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ObjectiveAssess the impact of introducing a consensus guideline incorporating an adapted Sepsis Risk Calculator (SRC) algorithm, in the management of early onset neonatal sepsis (EONS), on antibiotic usage and patient safety.DesignMulticentre prospective studySettingTen perinatal hospitals in Wales, UK.PatientsAll live births ≥34 weeks’ gestation over a 12-month period (April 2019–March 2020) compared with infants in the preceding 15-month period (January 2018–March 2019) as a baseline.MethodsThe consensus guideline was introduced in clinical practice on 1 April 2019. It incorporated a modified SRC algorithm, enhanced in-hospital surveillance, ongoing quality assurance, standardised staff training and parent education. The main outcome measure was antibiotic usage/1000 live births, balancing this with analysis of harm from delayed diagnosis and treatment, disease severity and readmissions from true sepsis. Outcome measures were analysed using statistical process control charts.Main outcome measuresProportion of antibiotic use in infants ≥34 weeks’ gestation.Results4304 (14.3%) of the 30 105 live-born infants received antibiotics in the baseline period compared with 1917 (7.7%) of 24 749 infants in the intervention period (45.5% mean reduction). All 19 infants with culture-positive sepsis in the postimplementation phase were identified and treated appropriately. There were no increases in sepsis-related neonatal unit admissions, disease morbidity and late readmissions.ConclusionsThis multicentre study provides evidence that a judicious adaptation of the SRC incorporating enhanced surveillance can be safely introduced in the National Health Service and is effective in reducing antibiotic use for EONS without increasing morbidity and mortality.

Fraudsters often alter handwritten contents in a document in order to achieve illicit purposes. At times, this may result in financial and mental loss to an individual or an organization. Hence, ink analysis is necessary to identify such an alteration. Convolution Neural Network (CNN) can be used to identify such cases of alteration, as CNN has emerged as a monumental success in the field of computer vision for varieties of classification tasks. But, CNN requires large amount of labeled data for training. Hence, there is a need to generate a large dataset for the experiments relating to handwritten word alteration detection. Collection, digitization, and cropping of a large number of altered and unaltered handwritten words are tedious and time consuming. To overcome such an issue, an approach for synthetic word data generation is presented in this paper for handwritten word alteration detection experiments. This scheme is designed in such a way that the synthetically generated words are very similar to the original ones. In order to achieve this, handwritten character data set is prepared using 10 blue and 10 black pens. These handwritten characters are used for creating synthetic word alteration data set. The presented approach uses relatively less number of handwritten character images to create a huge word alteration data set. Further, deep learning models are trained on the synthetically generated data set for word alteration detection.

The addition of new words in handwritten documents such as bank cheques, bills, and notes is considered as common crime. Such immoral activities on handwritten documents have a bad effect on the victim in terms of mental and financial loss. For facilitating an impartial judicial process, it is important to differentiate between the used pen inks. Earlier, image processing and pattern recognition-based techniques for pen ink differentiation gained attention among researchers due to their non-destructive nature. Existing techniques use various colour models to represent the colour of pen inks. Thus, it is crucial to assess the capabilities of various colour models for differentiating pen inks in handwritten documents. In this paper, we propose a histogram distance based quantitative assessment technique for suitable colour model identification for differentiating pen inks. Seven blue and seven black pen ink samples are acquired on 112 cheque leaves. The k-means binarisation is used to identify pen ink pixels. Y C b C r colour model has been identified as the best colour model for this task. The statistical features of the ink colours in the identified colour model representation are extracted and a multi-layer perceptron (MLP) classifier validates the capability of the identified colour model for pen ink discrimination.

Multimodal biometric system fuses information from multiple biometric modalities to overcome limitations of unimodal biometric system. This fusion significantly enhances the performance of the system. One of the ways of fusing information for multimodal biometrics is score level fusion. In this paper, a novel score level fusion method is proposed. Here, fusion at score level is formulated as an optimization problem. The paper proposes a genetic algorithm (GA) based approach to solve this optimization problem. It minimizes the distances between an aggregated score list and each input score list from individual biometric modality. The proposed GA based method uses weighted Spearman footrule distance metric to compute the distance between a pair of score lists. Superiority of the proposed method over several state-of-the-art score level and rank level fusion methods is demonstrated experimentally.

