Traditional Laboratory Methods Research Articles

The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa plain (Morocco)

The use of standard laboratory methods to estimate the soil texture is complicated, expensive, and time-consuming and needs considerable effort. The reflectance spectroscopy represents an alternative method for predicting a large range of soil physical properties and provides an inexpensive, rapid, and reproducible analytical method. This study aimed to assess the feasibility of Visible (VIS: 350-700 nm) and Near-Infrared and Short-Wave-Infrared (NIRS: 701-2500 nm) spectroscopy for predicting and mapping the clay, silt, and sand fractions of the soils of Triffa plain (north-east of Morocco). A total of 100 soil samples were collected from the non-root zone of soil (0-20 cm) and then analyzed for texture using the VIS-NIRS spectroscopy and the traditional laboratory method. The partial least squares regression (PLSR) technique was used to assess the ability of spectral data to predict soil texture. The results of prediction models showed excellent performance for the VIS-NIRS spectroscopy to predict the sand fraction with a coefficient of determination R2 = 0.93 and Root Mean Squares Error (RMSE) =3.72, good prediction for the silt fraction (R2=0.87; RMSE = 4.55), and acceptable prediction for the clay fraction (R2 = 0.53; RMSE = 3.72). Moreover, the range situated between 2150 and 2450 nm is the most significant for predicting the sand and silt fractions, while the spectral range between 2200 and 2440 nm is the optimal to predict the clay fraction. However, the maps of predicted and measured soil texture showed an excellent spatial similarity for the sand fraction, a certain difference in the variability of clay fraction, while the maps of silt fraction show a lower difference.

The Effectiveness of Triple Fixed-Dose Combination Therapy in the Management of Uncontrolled Arterial Hypertension

The aim of our study was to identify patients with uncontrolled hypertension (UCH) who were resistant to previous antihypertensive therapy (AHT) and to assess the clinical efficacy of the fixed-dose triple combination (FDTC) therapy during 6 months. Methods and Results: The study included 156 patients with UCH. The mean age of patients was 57.81±10.8 years; the average duration of AH was 8.87±5.34 years. According to the questionnaire data, all patients received dual or triple free-combination AHT, but did not reach the target blood pressure (BP) level. After the screening stage, all patients were discontinued from previous therapy and assigned to the FDTC therapy. Patients initially taking angiotensin-converting enzyme inhibitors (n=96/61.5%) were switched to single-pill triple combination (SPTC) of perindopril, indapamide and amlodipine (Per/Ind/Aml [5mg/1.25mg/5mg]). Patients initially taking angiotensin receptor blockers (n=60/38.5%) were switched to SPTC of telmisartan, hydrochlorothiazide and amlodipine (Tel/HCTZ/Aml [40mg/12.5mg/5mg]). The primary target level for SBP and DBP was <140mmHg and <90mmHg, respectively. With good tolerability of therapy, we aimed for the recommended target level of SBP/DBP (<130mmHg/<80 mmHg). All patients underwent the following examinations: assessment of traditional risk factors, physical examination, clinical and biochemical laboratory methods, 12-lead ECG, echocardiography, pulse contour analysis, and 24-hour ambulatory blood pressure monitoring. The results of our study showed that the FDTC therapy at the indicated initial doses in patients with UCT made it possible to achieve the primary target BP level in 73.7% of patients after 3 months of treatment. A further increase in SPTC doses made it possible to increase the number of patients (after 6 months of treatment)who achieved the primary goal to 92.3%, and the recommended goal in 82.4% of patients. After 6 months of treatment, against the background of selected doses of SPTC, 7.7% of patients were diagnosed with treatment-resistant hypertension. Conclusion: Markers of UCH are high SBP, DBP, and increased pulse wave velocity, which requires the maximum doses of a full-dose SPTC of antihypertensive drugs (Per/Ind/Aml or Tel/HCTZ/Aml), without wasting time on titration of drug doses.

