How to study basement membrane stiffness as a biophysical trigger in prostate cancer and other age-related pathologies or metabolic diseases

Here we describe a protocol that can be used to study the biophysical microenvironment related to increased thickness and stiffness of the basement membrane (BM) during age-related pathologies and metabolic disorders (e.g. cancer, diabetes, microvascular disease, retinopathy, nephropathy and neuropathy). The premise of the model is non-enzymatic crosslinking of reconstituted BM (rBM) matrix by treatment with glycolaldehyde (GLA) to promote advanced glycation endproduct (AGE) generation via the Maillard reaction. Examples of laboratory techniques that can be used to confirm AGE generation, non-enzymatic crosslinking and increased stiffness in GLA treated rBM are outlined. These include preparation of native rBM (treated with phosphate-buffered saline, PBS) and stiff rBM (treated with GLA) for determination of: its AGE content by photometric analysis and immunofluorescent microscopy, its non-enzymatic crosslinking by ((sodium dodecyl sulfate polyacrylamide gel electrophoresis)) (SDS PAGE) as well as confocal microscopy, and its increased stiffness using rheometry. The procedure described here can be used to increase the rigidity (elastic moduli, E) of rBM up to 3.2-fold, consistent with measurements made in healthy versus diseased human prostate tissue. To recreate the biophysical microenvironment associated with the aging and diseased prostate gland three prostate cell types were introduced on to native rBM and stiff rBM: RWPE-1, prostate epithelial cells (PECs) derived from a normal prostate gland; BPH-1, PECs derived from a prostate gland affected by benign prostatic hyperplasia (BPH); and PC3, metastatic cells derived from a secondary bone tumor originating from prostate cancer. Multiple parameters can be measured, including the size, shape and invasive characteristics of the 3D glandular acini formed by RWPE-1 and BPH-1 on native versus stiff rBM, and average cell length, migratory velocity and persistence of cell movement of 3D spheroids formed by PC3 cells under the same conditions. Cell signaling pathways and the subcellular localization of proteins can also be assessed

Analisis dan Perancangan Data Warehouse pada PT Gajah Tunggal Prakarsa

The purpose of this helpful in making decisions more quickly and precisely. Research methodology includes analysis study was to analyze the data base support in helping decisions making, identifying needs and designing a data warehouse. With the support of data warehouse, company leaders can be more of current systems, library research, designing a data warehouse using star schema. The result of this research is the availability of a data warehouse that can generate information quickly and precisely, thus helping the company in making decisions. The conclusion of this research is the application of data warehouse can be a media aide related parties on PT. Gajah Tunggal initiative in decision making

STUDY ON RISK FACTORS, CLINICAL AND THERAPEUTIC PROFILE OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE PATIENTS IN GOVERNMENT GENERAL HOSPITAL

Objective: This study mainly aims to assess and evaluate risk factors, clinical and therapeutical profile in chronic obstructive pulmonary disease (COPD) patients and promote rational drug therapy; to identify the prevalence of COPD symptoms and to assess the drugs prescribed in treating COPD patients; and to evaluate patterns of diagnosing COPD in patients and determine the severity of COPD in patients using COPD assessment test (CAT). Methods: A prospective observational study conducted in government general hospital among 220 patients for 6 months. Data were collected from patients by CAT questionnaire through interviewing each subject. Results: The majority of the patients (36%) were in the age group of 71–80 years. Males are more prone to the COPD (91%). Most common initiation interdependence of these habits are 31–40 years and 61.8% are suffering with comorbidities. A total of 145 (65%) have social habits. About 68% of the patients are suffering from occupational exposure, 78% of the patients are suffering from old pulmonary problems. Conclusion: Clinical pharmacist main provision is providing care to individual patients by patient counseling, regarding the rational usage of drug and also providing proper education regarding the usage of nebulizers and creating awareness to the patients

Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

The primary energy consumer of smart buildings are Heating, Ventilation, and Air-Conditioning (HVAC) systems, approximately 30% of the building energy use, which usually operate on a fixed schedule. Currently, most modern buildings still condition rooms with a set-point assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Occupancy-based controls can achieve significant energy savings by temporally matching the building energy consumption and building usage, conservative user behavior can save a third of expended energy.  In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, three techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we propose a method based on uncertain basis functions for modeling and prediction purposes. In literature, basis functions (e.g., radial basis functions, wavelets) are fixed; instead, we assume that the basis functions are random. We consider basis functions with three different distributions, which are Gaussian, Laplace and Uniform, respectively. Finally, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for HVAC control strategy.  To demonstrate effectiveness of proposed approach, a simulation-based experimental analysis is carried out using occupancy data. We define the estimation accuracy as the total number of correct estimations divided by the total number of estimations, and both Root Mean Squared Error (RMSE) and estimation accuracy analysis are provided. All the proposed estimation techniques could achieve at least 70% accuracy rate. Generally, accuracy for binary states estimation is much higher than that of detailed occupancy. For GSPS model, more training data improves performance of estimation. It should be remarked that although some mismatch exist for non-zero jumps, estimation performance tracks the zero base line (non-occupied status) perfectly. Therefore, the estimation techniques are effective for binary estimation with over 90% accuracy. Finally, the estimated occupancy is applied into temperature set algorithm to generate reference temperature curve. By adjusting temperature set curve, we can achieve significant energy without sacrificing customer’s comfort.  In this paper, we propose three real-time occupancy estimation methods that can be incorporated into HVAC controls . We have shown the effectiveness of all the proposed approaches by simulation examples. We have seen great potential of energy saving by integrating the proposed technique into real HVAC control system.   Â

Microstructure quality control of steels using deep learning

In quality control, microstructures are investigated rigorously to ensure structural integrity, exclude the presence of critical volume defects, and validate the formation of the target microstructure. For quenched, hierarchically-structured steels, the morphology of the bainitic and martensitic microstructures are of major concern to guarantee the reliability of the material under service conditions. Therefore, industries conduct small sample-size inspections of materials cross-sections through metallographers to validate the needle morphology of such microstructures. We demonstrate round-robin test results revealing that this visual grading is afflicted by pronounced subjectivity despite the thorough training of personnel. Instead, we propose a deep learning image classification approach that distinguishes steels based on their microstructure type and classifies their needle length alluding to the ISO 643 grain size assessment standard. This classification approach facilitates the reliable, objective, and automated classification of hierarchically structured steels. Specifically, an accuracy of 96% and roughly 91% is attained for the distinction of martensite/bainite subtypes and needle length, respectively. This is achieved on an image dataset that contains significant variance and labeling noise as it is acquired over more than 10 years from multiple plants, alloys, etchant applications, and light optical microscopes by many metallographers (raters). Interpretability analysis gives insights into the decision-making of these models and allows for estimating their generalization capability

Dissecting the RELICS cluster SPT-CLJ0615-5746 through the intracluster light: confirmation of the multiple merging state of the cluster formation

The intracluster light (ICL) fraction, measured at certain specific wavelengths, has been shown to provide a good marker for determining the dynamical stage of galaxy clusters, i.e., merging versus relaxed, for small to intermediate redshifts. Here, we apply it for the first time to a high-redshift system, SPT-CLJ0615-5746 at z=0.97, using its RELICS (Reionization Lensing Cluster Survey) observations in the optical and infrared. We find the ICL fraction signature of merging, with values ranging from 16 to 37%. A careful re-analysis of the X-ray data available for this cluster points to the presence of at least one current merger, and plausibly a second merger. These two results are in contradiction with previous works based on X-ray data, which claimed the relaxed state of SPT-CLJ0615-5746, and confirmed the evidences presented by kinematic analyses. We also found an abnormally high ICL fraction in the rest-frame near ultraviolet wavelengths, which may be attributed to the combination of several phenomena such as an ICL injection during recent mergers of stars with average early-type spectra, the reversed star formation-density relation found at this high redshift in comparison with lower-redshift clusters, and projection effects.Comment: 15 pages, 12 figures, submitted to A&

Experimental Biological Protocols with Formal Semantics

Both experimental and computational biology is becoming increasingly automated. Laboratory experiments are now performed automatically on high-throughput machinery, while computational models are synthesized or inferred automatically from data. However, integration between automated tasks in the process of biological discovery is still lacking, largely due to incompatible or missing formal representations. While theories are expressed formally as computational models, existing languages for encoding and automating experimental protocols often lack formal semantics. This makes it challenging to extract novel understanding by identifying when theory and experimental evidence disagree due to errors in the models or the protocols used to validate them. To address this, we formalize the syntax of a core protocol language, which provides a unified description for the models of biochemical systems being experimented on, together with the discrete events representing the liquid-handling steps of biological protocols. We present both a deterministic and a stochastic semantics to this language, both defined in terms of hybrid processes. In particular, the stochastic semantics captures uncertainties in equipment tolerances, making it a suitable tool for both experimental and computational biologists. We illustrate how the proposed protocol language can be used for automated verification and synthesis of laboratory experiments on case studies from the fields of chemistry and molecular programming