In a prediction error-based reversible data hiding technique, a pixel value is predicted using the values at its neighboring pixels in order to obtain the prediction error. Considering the varied neighborhood of the pixels in an image, it is difficult to come up with a mathematical relation or rule between a pixel value and the values in its neighborhood. Hence, in this paper, a novel multi-layer perceptron-based pixel prediction technique is proposed. The multi-layer perceptron is trained to predict a pixel value by considering the values only at the 12 not yet been traversed pixels (according to a raster scan traversal) in a \(5\times 5\) neighborhood of the pixel. Finally, according to an adaptive embedding strategy, one or two bits of data are hidden in the prediction error of the pixel. An adaptive prediction error histogram bin shifting technique is used for embedding the data bits. A better performance of the proposed method over other existing methods is experimentally observed.

Reversible data hiding is a branch of data hiding in which cover image can additionally be restored along with the extraction of hidden data. In prediction error expansion based reversible data hiding techniques, pixel values are predicted using the neighborhood pixels. Data bits are hidden in the expanded prediction error. Prediction error expansion based techniques have become popular due to their superior performances over other categories of techniques. In this paper, a novel reversible data hiding technique is proposed based on a multi-layer perceptron based pixel prediction. The multi-layer perceptron is trained to predict a pixel value based on its eight neighbors. Experiments have been conducted by varying the number of hidden layers and the number of neurons in each hidden layer in the multi-layer perceptron. The best architecture for the multi-layer perceptron is chosen for the proposed reversible data hiding technique. Finally, an adaptive embedding strategy is used to insert either one bit or two bits of data in the expanded prediction error of the pixel based on the standard deviation of the pixel values in the local neighborhood.

We present a numerical simulation based study of single graded Cu(In,Ga)Se2 (Copper Indium Gallium Diselenide) thin film solar cell. In this work, initially a basic CIGS single graded cell structure is optimized in terms of thickness, band-gap and doping concentration. CdS is kept as the buffer layer, which is widely used for high efficiency CIGS solar cells. In the next step, CdS is replaced with ZnMgO as the buffer layer in order to exploit its greater photon absorption ability due to its higher band-gap which further enhances the cell efficiency. A thorough analysis is carried out on the solar cell parameters open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF) and quantum efficiency (η) of the photovoltaic cell structure. An intermediate layer of p-type MoS2 is inserted in between the single graded CIGS absorber layers. The objective is to limit the unintentional Ga inter diffusion and maintain the desired grading during the high temperature annealing for the absorber preparation. The power conversion efficiency of the bilayer device structure with Ga fraction x=(0.31) of the top absorber layer along with Ga fraction y=(0.25) of the bottom absorber layer exhibits an improved efficiency from 24.02% (CdS as the buffer layer) to 25.37% (ZnMgO as buffer layer). An excellent power efficiency of η = 26.78% is reported after adding the intermediate layer of p-type MoS2 and optimizing its thickness and the carrier concentration.

Alteration of words in handwritten financial documents such as cheques, medical claims, and insurance claims may lead to monetary loss to the customers and financial institutions. Hence, automatic identification of such alteration in documents is a crucial task. Therefore, an ink color based analysis using Convolutional Neural Network (CNN) automation method has been introduced for alteration detection. Pre-trained AlexNet and VGG-16 architectures have been used to study the effect of transfer learning on the problem at hand. Further, two different shallow CNNs have been employed for recognition. A data set has been created using ten blue and ten black pens to simulate the word alteration problem. The dataset captures the word alteration by addition of the characters (or even pen strokes) in the existing word. Experiments have revealed that the transfer learning based deep CNN architectures have outperformed the shallow CNN architectures on both blue and black pens.