Processing of Mass Spectra of Exhaled Gases Based on Correlation Algorithms

Analyses of the composition of exhaled gases are among important areas of non-invasive medicine. Diagnostics based on the analysis of exhaled gases offers several advantages over traditional laboratory methods. An analysis of a gas mixture is safe for the staff, because it does not involve chemical and biological fluids. The analysis is relatively inexpensive, takes little time, and ensures the detection of volatile components in the exhaled gas at the trace level of substances in real time. This paper discusses algorithms for the processing of mass spectra of exhaled gases obtained on an MC7-200 quadrupole mass spectrometer with electron ionization and the direct capillary injection of a sample. A comparative evaluation of algorithms for the detection of spectral lines in mass spectra based on data convolution with a model of response function or its derivatives is given. The Hermite function, the Gaussian function, and its even-order derivatives are considered as convolution kernels. The results of evaluation of the potential for increasing the signal-to-noise ratio by algorithms of convolution with these functions are presented.

Minimum variance based-Bayes Combination for prediction of soil properties on Vis-NIR reflectance spectroscopy

Traditional laboratory methods for determining soil properties require a great deal of time and expense, while reflectance spectroscopy technology is a fast, inexpensive, and convenient way to predict physical and chemical soil properties. This technology in the spectral range of 400–2500 ​nm (Vis-NIR) as a suitable alternative method to get soil properties are accompanied by problems and challenges to extract considered properties. In this paper, we propose Minimum Variance based-Bayes Combination (MVBC) method to predict the soil properties. In the proposed MVBC method, we design two steps, prediction and combination for the training phase. Firstly, in the prediction step, five regression methods, i.e., partial least squares regression (PLSR), kernel Ridge regression (KRR), linear regression (LR), gradient boosting regression (GBR) and random forest (RF) method used to calculate and estimate nine soil properties, i.e., CaCo3, CEC, Clay, N, OC, PH in CaCl2, PH in H2O, Sand and Silt, separately. Secondly, in the combination step, the estimation errors of all regressions in the prediction step are determined to assign appropriate weight to each of them in the Bayesian framework based on minimum variance. These two steps are repeated until the final estimation error reaches an acceptable minimum value. Finally, these results and the trained system are used for the test phase. Experiments are reported to evaluate the effectiveness of the proposed MVBC method on real soil data, which shows the good performance of the proposed method with better results than other methods.

ANTIMICROBIAL PEPTIDES AND UBIQUITIN PROTEIN LIGASE E3 IN DESTRUCTIVE FORMS OF PANCREATITIS

The development of methods for predicting and pre-venting the development of infectious destructive forms of acute pancreatitis for clinical practice is one of the important problems of pancreatology, the solu-tion of which would reduce post-operative mortality in severe acute pancreatitis, reaching 30-80%. To date, traditional laboratory and instrumental methods of diagnosis do not fully satisfy clinicians, since they do not always allow timely and reliable determination of the probability and time of microbial contamina-tion of pancreatic and parapancreatic tissue in pa-tients with destructive pancreatitis. In this regard, it is of interest to search for new methods for predicting and timely diagnosis of infected forms of pancreatic necrosis, which would significantly improve the re-sults of treatment of severe forms of acute pancreatitis. The use of changes in the dynamics of levels of antimi-crobial peptides - α-defensins and Ubiquitin Protein Ligase E3 in the blood serum of patients for diagnos-tic purposes makes it possible to predict and deter-mine earlier the transformation of sterile pancreone-crosis into infected and can be used as markers of the development of contamination of destructive forms of acute pancreatitis.

