An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms

Detecting Bladder Biomarkers for Closed-Loop Neuromodulation: A Technological Review

Neuromodulation was introduced for patients with poor outcomes from the existing traditional treatment approaches. It is well-established as an alternative, novel treatment option for voiding dysfunction. The current system of neuromodulation uses an open-loop system that only delivers continuous stimulation without considering the patient’s state changes. Though the conventional open-loop system has shown positive clinical results, it can cause problems such as decreased efficacy over time due to neural habituation, higher risk of tissue damage, and lower battery life. Therefore, there is a need for a closed-loop system to overcome the disadvantages of existing systems. The closed-loop neuromodulation includes a system to monitor and stimulate micturition reflex pathways from the lower urinary tract, as well as the central nervous system. In this paper, we reviewed the current technological status to measure biomarker for closed-loop neuromodulation systems for voiding dysfunction

Preparation and evaluation of polymeric microparticulates for improving cellular uptake of gemcitabine

Ji-Ho Lim1,*, Sung-Kyun You1,*, Jong-Suep Baek1, Chan-Ju Hwang1, Young-Guk Na1, Sang-Chul Shin2, Cheong-Weon Cho11College of Pharmacy and Institute of Drug Research and Development, Chungnam National University, Gungdong, Yuseonggu, Daejeon, South Korea, 2College of Pharmacy, Chonnam National University, Buggu, Gwangju, South Korea *These authors contributed equally to this workBackground: Gemcitabine must be administered at high doses to elicit the required therapeutic response because of its very short plasma half-life due to rapid metabolism. These high doses can have severe adverse effects.Methods: In this study, polymeric microparticulate systems of gemcitabine were prepared using chitosan as a mucoadhesive polymer and Eudragit L100-55 as an enteric copolymer. The physicochemical and biopharmaceutical properties of the resulting systems were then evaluated.Results: There was no endothermic peak for gemcitabine in any of the polymeric gemcitabine microparticulate systems, suggesting that gemcitabine was bound to chitosan and Eudragit L100-55 and its crystallinity was changed into an amorphous form. The polymeric gemcitabine microparticulate system showed more than 80% release of gemcitabine in 30 minutes in simulated intestinal fluid. When mucin particles were incubated with gemcitabine polymeric microparticulates, the zeta potential of the mucin particles was increased to 1.57 mV, indicating that the polymeric gemcitabine microparticulates were attached to the mucin particles. Furthermore, the F53 polymeric gemcitabine microparticulates having 150 mg of chitosan showed a 3.8-fold increased uptake of gemcitabine into Caco-2 cells over 72 hours compared with gemcitabine solution alone.Conclusion: Overall, these results suggest that polymeric gemcitabine microparticulate systems could be used as carriers to help oral absorption of gemcitabine.Keywords: gemcitabine, polymeric microparticulates, mucoadhesive, enteric coating, cellular uptake, oral absorptio

Modeling the Fate and Transport of Malathion in the Pagsanjan-Lumban Basin, Philippines

Exposure to highly toxic pesticides could potentially cause cancer and disrupt the development of vital systems. Monitoring activities were performed to assess the level of contamination; however, these were costly, laborious, and short-term leading to insufficient monitoring data. However, the performance of the existing Soil and Water Assessment Tool (SWAT model) can be restricted by its two-phase partitioning approach, which is inadequate when it comes to simulating pesticides with limited dataset. This study developed a modified SWAT pesticide model to address these challenges. The modified model considered the three-phase partitioning model that classifies the pesticide into three forms: dissolved, particle-bound, and dissolved organic carbon (DOC)-associated pesticide. The addition of DOC-associated pesticide particles increases the scope of the pesticide model by also considering the adherence of pesticides to the organic carbon in the soil. The modified SWAT and original SWAT pesticide model was applied to the Pagsanjan-Lumban (PL) basin, a highly agricultural region. Malathion was chosen as the target pesticide since it is commonly used in the basin. The pesticide models simulated the fate and transport of malathion in the PL basin and showed the temporal pattern of selected subbasins. The sensitivity analyses revealed that application efficiency and settling velocity were the most sensitive parameters for the original and modified SWAT model, respectively. Degradation of particulate-phase malathion were also significant to both models. The rate of determination (R2) and Nash-Sutcliffe efficiency (NSE) values showed that the modified model (R2 = 0.52; NSE = 0.36) gave a slightly better performance compared to the original (R2 = 0.39; NSE = 0.18). Results from this study will be able to aid the government and private agriculture sectors to have an in-depth understanding in managing pesticide usage in agricultural watersheds

Environmental Enrichment Upregulates Striatal Synaptic Vesicle-Associated Proteins and Improves Motor Function

Environmental enrichment (EE) is a therapeutic paradigm that consists of complex combinations of physical, cognitive, and social stimuli. The mechanisms underlying EE-mediated synaptic plasticity have yet to be fully elucidated. In this study, we investigated the effects of EE on synaptic vesicle-associated proteins and whether the expression of these proteins is related to behavioral outcomes. A total of 44 CD-1® (ICR) mice aged 6 weeks were randomly assigned to either standard cages or EE (N = 22 each). Rotarod and ladder walking tests were then performed to evaluate motor function. To identify the molecular mechanisms underlying the effects of EE, we assessed differentially expressed proteins (DEPs) in the striatum by proteomic analysis. Quantitative real-time polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry were conducted to validate the expressions of these proteins. In the behavioral assessment, EE significantly enhanced performance on the rotarod and ladder walking tests. A total of 116 DEPs (54 upregulated and 62 downregulated proteins) were identified in mice exposed to EE. Gene ontology (GO) analysis demonstrated that the upregulated proteins in EE mice were primarily related to biological processes of synaptic vesicle transport and exocytosis. The GO terms for these biological processes commonly included Synaptic vesicle glycoprotein 2B (SV2B), Rabphilin-3A, and Piccolo. The qRT-PCR and western blot analyses revealed that EE increased the expression of SV2B, Rabphilin-3A and Piccolo in the striatum compared to the control group. Immunohistochemistry showed that the density of Piccolo in the vicinity of the subventricular zone was significantly increased in the EE mice compared with control mice. In conclusion, EE upregulates proteins associated with synaptic vesicle transport and exocytosis such as SV2B, Rabphilin-3A and Piccolo in the striatum. These upregulated proteins may be responsible for locomotor performance improvement, as shown in rotarod and ladder walking tests. Elucidation of these changes in synaptic protein expression provides new insights into the mechanism and potential role of EE

Abdominal pain without bruising or sign of trauma: pancreatic injuries in children is difficult to predict

Pancreatic injuries due to trauma in children are rare. An early diagnosis is difficult as the signs and symptoms are insidious, but delays in diagnosis can lead to significant complications. We report a case of a child who visited the emergency department with aggravating abdominal pain. The physicians first diagnosed the abdominal pain as being caused by a disease in the emergency department, but the patient was subsequently diagnosed with pancreatic injury. Clinicians should be aware of a possible trauma in children who complain of vague abdominal pain even in the absence of corresponding history