In unimodal biometric systems, there are several limitations like, non-universality, noisy data and other security risks. To overcome these, multimodal biometric systems are increasingly adopted. Multimodal biometric systems fuse information from multiple biometric traits. Rank level fusion is one of the approaches of information fusion for multimodal biometrics. In this paper, rank level fusion is considered as an optimization problem. Its aim is to minimize the distances between an aggregated rank list and each input rank list from individual biometric trait. A solution of this optimization problem has been proposed using cross-entropy (CE) Monte Carlo method. The proposed CE method uses two distance measures - namely, Spearman footrule and Kendall’s tau distances. Superiority of the proposed CE method based on above two distance measures over several existing rank level and score level fusion schemes is achieved on two different datasets.

Secure storage of a cryptographic key is a challenging task. In this paper, a fingerprint based cryptographic key generation approach has been proposed. It can be used in a symmetric key setup. The use of gray code makes the approach interesting, as gray code reduces the mismatch of binary representations of two successive integers. It has been experimentally shown that it is impossible to generate the same key from the fingerprint of an intruder. Moreover, it does not require to store neither the fingerprint of the user nor the cryptographic key. It makes the scheme more secure.

In recent years, thermal neutron radiography and tomography have gained much attention as one of the nondestructive testing methods. However, the application of thermal neutron radiography and tomography is hindered by their technical complexity, radiation shielding, and time-consuming data collection processes. Monte Carlo simulations have been developed in the past to improve the neutron imaging facility’s ability. In this paper, a new deterministic simulation approach has been proposed and demonstrated to simulate neutron radiographs numerically using a ray tracing algorithm. This approach has made the simulation of neutron radiographs much faster than by previously used stochastic methods (i.e., Monte Carlo methods). The major problem with neutron radiography and tomography simulation is finding a suitable scatter model. In this paper, an analytic scatter model has been proposed that is validated by a Monte Carlo simulation.

Predicting a pixel value in an image with the help of neighborhood pixels is important for several tasks, like image compression, reversible data hiding. The paper proposes a novel approach to predict the pixel intensities in an image using a multilayer perceptron. Experimental results show that the proposed method is more efficient than several popular pixel prediction methods.

Ability to draw an observer’s gaze toward an object in an image depends on the visual saliency of the object. Contrast and/or uniqueness of low-level features of an object (e.g., intensity, color, etc.) drives visual saliency in the absence of external influence. Hence, an increased difference between a user-defined target object (whose saliency is being enhanced) and its surrounding objects in terms of these low-level features enhances the visual saliency of the target object. In this paper, enhancement of saliency of the target object has been posed as a maximization problem. Feature values of every segment in an image have been modified within a limit to maximize the feature difference between the target segment and all other segments. The imposed limit restricts the amount of change in any segment in order to preserve the naturalness of the image. In the framework of the proposed maximization problem, a genetic algorithm-based approach has been devised to find suitable features values for every segment. Effectiveness of the proposed method has been demonstrated through suitable objective measures.

Nations have set-up Critical Information Infrastructure (CII) protection centers to provide safety of organizations which are deemed to be critical for the nation. In this context, this paper proposes a framework to identify Critical Information Infrastructures (CIIs). This framework has been adopted to identify the banks which are critical for Indian economy. Previously, Reserve Bank of India, the central bank of the country, has announced three banks as systemically important banks. The proposed approach, in this paper, considers a publicly available data set publishing the number (volume) and the amount (value) of financial transactions in the Indian domestic market involving all the banks. The proposed approach finds the individual ranks of each participating bank based on these two parameters. Finally, rank aggregation method combines these two rank lists (based on the value and the volume) to generate a final rank of the banks. Several rank aggregation methods have been compared in this context.

Authentication refers to verification of the identity of an user. There exist various types of authentication techniques, starting from simple password based authentication up to behavior biometric based authentication. In this paper, a new way of authentication is proposed where the user provides her password through eye gaze. It is based on graphical password scheme where she can choose her password from a large image data set. At the time of authentication, she needs to recall it and look at the chosen passcode appearing in a display in correct sequence. The method uses a machine learning technique where a convolutional neural network is used to determine gaze locations using inputs from a simple web camera. It takes her cropped eye as input and provides gaze location as output. Proposed method is cost effective solution as gaze tracking is done through a simple web camera. The proposed method is also free from attacks such as shoulder surfing, smudge, brute-force attacks. Experiments have been carried out to validate the system. It has been observed to perform accurately for all the volunteers.

