Skin sensitization resulting for allergic contact dermatitis (ACD) is an occupational and environmental health issue. The allergic hazard for workers and consumers is a serious problem for individuals, employers and marketing certain products. Consequently, it is necessary to accurately identify chemicals skin sensitization potential. According to the new EU chemical regulation REACH (Registration, Evaluation, Authorization and Restriction of Chemicals), information of skin sensitization of chemicals manufactured or imported at or above 1 ton/year should be available. Currently, valid approaches assessing skin sensitization rely on animal testing, such as local lymph node assay (LLNA). However, it now ultimately eliminates using animals for this purpose. Based on the fact that a key step in the skin sensitization process is formatting a covalent adduct between allergic sensitizers and proteins and/or peptides in skin, a lot of additional approaches are proposed and developed for replacing or reducing animal used. In this research, three bioassays, 24 h growth inhibition toward Tetrahymena pyriformis, long term (24 h) and short term (30 min) bacterial toxicity (to Vibrio fischeri), and a kinetic glutathione chemoassay are applied for predicting the organic chemicals’ skin sensitization potential. The major results and conclusions obtained are listed as follows: 1. Toxicity enhancement (Te) of 55 chemicals comprising different sensitization potencies were determined and compared with their narcotic toxicity to predict their skin sensitization. Three linear regressions yielded for all allergic sensitizer without nonsensitizers for each bioassay. The linear regressions are improved after classifying sensitizers into five different reaction mechanistic domains. Correspondingly, five different slopes from various reaction mechanisms indicate a decreased sensitivity of toxicity enhancement to skin sensitization potential with order SNAr > SN2 > acylation ≈ Schiff base > aromatic Michael addition. Based on the fact that a key step in the skin sensitization process is forming a covalent adduct between allergic sensitizers and proteins and/or peptides, Te > 10 as a threshold is applied to discriminate these allergic sensitizers, with 100% accuracy for strong (with extreme) and weaker sensitizers, up to 72% accuracy for moderate sensitizers and less than 69% accuracy for nonsensitizers. Compared with these bioassays, a decreasing order of sensitivities is 24 h growth inhibition (Tetrahymena pyriformis) > 24 h growth inhibition (Vibrio fischeri) > 30 min bioluminescence inhibition (Vibrio fischeri). These three bioassays are useful tools for screening sensitization potency of allergic chemicals, and the toxicity enhancement (Te) can be used to discriminate sensitizers from weak or nonsensitizers. However, in this context we should separate aromatic from aliphatic Mas (Michael acceptors). Moreover, metabolic biotransformation should be considered during predicting nonsensitizers’ skin sensitization. 2. Chemical reactivity of selected 55 compounds measuring through kinetic glutathione chemoassay applies to predict their skin sensitization. This chemoassay confirms the fact that the key step of sensitizers eliciting skin sensitization is formatting a covalent adduct between sensitizers and skin proteins or peptides. The chemical reactivity of tested sensitizers strongly relates with their sensitization potential, with strong (extreme) sensitizers presenting the highest reactivity as followed with moderate sensitizers, weak sensitizers as well as nonsensitizers. Moreover, an integrated platform of this chemoassay data and three bioassays data is performed, and this performance shows good sensitivity for monitoring skin sensitization potency, with more rational accuracy for each sensitizing classifications. 3. Thiol reactivity (kGSH) as well as toxicity enhancement (Te) of additional 21 aliphatic α,β-unsaturated compounds are determined for predicting their skin sensitization potential. The linear regressions of skin sensitization versus thiol reactivity and skin sensitization versus toxicity enhancement are significantly improved after classifying these 21 compounds to four chemical subgroups (acrylates, other esters, ketones and aldehydes). Thiol reactivity of these subgroups presented different sensitivity to skin sensitization, with a decreasing order as acrylates (－2.05) > other esters (－1.26) > ketones (－0.43) > aldehydes (－0.21). Moreover, thiol reactivity is confirmed to be a more sensitive tool for predicting skin sensitization, compared with toxicity enhancement. Although the datasets are probably too small to give a definite decision, hydrophobicity reveals contribution to skin sensitization for aliphatic MAs, which is different with literature report. This study suggests that aliphatic MAs should be treated separately into different chemical subgroups for analysis, and their skin sensitization potency can be predicted using kinetic glutathione chemoassay as well as toxicity enhancement bioassay.

