Glass is Heavy, Too: Testing the Field-Processing Model at the Modena Obsidian Quarry, Lincoln County, Southeastern Nevada

2015 ◽  
Vol 80 (3) ◽  
pp. 548-570 ◽  
Author(s):  
Michael J. Shott
Keyword(s):  
Great Basin ◽  
Early Stage ◽  
Principal Component ◽  
Ratio Scale ◽  
Round Trip ◽  
Reduction Stage ◽  
Linear Dimensions ◽  
Technological Characteristics ◽  
Lincoln County ◽  
Cumulative Count

The field-processing model (FPM) relates degree of resource processing at procurement site to round-trip travel distance. Typically applied to food getting, its extension to stone tools is part of a larger trend to broaden the scope and strengthen the theoretical foundation of inference from lithic data. This trend guided the FPM's application at the Modena obsidian quarry in the Great Basin, which follows earlier analyses by comparing the proportion by reduction stage of biface preforms between quarry and outlying sites and the technological characteristics of debris assemblages. It also uses two ratio-scale methods, slope of the regression of preform weight upon the first principal-component of linear dimensions, which declines with reduction, and slope of cumulative-count of flakes upon flake size, which increases with reduction. Results support the FPM and previous studies that document early-stage reduction at quarries vs. later-stage reduction across the landscape. The essential next step in FPM testing requires data on pattern and extent of biface reduction as a continuous function of distance from source. As useful as are existing measures and approaches, newly defined ratio-scale measures can particularly expedite testing of the FPM in its continuous terms.

scholarly journals Analysis of infrared spectra of blood serum of patients with multiple myeloma

2021 ◽  
Vol 2103 (1) ◽  
pp. 012052
Author(s):  
D A Chernyshev ◽  
E S Mikhailets ◽  
E A Telnaya ◽  
L V Plotnikova ◽  
A D Garifullin ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Blood Serum ◽  
Infrared Spectra ◽  
Early Stage ◽  
Principal Component ◽  
Healthy Donors ◽  
Components Analysis ◽  
Serious Disease

Abstract Multiple myeloma (MM) is a serious disease that is difficult to diagnose especially at early stage. Infrared spectroscopy is a promising approach for diagnosing MM. The principal component analysis (PCA) allows us to reduce the dimension of the data and keep only the important variables. In this study, we apply principal components analysis to infrared (IR) spectra of blood serum from healthy donors and multiple myeloma patients. As a result of the analysis by PCA, it was possible to visualize the separation of patient’s and donor’s samples into two clusters. The result indicates that this method is potentially applicable for diagnosis of multiple myeloma.

Validity and Reliability of Mathematical Visual Literacy Skills Based on Avgerinou’s Vilual Literacy Index

2021 ◽  
Vol 12 (3) ◽  
pp. 1102-1121
Author(s):  
Raja Lailatul Zuraida Et.al
Keyword(s):  
Content Validity ◽  
Visual Literacy ◽  
Early Stage ◽  
Reliability And Validity ◽  
Principal Component ◽  
Literacy Skills ◽  
Face Validity ◽  
Validity And Reliability ◽  
Principal Component Axis ◽  
Bartlett’S Test

There is much literature on visual literacy across different fields of knowledge. Even so, generally there is a gap of literature that deals with measuring mathematical visual literacy skills. The objective of this paper is to produce empirical data on reliability and validity of mathematical visual literacy skills instrument. The development of items was based on the skills outlined Avgerinou’s VL Index (2007. The early stage in validating the instrument required researchers to seek face validity and content validity from panels of experts. Face validity was based on subjective judgements of the items. Meanwhile, content validity was determined by Content Validity Index (CVI) which is computed using Item-CVI (I-CVI) and Scale-CVI (S-CVI). Each mathematical visual literacy skills had accepted S-CVI values ranging from 0.86 to 1.00 but items with low I-CVI values were deleted. Next, construct validity and reliability was determined by using Exploratory Factor Analysis (EFA) and Cronbach’s alpha respectively. The instrument, consisting of 43 items was assessed on 428 pre-university students. Students’ responses were scored using analytical rubric developed by researchers. Using Principal Component Axis (PCA) and varimax rotation, EFA was carried out where 40 retaining items were extracted to 7 factors, representing each visual literacy skills. Kaiser-Meyer-Olkin (KMO) of 0.721, significant Bartlett’s Test of Sphericity (BTS), communalities anti images ranging between 0.308-0.721 and 0.503-0.835 respectively, 7 extracted factors explaining 53.685% of the total variance, factor loadings of ±0.520 and more, and overall Cronbach’s alphas of instrument recorded at 0.82, explained the complete validity and reliability of the instrument.

