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BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 2. Three Corpus Calosum: Normal, MCI, AD

The three figures above present the Corpus Callosum relating to three topics: Normal Topic by MCI (Mild Cognitive Impairment), Alzheimer’s topic. Secondly, we will present our clustering method to classify the test subject between 3 classes: N (Normal), MCI (Mild Cognitive Impairment), and AD (Alzheimer's disease).

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 1. Three hippocampus: Normal, MCI, AD

In this context is our work: performing a diagnostic computer-aided system for detecting Alzheimer's disease. Like any diagnostic system, our system contains three parts: preprocessing, segmentation and classification. Initially, we will present a new segmentation method to segment the Hippocampus and Corpus Callosum regardless of the patient's condition.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training

A. Segmentation It seeks to establish a model that describes the shape and typical fluctuations. This requires first the preparation of a learning base to reflect the possible variations in shape of the structure. The preparation of the training set Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis

The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.

BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 4. Training

Each shape will be modeled by a vector X, built by concatenating the coordinates of the characteristic points placed on its outline: X=(X1, X2,…...Xn) (1) The training set can be modeled by a set of vectors: {Xi} Where i = 1. . N {N number of sample images} and {Si} surface, {Vi} standard deviation of the Area. The principle of this step is be illustrated by the figure below.

opencc-by-4.0Oct 2018View details →

Fig. 2. Potentially diagnostic distances and angles. 1 in Cochlostoma revised: the subgenus Lovcenia Zallot et al., 2015 (Caenogastropoda, Cochlostomatidae)

Fig. 2. Potentially diagnostic distances and angles. 1 = shell height (H). 2 = height of aperture at the columellar side. 3 = height of the first whorl. 4 = width of body whorl (Wbw). 5 = width of the suture above the body whorl. 6 = width of the penultimate whorl. 7 = width of the suture above the penultimate whorl. 8 = distance between the 5 most central ribs on the 4th whorl up. 9 = diameter of the 4th whorl up. 10 = aperture width (Wa). 11 = width of the lip at the columellar side. 12 = aperture height (Ha). 13 = rib inclination. 14 = suture inclination. 15 = aperture inclination. 16 = protoconch diameter in upper view (Dp). 17 = end of the smooth part of the protoconch. 18 = end of the protoconch.

opencc-by-4.0Dec 2019View details →

The Politecnico di Torino rolling bearing test rig: description of the open-access data for vibration monitoring and diagnostics

Accelerometric measurements from the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), Department of Mechanical and Aerospace Engineering, Politecnico di Torino. Goals:    • Vibration Monitoring, Bearing Diagnostics, Damage detection, Damage localization, Damage classification, Damage assessment. Features:    • high-speed spindle driving a hollow shaft supported by a couple of identical roller bearings B1 and B3. B1 is the bearing under analysis and features various damages.    • two damage types (indentations on a roller and on the inner ring) and severities (0, 150, 250, 450 µm).    • a central, larger roller bearing (B2) is loaded through a sledge generating a controlled radial force measured by a load cell.    • lubrication is obtained by oil injection into the hollow shaft.    • a K-type thermocouple is used to monitor the temperature (manually recorded).    • two triaxial accelerometers are mounted on the supports of bearings B1 and B2. Dataset:    • stationary acquisitions at different speed & load combinations (speed: 0, 100, 200, 300, 400, 500 Hz; load: 0, 1000, 1400, 1800 N).    • endurance acquisitions of the bearing featuring the 450µm roller indentation. Monitoring of the damage evolution for about 230 hours under the same speed and load condition.   The extended description of the dataset can be found in the attached pdf "Description and analysis of open access data" or in: A.P. Daga, A. Fasana, S. Marchesiello, L. Garibaldi, The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data, Mechanical Systems and Signal Processing 120 (2019) 252–273. doi:10.1016/j.ymssp.2018.10.010.

opencc-by-4.0Aug 2019View details →

Fig. 5 in Disentangling Leucocytozoon parasite diversity in the neotropics: Descriptions of two new species and shortcomings of molecular diagnostics for leucocytozoids

Fig. 5. (A) A Bayesian phylogenetic hypothesis of Leucocytozoon species constructed only with partial mitochondrial genomes (5485 bp excluding gaps) and (B) partial cytb gene sequences of leucocytozoids. Branch colors indicate the parasite morphology, with green branches representing parasites in fusiform host cells, and blue branches correspond to a species that develops in roundish host cells. Notice that, since parasite mitochondrial genomes (mtDNA) corresponding to the partial cytb fragments of the MH909275 and MH909276 sequences could not be amplified, they were not included in the phylogenetic hypothesis constructed with mtDNA (Fig. 5A). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

opencc-by-4.0Aug 2019View details →

Fig. 2 in Disentangling Leucocytozoon parasite diversity in the neotropics: Descriptions of two new species and shortcomings of molecular diagnostics for leucocytozoids

Fig. 2. Leucocytozoon neotropicalis sp. nov. from the peripheral blood of its type vertebrate host Greenand-black Fruiteater (Pipreola riefferii) captured at Los Nevados NNP, Colombia. Macrogametocytes (A–E) and microgametocytes (F–I). Black arrows () indicate the deformed host cell nuclei. Parasite nuclei are indicated by white arrow () and nucleoli are shown by double white arrowtips (). Volutin granules are indicated by double black arrowtips () and vacuoles – by white arrowtips (). Uneven cytoplasmic processes may acquire a ribbon-like appearance (asterisk *). Giemsa-stained thin blood films. Scale bar = 10 μm. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in A New Species Of Rana (Amphibia: Anura: Ranidae) From The Highlands Of The Malay Peninsula, With Diagnostic Larval Descriptions

Fig. 4. Ventral aspects of left forelimb (a) and left hindlimb (b) of adult male Rana banjarana (illustrated from holotype: ZRC.1.8325). Note nuptial pad (unpigmented, non-constricted) on inner face of first finger. Scale bar = 5mm.