Selective visual attention is an amazing capability of primate visual system to restrict the focus to few interesting objects (or portions) in a scene. Thus, primates are able to pay attention to the required visual content amidst myriads of other visual information. It enables them to interact with the external environment in real time through reduction of computational load in their brain. This inspires image and computer vision scientists to derive computational models of visual attention and to use them in varieties of applications in real-life, mainly to speed up the processing through reduction of computational burden which often characterizes image processing and vision tasks. This chapter discusses a wide variety of such applications of visual attention models in image processing, computer vision and graphics.

Age-related macular degeneration (AMD) is the foremost cause of blindness in people over the age of 60 worldwide. Clinically, this disease starts with distortion in central vision eventually leading to legal blindness. Vision loss has a significant impact on quality of life and incurs a substantial cost to the economy. Furthermore, AMD is a complex and progressive neurodegenerative disorder that triggers visual impairment due to the loss of retinal pigmented epithelium (RPE) and the light-sensitive photoreceptors that they support, protect and provide nutrition. Currently, there is no curative treatment for the most common form of this disease, i.e., dry AMD. A novel approach to treat AMD involves the transplantation of RPE cells derived from human induced pluripotent stem cells (iPSCs) in the outer retina. These iPSC-derived RPE cells not only show characteristics similar to native RPE but also could replace as well as regenerate damaged pathologic RPE and produce supportive growth factors and cytokines. Several clinical trials are being conducted taking advantage of a variety of cell- and tissue engineering-based approaches. Here, we present a simple, cost effective, and scalable cell-culture model for generation of purified RPE thus providing the foundation for developing an allogeneic cell therapy for AMD.

Retinal degenerative disorders are a leading cause of the inherited, irreversible and incurable vision loss. While various rodent model systems have provided crucial information in this direction, lack of disease-relevant tissue availability and species-specific differences have proven to be a major roadblock. Human induced pluripotent stem cells (iPSC) have opened up a whole new avenue of possibilities not just in understanding the disease mechanism but also potential therapeutic approaches towards a cure. In this review, we have summarized recent advances in the methods of deriving retinal cell types from iPSCs which can serve as a renewable source of disease-relevant cell population for basic as well as translational studies. We also provide an overview of the ongoing efforts towards developing a suitable in vitro model for modeling retinal degenerative diseases. This basic understanding in turn has contributed to advances in translational goals such as drug screening and cell-replacement therapies. Furthermore we discuss gene editing approaches for autologous repair of genetic disorders and allogeneic transplantation of stem cell-based retinal derivatives for degenerative disorders with an ultimate goal to restore vision. It is pertinent to note however, that these exciting new developments throw up several challenges that need to be overcome before their full clinical potential can be realized.

It is often required to emphasize an object in an image. Artists, illustrators, cinematographers and photographers have long used the principles of contrast and composition to guide visual attention. In order to achieve this, a novel perceptually-driven approach is put forth which leads to the enhancement of visual saliency of target object without destroying the naturalness of the contents of the image. The proposed approach computes new feature values for the intended object by maximizing the feature dissimilarity (which is weighted by positional proximity) with other objects. Too much change in feature values in the target segment may destroy naturality of the image. This poses as the constraint in the proposed maximization problem. Genetic algorithm has been used, in this context, to find the feature values which maximize the saliency of the target object. Experimental validation through objective evaluation metrics using saliency maps, as well as analysis of eye-tracking data, establish the success of the proposed method.

Stem cell transplantation is a generic term covering different techniques. However there is argument over the pros and cons of autologous and allogeneic transplants of mesenchymal stem cells (MSCs) for regenerative therapy. Given that the MSCs have already been proven to be safe in patients, we hypothesize that allogeneic transplantation could be more effective and cost-effective as compared to autologous transplantation specifically in older subjects who are the likely victims of degenerative diseases. This analysis is based on the scientific logic that allogeneic stem cells extracted in large numbers from young and healthy donors could be physiologically, metabolically and genetically more stable. Therefore stem cells from young donors may be expected to exhibit higher vigor in secreting trophic factors leading to activation of host tissue-specific stem cells and also be more efficient in remodeling the micro-environmental niche of damaged tissue.

