Heterogeneous Collection Research Articles

Understanding the Effects of Process Conditions on Thermal–Defect Relationship: A Transfer Machine Learning Approach

AbstractThis study aims to develop an intelligent, rapid porosity prediction methodology for additive manufacturing (AM) processes under varying process conditions by leveraging knowledge transfer from the existing process conditions. Conventional machine learning (ML) algorithms are extensively used in porosity prediction for AM processes. These approaches assume that the underline distribution of the source (training) and target (testing) is the same and that target labels are available for modeling purposes. However, the source and target sometimes follow different distributions in real-world manufacturing environments as the diversity of industrialization processes leads to heterogeneous data collection under different production conditions. This will reduce the ability of decision-making with conventional approaches. Transfer learning (TL) is one of the robust techniques that enables transferring learned knowledge between the target and source to establish a robust relationship while the target has fewer data. Therefore, this paper presents an unsupervised grouping-based transfer learning method to characterize the relationship between an unknown target and sources. The similarities between sources and targets are learned by forming a new mixed domain, which organizes data into identity groups. Then, a group-based learning process is designated to transfer knowledge to make target predictions. The effectiveness of the proposed method is evaluated by predicting porosity based on thermal images collected from the AM process under different process conditions, i.e., single-source and multi-source transfer to target porosity prediction. The performance comparison demonstrates that the in situ porosity prediction using the proposed method outperformed state-of-art classification models support vector machine (SVM), convolutional neural network (CNN), and different TL methods such as TL with NNs (TLNN), and TL with CNNs (TLCNN).

CT texture analysis reliability in pulmonary lesions: the influence of 3D vs. 2D lesion segmentation and volume definition by a Hounsfield-unit threshold

ObjectiveReproducibility problems are a known limitation of radiomics. The segmentation of the target lesion plays a critical role in texture analysis variability. This study’s aim was to compare the interobserver reliability of manual 2D vs. 3D lung lesion segmentation with and without pre-definition of the volume using a threshold of − 50 HU.MethodsSeventy-five patients with histopathologically proven lung lesions (15 patients each with adenocarcinoma, squamous cell carcinoma, small cell lung cancer, carcinoid, and organizing pneumonia) who underwent an unenhanced CT scan of the chest were included. Three radiologists independently segmented each lesion manually in 3D and 2D with and without pre-segmentation volume definition by a HU threshold, and shape parameters and original, Laplacian of Gaussian–filtered, and wavelet-based texture features were derived. To assess interobserver reliability and identify the most robust texture features, intraclass correlation coefficients (ICCs) for different segmentation settings were calculated.ResultsShape parameters had high reliability (64–79% had excellent and good ICCs). Texture features had weak reliability levels, with the highest ICCs (38% excellent or good) found for original features in 3D segmentation without the use of a HU threshold. A small proportion (4.3–11.5%) of texture features had excellent or good ICC values at all segmentation settings.ConclusionInterobserver reliability of texture features from CT scans of a heterogeneous collection of manually segmented lung lesions was low with a small proportion of features demonstrating high reliability independent of the segmentation settings. These results indicate a limited applicability of texture analysis and the need to define robust texture features in patients with lung lesions.Key Points• Our study showed a low reproducibility of texture features when 3 radiologists independently segmented lung lesions in CT images, which highlights a serious limitation of texture analysis.• Interobserver reliability of texture features was low regardless of whether the lesion was segmented in 2D and 3D with or without a HU threshold.• In contrast to texture features, shape parameters showed a high interobserver reliability when lesions were segmented in 2D vs. 3D with and without a HU threshold of − 50.

The aesthetics of the self: The meaning-making of Internet aesthetics

In this study we explore the so-called Internet aesthetics, labels applied on heterogenous collections of materials and activities by Internet users, which are discussed and constructed primarily on the Internet. We conducted qualitative content analyses on entries of AestheticsWiki, as well as interviews with 11 Internet aesthetics enthusiasts. In contrast to established notions such as genre, style or subculture, Internet aesthetics are characterized by few conventions, but seem fundamentally open for individual interpretations. We therefore theorize that they primarily serve as toolkits, through which individuals give sense and coherence to personal experience, in line with a situation-specific self-image. In doing so, they contribute to a reflexive myth of the self.