1 ABSTRACT Background Breast cancer cell population characteristics are used in common clinical practice for estimation of prognosis of the malignant disease (prognostic factors) and for prediction of reactivity of the tumor to certain therapeutic modality (predictive factors). Also axillary lymph node status is an independent prognostic factor in women with early breast cancer. Therefore, surgical excision and following histopathological examination of the nodes is the obligatory part of primary breast cancer surgery. The extension of axillary surgery varies widely, although sentinel lymph node biopsy is considered to be the standard procedure. However, it must be admitted that this type of procedure need not be optimal for all the breast cancer patients. Aims of the study The aim of this study is the verify the hypothesis whether or not the axillary lymph node metastatic affection can be effectively estimated using non-surgical methods - i.e. by evaluation of the combination of prognostic and predictive factors of the primary breast tumor. Statistical model composed on the basis of data of early breast cancer patients is the basic tool for this prediction. Application of this model In everyday practice can enable to adjust the extent of axillary surgery for each individual patient. Patients and methods A...

Background: A landmark study suggested that wide excision of intermediate-thickness melanoma with sentinel lymph node biopsy (SLNB) and subsequent completion lymph node dissection (CLND) for regional disease may improve prognostication and disease-free survival (DFS) compared with those undergoing wide excision alone. However, these benefits were relatively small and not associated with an improvement in disease-specific survival (DSS). It remains unknown if SLNB and subsequent treatments are beneficial in elderly patients who have a decreased overall (OS) due to other causes. Methods: Adults ≥ 70 years of age, who underwent surgical intervention for intermediate-thickness cutaneous melanoma from 2000-2013 were identified from a prospectively-maintained database. Clinicopathologic variables measured included age, gender, anatomic site, histologic type, tumor thickness, ulceration, receipt and result of SLNB, completion of CLND, OS, and DFS. Results: Ninety-one patients underwent excision of an intermediate-thickness melanoma. Forty-nine patients (54%) received a SLNB. Seven of these biopsies (14%) were positive, and five patients went on to receive CLND. Five-year OS was 41% in patients who did not receive SLNB and 52% in patients who did receive SLNB (p=0.11). DFS was similar between groups independent of receipt of SLNB. Conclusion: Among elderly patients with intermediate-thickness melanoma, patients who received SLNB had similar 5-year OS and DFS compared with those who did not receive SLNB. Routine SLNB for intermediate-thickness melanoma patients may not significantly change outcomes for this age group, and clinical decision-making should consider individual patient comorbidities and goals of care.

The evolution of IoT has revolutionized industrial automation. Industrial devices at every level such as field devices, control devices, enterprise level devices etc., are connected to the Internet, where they can be accessed easily. It has significantly changed the way applications are developed on the industrial automation systems. It led to the paradigm shift where novel IoT application development tools such as Node-RED can be used to develop complex industrial applications as IoT orchestrations. However, in the current state, these applications are bound strictly to devices from specific vendors and ecosystems. They cannot be re-used with devices from other vendors and platforms, since the applications are not semantically interoperable. For this purpose, it is desirable to use platform-independent, vendor-neutral application templates for common automation tasks. However, in the current state in Node-RED such reusable and interoperable application templates cannot be developed. The interoperability problem at the