In modern societies, rhetoric focused on body and health is common as biomedical sciences have taken a big place in people’s lives. They must enhance the society, health and living conditions of citizens. Solving criminality problems remains a major challenge and the early detection of antisocial children - future offenders - promises to offer a solution to criminality thanks to science and medical advances. But in a democratic society that values ​​solidarism and pluralism and tends to preserve the (...) social link, it is necessary to question the ethical relevance of this method of managing criminality. This paper proposes to analyze these questions in the light of debates that have troubled France for a number of years. (shrink)

Precise and timely diagnosis of Parkinson’s disease is important to control its progression among subjects. Currently, a neuroimaging technique called dopaminergic imaging that uses single photon emission computed tomography (SPECT) with 123I-Ioflupane is popular among clinicians for detecting Parkinson’s disease in early stages. Unlike other studies, which consider only low-level features like gray matter, white matter, or cerebrospinal fluid, this study explores the non-linear relation between different biomarkers (SPECT + biological) using deep learning and multivariate logistic regression. Striatal binding (...) ratios are obtained using 123I-Ioflupane SPECT scans from four brain regions which are further integrated with five biological biomarkers to increase the diagnostic accuracy. Experimental results indicate that this investigated approach can differentiate subjects with 100% accuracy. The obtained results outperform the ones reported in the literature. Furthermore, logistic regression model has been developed for estimating the Parkinson’s disease onset probability. Such models may aid clinicians in diagnosing this disease. (shrink)

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research (...) work presents that Social Media Analysis can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest, K-Nearest Neighbor, Support Vector Machine, and Decision Tree, with the help of Term Frequency and Inverse Document Frequency. For experimental analysis, two datasets are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques. (shrink)

The paper starts with the research on the early discovery of college students' psychological problems. Besides, it analyzes the data of the general survey of college students' mental health in a certain university, the existing data of students with psychological problems, and the questionnaire data of students' basic information in school. By comprehensively using the decision tree model and Kendall correlation analysis and other methods, using Python and SPSS software to preprocess the data and realize the model, it can (...) obtain a psychological problem prediction model based on the objective behavior data of college students. The model is actually analyzed, and it gets good results. (shrink)

Background Scientific and technological advancements in mapping and understanding the interrelated pathways through which biological and environmental exposures affect disease development create new possibilities for detecting disease risk factors. Early detection of such risk factors may help prevent disease onset or moderate the disease course, thereby decreasing associated disease burden, morbidity, and mortality. However, the ethical implications of screening for disease risk factors are unclear and the current literature provides a fragmented and case-by-case picture. Methods To identify key (...) ethical considerations arising from the early detection of disease risk factors, we performed a systematic scoping review. The Scopus, Embase, and Philosopher’s Index databases were searched for peer-reviewed, academic records, which were included if they were written in English or Dutch and concerned the ethics of (1) early detection of (2) disease risk factors for (3) disease caused by environmental factors or gene-environment interactions. All records were reviewed independently by at least two researchers. Results After screening 2034 titles and abstracts, and 112 full papers, 55 articles were included in the thematic synthesis of the results. We identified eight common ethical themes: (1) Reliability and uncertainty in early detection, (2) autonomy, (3) privacy, (4) beneficence and non-maleficence, (5) downstream burdens on others, (6) responsibility, (7) justice, and (8) medicalization and conceptual disruption. We identified several gaps in the literature, including a relative scarcity of research on ethical considerations associated with environmental preventive health interventions, a dearth of practical suggestions on how to address expressed concerns about overestimating health capacities, and a lack of insights into preventing undue attribution of health responsibility to individuals. Conclusions The ethical concerns arising with the early detection of risk factors are often interrelated and complex. Comprehensive ethical analyses are needed that are better embedded in normative frameworks and also assess and weigh the expected benefits of early risk factor detection. Such research is necessary for developing and implementing responsible and fair preventive health policies. (shrink)

A screening test for more than 50 cancers at earlier stages would strike many as a godsend. Such a test would promise, prima facie, to save 160,000 lives annually from a premature death from cancer, reduce the intensity of medical treatment, and reduce social costs. In brief, this is what is promised by the Galleri test. We will delineate those claims in greater detail and critically assess them from medical, economic, and ethical perspectives. We conclude, with many others, that this (...) test lacks clinical validity and clinical utility. In addition, annual public funding of $100 billion for this test would be socially unaffordable; the opportunity costs would be unacceptable for both ethical and economic reasons. Further, the least well off with respect to cancer care would be made worse off if this test were publicly funded for everyone over the age of fifty. (shrink)