A unique soft tissue injury with devastating delayed consequences: A video case report.

A 52-year-old male presented to the emergency department (ED) after a fall down 5 stairs resulting in a loss of consciousness. On physical examination, a 4-cm hematoma was identified on the right lateral flank that emergency medical services personnel stated was the size of a golf ball on initial evaluation and appeared to expand over the course of his ED stay. Initial computed tomography (CT) (Figure 1) showed the hematoma; however, a bedside ultrasound (Video 1) and subsequent CT (Figure 2) were performed, demonstrating expansion of the hematoma with active extravasation. The qualities of this hematoma are suspicious for an uncommon condition called a Morel-Lavallée lesion (MLL), a closed degloving injury in which the subcutaneous tissues and fascia are separated by a hematoma that forms most frequently because of shearing forces.1, 2 Previous reports have documented MLLs secondary to motor vehicle collisions1 or biking accidents3 and some in conjunction with traumatic ventral wall hernias.2, 4 Diagnosis of this condition can occur through the use of ultrasound or magnetic resonance imaging, demonstrating a heterogenous fluid collection between the subcutaneous tissue and fascia.3 Although early detection of this condition is critical as the patient may continue to hemorrhage during evaluation, sequelae of this condition can be lifethreatening for a patient. Documented sequelae of MLLs range from infection of the lesion and neurovascular compromise to capsulation and scar tissue formation.3 A high index of suspicion for these lesions in patients with acute or remote trauma is critical as complications can occur up to a year later.3

Heterogeneous Traffic Condition Dataset Collection for Creating Road Capacity Value

Indonesia has the third highest number of motorcycles, which means the traffic flow in Indonesia is heterogeneous. Traffic flow can specify its condition, whether it is a free flow or very heavy traffic. Traffic condition is the most important criterion used to find the best route from an origin to a destination. This paper collects the traffic condition for several road segments which are calculated based on the degree of saturation by using two methods, namely, (1) by counting the number of vehicles using object detection in the public closed-circuit television (CCTV) stream, and (2) by requesting the traffic information (vehicle’s speed) using TomTom. Both methods deliver the saturation degree and calculate the traffic condition for each road segment. Based on the experiments, the average error rate obtained by counting the number of vehicles on Pramuka–Cihapit and Trunojoyo was 0–2 cars, 2–3 motorcycles, and 0–1 for others. Meanwhile, the average error on Merdeka-Aceh Intersection reached 6 cars, 11 motorcycles, and 1 for other vehicles. The average speed calculation for the left side of the road is more accurate than the right side, and the average speed on the left side is less than 3.3 km/h. Meanwhile, on the right side, the differences between actual and calculated vehicle speeds are between 11.088 and 22.222 km/h. This high error rate is caused by (1) the low resolution of the public CCTV, (2) some obstacles interfering with the view of CCTV, (3) the misdetection of the type of vehicles, and by (4) the vehicles moving too fast. The collected dataset can be used in further studies to solve the congestion problem, especially in Indonesia.

Novel WDR72 Mutations Causing Hypomaturation Amelogenesis Imperfecta.

Amelogenesis imperfecta (AI) is a heterogeneous collection of hereditary enamel defects. The affected enamel can be classified as hypoplastic, hypomaturation, or hypocalcified in form. A better understanding of normal amelogenesis and improvements in our ability to diagnose AI through genetic testing can be realized through more complete knowledge of the genes and disease-causing variants that cause AI. In this study, mutational analysis was performed with whole exome sequencing (WES) to identify genetic etiology underlying the hypomaturation AI condition in affected families. Mutational analyses identified biallelic WDR72 mutations in four hypomaturation AI families. Novel mutations include a homozygous deletion and insertion mutation (NM_182758.4: c.2680_2699delinsACTATAGTT, p.(Ser894Thrfs*15)), compound heterozygous mutations (paternal c.2332dupA, p.(Met778Asnfs*4)) and (maternal c.1287_1289del, p.(Ile430del)) and a homozygous 3694 bp deletion that includes exon 14 (NG_017034.2:g.96472_100165del). A homozygous recurrent mutation variant (c.1467_1468delAT, p.(Val491Aspfs*8)) was also identified. Current ideas on WDR72 structure and function are discussed. These cases expand the mutational spectrum of WDR72 mutations causing hypomaturation AI and improve the possibility of genetic testing to accurately diagnose AI caused by WDR72 defects.