Application of the discrete element method and CT scanning to investigate the compaction characteristics of the soil–rock mixture in the subgrade

The soil–rock mixture (SRM) usually contains a large amount of gravels exceeding 40 mm in size, so the traditional laboratory method cannot directly test its maximum dry density (MD), making it difficult to evaluate the compaction degree of the SRM subgrade during construction. In this paper, a numerical simulation method of the vibration compaction method for the SRM (NSM-VCM) was developed based on a discrete element method (DEM) and CT scanning. Based on the established NSM-VCM, the MD of SRMs with a maximum particle size greater than 40 mm (SRM-G) was investigated comprehensively. Based on the results of laboratory tests and the NSM-VCM, a predictive model and determination method of the MD of SRM-G were developed. Finally, field measurements were conducted to validate the laboratory investigations. The results showed that the maximum error between the MD of the SRM obtained from the NSM-VCM and the laboratory test was 0.1%, indicating that the established NSM-VCM has high predictive accuracy. The MD of SRM-G increases with an increasing maximum particle size and dosage of giant granules. Only when the soil–rock ratio is appropriate can SRM-G form a better skeleton dense structure, which is important for improving the MD and mechanical strength. The maximum error between the estimated MD and the measured MD from the field site is 1.3%, which indicates that the prediction model and method for SRM-G established in this paper have high precision. These results address the issue that the MD of SRM-G cannot be determined in a laboratory.

Antimicrobial Efficacy of Toothpastes Containing Fluoride against Clinically Isolated Streptococci mutans

Dental caries is one of the prevalent oral diseases which affect all ages. Many chemical substances with antimicrobial properties, are effective against microorganisms causing dental caries. The objective of this study was to check the antimicrobial efficacy of some toothpastes found in Enugu metropolis, Nigeria more specifically against clinically isolated Streptococci mutans. The antimicrobial efficacy of different brands of toothpastes marketed in Enugu metropolis was assayed against Streptococci mutans using the agar well diffusion method. The clinical Streptococci mutans used for this study were taken from carious lesions of patients that attended the clinic at Federal College of Dental Therapy and Technology, Trans Ekulu, Enugu, Nigeria and were isolated and cultivated using traditional laboratory methods. The present study has shown that toothpaste which contains mint extract as herbal constituent and sodium fluoride had the highest antimicrobial activity, followed by toothpastes containing only sodium monofluorophosphate. Herbal toothpaste with mint can be a safe and effective alternative in the prevention of dental caries.

NMR relaxation times for soil texture estimation in the laboratory: A comparison to the laser diffraction and sieve–pipette methods

AbstractNuclear magnetic resonance (NMR) relaxation time (T2) distributions are well known to be linked to the pore size distribution and show promise as a method of estimating soil texture. As traditional laboratory methods used for soil texture estimates in soil science are generally time consuming, in this study, we explore an alternative approach based on NMR T2 distributions to estimate the soil texture of water‐saturated soil samples collected from three field sites. Using two T2 cut‐off times, T2a and T2b, the T2 distribution of a soil was partitioned into three regions, short, intermediate and long relaxation times, each of which represents the fraction of clay, silt and sand, respectively. Two approaches for determining the cut‐off times were used: the first used T2 cut‐off times determined from the data from all sites and the second used site‐specific T2 cut‐off times. The NMR estimates of soil texture were compared to measurements of soil texture made using the sieve–pipette method and laser diffraction particle size analysis (LDPSA). The results show that there is no universal cut‐off time for estimating the clay, silt and sand fractions based on the NMR T2 distributions. The accuracy of NMR measurements to estimate the soil texture depends on the magnetic susceptibility of the measured material. For soils with low magnetic susceptibility (<2 × 10−4 SI) using site‐specific cut‐off times, the NMR‐derived soil texture (root mean squared error [RMSE] = 9.43%) more closely matches the soil texture measured from the sieve–pipette method than the soil texture determined using LDPSA (RMSE = 11.88%). However, the NMR estimate of soil texture breaks down for soils with high magnetic susceptibility (>4 × 10−4 SI). These results suggest that the NMR method can provide reasonable estimates of the soil texture for soils with low magnetic susceptibility.Highlights No universal cut‐off time exists for estimating the clay, silt and sand fraction based on the NMR T2 distributions. NMR can provide better estimates of clay, silt and sand fractions for soils with low magnetic susceptibility than LDPSA. Site‐specific cut‐off times are determined through comparison between NMR data and sieve–pipette measurements. NMR yields reasonable estimates of the soil texture for soils with low magnetic susceptibility.