In handwritten Bank cheques, addition of new words using similar color pen can cause huge loss. Hence, it is important to differentiate pen ink used in these types of documents. In this work, we propose a non-destructive pen ink differentiation method using statistical features of ink and multi-layer perceptron (MLP) classifier. Large sample of blue and black pen ink is acquired from 112 Bank cheque leaves, written by nine different volunteers using fourteen different blue and black pens. Handwritten words are extracted from scanned cheque images manually. Pen ink pixels are identified using K-means binarization. Fifteen statistical features from each color handwritten words are extracted and are used to formulate the problem as a binary classification problem. MLP classifier is used to train the model for differentiating pen ink in handwritten Bank cheques. The proposed method performs efficiently on both known and unknown pen samples with an average accuracy of 94.6% and 93.5% respectively. We have compared the proposed method with other existing method to show its efficiency.

This paper presents a novel prediction error expansion (PEE) based reversible watermarking using 3 \(\times \) 3 neighborhood of a pixel. Use of a good predictor is important in this kind of watermarking scheme. In the proposed predictor, the original pixel value is predicted based on a selected set, out of the eight neighborhood of a pixel. Moreover, the value of prediction error expansion (PEE) is optimally divided between current pixel and top-diagonal neighbor such that distortion remains minimum. Experimental results show that the proposed predictor with optimal embedding outperforms several other existing methods.

The problem of image retargeting is to fit a large image on a small display. Simple down-scaling resizes all contents in the image at a uniform factor. It may shrink the important contents too much so that viewers may face difficulty in recognizing the contents. In this paper, a novel content-aware image retargeting technique has been proposed. According to this technique, the important objects are identified based on the importance map. Each important object is encompassed with minimum bounding rectangle. These important rectangles are mapped in the target space. The remaining areas in the source as well as in the target space are divided into minimum number of rectangles. These rectangles are referred as unimportant rectangles. The sizes of important rectangles in the target space are enlarged and the sizes of unimportant rectangles are shrunk iteratively. To control the distortions in unimportant areas, the similarity scores for corresponding unimportant rectangles in source and retargeted images are computed. If the similarity score for any unimportant rectangle drops below an acceptable threshold, the growth/shrinkage of each rectangle is stopped. Subjective quality assessment by a group of volunteers shows that the proposed technique performs better than several existing content-aware image retargeting techniques.

This paper proposes an efficient method to detect any fraud document. It considers texture features, such as Local binary pattern and Gabor filters and performs a histogram matching to analyze the document. Texture features and RGB color information of each word in the document are extracted. Normalized histograms of two different images of a document are compared to generate a matching score for decision making. The advantage of the proposed method is that it is fully unsupervised and hence, it does not require any prior knowledge and is less time consuming. The method is found to be very efficient.

Neurodegenerative diseases are devastating because they cause increasing loss of cognitive and physical functions and affect an estimated 1 billion individuals worldwide. Unfortunately, no drugs are currently available to halt their progression, except a few that are largely inadequate. This mandates the search of new treatments for these progressively degenerative diseases. Neural stem cells (NSCs) have been successfully isolated, propagated, and characterized from the adult brains of mammals, including humans. The confirmation that neurogenesis occurs in the adult brain via NSCs opens up fresh avenues for treating neurological problems. The proof‐of‐concept studies demonstrating the neural differentiation capacity of stem cells both in vitro and in vivo have raised widespread enthusiasm toward cell‐based interventions. It is anticipated that cell‐based neurogenic drugs may reverse or compensate for deficits associated with neurological diseases. The increasing interest of the private sector in using human stem cells in therapeutics is evidenced by launching of several collaborative clinical research activities between Pharma giants and research institutions or small start‐up companies. In this review, we discuss the major developments that have taken place in this field to position stem cells as a prospective candidate drug for the treatment of neurological disorders.