Design and Development of a Web-Based Prospective Nationwide Registry for Ocular Inflammatory Diseases: UVEITE.PT – The Portuguese Ocular Inflammation Registry

ABSTRACT Uveitis is a heterogeneous collection of infrequent diseases, which poses significant challenges to cost-effective research in the field. Medical registries are being increasingly recognized as crucial tools to provide high-quality data, thus enabling prospective clinical research. This paper describes the design and technical structure development of an innovative countrywide electronic medical record for uveitis, Uveite.pt, and gives an overview of the cohort registered since its foundation, March 2020. Uveite.pt is an electronic medical record platform developed by the Portuguese Ocular Inflammation Group (POIG), a scientific committee of the Portuguese Ophthalmology Society. This is a nationwide customized web-based platform for uveitis patients useful for both clinical practice and real-world-based research, working as a central repository and reporting tool for uveitis. This paper describes the technical principles, the design and the development of a web-based interoperable registry for uveitis in Portugal and provides an overview of more than 400 patients registered in the first 18 months since inception. In infrequent diseases, the existence of registries enables to gather evidence and increase research possibilities to clinicians. The adoption of this platform enables standardization and improvement of clinical practice in uveitis. It is useful to apprehend the repercussion of medical and surgical treatments in uveitis and scleritis, supporting clinicians in the strict monitoring of drug adverse reactions and surgical outcomes.

Inflammation and the Potential Implication of Macrophage-Microglia Polarization in Human ASD: An Overview.

Autism spectrum disorder (ASD) is a heterogeneous collection of neurodevelopmental disorders, difficult to diagnose and currently lacking treatment options. The possibility of finding reliable biomarkers useful for early identification would offer the opportunity to intervene with treatment strategies to improve the life quality of ASD patients. To date, there are many recognized risk factors for the development of ASD, both genetic and non-genetic. Although genetic and epigenetic factors may play a critical role, the extent of their contribution to ASD risk is still under study. On the other hand, non-genetic risk factors include pollution, nutrition, infection, psychological states, and lifestyle, all together known as the exposome, which impacts the mother's and fetus's life, especially during pregnancy. Pathogenic and non-pathogenic maternal immune activation (MIA) and autoimmune diseases can cause various alterations in the fetal environment, also contributing to the etiology of ASD in offspring. Activation of monocytes, macrophages, mast cells and microglia and high production of pro-inflammatory cytokines are indeed the cause of neuroinflammation, and the latter is involved in ASD's onset and development. In this review, we focused on non-genetic risk factors, especially on the connection between inflammation, macrophage polarization and ASD syndrome, MIA, and the involvement of microglia.

The Boy in the Borstal: Gene Hunting, Dopaminergic Dogma, and the Science of Schizophrenia

The Boy in the Borstal: Gene Hunting, Dopaminergic Dogma, and the Science of Schizophrenia

ScAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell-type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, no current cell annotation method explicitly utilizes dropout information. Fully utilizing dropout information motivated this work. We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene's marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using 14 real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate's misclassified cells differ greatly from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy. We implemented scAnnotate as an R package and made it publicly available from CRAN: https://cran.r-project.org/package=scAnnotate. Supplementary data are available at Bioinformatics Advances online.