The aim of this study was to assess the predictive capacity of some of the most relevant cognitive skills pertaining to the academic field as measured by the Spanish Primary School Aptitude Test Battery. This psychometric tool was applied to all students who were enrolled in the final year of Early Childhood Education in the public schools of the province of Alicante and a follow-up of their academic progress was carried out when they completed Primary Education. The results obtained (...) show that medium-high and high scores in Verbal Aptitude and Numerical Aptitude tests in Early Childhood Education, can predict academic success at the end of Primary Education in instrumental subjects such as: Language and Mathematics. We have determined the importance of developing pedagogical programs that stimulate the development of these skills during Early Childhood Education, while implementing support strategies during Primary Education, for those students who present underdeveloped aptitudes in these areas. In this way, school difficulties would be prevented in the instrumental subjects that provide access to other academic areas. (shrink)

Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current (...) literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively. (shrink)

Medical screening is justified on the strength of the assumption that the earlier disease is detected, the better it is for the patient. On examination, however, the assumption turns out to be severely flawed, and inadequate anyway, since it is not only the patient with whom we should be concerned, but healthy people as well. Instead of making assumptions about the ill, we should prove a test's overall benefit to the individual taking it before we recommend it.

This essay focuses on the ethical considerations and implications of providing a universal multi-cancer screening test as the best approach to reduce societal cancer burden in a society with limited funds, resources, and infrastructure. With 1.9 million cancer diagnoses each year in the United States, with 86% of all cancers diagnosed in individuals over the age of 50, and with screening tools approved for only four cancer types (breast, cervical, colorectal, and lung cancer), it seems that a multi-cancer screening test (...) to detect most cancer early that is easy to administer, and is accurate and cost-effective, would be worth considering. Whole-body magnetic resonance imaging and a multi-marker blood test are the two main technologies that we will discuss as a universal screening test. However, to understand and appreciate the societal and clinical breakthrough of such a screening test, we must first consider the accessibility and efficacy of current screening methods. We conclude with a closer examination of the ethical implications of implementing the Galleri test as a multi-cancer detection screening tool as adamantly advocated by the company that developed this blood-based test. (shrink)

Prostate cancer has become a major health concern of male Americans. It is now the most common nondermatologic cancer and the second leading cause of cancer death among men. The incidence of detected prostate cancer rose rapidly in recent years, partly because of prostate-specific antigen testing; it is only now tapering off. Screening for prostate cancer with PSA is widespread in the United States, yet controversial: the American Urological Association recommends PSA screening and the American Cancer Society recommends offering screening; (...) however, the United States Preventive Services Task Force and the American College of Physicians recommend against routine screening; and the American Academy of Family Physicians believes that the decision to screen should be left to the patient. (shrink)

GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with (...) class='Hi'>early- to middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (shrink)

Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer (...) application to the humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors’ status, were determining factors. (shrink)

This book explores the thought of - and is dedicated to - David Frisby, as one of the leading sociologists of the late twentieth and early twenty-first centuries. Presenting original examinations of his unique social theory and underlining his interdisciplinary approach to the critical interpretation of modern metropolitan society and culture, it emphasises Frisby's legacy in highlighting the role of the social researcher as a collector, reader, observer, detective and archivist of the phenomena and ideas that exemplify the modern (...) metropolis as society. With contributions from sociologists, historians of the city, urban geographers, cultural theorists, architectural historians, architectural theorists and urban designers, The Detective of Modernity constitutes a wide-ranging engagement with Frisby's profound legacy in social and cultural theory. (shrink)

In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors (...) from a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use. (shrink)

Detection of Alzheimer’s disease in an early stage is receiving increasing attention for a number of reasons, such as the failure of drug trials in more advanced disease stages, the demographic evolution, the financial impact of AD, and the approval of amyloid tracers for clinical use. Five focus group interviews with stakeholders were conducted.. The verbatim transcripts were analysed via the Nvivo 11 software. Most stakeholder groups wanted to know their own amyloid PET scan result. However, differences occurred (...) between FGs: two groups wanted to know, whilst in the three other groups FG members opted not to know or were still in doubt about their decision. Stakeholders provided insight into their reasons for wanting to know their amyloid PET scan result, for not wanting to know their result, or why they were in doubt about their decision. Several advantages and disadvantages were mentioned as part of knowing their amyloid PET scan result. Certain considerations were clustered in a grey zone, in between advantage and disadvantage, such as the emotional consequences. Clinicians, researchers, and policymakers ought to be aware of the diversity of reasons for wanting to know their result and how possible benefits and risks can be viewed differently. The current findings are of importance for future early diagnosis and disclosure of results in the research setting. (shrink)