Stroke causes a devastating insult to the brain resulting in severe neurological deficits because of a massive loss of different neurons and glia. In the United States, stroke is the third leading cause of death. Stroke remains a significant clinical unmet condition, with only 3% of the ischemic patient population benefiting from current treatment modalities, such as the use of thrombolytic agents, which are often limited by a narrow therapeutic time window. However, regeneration of the brain after ischemic damage is still active days and even weeks after stroke occurs, which might provide a second window for treatment. Neurorestorative processes like neurogenesis, angiogenesis and synaptic plasticity lead to functional improvement after stroke. Stem cells derived from various tissues have the potential to perform all of the aforementioned processes, thus facilitating functional recovery. Indeed, transplantation of stem cells or their derivatives in animal models of cerebral ischemia can improve function by replacing the lost neurons and glial cells and by mediating remyelination, and modulation of inflammation as confirmed by various studies worldwide. While initially stem cells seemed to work by a 'cell replacement' mechanism, recent research suggests that cell therapy works mostly by providing trophic support to the injured tissue and brain, fostering both neurogenesis and angiogenesis. Moreover, ongoing human trials have encouraged hopes for this new method of restorative therapy after stroke. This review describes up-to-date progress in cell-based therapy for the treatment of stroke. Further, as we discuss here, significant hurdles remain to be addressed before these findings can be responsibly translated to novel therapies. In particular, we need a better understanding of the mechanisms of action of stem cells after transplantation, the therapeutic time window for cell transplantation, the optimal route of cell delivery to the ischemic brain, the most suitable cell types and sources and learn how to control stem cell proliferation, survival, migration, and differentiation in the pathological environment. An integrated approach of cell-based therapy with early-phase clinical trials and continued preclinical work with focus on mechanisms of action is needed.

Wavelet to DCT transcoding provides inter-operability between standards using the two transforms for encoding. Transcoding in transform domain avoids inverse transform and re-transform operations and saves computation. In this paper, we propose new algorithms for transcoding wavelet coefficients to block DCT coefficients. In the first step, the wavelet coefficients are transformed into upsampled DCT coefficients. Subsequently, these trans-formed coefficients are synthesized in the block DCT space for transcoding. The proposed approach restricts all operations in the DCT domain that makes filtering involved in the synthesis process computationally efficient. The proposed technique could be used by the block DCT based services when the input is available as wavelet coefficients.

Image matching and object extraction have been at the heart of computer vision research since the last two decades invigorated by the introduction of SIFT features by David Lowe. This paper proposes an approach for the same for its application to a specific problem, i.e. to extract the rectangular pantograph region from the cheque images used by banks in India. The proposed approach relies upon maximally stable extremal regions (MSER) for the keypoint localization and followed by speeded up robust features (SURF) for feature description. Then, these features from the template are matched with those in the cheque image. After the matching is performed, location of the detected blobs is identified and a binary image is generated with these locations as white and the rest of the background as black. From this binary image, the region of interest can be figured out by simple procedure based on iteration of a rectangular mask sized window throughout the binary image. The location having the largest density of the minority pixels is determined and this region is further extracted out from the initial input image. This is the first attempt of its kind to create an automated technique for extraction of the pantograph region from a cheque image. The results are encouraging. Also, this extraction of the pantograph region can be further applied on other images where it is being used to provide document security.

Displaying a large image in a small screen of a handheld gadget is a challenging task. Simple down-scaling of the image may reduce some objects too small to be perceptible. This gives rise to content-aware retargeting of the image. Important contents are allotted more screen space as compared to relatively less important contents of the image. Various types of content-aware image retargeting approaches have been proposed in a span of just over a decade. Another challenging area is to estimate importance of importance of the contents. Lot of researches has been carried out in this direction too to identify the important contents in the context of image retargeting. Equally important aspect is evaluation of these retargeting methods. This article contains a brief survey of related research in all of these aspects.

Saliency of an object in an image determines the attentiveness of the object with respect to human visual system. Saliency, in the absence of any external stimuli, is determined by contrast of features (like intensity, color, etc.) of the object with its surroundings. This paper proposes a novel technique to enhance the saliency of an object, which is not intrinsically salient. In order to enhance the attentiveness, the feature values of a used-defined target object (whose saliency has to be enhanced) should have more differences with the feature values of its surrounding objects. But too much modification of these values of the target object will destroy the naturalness of the image. So this problem of enhancing saliency of a target object is treated as a maximization problem under some constraints.