data level can be addressed in IoT, using Semantic Web (SW) technologies. However, for an industrial engineer or an IoT application developer, SW technologies are not very easy to use. In order to enable efficient use of SW technologies to create interoperable IoT applications, novel IoT tools are required. For this purpose, in this paper we propose a novel semantic extension to the widely used Node-RED tool by introducing semantic definitions such as iot.schema.org semantic models into Node-RED. The tool guides a non-expert in semantic technologies such as a device vendor, a machine builder to configure the semantics of a device consistently. Moreover, it also enables an engineer, IoT application developer to design and develop semantically interoperable IoT applications with minimal effort. Our approach accelerates the application development process by introducing novel semantic application templates called Recipes in Node-RED. Using Recipes, complex application development tasks such as skill matching between Recipes and existing things can be automated.We will present the approach to perform automated skill matching on the Cloud or on the Edge of an automation system. We performed quantitative and qualitative evaluation of our approach to test the feasibility and scalability of the approach in real world scenarios. The results of the evaluation are presented and discussed in the paper. / Die Entwicklung des Internet der Dinge (IoT) hat die industrielle Automatisierung revolutioniert. Industrielle Geräte auf allen Ebenen wie Feldgeräte, Steuergeräte, Geräte auf Unternehmensebene usw. sind mit dem Internet verbunden, wodurch problemlos auf sie zugegriffen werden kann. Es hat die Art und Weise, wie Anwendungen auf industriellen Automatisierungssystemen entwickelt werden, deutlich verändert. Es führte zum Paradigmenwechsel, wo neuartige IoT Anwendungsentwicklungstools, wie Node-RED, verwendet werden können, um komplexe industrielle Anwendungen als IoT-Orchestrierungen zu entwickeln. Aktuell sind diese Anwendungen jedoch ausschließlich an Geräte bestimmter Anbieter und Ökosysteme gebunden. Sie können nicht mit Geräten anderer Anbieter und Plattformen verbunden werden, da die Anwendungen nicht semantisch interoperabel sind. Daher ist es wünschenswert, plattformunabhängige, herstellerneutrale Anwendungsvorlagen für allgemeine Automatisierungsaufgaben zu verwenden. Im aktuellen Status von Node-RED können solche wiederverwendbaren und interoperablen Anwendungsvorlagen jedoch nicht entwickelt werden. Diese Interoperabilitätsprobleme auf Datenebene können im IoT mithilfe von Semantic Web (SW) -Technologien behoben werden. Für Ingenieure oder Entwickler von IoT-Anwendungen sind SW-Technologien nicht sehr einfach zu verwenden. Zur Erstellung interoperabler IoT-Anwendungen sind daher neuartige IoT-Tools erforderlich. Zu diesem Zweck schlagen wir eine neuartige semantische Erweiterung des weit verbreiteten Node-RED-Tools vor, indem wir semantische Definitionen wie iot.schema.org in die semantischen Modelle von NODE-Red einführen. Das Tool leitet einen Gerätehersteller oder Maschinebauer, die keine Experten in semantische Technologien sind, an um die Semantik eines Geräts konsistent zu konfigurieren. Darüber hinaus ermöglicht es auch einem Ingenieur oder IoT-Anwendungsentwickler, semantische, interoperable IoT-Anwendungen mit minimalem Aufwand zu entwerfen und entwicklen Unser Ansatz beschleunigt die Anwendungsentwicklungsprozesse durch Einführung neuartiger semantischer Anwendungsvorlagen namens Rezepte für Node-RED. Durch die Verwendung von Rezepten können komplexe Anwendungsentwicklungsaufgaben wie das Abgleichen von Funktionen zwischen Rezepten und vorhandenen Strukturen automatisiert werden. Wir demonstrieren Skill-Matching in der Cloud oder am Industrial Edge eines Automatisierungssystems. Wir haben dafür quantitative und qualitative Bewertung unseres Ansatzes durchgeführt, um die Machbarkeit und Skalierbarkeit des Ansatzes in realen Szenarien zu testen. Die Ergebnisse der Bewertung werden in dieser Arbeit vorgestellt und diskutiert.