scholarly journals Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xianlan Zhu ◽  
Kun Wang ◽  
Gaoshuang Liu ◽  
Yuqing Wang ◽  
Jin Xu ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Early Stage ◽  
Principal Component ◽  
Roc Curves ◽  
Tca Cycle ◽  
Metabolic Reprogramming ◽  
Serum Samples ◽  
Diagnosis And Prognosis ◽  
Flight Mass Spectrometry ◽  
Amino Acid Turnover

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

The predictive potential of the spatial signature of lymphocytes in breast cancer patients.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 530-530
Author(s):  
Nora Balint-Lahat ◽  
Chen Mayer ◽  
Noa Ben-Baruch ◽  
Ady Yosepovich ◽  
Kira Sacks ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Early Stage ◽  
Principal Component ◽  
Treatment Decision ◽  
High Ratio ◽  
Luminal Breast Cancer ◽  
Breast Cancers ◽  
Breast Cancer Patients ◽  
Validation Set

530 Background: Tumor-infiltrating lymphocytes in breast cancer have emerged as both a prognostic and a potentially predictive immunotherapy biomarker. Advancements in artificial intelligence can extract pathology-based spatial immune fingerprints for use as treatment decision support tools. Methods: We examined 908 primary breast cancer patients with whole slide images (WSI) available from TCGA database. Digital structuring of WSIs included automated detection of lymphocytes, tumor and tumor adjacent stroma, using deep learning-based semantic segmentation. Prognosis was defined as progression free interval (PFI). A Cox Survival analysis was used to detect prognostic spatial features. We used principal component analysis (PCA) to reduce and decorrelate significant features. The resulting PCA features were used to fit the final model. The model was then validated on an independent database of WSI of breast lumpectomies, from two tertiary hospitals in Israel. Results: The analysis included 908 WSI. The average age was 58.4 years old, with a majority of early stage breast cancer (76.7%, stage I and II). The detection performance for tumor area and lymphocytes reached F1 scores of 99% and 97% respectively, in comparison to human annotation. In the Kaplan Meier (KM) analysis of 414 early stage luminal breast cancers, a high number of lymphocyte clusters (LC) and a high ratio between stromal lymphocyte density and tumor lymphocyte density (LD-S/LD-T) were significantly associated with longer PFI (p = 0.005 and p = 0.038, respectively). Based on these features, two continuous PCA features were added to the multivariate model, and remained significantly associated with PFI after adjusting for age (HR = 1.19, 95% CI 1.05-1.35; HR = 1.26 95% CI 1.03-1.55). The validation set was underpowered (n = 79) and data is still being collected. In a preliminary KM analysis of 37 early stage luminal breast cancer cases from the validation set, LD-S/LD-T was significantly associated with longer PFI (p = 0.046). Conclusions: In our study, LC and LD-S/LD-T, presumably surrogate measures of peritumoral lymphocytes, were found significantly associated with longer PFI.

Genotypic variation for seed yield components in white clover (Trifolium repens L.)