Beyond the Lab and the Field: Infrastructures as Places of Knowledge Production since the Late Nineteenth Century ed. by Eike-Christian Heine and Martin Meiske

Reviewed by: Beyond the Lab and the Field: Infrastructures as Places of Knowledge Production since the Late Nineteenth Century ed. by Eike-Christian Heine and Martin Meiske Thomas Kaiserfeld (bio) Beyond the Lab and the Field: Infrastructures as Places of Knowledge Production since the Late Nineteenth Century Edited by Eike-Christian Heine and Martin Meiske. Pittsburgh: University of Pittsburgh Press, 2022. Pp. 292. The contributors to this anthology, comprising an introduction and ten empirical chapters, argue that infrastructures have been an overlooked site for knowledge production. New knowledge, they claim, has traditionally been traced back to spaces like labs and field studies. In these environments, control has to a varying degree been exercised over conditions and elements. Not in this volume, where instead infrastructures—characterized as a second nature—are branded as a hitherto ignored arena for the generation of knowledge. Beyond the Lab and the Field examines projects characterized as "scientific bonanzas," a metaphor for infrastructures as junctions for opportunities as well as distress. The cases include the Panama Canal; dam constructions in Germany, the United States, and Spain; the German autobahns during the Third Reich; Siberian oil extraction and irrigation in the Caucasus during the Soviet regime; European and Dutch power lines; a highway in Tel Aviv, Israel; and an airstrip in Antarctica. Timewise, the empirical examples stretch from the late nineteenth century to the early twentieth. The different chapters constitute a range of relations between infrastructures and knowledge, including social sciences and the humanities. Most straightforward are probably examples of how construction of infrastructures such as the Panama Canal led to the emergence of tropical medicine and to the further development of geology and paleontology. But infrastructures, such as waterscapes exploited for hydropower, can also exhibit more complicated patterns of knowledge management, including the coexistence of different knowledge traditions. In addition, the monograph includes examples of how new knowledge may constitute formative elements of infrastructure projects. In the case of autobahn construction in the Third Reich, archaeological excavations pointed backward to an imagined glorious Germanic past while the new road system under construction pointed forward to modernization and motorization. Ethnographical research was mobilized in Siberia in order to understand how indigenous people could be transformed to contribute to the extraction of oil, resulting in new knowledge about indigenous Siberian lifestyles. Related to this are examples of counterexpertise knowledge opposing new infrastructures by introducing alternative value scales for the assessment of their impact, such as accessibility or the preservation of cultural and natural heritage. Taken together, the different contributions make up a heterogenous collection in which the theme is analyzed from a number of perspectives. One drawback is that the concept of infrastructure becomes a bit stretched. [End Page 268] Although briefly discussed in the introduction, where it is characterized by "profound interdependencies" (p. 5), and in a chapter on the European electrical system as arrangements that enable "flows of energy, information, people, and goods" (p. 163), many of the chapters focus on isolated parts of infrastructures. The resulting impression is that the common denominator for the sites of knowledge production analyzed is more ambitious construction projects than infrastructures. For instance, an Antarctic airstrip is certainly a project that is part of an infrastructure. But when the analysis is focused on ecological knowledge mobilized in opposition to the exploration of Antarctica, the infrastructural aspects are easily lost. Another concern is the choice of analytical concepts. Although Thomas P. Hughes's notion of technological momentum is employed to elucidate the suitability of infrastructures for the study of knowledge production as they often involve substantial investments in order to mobilize extensive resources to be managed by numerous stakeholders (p. 8), his ideas regarding reverse salients are never put to work despite their relevance to knowledge production in the contexts of infrastructures and large technical systems alike. In fact, many of the contributions could easily be brought together as examples of how reverse salients are handled by the production or engagement of knowledge—or, alternatively, how knowledge may create reverse salients where there were none due to ignorance. Still, the different contributions make a strong, albeit somewhat less concerted, effort to highlight infrastructures as an important space for knowledge production and...

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data, Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation, data sensing and collection, real-time data processing, and high request arrival rates. The classical intrusion detection system (IDS) is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity. To resolve these issues, this paper designs a new Chaotic Cuckoo Search Optimization Algorithm (CCSOA) with optimal wavelet kernel extreme learning machine (OWKELM) named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform. The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complexity and maximum detection accuracy. The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique, which incorporates the concepts of chaotic maps with CSOA. Besides, the OWKELM technique is applied for the intrusion detection and classification process. In addition, the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization (SFO) algorithm. The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance. In order to guarantee the supreme performance of the CCSOA-OWKELM technique, a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promising performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Współczesne tendencje urbanonimiczne w Szczecinie na przykładzie nazw restauracji

The research task was to trace the names of Szczecin restaurants existing in 2020–2022. The aim of the study was to show which naming tendencies exist in the contemporary urban names of Szczecin. The gathered onymic collection was subjected to semantic and formal analysis. On the basis of the study of the names of selected restaurants in Szczecin, it can be concluded that two opposing linguistic tendencies clash in the contemporary urban naming of Szczecin: 1. striving to revert to native names and 2. adopting foreign names. The onyms created in this way create a semantically and structurally heterogeneous collection of urbanonyms.