Fig. 2 in A New Species Of Rana (Amphibia: Anura: Ranidae) From The Highlands Of The Malay Peninsula, With Diagnostic Larval Descriptions

Fig. 2. Lateral (a) and dorsal (b) aspects of adult male Rana banjarana, new species (Holotype: ZRC.1.8325, SVL 34.6mm). Note dorsolateral dermal folds, longitudinal ridges on tibia, raised granules on dorsum and flanks, and absence of humeral gland.

Fig. 1 in A New Species Of Rana (Amphibia: Anura: Ranidae) From The Highlands Of The Malay Peninsula, With Diagnostic Larval Descriptions

Fig. 1. Schematic map of the Malay Peninsula, illustrating the five mountain ranges (from west to east): A, Banjaran Bintang; B, Banjaran Titi Wangsa; C, Banjaran Benom; D, Banjaran Gunong Tahan; E, Banjaran Timur, and the currently known distribution of Rana banjarana, new species, in the highlands (from north to south): Hala Bala Wildlife Sanctuary (Yala Province, South Thailand), Maxwell's Hill, Cameron Highlands, Fraser's Hill, Genting Highlands, Gunong Bunga Buah. Banjaran = mountain range (Malay). Hatched areas represent elevation of 300m asl and above.

Fig. 3 in A New Species Of Rana (Amphibia: Anura: Ranidae) From The Highlands Of The Malay Peninsula, With Diagnostic Larval Descriptions

Fig. 3. Dermal segment of Rana banjarana dorsum, depicting dorsolateral skin fold and raised granules bearing apical asperities/ glandules, each surrounded by smaller ones (head towards left; illustrated from holotype: ZRC.1.8325). Scale bar = 1mm.

Fig. 7 in A New Species Of Rana (Amphibia: Anura: Ranidae) From The Highlands Of The Malay Peninsula, With Diagnostic Larval Descriptions

Fig. 7. Oral disc of larval Rana banjarana (Stage 39). Note nonemarginate condition; LTRF 3(2-3)/3(1). Scale bar = 1mm.

opencc-by-4.0Apr 2020View details →

Figure 1 in The study of fidelity measures in the context of using them as a threshold criterion in the allocation of diagnostic species

Figure 1. Detection of optimal number of clusters by the Optimclass-2 approach. From left to right: the case study of the test data set 1 and 2.

opencc-by-4.0Apr 2020View details →

Figure 2 in The study of fidelity measures in the context of using them as a threshold criterion in the allocation of diagnostic species

Figure 2. Representing distributions of some fidelity measures with different degree of asymmetry with kernel density estimates. From left to right: the case study of the test data set 1 and 2.

opencc-by-4.0Nov 2023View details →

Figure 2 in The tegumen morphology is inappropriate as a diagnostic character for Trabala niphanae-inga-pinratanai sp. group (Lepidoptera: Lasiocampidae)

Figure 2. Male genitalia of Trabala niphanae variabilis ssp. nov. from Laos: A) Holotype (Sekong province, Lamam town); B–D) Paratypes (Sekong province, Lamam town); E–I) Paratypes (Champasak province, Wopakok).

opencc-by-4.0Aug 2020View details →

Fig. 2. The PCR products identified within the 18S in Usefulness of PCR-RFLP of 18S rRNA gene for rapid post-mortem diagnostics of highly pathogenic Eimeria spp. (Apicomplexa: Eimeriidae) of European bison, Bison bonasus L. with histopathological correlation

Fig. 2. The PCR products identified within the 18S rRNA of Eimeria bovis following digestion with two restriction endonucleases: AluI recognising AG∧CT and Hin1II recognising CATG∧. M1: GeneRuler 100 bp Plus DNA Ladder (Thermo Fisher Scientific); M2: GeneRuler 50bp DNA Ladder (Thermo Fisher Scientific); lane 1: European bison colon wall tissue; lane 2: European bison colon wall tissue after digestion; lane 3: E. bovis oocysts of European bison; lane 4: E. bovis oocysts of European bison after digestion.

opencc-by-4.0Aug 2020View details →

Fig. 3 in Usefulness of PCR-RFLP of 18S rRNA gene for rapid post-mortem diagnostics of highly pathogenic Eimeria spp. (Apicomplexa: Eimeriidae) of European bison, Bison bonasus L. with histopathological correlation

Fig. 3. The virtual double digestion of the 18S rRNA gene of eimerians infecting the large intestine of the European bison with the restriction enzymes Mval (BstNI) recognising CC∧WGG, and KpnI recognising GGTAC∧C, simulated with SnapGene version 5.0.6 (GSL Biotech LLC); M: GeneRuler 50 bp DNA Ladder (Thermo Fisher Scientific). (A) A three-band pattern for E. bovis (20 bp, 210 bp, 343 bp). (B) A four-band pattern for E, zuernii (20 bp, 100 bp, 210 bp, 242 bp). (C) A two-band pattern for E. alabamensis (212 bp, 362 bp).