2000 ◽  
Vol 51 (6) ◽  
pp. 657 ◽  
Author(s):  
M. Z. Z. Jahufer ◽  
F. I. Gawler
Keyword(s):  
Seed Yield ◽  
White Clover ◽  
Yield Components ◽  
Early Stage ◽  
Genotypic Variation ◽  
Principal Component ◽  
Positive Association ◽  
Genotypic Correlation ◽  
Yield Attributes ◽  
Seed Yield Components

Seed yield is an important criterion that determines the commercial acceptability of new cultivars. Often, the seed yielding capacity of a forage cultivar is tested only in the final stages of its development. A more efficient strategy would be to screen and select plants for seed yield at an early stage of breeding. An important objective of the National White Clover Breeding Program based at the Agriculture Victoria Pastoral and Veterinary Institute, Hamilton, is to assess the genetic diversity for important morphological attributes among germplasm accessions. A set of 53 accessions, which included germplasm collected from Morocco and Tunisia and a range of commercial cultivars, was characterised for seed yield components. The seed yield components were number of ripe inflorescences, number of florets per ripe inflorescence, number of seeds per pod, floret size, and inflorescence height. Potential seed yield was estimated. The magnitude of genotypic variation, together with the accession mean repeatability estimates, indicated the presence of genetic variation among the 53 accessions for all attributes. Both phenotypic and genotypic correlation coefficients indicated a strong positive association between total number of ripe inflorescences and seed yield. There was also a positive phenotypic and genotypic correlation between seed yield and number of florets per inflorescence. Cluster analysis of the 53 accessions based on seed yield components resulted in the generation of 6 groups. Principal component analysis helped to identify 5 accessions that could be potentially useful in improving the seed yield of white clover germplasm selected for superior agronomic and herbage yield attributes.

scholarly journals Combining High Yields and Blast Resistance in Rice (Oryza spp.): A Screening under Upland and Lowland Conditions in Benin

2018 ◽  
Vol 10 (7) ◽  
pp. 2500 ◽  
Author(s):  
Octaviano Yelome ◽  
Kris Audenaert ◽  
Sofie Landschoot ◽  
Alexandre Dansi ◽  
Wouter Vanhove ◽  
...  
Keyword(s):  
Early Stage ◽  
Blast Resistance ◽  
Phenotypic Variability ◽  
Screening Method ◽  
Principal Component ◽  
Yield Potential ◽  
High Yield ◽  
Cycle Duration ◽  
Early Screening ◽  
High Yield Potential

The future security of the supply of rice for food in Africa depends on improving the level of local production to achieve self-sufficiency. In order to cope with the existing gap between production and actual demand, combining a high level of rice blast tolerance and a high-yield potential is necessary. The current study was conducted under upland and lowland conditions in Benin to gain insight into the performance of selected blast-resistant accessions along with some currently grown varieties. This study revealed a high phenotypic variability among these accessions. Furthermore, differences in the performance of these accessions under lowland and upland conditions were observed. Principal component analysis showed their grouping in three clusters. The analysis also demonstrated a high yield potential among the blast-resistant rice accessions whether they were Oryza sativa or O. glaberrima. Furthermore, there was a significant correlation between yield and both spikelet fertility and growth cycle duration. In conclusion, the present study identified promising rice accessions for future breeding. High phenotypic variability in combination with interesting traits can help to develop new resilient varieties. Finally, when the traits correlate with yield, they can be used as markers for an early screening method for identifying promising accessions at an early stage.

scholarly journals Выращивание столовых сортов винограда в теплице, накрытой полиэтиленовой пленкой

2019 ◽  
Author(s):  
A. Ivanov ◽  
V. Roytchev
Keyword(s):  
Early Stage ◽  
Vegetation Period ◽  
The Third ◽  
Grapevine Cultivar ◽  
Technological Characteristics ◽  
Long Shoots ◽  
Second Year

