Book Review: Further understanding of the human machine. The road to bioengineering.
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ABSTRACT:
SUBMITTER: Wald A
PROVIDER: S-EPMC5541753 | biostudies-other | 2017 Aug
REPOSITORIES: biostudies-other
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Project description:This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.
| S-EPMC5805011 | biostudies-literature
Project description:In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own--surprisingly few--driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.
| S-EPMC3526957 | biostudies-literature
Project description:BackgroundFrom patient-reported surveys and individual interviews by health care providers, we attempted to identify the significant factors related to the improvement of distress and fatigue for cancer survivors by text analysis with machine learning techniques, as the secondary analysis using the single institute data from the Korean Cancer Survivorship Center Pilot Project.MethodsSurveys and in-depth interviews from 322 cancer survivors were analyzed to identify their needs and concerns. Among the keywords in the surveys, including EQ-VAS, distress, fatigue, pain, insomnia, anxiety, and depression, distress and fatigue were focused. The interview transcripts were analyzed via Korean-based text analysis with machine learning techniques, based on the keywords used in the survey. Words were generated as vectors and similarity scores were calculated by the distance related to the text's keywords and frequency. The keywords and selected high-ranked ten words for each keyword based on the similarity were then taken to draw a network map.ResultsMost participants were otherwise healthy females younger than 50 years suffering breast cancer who completed treatment less than 6 months ago. As the 1-month follow-up survey's results, the improved patients were 56.5 and 58.4% in distress and fatigue scores, respectively. For the improvement of distress, dyspepsia (p = 0.006) and initial scores of distress, fatigue, anxiety, and depression (p < 0.001, < 0.001, 0.043, and 0.013, respectively) were significantly related. For the improvement of fatigue, economic state (p = 0.021), needs for rehabilitation (p = 0.035), initial score of fatigue (p < 0.001), any intervention (p = 0.017), and participation in family care program (p = 0.022) were significant. For the text analysis, Stress and Fatigue were placed at the center of the keyword network map, and words were intricately connected. From the regression anlysis combined survey scores and the quantitative variables from the text analysis, participation in family care programs and mention of family-related words were associated with the fatigue improvement (p = 0.033).ConclusionCommon symptoms and practical issues were related to distress and fatigue in the survey. Through text analysis, however, we realized that the specific issues and their relationship such as family problem were more complicated. Although further research needs to explore the hidden problem in cancer patients, this study was meaningful to use personalized approach such as interviews.
| S-EPMC8237475 | biostudies-literature
Project description:IntroductionAutomated vehicles need to gain the trust of all road users in order to be accepted. To make technology trustworthy, automated vehicles must transmit crucial information to pedestrians through a human-machine interface, allowing pedestrians to accurately predict and act on their next behavior. However, the unsolved core issue in the field of vehicle automation is to know how to successfully communicate with pedestrians in a way that is efficient, comfortable, and easy to understand. This study investigated the impact of three human-machine interfaces specifically designed for pedestrians' trust during the street crossing in front of an automated vehicle. The interfaces used different communication channels to interact with pedestrians, i.e., through a new road infrastructure, an external human-machine interface with anthropomorphism, or with conventional road signaling.MethodsMentally projected in standard and non-standard use cases of human-machine interfaces, 731 participants reported their feelings and behavior through an online survey.ResultsResults showed that human-machine interfaces were efficient to improve trust and willingness to cross the street in front of automated vehicles. Among external human-machine interfaces, anthropomorphic features showed significant advantages in comparison with conventional road signals to induce pedestrians' trust and safer crossing behaviors. More than the external human-machine interfaces, findings highlighted the efficiency of the trust-based road infrastructure on the global street crossing experience of pedestrians with automated vehicles.DiscussionAll of these findings support trust-centered design to anticipate and build safe and satisfying human-machine interactions.
| S-EPMC10196377 | biostudies-literature
Project description:ObjectivesThis study examines gang group processes on the digital street to understand if gang processes in the online environment mimic those on geographic street corners. Specifically, this paper examines what conditions influence whether gangs interact negatively or positively in online spaces and how online interactions relate to geographic proximity of gangs.MethodsThis study uses digital trace data web scraped from a public Facebook about Chicago Latina/o gangs combined with geographic locations of gang territories. Negative binomial regression models are used to investigate the conditions under which gangs interact with one another.ResultsThis study finds that interactions among gangs are conditional on the type of post displayed and negative gang interactions in online spaces are moderately correlated with geographic proximity.ConclusionsThe tendency to show identification with the gang group or hostility to rival groups is not always evident. Rather, interactions are contextualized in the situation of the online environment. In addition, in our sample geographic proximity is not a primary condition of gang interactions. The digital street enables gang members to interact with other gangs in faraway locales and individuals in close geographic proximity. We find gang members take the opportunity to interact with both groups.
