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The application of unglazed transpired collectors (UTCs) in livestock barns is a good solution for reducing not only the energy lost though ventilation and structures but also the electricity consumed by heat lamps. However, a control system and operating strategies need to be developed for the application of UTC systems in pig barns in order to maintain room temperature and increase the efficiency of the UTC systems. Therefore, this research focused on analyzing the control signals and temperatures of pig barns that depended on UTC control systems, thermal performance of the UTC, and electricity consumption through a field test. A UTC fan was operated for a daily average of 7 h with the application of a UTC control system and the possibility of reducing the electricity consumption was confirmed. Through the UTC control system, the effect of a temperature rise was confirmed, with an average rise of up to 27.6 °C compared with the ambient temperature. A correlation model was verified between solar radiation and collector surface temperature: the linear model was R 2 = 0.93, whereas the nonlinear model was R 2 = 0.92 of solar radiation with a temperature rise (Δ T ). It was verified that the heat exchange effectiveness was distributed roughly between 0.5 and 0.7, and tended to decrease as the amount of solar radiation increased (R 2 = 0.49). The efficiency was distributed roughly between 20% and 60%, and the efficiency equation was presented through the inverse relation with the amount of solar radiation (R 2 = 0.77). It was confirmed that it is possible to reduce electricity consumption by approximately 30.8% through the application of a UTC control system. [ABSTRACT FROM AUTHOR]

To produce beef with a higher marbling standard, Japanese black cattle farmers usually actively attempt to lower the serum vitamin A level in the fattening period to a minimum desired level (about 30 IU/dL). However, early identification of suboptimal vitamin A deficiency in the cattle is important to prevent them from becoming susceptible to contracting serious diseases. In this study, we investigated the feasibility of using Pupillary Light Reflex (PLR) analysis to identify vitamin A deficient cattle during this fattening period. PLRs of 43 cattle were recorded monthly from June 2012 to February 2013 using a 2CCD camera based handheld machine vision system. A new image processing algorithm to segment the pupil from the background was developed. Compared with manually selected results, the root mean square error associated with the constriction amplitude ( CA ) acquired by the image processing algorithm was only 2.3%, indicating the effectiveness of this algorithm. No significant differences were found between results of CA from fattening cattle in the high (>60 IU/dL) and low (<30 IU/dL) vitamin A period. However, two fattening cattle were identified with severe vitamin A deficiency, because of their weak PLR ( CA < 10%). Initial Pupil Roundness ( IPR ) results showed cattle in a dark environment possessed less-dilated pupils during the vitamin A deficient period ( p < 0.05). These results highlight the potential, as well as the limitations of this method. Due to the natural variation of PLR in healthy cattle, monthly measurements are not sufficient to make accurate identification. To realize early identification of severe vitamin A deficiency in fattening cattle, a more frequent PLR measurements regime needs to be explored. [ABSTRACT FROM AUTHOR]

Strawberry cultivation depends on environmental factors, making its cultivation in the greenhouse a challenge in the winter. This study investigated the most appropriate greenhouse cladding material and thermal screen configuration for strawberry production in the winter by considering greenhouse air temperature, relative humidity (RH, vapor pressure deficit (VPD, and solar radiation (SR). Two gothic greenhouses with different cladding materials and thermal screen configurations, namely, the single-layer greenhouse and double-layer greenhouse, were used for strawberry cultivation. The greenhouse microclimate was controlled by natural ventilation aided with circulating fans and boilers. Strawberries were planted on 5 greenhouse benches, 660 stands per greenhouse. Daily environmental parameters were recorded and processed into daytime and nighttime. The impacts of cladding material-thermal screen configurations on temperature, RH, VPD, and SR, and the subsequent effect on strawberry yield in both greenhouse systems, were evaluated. Comparing the environmental parameters recorded in the single-layer and double-layer greenhouse showed that VPD and SR were significantly different in the daytime, whereas RH and VPD were significantly different in the nighttime. The post hoc test further showed that RH, VPD, and SR in both greenhouses were significantly different. The significant difference in RH and VPD can be attributed to the inner layer of polyethene in the double-layer greenhouse, which sealed up the pores of the thermal screen, resulting in humidity buildup, causing a lower VPD than in the single-layer greenhouse. The single-layer greenhouse yield was 14% greater than the double-layer greenhouse yield and can be attributed to the higher daytime VPD and lower RH achieved in the single-layer greenhouse at night. The study established that though the single-layer greenhouse system was cost-effective regarding construction, the operating cost of the single-layer greenhouse was higher than that of the double-layer greenhouse. [ABSTRACT FROM AUTHOR]