Characterization of Mannitol Fermenter and Salt Tolerant Staphylococci from Breast Tumor Biopsies of Iraqi Women

The emergence of staphylococci, either coagulase negative (CNS) or coagulase positive (CPS), as important human pathogens has implied that reliable methods for their identification are of large significance in understanding the diseases caused by them. The identification and characterization of staphylococci from biopsies taken from human breast tumors is reported here. Out of 32 tissue biopsies, a total of 12 suspected staphylococci grew on mannitol salt agar (MSA) medium, including 7 fermenters and 5 non-fermenter staphylococci based on traditional laboratory methods. Polymerase chain reaction (PCR) successfully identified seven isolates at the genus level as methicillin resistant Staphylococcus spp. by targeting a common region of the mecA gene. Only two of the seven bacteria were S. aureus based on the three-specific primers designed to amplify the housekeeping gene recN, and two of the virulence genes icaD and pvl. Diagnosing the isolates using the Vitek system revealed different findings. Although 6 of 7 isolates belonged to the Staphylococcus genus, including: S. cohnii subsp. cohnii, 2 isolates; S. lentus, 2 isolates; and one isolate for each S. auricularis and S. xylosus, the last bacterium was completely different (Aerococcus viridans). Concerning the two bacteria characterised as S. aureus by PCR, they were identified as S. lentus by Vitek with comparatively low detection probabilities of 93% and 88%. The data of this study indicate that undoubtedly PCR is a reliable and accurate test for identification of mannitol fermenter and salt tolerant bacteria in comparison with other tests that depend mainly on biochemical characteristics.

Detection of Soft‐Deodorized Olive Oil and Refined Vegetable Oils in Virgin Olive Oil Using Near Infrared Spectroscopy and Traditional Analytical Parameters

AbstractThe European Parliament identifies virgin olive oil (VOO) as one of the foods which are often subject to fraudulent activities. Possibilities of adulteration are the application of illegal soft deodorization of extra virgin olive oil (EVOO) or the commercialization of blends of EVOO with soft‐deodorized EVOO or refined vegetable oils. Despite the search for possibilities to prove the illegal soft deodorization of EVOO or the addition of cheaper vegetable oils to EVOO, suitable methods are still missing. Therefore, the aim of the study is to develop a new analytical and statistical approach addressing detection of mild deodorization or addition of refined foreign oils. For this purpose, VOOs are treated in lab‐scale for 1 h up to 28 days at different temperatures (20, 50, 60, 80,100, 110, and 170 °C) in order to simulate and study the effect of heat treatment on known analytical parameters by near infrared spectroscopy (NIR). A logit regression model enabling the calculation of the probability for a heat treatment is developed. This new methodology allows detecting both soft deodorized olive oils and blends of EVOO with cheaper full refined vegetable oils. Adding only 10% of full refined oil could be detected in extra VOO.Practical Applications: NIR methods combined with chemometrics have become one of the most attractive analytical tools to control quality of food. It is a simple, precise, and rapid method. All relevant analytical parameters of oxidative and thermal fat degradation can be determined in a single run and be used to detect adulterated virgin olive oils (VOOs). The use of a simple equation developed from the logistic regression using peroxide value, K‐values, p‐anisidine value, pyropheophytine, 1,2‐diacylglycerols, total polar compounds and monomeric oxidized triacylglycerols, and other well‐known parameters allows to detect mild deodorized olive oils or also blends of VOO with soft‐deodorized ones or the addition of low amounts of foreign vegetable oils. This technique has potential to be used as a screening method for the detection of adulterated olive oils using both the traditional laboratory methods and the corresponding NIR‐methods.

Rapid identification of the Candida glabrata species complex by high-resolution melting curve analysis.

