Leishmania pyrimidine salvage is replete with opportunities for therapeutic intervention with enzyme inhibitors or antimetabolites. Their uptake into cells depends upon specific transporters; therefore it is essential to establish whether various Leishmania species possess similar pyrimidine transporters capable of drug uptake. Here, we report a comprehensive characterization of pyrimidine transport in L. major and L. mexicana. In both species, two transporters for uridine/adenosine were detected, one of which also transported uracil and the antimetabolites 5-fluoruracil (5-FU) and 5F,2'deoxyuridine (5F,2'dUrd), and was designated uridine-uracil transporter 1 (UUT1); the other transporter mediated uptake of adenosine, uridine, 5F,2'dUrd and thymidine and was designated Nucleoside Transporter 1 (NT1). To verify the reported L. donovani model of two NT1-like genes encoding uridine/adenosine transporters, and an NT2 gene encoding an inosine transporter, we cloned the corresponding L. major and L. mexicana genes, expressing each in T. brucei. Consistent with the L. donovani reports, the NT1-like genes of either species mediated the adenosine-sensitive uptake of [3H]-uridine but not of [3H]-inosine. Conversely, the NT2-like genes mediated uptake of [3H]-inosine but not [3H]-uridine. Among pyrimidine antimetabolites tested, 5-FU and 5F,2'dUrd were the most effective antileishmanials; resistance to both analogs was induced in L. major and L. mexicana. In each case it was found that the resistant cells had lost the transport capacity for the inducing drug. Metabolomics analysis found that the mechanism of action of 5-FU and 5F-2'dUrd was similar in both Leishmania species, with major changes in deoxynucleotide metabolism. We conclude that the pyrimidine salvage system is highly conserved in Leishmania species - essential information for the development of pyrimidine-based chemotherapy.

Malignant melanoma is one of the most rapidly increasing cancers globally and it is the most dangerous form of human skin cancer. Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma. Early detection of melanoma can be helpful and usually curable. Due to the difficulty for dermatologists in the interpretation of dermoscopy images, Computer Aided Diagnosis systems can be very helpful to facilitate the early detection. The automated detection of the lesion borders is one of the most important steps in dermoscopic image analysis. In this paper, we present a fully automated method for melanoma border detection using image processing techniques. The hair and several noises aredetected and removed by applying a bank of directional filters and Image Inpainting method respectively. A hybrid method is developed by combining Particle Swarm Optimization and Markov Random Field methods, in order to delineate the border of the lesion area in the images. The method was tested on a dataset of 200 dermoscopic images, and the experimental results show that our method is superior in terms of the accuracy of drawing the lesion borders compared to alternative methods.

The current paper proposes a novel scheme for non-blind watermarking of images, making use of discrete wavelet transform (DWT), discrete time Fourier transform (DTFT), as well as singular value decomposition, or SVD. During the process of embedding, 1-level DWT is used to decompose the host image into its various frequency sub-bands. After this, the high-frequency sub band receives an application of DTFT. This is followed then by SVD, after which the watermark becomes embedded into the now-transformed host image's singular matrix. Then, the inverses of 1-level DWT, DTFT and SVD are applied in order to obtain a watermarked final image. This paper evaluates the performance of the proposed method of watermarking against a number of attacks, including sharpening, salt and pepper noise, AWGN, gamma correction, histogram equalisation, flipping and cropping. Results obtained during experiments have found that the scheme as proposed does provide high levels of robustness and imperceptibility against various signal processing attacks.

Malignant melanoma is the most hazardous type of human skin cancer and its incidence has been rapidly increasing. Early detection of malignant melanoma in dermoscopy images is very important and critical, since its detection in the early stage can be helpful to cure it. Computer Aided Diagnosis systems can be very helpful to facilitate the early detection of cancers for dermatologists. In this paper, we present a novel method for the detection of melanoma skin cancer. To detect the hair and several noises from images, pre-processing step is carried out by applying a bank of directional filters. And therefore, Image inpainting method is implemented to fill in the unknown regions. Fuzzy C-Means and Markov Random Field methods are used to delineate the border of the lesion area in the images. The method was evaluated on a dataset of 200 dermoscopic images, and superior results were produced.

One of the most important structures in dermoscopy images is the pigment network, which is also one of the most challenging and fundamental task for dermatologists in early detection of melanoma. This paper presents an automatic system to detect pigment network from dermoscopy images. The design of the proposed algorithm consists of four stages. First, a pre-processing algorithm is carried out in order to remove the noise and improve the quality of the image. Second, a bank of directional filters and morphological connected component analysis are applied to detect the pigment networks. Third, features are extracted from the detected image, which can be used in the subsequent stage. Fourth, the classification process is performed by applying feed-forward neural network, in order to classify the region as either normal or abnormal skin. The method was tested on a dataset of 200 dermoscopy images

Six Hakea species varying greatly in seed size were selected for cotyledon damage experiments. The

growth of seedlings with cotyledons partially or completely removed was monitored over 90 days. All

seedlings perished by the fifth week when both cotyledons were removed irrespective of seed size.