| S-EPMC8356630 | biostudies-literature
Project description:BackgroundDespite a remarkable increase in the depth of our understanding and management of breast cancer in the past 50 years, the disease is still a major public health problem worldwide and poses significant challenges. The palpability of breast tumors has facilitated diagnosis and documentation since ancient times. The earliest descriptions of breast cancer date back to around 3500 BCE. For centuries to follow, theories by Hippocrates (460 BCE) and Galen (200 CE), attributing the cause of breast cancer to an "excess of black bile" and treatment options including the use of opium and castor oil, prevailed. Surgical resection was introduced in the 18th century. The advent of modern medicine led to the development of novel treatment options that include hormonal, targeted and chemo-therapies. There are still several therapeutic challenges including the treatment of triple negative breast cancer (TNBC), and overcoming drug resistance.Scope of reviewThe increased incidence and awareness of breast cancer has led to significant changes in diagnosis and treatment in recent decades. But, mankind has come a long way. Herein, I have traced how our understanding of breast cancer has evolved from the early description of the disease around 460 BCE as "black bile-containing crab-like tumors" to the conventional as a heterogeneous disease with high degree of diversity between and within tumors, as well as among breast cancer patients. How is breast cancer treated today and how do risk factors, breast cancer subtype and drug resistance contribute to the therapeutic challenges at the turn of the 21st century?Major conclusionsBreast cancer remains a serious public health issue worldwide. However, appreciable growth in our understanding of breast cancer in the past century has led to remarkable progress in the early detection, treatment and prevention of the disease. The clinical focus is shifting more towards tailored therapy as more targets are characterized and novel highly innovative approaches are developed.General significanceTracing the history of breast cancer, highlights how increased awareness of the disease, and progress in research and development have enhance our understanding of the disease.
| S-EPMC5300293 | biostudies-literature
Project description:BackgroundAs the number of patients with end-stage lung disease continues to rise, there is a growing need to increase the limited number of lungs available for transplantation. Unfortunately, attempts at engineering functional lung de novo have been unsuccessful, and artificial mechanical devices have limited utility as a bridge to transplant. This difficulty is largely due to the size and inherent complexity of the lung; however, recent advances in cell-based therapeutics offer a unique opportunity to enhance traditional tissue-engineering approaches with targeted site- and cell-specific strategies.MethodsHuman lungs considered unsuitable for transplantation were procured and supported using novel cannulation techniques and modified ex-vivo lung perfusion. Targeted lung regions were treated using intratracheal delivery of decellularization solution. Labeled mesenchymal stem cells or airway epithelial cells were then delivered into the lung and incubated for up to 6 hours.ResultsTissue samples were collected at regular time intervals and detailed histologic and immunohistochemical analyses were performed to evaluate the effectiveness of native cell removal and exogenous cell replacement. Regional decellularization resulted in the removal of airway epithelium with preservation of vascular endothelium and extracellular matrix proteins. After incubation, delivered cells were retained in the lung and showed homogeneous topographic distribution and flattened cellular morphology.ConclusionsOur findings suggest that targeted cell replacement in extracorporeal organs is feasible and may ultimately lead to chimeric organs suitable for transplantation or the development of in-situ interventions to treat or reverse disease, ultimately negating the need for transplantation.
| S-EPMC6351169 | biostudies-literature
Project description:Recreating the structure of human tissues in the laboratory is valuable for fundamental research, testing interventions, and reducing the use of animals. Critical to the use of such technology is the ability to produce tissue models that accurately reproduce the microanatomy of the native tissue. Current artificial cell-based skin systems lack thorough characterisation, are not representative of human skin, and can show variation. In this study, we have developed a novel full thickness model of human skin comprised of epidermal and dermal compartments. Using an inert porous scaffold, we created a dermal construct using human fibroblasts that secrete their own extracellular matrix proteins, which avoids the use of animal-derived materials. The dermal construct acts as a foundation upon which epidermal keratinocytes were seeded and differentiated into a stratified keratinised epithelium. In-depth morphological analyses of the model demonstrated very close similarities with native human skin. Extensive immunostaining and electron microscopy analysis revealed ultrastructural details such as keratohyalin granules and lamellar bodies within the stratum granulosum, specialised junctional complexes, and the presence of a basal lamina. These features reflect the functional characteristics and barrier properties of the skin equivalent. Robustness and reproducibility of in vitro models are important attributes in experimental practice, and we demonstrate the consistency of the skin construct between different users. In summary, a new model of full thickness human skin has been developed that possesses microanatomical features reminiscent of native tissue. This skin model platform will be of significant interest to scientists researching the structure and function of human skin.
| S-EPMC6422806 | biostudies-literature
Project description:Cardiac autonomic neurons control cardiac contractility. Dysregulation of the autonomic nervous system can lead to sympathetic overdrive resulting in heart failure and an increased incidence of fatal arrhythmias. Here, we introduce innervated engineered human myocardium (iEHM), a novel model of neuro-cardiac junctions, constructed by fusion of a bioengineered neural organoid (BENO) patterned to autonomic nervous system and engineered human myocardium (EHM). Projections of sympathetic neurons into engineered human myocardium formed presynaptic terminals in close proximity to cardiomyocytes and an extensive vascular network co-developing in the tissues. Contractile responses to optogenetic stimulation of the accordingly engineered neuronal component demonstrated functionality of the engineered neuro-cardiac junctions in iEHM. This model will serve as a human surrogate system to delineate neuron and cardiac cell contribution to brain and heart diseases and is an important step towards engineering a human brain to heart axis in a dish.
2024-03-31 | GSE232464 | GEO