Background Candida glabrata is a common pathogen that causes invasive candidiasis. Among non‐albicans Candida infections, C glabrata infections are associated with the highest fatality rates. Candida glabrata sensu stricto, Candida nivariensis, and Candida bracarensis have been identified and together form the C glabrata species complex. It is difficult to detect the two rare species by traditional laboratory methods. This study established a method for the rapid identification of members of the C glabrata species complex based on high‐resolution melting curve (HRM) analysis and evaluated its practical application.MethodsThe internal transcribed spacer (ITS) region was used as target gene region to design specific primers. HRM analysis was performed with three subspecies of the C glabrata species complex and negative controls to test its specificity and sensitivity. To evaluate its practical application, the HRM technique was tested with clinical isolates, and the results were compared with the DNA sequencing results.ResultsDifferences were detected among the melting profiles of the members of the C glabrata species complex. The negative controls were not amplified, indicating the high specificity of the method. The minimum detection limits of C glabrata sensu stricto, C nivariensis, and C bracarensis were approximately 1 × 101 copies/µL or less. The results of the HRM analysis of the clinical isolates were consistent with the DNA sequencing results.ConclusionsThe HRM method is sensitive and can be used to rapidly identify the members of the C glabrata species complex. The method can allow early and targeted treatment of patients with invasive candidiasis.

Installation design of on-line near infrared spectroscopy for the production of compound fertilizer

Low-analysis fertilizer composition highly poses risk to soil infertility, crop yield and farmer livelihood. Fertilizer testing via traditional laboratory methods is a tool for quality control but with the significantly high cost of investment and maintenance in the long run. Physiochemical data from vibrational spectra obtained from the near-infrared reflectance (NIR) analyzer shows successful installation design for real-time quality monitoring of compound fertilizer production in this study overcoming exhaustive laboratory methods offering competitive advantages to the fertilizer industry becoming a great tool for product development and lower the risk of farmers buying the low-analysis fertilizer. The NIR analyzer was designed for on-line analysis of nutrient contents of the fertilizer before bagging. Total nitrogen (TN%), available phosphorus (available P2O5%), water-soluble potassium (water-soluble K2O%), total organic matter (TOM%), moisture content (TMC%) including pH were required to be monitored to ensure final product release being within limits legislatively established. Spectral variations across different times of measurement over a day of the production was investigated in the condition of on-line system instead of a controlled condition to confirm the measurement will be reliable and in good reproducibility. Calibration models were developed using Partial Least Squares (PLS) regression with a series of data pretreatments on individual model per nutrient. Precision and accuracy figures of NIR results were obtained considerably good enough to the manufacturing practice. NIR prediction results were later designed for threshold and feed-back to the process control.

From the Laboratory to The Vineyard-Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis.

Compared to traditional laboratory methods, spectroscopic techniques (e.g., near infrared, hyperspectral imaging) provide analysts with an innovative and improved understanding of complex issues by determining several chemical compounds and metabolites at once, allowing for the collection of the sample “fingerprint”. These techniques have the potential to deliver high-throughput options for the analysis of the chemical composition of grapes in the laboratory, the vineyard and before or during harvest, to provide better insights of the chemistry, nutrition and physiology of grapes. Faster computers, the development of software and portable easy to use spectrophotometers and data analytical methods allow for the development of innovative applications of these techniques for the analyses of grape composition.

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts.

Prediction of Soil Texture Distributions by using PLSR and Reflectance Spectroscopy