Simple Summary: Animals exhibit their internal and external stimuli through changing behavior. Therefore, people intrinsically used animal physical activities as an indicator to determine their health and welfare status. A deep-learning-based pig posture and locomotion activity detection and tracking algorithm were designed to measure those behavior changes in an experimental pig barn at different greenhouse gas (GHG) levels. The naturally occurring GHGs in the livestock were elevated by closing ventilators for an hour in the morning, during the day, and at nighttime. Additionally, the corresponding pig posture and locomotion activity were measured before, during, and after an hour of treatment. With the increase in GHG concentration, the pigs became less active, increasing their lateral-lying posture duration. In addition, standing, sternal-lying, and walking activities were decreased with the increment in GHG levels. Therefore, monitoring and tracking pigs' physical behaviors using a simple RGB camera and a deep-learning object detection model, coupled with a real-time tracking algorithm, would effectively monitor the individual pigs' health and welfare. Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs' health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs' health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs' short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models' efficacy in the monitoring and tracking of pigs' physical activities non-invasively. [ABSTRACT FROM AUTHOR]

Simple Summary: The preeminent purpose of precision livestock farming (PLF) is to provide affordable and straightforward solutions to severe problems with certainty. Some data collection techniques in PLF such as RFID are accurate but not affordable for small- and medium-sized farms. On the other hand, camera sensors are cheap, commonly available, and easily used to collect information compared to other sensor systems in precision pig farming. Cameras have ample chance to monitor pigs with high precision at an affordable cost. However, the lack of targeted information about the application of cameras in the pig industry is a shortcoming for swine farmers and researchers. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors, and presents automated approaches for monitoring and investigating pigs' feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors. In addition, the review summarizes the related literature and points out limitations to open up new dimensions for future researchers to explore. Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human–machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs' feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors. [ABSTRACT FROM AUTHOR]

This study proposes a building energy simulation (BES) model of an air-to-water heat pump (AWHP) system integrated with a multi-span greenhouse using the TRNSYS-18 program. The proposed BES model was validated using an experimental AWHP and a multi-span greenhouse installed in Kyungpook National University, Daegu, South Korea (latitude 35.53° N, longitude 128.36° E, elevation 48 m). Three AWHPs and a water storage tank were used to fulfill the heat energy requirement of the three-span greenhouse with 391.6 m2 of floor area. The model was validated by comparing the following experimental and simulated results, namely, the internal greenhouse temperature, the heating load of the greenhouse, heat supply from the water storage tank to the greenhouse, heat pumps' output water temperature, power used by the heat pumps, coefficient of performance (COP) of the heat pump, and water storage tank temperature. The BES model's performance was evaluated by calculating the root mean square error (RMSE) and the Nash–Sutcliffe efficiency (NSE) coefficient of validation results. The overall results correlated well with the experimental and simulated results and encouraged adopting the BES model. The average calculated COP of the AWHP was 2.2 when the outside temperature was as low as −13 °C. The proposed model was designed simply, and detailed information of each step is provided to make it easy to use for engineers, researchers, and consultants. [ABSTRACT FROM AUTHOR]

Oil palm crops are essential for ensuring sustainable edible oil production, in which production is highly dependent on fertilizer applications. Using Landsat-8 imageries, the feasibility of macronutrient level classification with Machine Learning (ML) was studied. Variable rates of compost and inorganic fertilizer were applied to experimental plots and the following nutrients were studied: nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg) and calcium (Ca). By applying image filters, separability metrics, vegetation indices (VI) and feature selection, spectral features for each plot were acquired and used with ML models to classify macronutrient levels of palm stands from chemical foliar analysis of their 17th frond. The models were calibrated and validated with 30 repetitions, with the best mean overall accuracy reported for N and K at 79.7 ± 4.3% and 76.6 ± 4.1% respectively, while accuracies for P, Mg and Ca could not be accurately classified due to the limitations of the dataset used. The study highlighted the effectiveness of separability metrics in quantifying class separability, the importance of indices for N and K level classification, and the effects of filter and feature selection on model performance, as well as concluding RF or SVM models for excessive N and K level detection. Future improvements should focus on further model validation and the use of higher-resolution imaging. [ABSTRACT FROM AUTHOR]