Depression is a common mental health illness worldwide that affects our quality of life and ability to work. Although prior research has used EEG signals to increase the accuracy to identify depression, the rates of underdiagnosis remain high, and novel methods are required to identify depression. In this study, we built a model based on single-channel, dry-electrode EEG sensor technology to detect state depression, which measures the intensity of depressive feelings and cognitions at a particular time. To test the accuracy (...) of our model, we compared the results of our model with other commonly used methods for depression diagnosis, including the PHQ-9, Hamilton Depression Rating Scale, and House-Tree-Person drawing test, in three different studies. In study 1, we compared the results of our model with PHQ-9 in a sample of 158 senior high students. The results showed that the consistency rate of the two methods was 61.4%. In study 2, the results of our model were compared with HAM-D among 71 adults. We found that the consistency rate of state-depression identification by the two methods was 63.38% when a HAM-D score above 7 was considered depression, while the consistency rate increased to 83.10% when subjects showed at least one depressive symptom. In study 3, 68 adults participated in the study, and the results revealed that the consistency rate of our model and HTP drawing test was 91.2%. The results showed that our model is an effective means to identify state depression. Our study demonstrates that using our model, people with state depression could be identified in a timely manner and receive interventions or treatments, which may be helpful for the early detection of depression. (shrink)

We present an algorithm that can anticipate tax evasion by modeling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms (...) of tax schemes in response to changes in audit policies. This can serve as an early warning system to help focus enforcement efforts. In addition, the audit policies can be fine tuned to help improve tax scheme detection. We demonstrate our approach using the iBOB tax scheme and show it can capture the co-evolution between tax evasion and audit policy. Our experiments shows the expected oscillatory behavior of a biological co-evolving system. (shrink)

The sociological study of knowledge infrastructures and classification has traditionally focused on the politics and practices of classifying things or people. However, actors’ work to escape dominant infrastructures and pre-established classification systems has received little attention. In response to this, this article argues that it is crucial to analyze, not only the practices and politics of classification, but also actors’work to escape dominant classification systems. The article has two aims: First, to make a theoretical contribution to the study of classification (...) by proposing to pay analytical attention to practices of escaping classification, what the article dubsclassification egress. This concept directs our attention not only to the practices and politics of classifying things, but also to how actors work to escape orresist classification systemsin practice. Second, the article aims to increase our understanding of the history of quantified and statistical health surveillance. In this, the article investigates how actors in health surveillance assembled a knowledge infrastructure for surveilling, quantifying, and detecting unknown patterns of congenital malformations in the wake of the thalidomide disaster in the early 1960s. The empirical account centers on the actors’ work to detect congenital malformations and escape the dominant nosological classification of diseases, the International Classification of Diseases (ICD), by replacing it with a procedural standard for reporting of symptoms. Thus, the article investigates how actors deal with the tension between the-already-known-and-classified and the unknown-unclassified-phenomenon in health surveillance practice. (shrink)

Aiming at the problem of insufficient health monitoring of the elderly in the existing home care system, this paper designs a health information analysis and early warning system based on the Internet of Things technology, which can monitor the physiological data of the elderly in real time. It also can be based on the elderly real-time monitoring data, physical examination data, and other types of health data, which can be used to predict diseases, so as to achieve “early (...) detection and early treatment” of diseases. First, analyse and design the architecture and content of the home care monitoring system based on the Internet of Things. Secondly, based on the collected heart rate, blood pressure, and three-axis acceleration information of the elderly, it is analysed to determine whether the elderly are in danger of falling, and the designed system is used for early warning. Finally, this paper analyses the prediction algorithm theory of the disease prediction module in the health monitoring software of the home care system. In order to improve the accuracy of prediction, the DS evidence theory is used to optimize the traditional BP neural network algorithm and conduct experimental tests. The test results show that the health information analysis and early warning software of the home care system meet actual needs and achieve the expected goals. (shrink)

Cancer is the second leading cause of death in developed countries, making it a global public health problem. In this scenario, early detection is the key to successful treatment. Tissue biopsy, the current gold standard for cancer diagnosis, offers reliable results, but it is feasible only when the mass becomes detectable. On the other hand liquid biopsy, a promising experimental system, not yet implemented within clinical practice, allows early detection as its functioning relies on the analysis (...) of body fluids. Yet, its results are less reliable if compared to those of tissue biopsy as, for instance, false positives and false negatives might occur. Despite technical features, the tradeoff between a reliable diagnosis available at a later time and a potentially less reliable diagnosis available at an early stage poses significant ethical challenges in the clinical scenario which involve, among other aspects, informed consent, communication, and patient-physician encounter. (shrink)