The texture of soil i.e. Sand, Silt and Clay are the most important physical properties of soil for agricultural management. In the agricultural practices to increase the productivity of soil, moisture-holding capacity, aeration and to support the agronomic decisions the knowledge of soil texture is an essential task. For this purpose, the present research gives better results and fast acquisition of soil information with the use of Visible and Near Infrared (Vis- NIR) Diffuse Reflectance Spectroscopy. A total of 30 soil samples from two different locations from Aurangabad, Maharashtra, India were collected and analyzed for soil texture. To detect the soil texture the Vis-NIR DRS has shown levels of accurate results compared to the traditional laboratory method with less time, cost and effort. To measure the reflectance of soil the ASD FieldSpec4 Spectroradiometer (350-2500nm) was used. By the observation of captured spectra by using Spectroradiometer it showed that on the basis of different textural classes the soil samples could be spectrally separable. For database collection and pre-processing, we have used RS3 and ViewSpec Pro software respectively. The statistical analysis by using the combination of Principal Component Analysis (PCA) and Partial Least Square Regression method gives accurate results. To determine the texture of soil sample thirteen features were calculated. The main goal of this research was to determine the soil texture by using statistical methods and to test the performance of VNIR-SWIR reflectance spectroscopy by using the ASD FieldSpec4 Spectroradiometer for estimation of the texture of the soil. The results showed that R2 = 0.99 gives maximum accuracy for clay content and R2 = 0.988 for silt content and R2 = 0.989 for sand. The Root Mean Square Values (RMSE) for clay, silt, and sand are 0.02392, 0.02399 and 0.02289 respectively. With the use of reflectance spectroscopy and statistical analysis by using regression models we can determine the soil properties accurately in very less time.

Segmentation-driven Hierarchical RetinaNet for Detecting Protozoa in Micrograph

Protozoa detection and identification play important roles in many practical domains such as parasitology, scientific research, biological treatment processes, and environmental quality evaluation. Traditional laboratory methods for protozoan identification are time-consuming and require expert knowledge and expensive equipment. Another approach is using micrographs to identify the species of protozoans that can save a lot of time and reduce the cost. However, the existing methods in this approach only identify the species when the protozoan are already segmented. These methods study features of shapes and sizes. In this work, we detect and identify the images of cysts and oocysts of various species such as: Giardia lamblia, Iodamoeba butschilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba, which have round shapes in common and affect human and animal health seriously. We propose Segmentation-driven Hierarchical RetinaNet to automatically detect, segment, and identify protozoans in their micrographs. By applying multiple techniques such as transfer learning, and data augmentation techniques, and dividing training samples into life-cycle stages of protozoans, we successfully overcome the lack of data issue in applying deep learning for this problem. Even though there are at most 5 samples per life-cycle category in the training data, our proposed method still achieves promising results and outperforms the original RetinaNet on our protozoa dataset.

Point-of-care testing by healthcare workers for detection of meticillin-resistant Staphylococcus aureus, Clostridioides difficile, and norovirus

The feasibility of introducing three separate Cepheid GeneXpert® assays was assessed: Xpert SA Nasal Complete, Xpert C.difficile, and Xpert Norovirus for point-of-care testing (POCT) on a ward in a district general hospital. To establish a seven-day/24 h POCT service for meticillin-resistant Staphylococcus aureus (MRSA), Clostridioides difficile, and norovirus operated solely by healthcare workers (HCWs). The Cepheid GeneXpert assays performance characteristics were assessed by comparing the assays to traditional central laboratory methods in terms of clinical turnaround times, hands-on time, number of process steps, time to result and diagnostic accuracy. HCW feedback was collected to consider the potential added value of applying this technology to improve patient flow and clinical care. In total 1170 tests were carried out over the 16-month study period. The assays significantly reduced hands-on time, process steps, and time to result for identification of all three micro-organisms. Overall agreement with central laboratory testing was >98% for all three assays. Staff members fed back that POCT had a positive impact in terms of clinical utility. Xpert SA Nasal Complete for MRSA detection, Xpert C.difficile, and Xpert Norovirus can be used as POCT solely by HCWs in a ward setting. Each assay was used throughout a seven-day/24 h period with potential positive impact on bed management and patient care.