Image based cheque clearing systems enable faster clearing of cheques in banking system. But the paying bank should receive an unaltered image of a genuine cheque so that its decision does not support a case of fraud. The method, proposed in this paper, successfully detects tampered cheque images. The proposed method is based on a fragile and reversible watermarking technique, namely difference expansion based watermarking. But the major problem with these kinds of reversible watermarking techniques is that the pixel values in the watermarked image often fall outside the dynamic range of the image. This paper demonstrates how intelligent pairing of pixels can solve this problem. Therefore, the revised difference expansion based watermarking (based on intelligent pixel pairing) successfully detects tampered images of cheques.

Hiding secret message into images is known as image steganography. A good image steganography method aims to minimize visual degradation while maximizing embedding capacity. In this paper, visual saliency is used to determine the number of bits which can be embedded in a pixel. The relation between the saliency of a pixel and the number of embedded bits is obtained by solving a multi-objective optimization problem. A genetic algorithm has been developed which gives the optimum relation between saliency values and the number of embedded bits in the pixels. A variation of Single Digit Sum (SDS) based steganography scheme has been used to embed the secret message bits where the base of computing SDS is estimated based on the number of bits being embedded in a pixel. Experimental results reveal that the proposed technique can boast of a good embedding capacity while controlling the visual degradation of stego image.

Vitiligo is an autoimmune disorder that leads to depigmentation of skin via melanocyte dysfunction. Keratinocyte-induced toxicity is one among the several etiological factors implicated for vitiligo, and hence, autologous keratinocyte grafting is projected as one of the primary mode of treatment for vitiligo. However, reports indicate that perilesional keratinocytes not only display signatures of apoptosis but also could secrete cytokines and mediators which have antagonistic effect on proliferation or survival. Therefore, we investigated how vitiligo patients' derived keratinocytes respond to surplus amounts of inflammatory cytokines and whether they recapitulate events that take place during conventional wound healing. The primary objective of our study was to determine whether keratinocytes isolated from a vitiligo patient would undergo epithelial-mesenchymal transition similar to their normal counterparts upon induction with inflammatory cytokines such as TGF-b1 and EGF. We found that these keratinocytes undergo EMT during wound repair accompanied with increase in the levels of mesenchymal markers and ECM proteins; decrease in the levels of epithelial markers and enhanced migratory ability. Besides, we also demonstrated that EMT induction leads to activation of SMAD and MAPK pathways via Ras, Raf, PAI 1, Snail, Slug and ZO1. To our knowledge, this is the first report on the characterization of primary keratinocytes isolated from vitiligo patients with respect to their wound healing capacity.

The staggering number of publications featuring the use of stem cells has revolutionized regenerative medicine research. Preclinical studies indicate that allogeneic human mesenchymal stem cells (MSCs) may be useful for the treatment of several clinical disorders, including sepsis, acute renal failure, acute myocardial infarction, and more recently, acute lung injury (ALI). However, considerable success would not be obtained in clinical trials due to poor survival of transplanted cells under the influence of inflammatory conditions. Despite robust approaches like cellular reprogramming, scaffolds and conditioned media have been tested to overcome this problem; however the success rate of these approaches remain questionable. Recently, pretreatment of bioactive compounds in vitro have been shown to suppress cell apoptosis and promote cell survival. Quite likely a similar phenomenon can take place in vivo. Based on such studies, we hypothesize that MSCs derived from human post-natal tissues could be conditioned and prepared for targeted disease therapy. Depending on the disease condition, the MSCs could be treated prior to delivery with appropriate bioactive compounds to allow them survive longer and perform a better role as biocatalyst. The advantage of this approach could be the tailor made availability of MSCs preconditioned with appropriate bioactive compounds for disease specific therapy. Therefore, the choice of suitable bioactive molecule is likely to enhance the efficacy of targeted stem cell therapy and preconditioning may provide a novel strategy in maximizing biological and functional properties of MSCs.