Simple Summary: Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables. Among other livestock animals, pigs are more sensitive to environmental equilibrium; a lack of favorable environment in barns affects the productivity parameters such as voluntary feed intake, feed conversion, heat stress, etc. Machine learning (ML) based prediction models are utilized for solving various nonlinear problems in the current decade. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. Typically, most of the available IAT and IRH models are limited to feed the animal biological data as the input. Since the biological factors of the internal animals are challenging to acquire, this study used accessible factors such as external environmental data to simulate the models. Three different input datasets named S1 (weather station parameters), S2 (weather station parameters and indoor attributes), and S3 (Highly correlated values) were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes. [ABSTRACT FROM AUTHOR]

Recent advances in solar-driven interfacial evaporation (SDIE) have led to high evaporation rates that open promising avenues for practical utilization in freshwater production and industrial application for pollutant and nutrient concentration, and resource recovery. Breakthroughs in overcoming the theoretical limitation of 2D interfacial evaporation have allowed for developing systems with high evaporation rates. This study presents a comprehensive review of various evaporator designs that have achieved pure evaporation rates beyond 4 kg m -2  h -1 , including structural and material designs allowing for rapid evaporation, passive 3D designs, and systems coupled with alternative energy sources of wind and joule heating. The operational mechanisms for each design are outlined together with discussion on the current benefits and areas for improvement. The overarching challenges encountered by SDIE concerning the feasibility of direct integration into contemporary practical settings are assessed, and issues relating to sustaining elevated evaporation rates under diverse environmental conditions are addressed., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO 2 , light, pressure, and different types of gases, namely PM 1 , PM 2.5 , and PM 10 . The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Thermally localized solar-driven water evaporation (SWE) in recent years has increasingly been developed due to the potential of cost-efficient freshwater production from small-scale portable devices. In particular, the multistage SWE has attracted much attention as the systems possess mostly a simple foundational structure and high solar-to-thermal conversion output rates, enough to produce freshwater from 1.5 L m -2 h -1 (LMH) to 6 LMH. In this study, the currently designed multistage SWE devices were reviewed and examined based on their unique characteristics as well as their performances in freshwater production. The main distinguishing factors in these systems were the condenser staging design and the spectrally selective absorbers either in a form of high solar absorbing material, photovoltaic (PV) cells for water and electricity co-production, and coupling of absorber and solar concentrator. Other elements of the devices involved differences such as the direction of water flow, the number of layers constructed, and the materials used for each layer of the system. The key factors to consider for these systems include the heat and mass transport in the device, solar-to-vapor conversion efficiency, gain output ratio (representing how many times the latent heat has been reused), water production rate/number of stages, and kWh/number of stages. It was evident that most of the studied devices involved slightly different mechanisms and material compositions to draw out higher efficiency rates from the current limitations. The reviewed designs showed the ability to be adopted into small-scale solar desalination allowing for accessibility of sufficient freshwater in needing regions.

Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.

Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar irradiance prediction models. The 5 RNN classes are long-short term memory (LSTM), gated recurrent unit (GRU), Simple RNN, bidirectional LSTM (Bi-LSTM), and bidirectional GRU (Bi-GRU); the first 3 classes are unidirectional and the last two are bidirectional RNN models. The 26 months data under consideration, exhibits extremely volatile weather conditions in Jinju city, South Korea. Therefore, after different experimental processes, 5 hyper-parameters were selected for each model cautiously. In each model, different levels of depth and width were tested; moreover, a 9-fold cross validation was applied to distinguish them against high variability in the seasonal time-series dataset. Generally the deeper architectures of the aforementioned models had significant outcomes; meanwhile, the Bi-LSTM and Bi-GRU provided more accurate predictions as compared to the unidirectional ones. The Bi-GRU model provided the lowest RMSE and highest R 2 values of 46.1 and 0.958; additionally, it required 5.25*10 -5 seconds per trainable parameter per epoch, the lowest incurred computational cost among the mentioned models. All 5 models performed differently over the four seasons in the 9-fold cross validation test. On average, the bidirectional RNNs and the simple RNN model showed high robustness with less data and high temporal data variability; although, the stronger architectures of the bidirectional models, deems their results more reliable., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

