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"The rheology of dense red blood cell suspensions is investigated via computer simulations based on the lattice Boltzmann, the immersed boundary, and the finite element methods. The red blood cells are treated as extended and deformable particles immersed in the ambient fluid. In the first part of the work, the numerical model and strategies for stress evaluation are discussed. In the second part, the behavior of the suspensions in simple shear flow is studied for different volume fractions, particle deformabilities, and shear rates. Shear thinning behavior is recovered. The existence of a shear-induced transition from a tumbling to a tank-treading motion is demonstrated. The transition can be parameterized by a single quantity, namely the effective capillary number. It is the ratio of the suspension stress and the characteristic particle membrane stress. At the transition point, a strong increase in the orientational order of the red blood cells and a significant decrease of the particle diffusivity are observed. However, the average cell deformation shows no signature of the transition."

"Clustered Red Blood Cells are observed very frequently in the thin blood smear digital images. Separating clustered Red Blood Cells from the single Red Blood Cells and splitting of clustered Red Blood Cells into single Red Blood Cells is a challenging job in the computer-assisted diagnosis of blood for any disorder in many diseases like Complete Blood Count Test, Anemia, Leukemia and Malaria etc. The mentioned problems are highly laborious in manual microscopy for the hematologists. Many techniques currently existing for the solution suffer from both under- and over- splitting problems when highly complex clusters of Red Blood Cells occur. In addition, the existing techniques are not computationally efficient. In this paper, we address the aforementioned problems, firstly by considering the boundaries of the convex hulls of clustered Red Blood Cells and secondly, by splitting the boundaries according to the number of Red Blood Cells in relation to distance measures. Furthermore, we draw circles using a mid-point circle algorithm at each boundary cleavage to give an illusion of the Red Blood Cells. The test results of the proposed technique on a standard online dataset are presented in two ways. Statistically first of all by achieving an average recall of 0.964 and precision of 0.970 while their F-measure achieved is 0.962 as well as secondly through ground truth data with visual inspections."

"This book contain : * Enables both the haematologist and laboratory scientist to identify blood cell features, from the most common to the more obscure * Provides essential information on methods of collection, blood film preparation and staining, together with the principles of manual and automated blood counts * Completely revised and updated, incorporating much newly published information: now includes advice on further tests when a specific diagnosis is suspected *400 high quality photographs to aid with blood cell identification * Highlights the purpose and clinical relevance of haematology laboratory tests throughout."

"ABSTRACTWe studied ultrathin layers of 𝑛 cyanobiphenyl (𝑛 = 5,6,7,8) mesogene moleculesforming thin films on a graphene plane using molecular dynamics simulations in a wide temperaturerange (220 420 K). Each modeled ensemble was heated to the maximum temperature andthen cooled (reverse procedure). We calculated the second rank order parameter as a measureof the molecular order of mesogene molecules and we discuss the distribution of angles betweenthem and the global sample director."

"Jengkol merupakan makanan yang cukup populer karena rasa khasnya dan bau yang kuat di Indonesia. Meskipun diketahui punya pengaruh buruk terhadap saluran kemih, jengkol mengandung banyak gizi seperti protein, asam amino, lemak, mineral, dan juga beberapa vitamin. Selain itu jengkol mengandung suatu zat yg bernama asam jengkolat. Struktur molekular dari zat ini sangat mirip dengan struktur sistin, yang telah diketahui memiliki efek antioksidan. Oleh karena itu, jengkol memiliki potensi memiliki sifat antioksidan.Percobaan ini ingin melihat apakah ekstrak jengkol mampu memberikan efek antioksidan kepada sel darah merah domba SDMD . SDMD tersebut juga diberikan paparan oksidatif dengan menggunakan hidrogen peroksida dan divariasikan menjadi beberapa kelompok. Dengan begitu, efek protektif dan kuratif dari jengkol terhadap SDMD akan terlihat.Namun, hasil percobaan tidak seperti ekspektasi. SDMD yang diberikan jengkol saja memiliki tingkat methemoglobin yang tinggi, bahkan lebih tinggi daripada yang diberikan hidrogen peroksida saja. Campuran keduanya tentu menyebabkan tingkat methemoglobin yang jauh lebih tinggi. Dengan demikian, tidak terbukti bahwa jengkol memiliki sifat antioksidan, bahkan menurut hasil ternyata jengkol kemungkinan besar bersifat peroksidan.

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Jengkol is a popular food in Indonesia, known for its distinctive taste and strong odor. Despite being known to give bad effect to the urinary tract system, jengkol contain vital nutritions such as protein, amino acid, fat, minerals, and several vitamins. Other than that, its bean is known to contain a substance called Djenkolic acid. This substance is molecularly similar to cystine, which has been known to have antioxidant effect. Therefore, Jengkol has potential to have antioxidant characteristic.This study want to see whether jengkol water extract is able to give antioxidant effect to sheep red blood cells SRBC . These SRBC also exposed to oxidative stress with the help of hydrogen peroxide and the treatments are varieted into several groups. Therefore, the protective and curative effect of jengkol can be observed.Unfortunately, the results are not what is expected. SRBC treated with only jengkol have a high methemoglobin level, even higher than treated with only hydrogen peroxide. A combination of both also shows high methemoglobin level. Therefore, it is not proven that jengkol have antioxidant characteristics. Even from the result, indicate that jengkol may very well be peroxidant."

"Metode klasifikasi telah banyak digunakan dalam berbagai aspek, termasuk dalam bidang bioinformatika. Salah satu penggunaan metode klasifikasi ini adalah untuk menentukan tingkatan fase dari sebuah penyakit. Dalam penelitian ini akan dilakukan pengklasifikasian parasit plasmodium falciparum. Parasit tersebut merupakan parasit penyebab penyakit malaria. Penyakit ini dapat ditularkan oleh gigitan nyamuk Anopheles betina yang mengandung plasmodium di dalamnya. Hasil penelitian ini dapat digunakan untuk menentukan fase parasit plasmodium yang berada di sel darah orang yang terjangkit malaria. Tujuan penelitian ini adalah untuk mengetahui persentase keberhasilan dan menganalisis metode Multiclass Support Vector Machines untuk memprediksi tingkatan parasit tersebut. Data yang digunakan adalah data citra sel darah merah yang telah terjangkit tiga jenis tingkatan parasit plasmodium falciparum. Dalam prosesnya, penelitian ini akan menggunakan Canopy sebagai IDE bahasa pemrograman python. Dari 112 percobaan, didapatkan tingkat akurasi tertinggi sebesar 87,5% untuk metode Multclass SVM one vs rest dan one vs one menggunakan 4-fold cross validation dengan parameter linear kernel dan C=1.

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Classification methods has been frequently used in various aspects, including bioinformatics. One of its purpose of this classification is to  determine phase level of a disease. This research will classify the phase of plasmodium falciparum parasite which causes malaria.The disease is spread by an infected female Anopheles mosquito which contains Plasmodium. The result of this research could be use to determine Plasmodium parasite phase in infected peoples red blood cells. The purpose of this research is to discover the success rate of Multiclass Support Vector Machines method and analyze it in order to predict the parasite phase levels. The data of this study is image data of red blood cells which was infected by three kinds of Plasmodium falciparum parasite levels. In the process, this study will be using Canopy as Integration Development Environtments of phyton programming language.  From 112 trials, the highest number of accuracy is 87.5% for Multiclass Support Vector Machines one vs rest and one vs all methods which used the 4-fold cross validation with C=1 as parameter for linear kernel."