Faces are complex stimuli that can be described via intuitive facial features like the eyes, nose, and mouth, “configural” features like the distances between facial landmarks, and features that correspond to computations performed in the early visual system (e.g. oriented edges). With regard to this latter category of descriptors, adult face recognition relies disproportionately on information in specific spatial frequency and orientation bands: Many recognition tasks are performed more accurately when adults have access to mid-range spatial frequencies (8-16 cycles/face) (...) and horizontal orientations (Dakin & Watt, 2009). In the current study, we examined how this information bias develops in middle childhood. We recruited children between the ages of 5-10 years old to participate in a simple categorization task that required them to label images according to whether they depicted a face or a house. Critically, children were presented with face and house images comprised either of primarily horizontal orientation energy, primarily vertical orientation energy, or both horizontal and vertical orientation energy. We predicted that any bias favoring horizontal information over vertical should be more evident in faces than in houses, and also that older children would be more likely to show such a bias than younger children. We designed our categorization task to be sufficiently easy that children would perform at near-ceiling accuracy levels, but with variation in response times that would reflect how they rely on different orientations as a function of age and object category. We found that horizontal bias for face detection (but not house detection) correlated significantly with age, suggesting an emergent category-specific bias for horizontal orientation energy that develops during middle childhood. These results thus suggest that the tuning of high-level recognition to specific low-level visual features takes take place over several years of visual development. (shrink)

Zeno's extraordinary and disturbing paradoxes, the atomic theories of Democritus that so strikingly anticipate contemporary physics, the enigmatic and haunting epigrams of Heraclitus - these are just some of the riches to be found in this collection of writings of the early Greek philosophers. Jonathan Barnes's masterly Introduction shows how the most skilled detective work is often needed to reconstruct the ideas of these thinkers from the surviving fragments of their work. But the effort is always worth while. In (...) forging the first truly scientific vocabulary and offering rational arguments for their views, the pre-Socratics were doing something new and profoundly important; they also posed the questions that have remained at the centre of philosophy to this day. (shrink)

Recent EEG studies on the early postmortem interval that suggest the persistence of electrophysiological coherence and connectivity in the brain of animals and humans reinforce the need for further investigation of the relationship between the brain’s activity and the dying process. Neuroscience is now in a position to empirically evaluate the extended process of dying and, more specifically, to investigate the possibility of brain activity following the cessation of cardiac and respiratory function. Under the direction of the Center for (...) Healthy Minds at the University of Wisconsin-Madison, research was conducted in India on a postmortem meditative state cultivated by some Tibetan Buddhist practitioners in which decomposition is putatively delayed. For all healthy baseline and postmortem subjects presented here, we collected resting state electroencephalographic data, mismatch negativity, and auditory brainstem response. In this study, we present HB data to demonstrate the feasibility of a sparse electrode EEG configuration to capture well-defined ERP waveforms from living subjects under very challenging field conditions. While living subjects displayed well-defined MMN and ABR responses, no recognizable EEG waveforms were discernable in any of the tukdam cases. (shrink)

As mentioned in the narrations, the messenger of Allah (pbuh) used to recite some prayers known as opening prayers such as Subhaneke, Wejjehtu and Allâhümme Bâid before reciting Fatiha surah while starting the obligatory and supererogatory worships (salat). In this study, it is aimed to find out the reflections of the abovementioned prayers in narrations, and to determine their authenticity and to reveal the reasons why jurisprudential sects preferred different prayers. Benefitting from literature review method, hadith inferences were made, and (...) moving from the books of refutation & corrections and biographies, the cases of the narrators of the hadiths, who took part in the ascription of the narrations, were put forth. After the investigation, it was determined that the narrated narrations about Subhaneke, an opening prayer, were found to have been narrated by eight different prophet companions in their later works through Aisha and Abu Sâ’id al-Khudrî in early hadiths resources; and that all these narrations were week in terms of authenticity. However, it was revealed that the most authentic narrations about the issue were those depending on the practice of Umar. In the hadith resources, it was seen that five different prophet companions narrated some narrations about the Wejjechtu prayer, and that the great majority of these narrations were through Ali, and that most of these narrations were authentic, yet only one reference was found to be weak. On the other hand, it was revealed that the narrations narrated by Abu Huraira between the opening prayer, Allâhümme Bâid, turned out to be genuine in terms of authenticity. For instance, while Hanafis and Hanbalis have preferred reciting Subhaneke during salat, Shafiîs have found it appropriate to recite the prayer Wejjectu in the salat. On the other hand, Malikis have thought that it was obligatory to start the salat with Surah Fatiha, and that it was makruh to recite these prayers in salat. (shrink)

We show that early vision can use monocular cues to rapidly complete partially-occluded objects. Visual search for easily detected fragments becomes difficult when the completed shape is similar to others in the display; conversely, search for fragments that are difficult to detect becomes easy when the completed shape is distinctive. Results indicate that completion occurs via the occlusion-triggered removal of occlusion edges and linking of associated regions. We fail to find evidence for a visible filling-in of contours or surfaces, (...) but do find evidence for a "functional" filling-in that prevents the constituent fragments from being rapidly accessed. As such, it is only the completed structures—and not the fragments themselves—that serve as the basis for rapid recognition. (shrink)