Objective: Anesthesia preoperative evaluation clinics (APECs) are currently operating in several South Korean hospitals. While several studies have investigated the impact of APEC operations on the length of total hospital stay (LTHS), few have investigated their impact on the length of preoperative hospital stay (LPHS) for patients. In this study, we aimed to determine whether APEC affected the LPHS and LTHS., Methods: Data of all patients who underwent surgery at Chungbuk National University Hospital between September 2009 and August 2019 were analyzed retrospectively. All patients who had undergone laparoscopic cholecystectomy over the last 10 years were categorized into two groups: those who visited the APEC (Group A), and those who did not (Group B). The age, sex, American Society of Anesthesiologists physical status score, LPHS, and LTHS of the two groups were compared., Results: The LPHS was 1.03±0.2 days in Group A and 1.61±1.6 days in Group B. The LTHS was 4.77±1.9 days in Group A and 5.63±2.6 days in Group B. The LPHS and LTHS of the two groups differed by 0.58 and 0.9 days, respectively., Conclusion: We evaluated the effect of APEC operations on the LPHS and LTHS of inpatients undergoing laparoscopic cholecystectomy and observed a decrease in both the LPHS and LTHS. Understanding and accepting the importance of APEC is significant for physicians and administrators working to improve hospital efficiency and patient outcomes. Further research is needed to investigate the need and benefits of APECs., Competing Interests: None., (AJTR Copyright © 2021.)

The optimal production of strawberries requires the essential nutrients and favourable media for vegetative and reproductive growth. The present study sought to determine the effectiveness of growth parameters and fruit yield of strawberries in different media growing under a greenhouse. To analyze the significant effect for the growth and fruit yield among the growing media, four treatments such as control soil (CS), bio plus compost (T 1 ), the combination of bio plus compost, and synthetic nutrient applied media/integrated media (T 2 ) and synthetic nutrient applied soil media (T 3 ) were assayed. Morphology parameters like plant height, canopy area, fresh weight, dry weight of roots were measured in each stage after eight weeks and sixteen weeks and yield attributing parameter as the number of fruits set per plant and number of fruits per plant were measured at the beginning and end of the reproductive stage eight and sixteen weeks respectively. The effects of growing media for the strawberry plant growth and productivity were analyzed using completely randomized block designs through analyzing the variance with a significance level of p < 0.05. The canopy area of the strawberry plants was calculated using the image processing technique applied in HSV colour space. Correspondingly, the vegetative stage and reproductive stage of T 2 plants attained the maximum plant height of 16.93 ± 0.31 cm and 19.34 ± 0.21 cm, canopy area with 23.02 ± 1.94 cm 2 and 28.78 ± 0.93 cm 2 , fresh weight of 18.00 ± 3.06 g, and 20.15 ± 3.49 g, dry weight of 5.15 ± 1.26 g and 6.66 ± 2.34 g and the number of fruits set per plant 18.83 ± 2.64 and number of fruits per plant 24.17 ± 2.14 followed by T 1 , T 3, and CS respectively. A comparison of the relative growth and fruit yield at the vegetative and reproductive phases of plants T 2 implied better performance. This study demonstrated that bio plus compost with synthetic nutrients act as a better source for the growth and production of strawberries under the greenhouse., Competing Interests: The authors declare no conflict of interest., (© 2021 The Author(s).)

The present study aims to compare the contents of minerals (essential major-K, Ca, Mg, Na, P, S; essential trace-Fe, Mn, Zn, Cu, B, Mo, As, Se, Ni, V, Cr, Co; non-essential-Sn, Ga, Li, Be, Rb, Sr, Al, Pd, Cd, Hg, Pb, Ge) in strawberry (stem, leaf, and fruit) cultivated in two different cultivation systems, soil and hydroponic. The concentrations of 30 minerals in the acid-digested strawberry samples were determined by ICP-MS and ICP-OES. Hydroponic strawberry (leaf > fruit > stem) indicated higher values for most minerals which were below the plant toxicity levels. In leaves collected from the hydroponic system, it was observed there were larger amounts of Fe, Zn, B, As, Se, Ni, V, Cr, Al, Cd, and Pd. Hydroponic fruits were the significant sources of K, P, Mn, Zn, Cr, and Co. Hydroponic strawberry leaves could contribute twice as many higher and safe daily intake of minerals to humans than other fruits. This analysis shows that, firstly, higher quality and safely edible produce can be provided by the hydroponic system; and secondly, strawberry leaf is a potential mineral source.