Partial removal of cotyledons caused a significant delay in the emergence of the first leaf, and reduction

in root and shoot growth of the large-seeded species. The growth of seedlings of small-seeded species

was less impacted by cotyledon damage. The rate of survival, root and shoot lengths and dry biomass

of the seedlings were determined after 90 days. When seedlings were treated with balanced nutrient

solutions following removal of the cotyledons, survival was 95–98%, but 0% when supplied with

nutrient solutions lacking N or P or with water only. The addition of a balanced nutrient solution failed

to restore complete growth of any species, but the rate of root elongation for the small-seeded species

was maintained. Cotyledons provide nutrients to support early growth of Hakea seedlings, but other

physiological roles for the cotyledons are also implicated. In conclusion, small-seeded Hakea species can

tolerate cotyledons loss better than large-seeded species.

Abstract-A series of polyelectrolyte hydrogels ranging from 92-98wt% were synthesized by copolymerization of acrylamide,

AAM with 2-acrylamido-2-methyl propane sulphonic acid, AMPS using 0.001g APS as initiator in the presence of 30wt% H2O

and 1.0wt% ethylene glycol dimethacrylate,EDMA as cross-linking agent. The final copolymers was obtained in the form of

glassy and transparent roads at room temperature, these roads were soaked in water for two days to remove unreacted

monomers. The swelling behavior of the hydrogels was studied in distilled water and salt solutions of 1.5 mol/l each of NaCl

and KCl. The Swelling in water shows decreasing values of q, LE, Ø1, EWC% and increasing polymer volume fraction, Ø2 by

increasing acrylamide, due to increasing hydrophobicity and decreasing the hydrophilicity. The swelling in salt solutions

shows a decreasing in the values of ESSNa%, WCNa%, SCNa%, ESSK%, WCK% and SCK% by increasing acrylamid

monomer in the feed due to increasing the hydrophobicity and decreasing the ionized ionic groups (SO3H). The increasing

values of ESSNa%, WCNa% and SCNa% compared with the values of ESSK%, WCK% and SCK% respectively, is due to

the higher charge density of sodium ion than that of potassium ion.

Abstract

Walking considered as one of the safest modes of

travel available, sustainable to human society as well as

environmentally beneficial. In this context, the aim of this

research is to investigate pedestrians’ traffic gap acceptance

and crossing decision for Mid-block Street crossing in urban

areas in Malaysia. Pedestrian crossing behaviour at

Rughaya Street has been examined in terms of the decision

to cross or not the street and size of traffic gaps accepted by

pedestrian, as well as the related contributing factors. A

field study was conducted to collect the data of pedestrians’

decisions under real mix traffic Condition using video

camera on a typical unsignalized urban street section. JPEG

files were obtained from video recording by using Snapshot

Wizard software. The data extracted included traffic

characteristics such as traffic size, traffic speed, etc.

pedestrian individual characteristics such as gender, in

addition to individual behavior such as waiting, frequency

of attempt, etc.). Furthermore, The extracted data were

used to develop and examine a pedestrian gap acceptance

model based on A lognormal regression model and binary

logistic model by SPSS (22) in order to validate the impact

of various parameters on the size of traffic gaps accepted by

pedestrians as well as the effect on the decision of

pedestrians to cross the street or not. So that the effect of

the gap accepted available and of other factors on the

decision of pedestrians to cross the street or not is examined.

These results indicate that the data set for this particular

location has a majority of male pedestrians which were

insignificant variables in both models moreover a lognormal

regression results shows that accepted gaps size depend on

traffic size, crossing distance, speed of approaching vehicle

and time spent by pedestrian at the curb waiting for a

suitable gap size to start crossing. The BL model performs

well for the reason that it captures the pedestrian decision

making process with traffic taking the relevant attributes

into consideration. According to the coefficients of BL

regression analysis equation we noticed that the illegal

parking, traffic size, traffic waiting time and gap size are the

vital attributes for the Pedestrian gap acceptance model..

Index Terms: Pedestrian crossing, gap acceptance, crossing

decision, multiple linear regression, binary logistic

regression.

This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

Abstract—With a rapidly increasing incidence of melanoma

skin cancer, there is a need for decision support systems to

detect it in its early stages, which would lead to better decisions

in treating it successfully. However, developing such systems is

still a challenging task for researchers. Several Computer Aided-

Diagnosis (CAD) systems have been proposed in the last two

decades to increase the accuracy of melanoma detection. Image

feature extraction is a critical step in differentiating between

melanoma and normal skin lesions. In this paper, we propose

to improve a bag-of-words approach by combining features

consisting of the color histogram and first order moments with the

Histogram of Oriented Gradients (HOG). Experimental results

show that the proposed technique significantly improves the

detection accuracy, with an average sensitivity of 91% and

specificity of 85%. The proposed system was validated on a

dataset of 200 medically annotated images (40 melanomas and

160 non-melanomas) obtained from the database of the Hospital

Pedro Hispano.