Nowadays, prolonged sitting among office workers is a widespread problem, which is highly related to several health problems. Many proposals have been reported and evaluated to address this issue. However, motivating and engaging workers to change health behavior to a healthier working life is still a challenge. In this project, a specific application has been deployed for real-time monitoring and alerting office workers for prolonged sitting. The proposed system consists of three distinct parts: The first one is an android smartwatch, which was used to collect sensor data e.g., accelerometer and gyro data, with a custom android wear app. The second one is an android application, which was developed to act as a gateway for receiving the smartwatch’s data and sending them to IBM Bluemix cloud with MQTT protocol. The final part is a Node-Red cloud application, which was deployed for storing, analyzing and processing of the sensor data for activity detection i.e., sitting or walking/standing. The main purpose of the last one was to return relevant feedback to the user, while combining elements from gaming contexts (gamification methods), for motivating and engaging office workers to a healthier behavior. The system was firstly tested for defining appropriate accelerometer thresholds to five participants (control group), and then evaluated with five different participants (treatment group), in order to analyze its reliability for prolonged sitting detection. The results showed a good precession for the detection. No confusing between sitting and walking/standing was noticed. Communication, storage and analysis of the data was successfully done, while the push notifications to the participants, for alerting or rewarding them, were always accurate and delivered on time. Every useful information was presented to the user to a web-based dashboard accessed through a smartphone, tablet or a PC.     The proposed system can easily be implemented at a real-life scenario with office workers. Certainly, there is a lot space for improvement, considering mostly the type of data registered at the system, the method for sitting detection, and the user interface for presenting relevant information. / Numera är förlängt sittande bland kontorsarbetare ett utbrett problem som är väldigt relaterat till flera hälsoproblem. Många förslag har rapporterats och utvärderas för att ta itu med denna fråga. Tydligen är det fortfarande en utmaning att motivera och engagera arbetstagare för att förändra deras hälsobeteende till hälsosammare arbetsliv. I detta projekt har en särskild applikation använts för realtidsövervakning och varnar kontorsarbetare för förlängt sittande. Det föreslagna systemet består av tre olika delar: Den första är en android smartwatch, som användes för att samla sensordata t.ex. accelerometer och gyrodata, med en anpassad android wear app. Den andra är en en androidapplikation som fungerade som en gateway för att ta emot smartwatchens data och skickar datan till IBM Bluemix-Cloud med MQTT-protokollet. Den sista delen är en Node-RED Cloud-Applikation som användes för lagring, analysering och behandling av sensordata för aktivitetsdetektering. Detta innebär sittande eller gå/stående med det huvudsakliga ändamålet att returnera relevant återkoppling till användaren, samtidigt som man kombinerar element från spelkontekster (gamification metoder), för att motivera och engagera arbetarna till ett hälsosammare beteende. Systemet testades först för att definiera lämpliga accelerometertrösklar till fem deltagare (kontroll grupp) och utvärderades sedan med fem olika deltagare (behandingsgrupp) för att analysera dess tillförlitlighet för långvarig sittdetektering. Resultaten visade en bra precession för detektionen. Ingen förvirring mellan att sitta och gå / stående märktes. Kommunikation, lagring och analys av data gjordes framgångsrikt, medan push-meddelandena till deltagarna, för att varna eller belöna dem, var alltid korrekta och levererade i tid. All användbar information presenterades för användaren på en webbaserad dashboard som nås via en smartphone surfplatta eller en dator. Det föreslagna systemet kan enkelt implementeras i ett verkligt scenario med kontorsarbetare. Visst finns det mycket utrymme för förbättring om man tänker på majoriteten av data som registrerats i systemet, metoden för sittande detektion och användargränssnittet för presentering av relevant information.