Objective: When conducting a caesarean section under regional anaesthesia, either epidural anaesthesia or spinal anaesthesia can be used. Patients who underwent caesarean section in our hospital were surveyed retrospectively to confirm and compare the merits and demerits of spinal anaesthesia and epidural anaesthesia to determine the most efficient approach., Methods: Mothers meeting the American Society of Anesthesiologists physical status classification system (ASA) I or II, who underwent caesarean sections at our hospital were surveyed retrospectively. The survey targeted one hundred patients each who received spinal anaesthesia and epidural anaesthesia. The time from anaesthesia to surgical incision (A to S time), entire anaesthesia time, and the usage of vasopressor and midazolam were compared according to anaesthetic approach., Results: The A to S time and the entire anaesthesia time of the group that underwent spinal anaesthesia were significantly short compared to the times recorded for the group who underwent epidural anaesthesia, and the use of vasopressor was more frequent in the spinal anaesthesia group because their blood pressure decline was larger., Conclusion: The A to S time and the entire anaesthetic time were longer for epidural anaesthesia than for spinal anaesthesia. However, the haemodynamic change was smaller and vasopressor was hardly used in the former group. Therefore, the choice of the technical method will depend on the clinical, anaesthetic, and obstetric situation.

E-cadherin expression has been suggested to be related with response to EGFR-TK inhibitors. To evaluate the prognostic value of E-cadherin expression in lung cancer treated with gefitinib, we retrospectively reviewed patients with inoperable stage IIIb or IV non-small cell lung cancer treated with gefitinib and compared immunohistochemical staining for E-cadherin on biopsied specimens. The association between gefitinib sensitivity and E-cadherin expression by Western blot and immunocytochemistry was also examined in 10 lung cancer cell lines. Of 52 eligible patients, 15 (28.8%) showed a partial response, and the disease control rate was 42.3%. Median progression-free survival (PFS) and overall survival (OS) in responders were 8 and 20 months, respectively. Response rates (RR), PFS, and disease control rates were higher in never-smokers and patients showing adenocarcinoma histology, and improved OS was observed in patients with adenocarcinoma. E-cadherin expression did not impact any parameters (RR, PFS, or OS). Although there was a tendency for cell lines with lower IC50 to have E-cadherin expression, H2009 cells were the least sensitive to gefitinib, with an IC50 of 30 microM, and H1650 cells had an intermediate sensitivity despite high E-cadherin expression. E-cadherin expression was not a useful indicator of response and survival after treatment with gefitinib.

The objective of the study is to investigate the distribution patterns of airborne bacteria and fungi in three feedstuff-manufacturing factories in Korea. The geometric mean (+/-GSD) levels of airborne bacteria and fungi in the feedstuff-manufacturing factory were 113(+/-18)cfu/m(3) and 89(+/-5)cfu/m(3) for pelleting process and 198(+/-5)cfu/m(3) and 124(+/-12)cfu/m(3) for powdering process, respectively. The percentage of respirable (0.65-4.7 microm) and total (>4.7 microm) concentration of airborne bacteria and fungi in the feedstuff-manufacturing factory ranged from 60% to 90% and were higher in pelleting process than powdering process. The ratio of indoor concentration and concentration in ventilation air from outside for airborne bacteria and fungi exceeded 1.0 regardless of types of feedstuff manufacture process. The predominant airborne microorganisms in the feedstuff-manufacturing factory were Staphylococcus spp., Micrococcus spp., Corynebacterium spp. and Bacillus spp. for airborne bacteria and Cladosporium spp., Penicillium spp. and Aspergillus spp. for airborne fungi, respectively. Based on the result of the study, there would be an association between relative humidity among environmental factors and airborne microorganism's bioactivity.