Mapping the lab-on-a-chip patent landscape through bibliometric techniques

Lab-on-chip are miniaturized devices capable of performing a variety of chemical, biochemical or biological analyzes of small volumes of fluids into a single chip. This is an emerging technology that holds potential to deliver more reliable and faster results at a lower cost than traditional laboratory methods. The aim of this paper is to give an overview of the products and processes related to lab-on-a-chip based on patent documents. We use patents data from Thomson Reuters Derwent Innovations Index and combine bibliometrics and social network analysis techniques. We found 2984 patents related to lab-on-a-chip technology, of which only 221 claims for a new lab-on-a-chip device. Considering the total of patents, our results show a significant increase in patenting from 2000 to 2008. As of 2006, the interest in patenting in several countries has risen. USA and Japan are the two most frequent countries developing related technologies, and the USA and the European Patent Office are the top target of patenting by non-residents. Overall, one can see a wide dispersion of organizations involved in researching and developing this technology. Technological developments are most frequently associated with the areas of physics, performing operations and chemistry. Most of the documents are aimed to protect inventions related to instruments for measuring and testing, and processes or apparatus for separation or mixing. By providing a lab-on-a-chip patent landscape worldwide, our findings can be used to support R&D decisions and foster new partnerships between organizations willing to develop the capabilities needed to enter this market.

Using Inquiry-Based Laboratory Instruction to Improve Critical Thinking and Scientific Process Skills among Preservice Elementary Teachers

Purpose: Although critical thinking skills (CTS) and scientific process skills (SPS) are the beneficial skills needed in the 21 st century learning, the fact is that the acquisition of these two skills is still low. Research w ith a focus on improving these skills by using inquiry-based approach is also limited. Therefore, this quasi-experimental study aimed to enhance preservice elementary teachers’ CTS and SPS by using Inquiry-Based Laboratory Instruction (IBLI). Method: A pretest-posttest control group design was executed. A total of 43 students who attended Teaching Science in Elementary School-II Laboratory Course at the Muhammadiyah University of Ponorogo were divided into two groups using cluster random sampling. The experimental group ( n =21) was taught by using IBLI, while the control group ( n =22) was taught by using traditional laboratory method. The Oliver-Hoyo Rubric for Critical Thinking (OHRCT) and the Observation Checklist for SPS (OCSPS) were administered. The data were then analyzed by using normalized gain score and Mann-Whitney U test at significance level .05. Findings: There was a significant difference in terms of CTS and SPS between control and experimental groups in favor of experimental group students. It was found out that gained CTS score of control and experimental group students was .58 and .80, while gained SPS score was .60 and .81, respectively. It can be highlighted that IBLI had a significant effect on preservice elementary teachers’ performance compared to the conventional group. Implications for Research and Practice: The findings suggest that IBLI is considered as the effective method to foster CTS and SPS of preservice elementary teachers. According to results, it is recommended that preservice teachers need to be given opportunities to develop hands-on and minds-on experiences in the science laboratory activities. The lecturers should utilize IBLI to develop students’ various lifelong learning skills.

Taming big data: Applying the experimental method to naturalistic data sets.

Psychological researchers have traditionally focused on lab-based experiments to test their theories and hypotheses. Although the lab provides excellent facilities for controlled testing, some questions are best explored by collecting information that is difficult to obtain in the lab. The vast amounts of data now available to researchers can be a valuable resource in this respect. By incorporating this new realm of data and translating it into traditional laboratory methods, we can expand the reach of the lab into the wilderness of human society. This study demonstrates how the troves of linguistic data generated by humans can be used to test theories about cognition and representation. It also suggests how similar interpretations can be made of other research in cognition. The first case tests a long-standing prediction of Gentner's natural partition hypothesis: that verb meaning is more subject to change due to the textual context in which it appears than is the meaning of nouns. Within a diachronic corpus, verbs and other relational words indeed showed more evidence of semantic change than did concrete nouns. In the second case, corpus statistics were employed to empirically support the existence of phonesthemes-nonmorphemic units of sound that are associated with aspects of meaning. A third study also supported this measure, by demonstrating that it corresponds with performance in a lab experiment. Neither of these questions can be adequately explored without the use of big data in the form of linguistic corpora.