Nusselt number is estimated as a function of the Rayleigh number and shown not to be strongly dependent on the Prandtl number.The analysis and the results of the numerical investigation are presented and compared to experimental data and other correlations available in literature. The documented correlation has an expanded range of applicability to high and low Rayleigh numbers, is supported by numerical and experimental results and is expressed in a simple form.Copyright © 2013 by ASME
Cutaneous Malignant Melanoma: A Review of Early Diagnosis and Management

Cutaneous melanoma (CM) is a malignant tumor formed from pigment-producing cells called melanocytes. It is one of the most aggressive and fatal forms of skin malignancy. In the last decades, CM’s incidence has gradually risen, with 351,880 new cases in 2015. Since the 1960s, its incidence has increased steadily, in 2019, with approximately 96,000 new cases. A greater understanding of early diagnosis and management of CM is urgently needed because of the high mortality rates due to metastatic melanoma. Timely detection of melanoma is crucial for successful treatment, but diagnosis with histopathology may also pose a significant challenge to this objective. Early diagnosis and management are essential and contribute to better survival rates of the patient. To better control this malignancy, such information is expected to be particularly useful in the early detection of possible metastatic lesions and the development of new therapeutic approaches. This article reviews the available information on the early diagnosis and management of CM and discusses such information’s potential in facilitating the future prospective.
Multi-Label and Multimodal Classifier for Affective States Recognition in Virtual Rehabilitation

Computational systems that process multiple affective states may benefit from explicitly considering the interaction between the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC) of CCC with the FSNBC are over <inline-formula><tex-math notation="LaTeX">$0.940 \pm 0.045$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>940</mml:mn><mml:mo>±</mml:mo><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>045</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="rivas-ieq1-3055790.gif"/></alternatives></inline-formula> (<inline-formula><tex-math notation="LaTeX">$mean \pm std.\;deviation$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>m</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mo>±</mml:mo><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>d</mml:mi><mml:mo>.</mml:mo><mml:mspace width="0.166667em"/><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>v</mml:mi><mml:mi>i</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="rivas-ieq2-3055790.gif"/></alternatives></inline-formula>) for the four states. Relationships of mutual exclusion between engagement and all the other states and co-occurrences between pain and anxiety were detected and discussed.
The impact of macroeconomic factors on suicide in 175 countries over 27 years

Abstract Background Every year, more than 800,000 people die by suicide, three‐quarters of which are males. Economic factors influence suicide rates, but a worldwide perspective of their impact according to age and sex is lacking. Method We queried publicly available datasets on economic factors and on suicide rates stratified according to sex and age, from 1991 to 2017, for 175 countries. Thus, we analyzed approximately 21 million deaths by suicide using a multivariable regression model approach. Results Every 1% increase in global unemployment rates is associated with a 1% upsurge in male deaths by suicide (Relative risk (RR) = 1.01 [CI 95% 1.00–1.01] with respect to females) or 5000 excess male deaths. A 1% higher unemployment rate also exerts age‐specific effects on suicide rates, since, among adults aged 30–59, the suicide rate is increased by 2–3%. Lastly, for every 1000 US dollar increase in the GDP per capita, suicide rates are reduced by 2% (RR = 0.98 [0.98–0.98]), corresponding to a reduction of 14,000–15,000 suicide deaths per year globally. Conclusions Males who have lost their jobs in adulthood are those at higher risk of suicide and to whom financial support measures should be delivered in a timely manner.
Group B streptococci in human disease

INTRODUCTION . 774 History and Nomenclature . 774 Human Disease . 774 THE AGENT . 775 Strains of Bovine and Human Origin . 775 Seroclassification . 775 Virulence Factors . 776 Metabolism . 777 CLINICAL DISEASE SPECTRUM . 777 Early Recognition . 777 Neonatal Disease . 778 Disease in Older Children and Adults . 779 PATHOGENESIS . 779 Perinatal Colonization . 779 Postnatal Colonization . 780 LABORATORY DIAGNOSIS . 780 Selective Broth Medium for Primary Isolation . 780 Presumptive Identification . 781 Serological Identification . 782 IMMUNOLOGY . 782 Host Antibody Response . 782 Leukocytic Response . 783 EPIDEMIOLOGY . 783 Incidence . 783 Source . 784 Transmission . 784 Microbial Persistence . 784 Race . 785 Sex . 785 Other Factors . 785 PROPHYLAXIS AND TREATMENT . 785 Spectrum of Antimicrobial Susceptibility . 785 Carrier Prophylaxis . 785 CONCLUDING REMARKS . 787 LITERATURE CITED . 787
Champions of Change: How Ceos and Their Companies Are Mastering the Skills of Radical Change

What It Takes: Confronting the Realities of Change. Where to Start: Understanding Organizations. From Tuning to Overhaul: The Dimensions of Change. Reshaping the Entire Enterprise: The Special Challenges of Discontinuous Change. Winning Hearts and Minds: Overcoming the Obstacles to Change. Setting the Stage: Recognizing the Change Imperative. Waging the Great Campaign: Developing a Shared Direction. Building a New Strategy: The Strategic Choice Process. Redesigning the Organizational "Hardware": The Keys to Strategic Design. When Worlds Collide: Aligning Strategy And Culture. Finding the Right People: A Guide to Strategic Selection. Staying the Course: Consolidating and Sustaining Change. Leading the Charge: The Unique Role of Senior Management. Learning to Lead Change: The New Principles for CEOs and Companies.
Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning

A hallmark of intelligence is the ability to learn new flexible, cognitive behaviors - that is, behaviors that require memorizing and exploiting a certain information item for each new instance of the task. In meta-learning, agents are trained with an external, human-designed reinforcement learning algorithm to learn a specific cognitive task. However, animals are able to pick up such cognitive tasks automatically, from stimuli and rewards alone, as a result of their evolved neural architecture and synaptic plasticity mechanisms: evolution has designed animal brains as self-contained reinforcement (meta-)learning systems, capable not just of performing specific cognitive tasks, but of acquiring novel cognitive tasks, including tasks never seen during evolution. Can we harness this process to generate artificial agents with such abilities? Here we evolve neural networks, endowed with plastic connections and neuromodulation, over a sizable set of simple meta-learning tasks based on a framework from computational neuroscience. The resulting evolved networks can automatically acquire a novel simple cognitive task, never seen during evolution, through the spontaneous operation of their evolved neural organization and plasticity system. We suggest that attending to the multiplicity of loops involved in natural learning may provide useful insight into the emergence of intelligent behavior. An important feature of intelligent behavior is the ability to learn not just simple, reactive tasks (associating a stimulus with a response), but also more complex, cognitive tasks. By "cognitive", we mean a task which require the acquisition, storage, processing and appropriate exploitation of novel, unpredictable pieces of information from the environment for each new instance of the task. This ability to "learn how to learn", or meta-learning, has been studied quantitatively in animals [9] and implemented in artificial neural networks [3, 5, 10, 18, 22, 24].
Patrons and Pathfinders. Two Tales of Successful Strategic Evolution in the Finnish Music Industry

This thesis presents the entire lifespan of two major record companies in Finland, starting from their creation and ending with their present situation. In this research the strategic evolution of these companies is analyzed through the lens of the strategy logic perspective. In doing so, this thesis takes a qualitative look at the development of strategy in two individual companies. In addition, the study aims to reveal the main issues the strategists of these companies have faced over the decades and the key principles that have steered their companies through successes as well as certain failures. This thesis seeks answers to the following research questions. 1. What were the strategy logics of Fazer Music and Poko Rekords in the days when the companies were founded? 2. How have the strategy logics of these companies changed during their existence? Have there been any radical turning points or have the changes been incremental? What were the similarities and differences in the evolutionary paths of the two companies? 3. What were the reasons behind the changes in strategy logics? Were the changes deliberate or did they just somehow come about? The analysis shows how the strategy logics of Fazer Music and Poko Rekords evolved as the companies transformed themselves from their humble beginnings into serious players in the Finnish music industry. Both companies had a similar evolutionary path where they eventually outgrew their own capabilities and ended up divesting their operations. Similarly both companies also became a part of multinational conglomerates, even though these two acquisitions had very dissimilar results. From a theoretical perspective this thesis demonstrates that the number of essential elements in a certain strategy logic is not the key factor that determines its successfulness. On the other hand, this thesis also points out that any strategy logic inevitably changes gradually or more rapidly. Furthermore, even small changes in the essential elements of a strategy logic may bring about significant changes in the actual operations of a company. In addition, this thesis further indicates that in order to succeed a company should focus both on doing the right things as well as doing things right. Finally, the thesis demonstrates that successful strategies can be created either through a comprehensive analytical process or simply based on personal vision. Neither of these paths is fundamentally wrong, but their functionality should be evaluated on the basis of the characteristics of the organization in question.
On striking the balance between the fairness of service and throughput in roadside units based vehicular ad hoc networks

Data dissemination using Road Side Units (RSUs) in Vehicular Ad Hoc Networks (VANETs) got considerable attention to assist the inter-vehicles communication to overcome the vehicle-to-vehicle frequent disconnection problem. An RSU invokes the underlying scheduler to choose a data item to broadcast in order to satisfy the pending requests of vehicles. Conventionally, a scheduler selects a data item based on different metrics, which are usually the deadline of a request, the size of an item and the popularity of an item. In the conventional scheduling manner, normally the popular data items are broadcast many times for maximising the broadcast channel bandwidth. As a consequence, non-popular data items are broadcast very few times, which creates unfairness to the pending requests for the non-popular data items. However, the non-popular data items may also be important for a vehicle. Hence, there is trade-off of achieving higher fairness and gaining higher system throughput. In this study, we investigate this trade-off in the context of RSU-based VANETs and propose a fairness-friendly approach with which the integration of a scheduling algorithm can balance the trade-off of the fairness of service and the system throughput. Simulation results support our proposed approach and offer the expected results.
Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs

This paper addresses the task of conversational question answering (ConvQA) over knowledge graphs (KGs). The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG. However, creating such a gold logical form is not viable for each potential question in a real-world scenario. Hence, in the case of missing gold logical forms, the existing information retrieval-based approaches use weak supervision via heuristics or reinforcement learning, formulating ConvQA as a KG path ranking problem. Despite missing gold logical forms, an abundance of conversational contexts, such as entire dialog history with fluent responses and domain information, can be incorporated to effectively reach the correct KG path. This work proposes a contrastive representation learning-based approach to rank KG paths effectively. Our approach solves two key challenges. Firstly, it allows weak supervision-based learning that omits the necessity of gold annotations. Second, it incorporates the conversational context (entire dialog history and domain information) to jointly learn its homogeneous representation with KG paths to improve contrastive representations for effective path ranking. We evaluate our approach on standard datasets for ConvQA, on which it significantly outperforms existing baselines on all domains and overall. Specifically, in some cases, the Mean Reciprocal Rank (MRR) and Hit@5 ranking metrics improve by absolute 10 and 18 points, respectively, compared to the state-of-the-art performance.
Completions of Non-Symmetric Metric Spaces Via Enriched Categories

Abstract It is known from [Lawvere, Repr. Theory Appl. Categ. 1: 1–37 2002] that nonsymmetric metric spaces correspond to enrichments over the monoidal closed category [0, ∞]. We use enriched category theory and in particular a generic notion of flatness to describe various completions for these spaces. We characterise the weights of colimits commuting in the base category [0, ∞] with the conical terminal object and cotensors. Those can be interpreted in metric terms as very general filters, which we call filters of type 1. This correspondence extends the one between minimal Cauchy filters and weights which are adjoint as modules. Translating elements of enriched category theory into the metric context, one obtains a notion of convergence for filters of type 1 with a related completeness notion for spaces, for which there exists a universal completion. Another smaller class of flat presheaves is also considered both in the context of both metric spaces and preorders. (The latter being enrichments over the monoidal closed category 2.) The corresponding completion for preorders is the so-called dcpo completion.
Slow Coloring of 3k-Connected Graphs

The slow coloring game was introduced by Mahoney, Puleo, and West and it is played by two players, Lister and Painter, on a graph \(G\). In round \(i\), Lister marks a nonempty subset \(M\) of \(V(G)\). By doing this he scores \(|M|\) points. Painter responds by deleting a maximal independent subset of \(M\). This process continues until all vertices are deleted. Lister aims to maximize the score, while Painter aims to minimize it. The best score that both players can guarantee is called the \textit{slow coloring number} or \textit{sum-color cost} of \(G\), denoted \(\spo{(G)}\). Puleo and West found that for an \(n\)-vertex tree \(T\), the slow coloring number is at most \(\lfloor \frac{3n}{2} \rfloor\), and that the maximum can be reached when \(T\) contains a spanning forest with vertices of degree 1 or 3. This implies that every n-vertex graph \(G\) having a perfect matching satisfies \(\spo(G) \geq \lfloor{\frac{3n}{2}}\rfloor\). In this paper, we prove that for \(3k\)-connected graphs with \(|V(G)| \geq 4k\) and with a perfect matching the lower bound is higher: \(\spo(G) \geq \frac{3n}{2} + k\).
PREVALENCE OF PHYSICAL ACTIVITY, AND PERCEIVED BARRIERS AMONG 6TH YEAR MEDICAL STUDENTSAT IN UMM AL-QURA UNIVERSITY IN MAKKAH AL-MUKARRAMAH, IN 2016.

Reem Hamed Al Harbi 1 , Hanan Mosad Al Matrafi 2 and Osama Mohammed Al Wafi 3 . 1. MBBS, Family Medicine Residents. 2. MBBS, SBFM, ABFM, Family Medicine Consultant. 3. MBBS, SBFM, ABFM, MPH, Family Medicine/ Public Health Consultant. Manuscript Info Abstract . Manuscript History
Comparative Analysis and Validation for Diagnosis of Pneumonia through Chest X-rays using Deep Learning Models

—Pneumonia has been the widespread disease caused by a respiratory infection and has rapid spread and relatively high mortality rate. It is mostly found in the children of age group of five. Early detection and treatment of pneumonia will significantly reduce its mortality rate. X-ray diagnosis is currently recognized as a relatively effective method to diagnose the pneumonia. An experienced doctor's visual analysis of a patient's X-ray chest radiograph takes about 5 to 15 minutes. When cases are concentrated, the doctor's clinical diagnosis is undoubtedly put under tremendous strain. As a result, relying on the imaging doctor's naked eye has a very low efficiency. So, it is important to use AI to help doctors diagnose pneumonia from clinical images. Furthermore, artificial intelligence recognition is extremely fast, and convolutional neural networks (CNNs) have outperformed humans in image identification. To achieve a solution to the problem we used the Kaggle dataset with chest X-ray images for classification, which included 5216 train and 624 test images and two classes: normal and pneumonia. We conducted studies in which we used five mainstream network algorithms to classify these diseases in the dataset and compared the results, in which custom CNN model achieved a higher accuracy rate than other methods. The accuracy gained was about 92.7 % Additionally, the improved Custom CNN network may be extended to other areas for application and better results.