What Are UN General Assembly Resolutions for? Four Views on Parliamentary Diplomacy

Abstract The United Nations General Assembly (UNGA) has passed over eighteen thousand resolutions since its foundation. It is a very heterogeneous collection, containing at once landmark documents, such as the Universal Declaration of Human Rights, and scores of less important and even controversial pieces. Hence, scholarship for the past 75 years has been divided on the actual relevance of UNGA resolutions and on member states’ motivations in engaging with their drafting. We propose a novel theoretical typology to organize prevailing views on the role of UNGA resolutions. Relying on the dimensions of effect and consensus, that is, whether or not resolutions are deemed to have a real-world impact and to what extent they represent world opinion, we sort the literature into four ideal types: resolutions can be regarded as the fruit of deliberation, dispute, diversion, or drama. We discuss the rationale of each view and indicate proposals within the UNGA that exemplify these perspectives. Our typology contributes to scholarship by both tidying previous debates and highlighting unnoticed commonalities between the UNGA and topics from the political representation literature.

Statistical considerations for assessing precision of heterogeneous duplicate measurements: An application to pharmaceutical bioanalysis.

Duplicate analysis is a strategy commonly used to assess precision of bioanalytical methods. In some cases, duplicate analysis may rely on pooling data generated across organizations. Despite being generated under comparable conditions, organizations may produce duplicate measurements with different precision. Thus, these pooled data consist of a heterogeneous collection of duplicate measurements. Precision estimates are often expressed as relative difference indexes (RDI), such as relative percentage difference (RPD). Empirical evidence indicates that the frequency distribution of RDI values from heterogeneous data exhibits sharper peaks and heavier tails than normal distributions. Therefore, traditional normal-based models may yield faulty or unreliable estimates of precision from heterogeneous duplicate data. In this paper, we survey application of the mixture models that satisfactorily represent the distribution of RDI values from heterogeneous duplicate data. A simulation study was conducted to compare the performance of the different models in providing reliable estimates and inferences of percentile calculated from RDI values. These models are readily accessible to practitioners for study implementation through the use of modern statistical software. The utility of mixture models are explained in detail using a numerical example.

Optimizing the Training of Models for Automated Post-Correction of Arbitrary OCR-ed Historical Texts

Systems for post-correction of OCR-results for historical texts are based on statistical correction models obtained by supervised learning. For training, suitable collections of ground truth materials are needed. In this paper we investigate the dependency of the power of automated OCR post-correction on the form of ground truth data and other training settings used for the computation of a post-correction model. The post-correction system A-PoCoTo considered here is based on a profiler service that computes a statistical profile for an OCR-ed input text. We also look in detail at the influence of the profiler resources and other settings selected for training and evaluation. As a practical result of several fine-tuning steps, a general post-correction model is achieved where experiments for a large and heterogeneous collection of OCR-ed historical texts show a consistent improvement of base OCR accuracy. The results presented are meant to provide insights for libraries that want to apply OCR post-correction to a larger spectrum of distinct OCR-ed historical printings and ask for "representative" results.

Stability and spatial autocorrelations of suspensions of microswimmers with heterogeneous spin

In this paper, suspensions of active swimmer-rotor particles have been mathematically modeled by extending a previously developed stochastic kinetic theory to analyze heterogeneous collections of microswimmers and microrotors with multiple spin velocity populations. The paper uses this modeling approach to derive insight on large-scale properties, such as suspension instabilities and spatial correlations of the active particles, and highlights the role of active particle rotations on the behavior of the suspension. A secondary aim is to demonstrate an analytical approach for understanding the emergence of patterns that have been observed for self-propelled micro-particles using numerical and lab experiments.