Представлены первые результаты выращивания десертных сортов винограда Виктория, Италия и Ред глоуб в теплице, накрытой полиэтиленовой пленкой, в условиях Южной Болгарии. Установлено, что фенофазы их развития начинаются раньше и протекают значительно более ускоренно по сравнению с их культивированием на открытом воздухе в полевых условиях. Ввиду вырастания достаточно длинных побегов виноградные кусты можно начать формировать ускоренно еще в первый вегетационный период. Виноград исследуемых сортов во второй и третий годы выращивания в теплице достигает потребительской зрелости приблизительно на месяц раньше по сравнению с виноградом, выращиваемым в полевых условиях, а урожай с одного куста возрастает с 25 кг на второй год до 710 кг на третий. Полученный виноград отличается очень хорошими, хозяйственно важными агробиологическими и технологическими показателями.The article summarizes first findings on cultivation of dessert grapevine cultivar Victoria, Italia and Red Gloube in the greenhouse covered with plastic wrap in the conditions of south Bulgaria. It has been established that phenophases of their development begin earlier, and proceed much more rapidly, as compared to cultivationin in the open air. Development of sufficiently long shoots allows shaping grapevine bushes at an early stage during the first vegetation period. In the second and third years of their cultivation in the greenhouse, the grapes of the studied cultivars reach consumer ripeness approximately one month earlier than the grapes grown in the field. The yield from one bush increases from 25 kg in the second year to 710 kg in the third. The grapes obtained demonstrated strong, economically significant agro-biological and technological characteristics.

Vibration-Based Structural Health Monitoring of a Simulated Beam with a Breathing Crack

2013 ◽  
Vol 569-570 ◽  
pp. 1093-1100 ◽  
Author(s):  
Jyrki Kullaa ◽  
Kari Santaoja ◽  
Anthony Eymery
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Crack Length ◽  
Health Monitoring ◽  
Early Stage ◽  
Novelty Detection ◽  
Principal Component ◽  
Extreme Value Statistics ◽  
Ratio Test ◽  
Structural Health

Cracking is a common type of failure in machines and structures. Cracks must be detected at an early stage before catastrophic failure. In structural health monitoring, changes in the vibration characteristics of the structure can be utilized in damage detection. A fatigue crack with alternating contact and non-contact phases results in a non-linear behaviour. This type of damage was simulated with a finite element model of a simply supported beam. The structure was monitored with a sensor array measuring transverse accelerations under random excitation. The objective was to determine the smallest crack length that can be detected. The effect of the sensor locations was also studied. Damage detection was performed using the generalized likelihood ratio test (GLRT) in time domain followed by principal component analysis (PCA). Extreme value statistics (EVS) were used for novelty detection. It was found that a crack in the bottom of the midspan could be detected once the crack length exceeded 10% of the beam height. The crack was correctly localized using the monitoring data.

An Effective Approach Based on Improved Convolutionary Neural Network Classifier for the Detection of Diabetic Retinopathy

2021 ◽  
Vol 11 (12) ◽  
pp. 3082-3089
Author(s):  
B. Sakthi Karthi Durai ◽  
J. Benadict Raja
Keyword(s):  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Early Stage ◽  
Principal Component ◽  
Gabor Filtering ◽  
Processing Scheme ◽  
Successful Approach ◽  
Image Pixels ◽  
Watershed Method ◽  
In The Beginning

In diabetic individuals, diabetic retinopathy (DR) causes blindness. Therefore, detecting diabetic retinopathy at an early stage decreases vision loss. An successful approach for diabetic retinopathy prediction is discussed in this article. In the beginning, the input pictures of human retinal fundus images are preprocessed using histogram equalisation followed by Gabor filtering to reduce noise for enhancement. Then, using the Watershed method, segmentation is performed, and the features are retrieved through feature extraction. The best optimum features are selected using PCA (principal component analysis) approach. The morphological based post processing scheme was employed to further enhance the quality of selected features. At last, the classification approach is carried with the utilization of Google NET CNN classifier to classify/predict the retinal image as normal, abnormal, and severe. Google NET CNN has been developed with limited preprocessing step to distinguish visual features directly from image pixels. The findings are then evaluated and the efficacy of the new method is contrasted with other current methods. The quantitative findings were evaluated for Accuracy, precision, reliability, positive predictive levels and false predictive levels in parameters and were seen to deliver better results than current techniques.

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