Belastningsskador är idag ett allt större problem i samhället som leder till flera sociala och ekonomiska problem. Tillsammans med en ökande ålder på befolkningen kan belastningsbesvär skapa ännu mer utmaningar i framtiden. Att utöka kunskapen om ergonomi är ett bra sätt att minska risken för att drabbas av belastningsskador. Idag används primärt en visuell bedömning av ergonomi, men varje bedömning är individuella och inte konsistenta. Tillgängligheten av små och billiga Inertial Measurement Unit (IMU) har ökat möjligheten att mäta objektivt istället för subjektivt. I detta exjobb har Texas Instruments SensorTag CC2650 använts som sensor, då den är billig och har många inbyggda sensorer där den 9-axliga rörelsesensorn var den eftersökta. En webbaserad utvecklingsmiljö kallad Node-RED användes för utveckling av en prototyp och sensorn kopplades till en smarttelefon tillsammans med Texas Instruments egna applikation. Data från sensorn skickades från smarttelefonen till webbapplikationen. Mätningar utfördes på en arm för att mäta belastningen på en axel, när handens position passerade axelns höjd finns det risk för belastningsskador efter en längre tid. För att beräkna vinkel av armen användes accelerometer samt gyroskop tillsammans med ett komplementärt filter. Prototypen testades i flera tester, där ena testet undersökte en högre risk t.ex. rita på en tavla samt lägre risk t.ex. papperssortering. Det visade sig att sensorn är kapabel att utföra bedömningar av belastningsriskerna. Men det finns kända begränsningar i form av låg samplingshastighet på 5 Hz (begränsningar i Texas Instruments applikation). Nästa steg för vidare arbete är att lägga till en magnetometer till prototypens sensor fusion och undersöka dess tillförlitlighet. / Musculoskeletal disorders (MSD) is a rising problem in society leading to several socio-economic problems. In combination of the trend of an aging population, MSD can cause even more challenges in the future. Expanding the knowledge of ergonomics is a good way to reduce the risk of stress injury. Today, a visual assessment of ergonomics is primarily used. These observations are individual and not consistent. Availability of cheaper and smaller Inertial Measurement Unit (IMU) has increased the possibility of using objective measurements instead of subjective observations. In this thesis work, the Texas Instruments SensorTag CC2650 was used as a sensor, because it is cheap and has many built-in sensors where the 9-axis motion sensor was the most sought one. A web-based development environment called Node-RED was used to develop a prototype and the sensor was connected to a smartphone along with Texas Instruments own application. The data from the sensor was sent from the smartphone to the web application. Measurements were performed on an arm to measure the load on a shoulder, when the hand's position passed the height of the shoulder it indicates that there is a risk of musculoskeletal disorders after an amount of time. To calculate the angle of the arm, sensor fusion of an accelerometer and a gyroscope were used together with a complementary filter. Developed system was evaluated by series of tasks exposing the shoulder to a higher risk e.g., painting compare to lower risk tasks e.g., paper sorting. It turned out that measurement are useful for assessment of the risk. However, there are known limitations like low sampling rate of 5Hz (due to limitation in the android app from Texas Instruments). Adding magnetometer to the sensor fusion and evaluation of reliability of calculations are the next natural steps for future work.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - clustering and classification. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.

Nowadays, wireless sensor networks can be found in many new application areas. In these sensor networks there may exit a part of the network which are difficult to access or lie in a wide area, far apart. A change in the software (e.g., function update or bug fix) can entail reprogramming of all sensor nodes. This is very time consuming and labour intensive, if the patching has to be done manually for each individual sensor nodes. In the area of mobile phones, the over the air (OTA) update function has been established very well with good reliability. In embedded systems such as sensor nodes, where resources are severely restricted, an update cannot be stored but must be programmed directly with the transfer. For this to be possible, a lot of basic functionality is needed to be established to correct errors or to be able to resume a failed programming. Within the framework of this thesis a concept for the transmission and distribution of the firmware and programming the sensor node is established. Focus here is to optimize the use of resources and to provide basic functionality within the programming mode.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Graph Theory is a widely studied topic. A graph is defined by two important features: nodes and edges. Nodes can represent people, cities, variables, resources, products, while the edges represent a relationship between two nodes. Using graphs to solve problems has played a major role in a diverse set of industries for many years. Integer Programs (IPs) are mathematical models used to optimize a problem. Often this involves maximizing the utilization of resources or minimizing waste. IPs are most notably used when resources must be of integer value, or cannot be split. IPs have been utilized by many companies for resource distribution, scheduling, and conflict management. The node packing or independent set problem is a common combinatorial optimization problem. The objective is to select the maximum nodes in a graph such that no two nodes are adjacent. Node packing has been used in a wide variety of problems, which include routing of vehicles and scheduling machines. This thesis introduces several new graph structures, cliqued hole, odd bipartite hole, and odd k-partite hole, and their corresponding valid inequalities for the node packing polyhedron. These valid inequalities are shown to be new valid inequalities and conditions are provided for when they are facet defining, which are known to be the strongest class of valid inequalities. These new valid inequalities can be used by practitioners to help solve node packing instances and integer programs.