Overcoming the data processing challenges of unifying tomography techniques at ESRF

The ESRF tomography software is undergoing a major re-write, targeted at unifying the tomography user experience, data acquisition, data format, and processing tools. To cope with the inherent large complexity of handling data coming from multiple beamlines, techniques and facilities, we are developing an open source software called NXtomomill. It is named after the NXtomo application class of the NeXus data format, and it will offer advanced data conversion, manipulation and reduction functions. This includes azimuthal integration for X-ray Diffraction CT, and elemental fitting for X-ray Fluorescence CT.This work will have a remarkable impact on resource management, software robustness, and data storage. With the addition of the new functionality of the ESRF data portal, it will also unlock unimagined opportunities with respect to the automation of artificial intelligence techniques on large and heterogeneous collections of datasets.

Real-Time Energy Data Acquisition, Anomaly Detection, and Monitoring System: Implementation of a Secured, Robust, and Integrated Global IIoT Infrastructure with Edge and Cloud AI.

The industrial internet of things (IIoT), a leading technology to digitize industrial sectors and applications, requires the integration of edge and cloud computing, cyber security, and artificial intelligence to enhance its efficiency, reliability, and sustainability. However, the collection of heterogeneous data from individual sensors as well as monitoring and managing large databases with sufficient security has become a concerning issue for the IIoT framework. The development of a smart and integrated IIoT infrastructure can be a possible solution that can efficiently handle the aforementioned issues. This paper proposes an AI-integrated, secured IIoT infrastructure incorporating heterogeneous data collection and storing capability, global inter-communication, and a real-time anomaly detection model. To this end, smart data acquisition devices are designed and developed through which energy data are transferred to the edge IIoT servers. Hash encoding credentials and transport layer security protocol are applied to the servers. Furthermore, these servers can exchange data through a secured message queuing telemetry transport protocol. Edge and cloud databases are exploited to handle big data. For detecting the anomalies of individual electrical appliances in real-time, an algorithm based on a group of isolation forest models is developed and implemented on edge and cloud servers as well. In addition, remote-accessible online dashboards are implemented, enabling users to monitor the system. Overall, this study covers hardware design; the development of open-source IIoT servers and databases; the implementation of an interconnected global networking system; the deployment of edge and cloud artificial intelligence; and the development of real-time monitoring dashboards. Necessary performance results are measured, and they demonstrate elaborately investigating the feasibility of the proposed IIoT framework at the end.

Review of the endocrine organ-like tumor hypothesis of cancer cachexia in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal types of solid tumors, associated with a high prevalence of cachexia (~80%). PDAC-derived cachexia (PDAC-CC) is a systemic disease involving the complex interplay between the tumor and multiple organs. The endocrine organ-like tumor (EOLT) hypothesis may explain the systemic crosstalk underlying the deleterious homeostatic shifts that occur in PDAC-CC. Several studies have reported a markedly heterogeneous collection of cachectic mediators, signaling mechanisms, and metabolic pathways, including exocrine pancreatic insufficiency, hormonal disturbance, pro-inflammatory cytokine storm, digestive and tumor-derived factors, and PDAC progression. The complexities of PDAC-CC necessitate a careful review of recent literature summarizing cachectic mediators, corresponding metabolic functions, and the collateral impacts on wasting organs. The EOLT hypothesis suggests that metabolites, genetic instability, and epigenetic changes (microRNAs) are involved in cachexia development. Both tumors and host tissues can secrete multiple cachectic factors (beyond only inflammatory mediators). Some regulatory molecules, metabolites, and microRNAs are tissue-specific, resulting in insufficient energy production to support tumor/cachexia development. Due to these complexities, changes in a single factor can trigger bi-directional feedback circuits that exacerbate PDAC and result in the development of irreversible cachexia. We provide an integrated review based on 267 papers and 20 clinical trials from PubMed and ClinicalTrials.gov database proposed under the EOLT hypothesis that may provide a fundamental understanding of cachexia development and response to current treatments.