Cheque Truncation System (CTS) is an image based cheque clearing system to speed up the process of clearing the cheques. Financial frauds are being carried out by tampering the content of the cheque image. Therefore, there is a need of detecting this kind of tampering of images. This paper proposes a method to detect whether a cheque image has been tampered or not. A difference expansion based watermarking technique is applied for this purpose. Experimental results demonstrate that the proposed method successfully distinguishes between genuine and tampered cheque images.

Hiding an important message within an image is known as image steganography. Imperceptibility of the message is a major concern of an image steganography scheme. A novel single digit sum (SDS) based image steganography scheme has been proposed in this paper. At first, the computation of SDS has been generalized to support a number system with any given base. Then, an image steganography scheme has been developed, where the base for computing SDS is varied from one pixel to another. Therefore, the number of embedding bits in a pixel is varied across pixels. The purpose of this technique is to control the amount of change in a pixel. A lossy compressed version of the cover image is used to determine the upper limit of change in each pixel value. The base for computing SDS is determined by using this upper limit for a pixel. Thus, it is ensured that the stego image does not degrade beyond the degradation in the lossily compressed image.

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

Hand-written signature is commonly used for authenticating a person. Extraction of desired features from the captured signature image is crucial for automated signature verification. Presence of a background line (which may be a part of a paper form) is common in an offline signature. Removal of this kind of background line is necessary for correct extraction of features. But sometimes, a signature contains a line as part of it. This paper shows how intensity distributions can distinguish a background line from a line which is part of a signature.

Cheque Truncation System (CTS) is an image based cheque clearing system. It enables faster clearing of cheques. Indian banks have implemented this technology in selected areas of the country. Meanwhile, several fraud cases have already occurred by taking advantage of inadequate security mechanisms of this system. One of several mechanisms of committing frauds is to alter the digital image of the cheque. This paper proposes a method to tackle this type of fraud. It is based on inserting a watermark signal in the image of the cheque. But unlike traditional watermarking, this method is not interested in extracting the watermark at the receiver side. Rather it counts number of mismatching pixels in between the cover image and the watermarked image to detect occurrence of fraud. It also can identify which portions of the cheque image have been altered.

The problem of retargeting a larger image in a small display is to maintain recognizability of the objects. The retargeting scheme proposed in this paper provides a suitable solution to this. The input image is partitioned into rectangular strips such that alternate strips contain important or unimportant objects. Each of these rectangular strips shrinks in size to fit the entire image into the small display. Strips containing unimportant objects shrink at a higher rate than those containing important objects. It is experimentally found that this scheme performs better than many existing schemes. An estimate for the range of values for shrinkage controlling parameters is also attempted here.

Red, orange and yellow are termed as warm colors. A study regarding role of warm colors on visual attention is reported in this paper. Distributions of chromatic features (hue and saturation) are found to be different for warm colors that draw attention and those do not. It is observed that likelihood of drawing attention by a warm color depends on both of its hue and saturation component. Interestingly, this dependency is not related to the absolute hue and saturation, but relative to these chromatic components of other warm color pixels. Warm colors with hue relatively closer to red and/or higher saturation are more likely to guide attention.

In evaluating a model of a system, one should have the ideal response of the system as a reference. For evaluation of visual saliency models, one requires to obtain salient locations/objects in an image according to human visual system. In many experiments, eye-trackers are used for this purpose. But this technology is unavailable to many researchers. This paper deals with an alternate method of collecting groundtruth data. Salient locations are identified using a hand-eye coordination based approach. It does not require any instruments other than the usual computing resources. Then image segment based analysis generates groundtruth data which serves the reference for evaluating visual saliency models. The generated groundtruth data fits to the size and shape of the underlying objects.

Saliency computation must consider usage of suitable combination of features along with an appropriate model to deliver desired performance. In this paper, important features for visual saliency computation are identified by analysing the performance of all possible combinations of a set of common candidate features. Combination of intensity and orientation is found to be suitable for monochrome images. Principal component analysis (PCA) on selected features is also performed. Usage of only the first principal component of selected features achieves good performance and reduces feature channels to save computational resources to a great extent.

The challenge of displaying an image in a much smaller target area is to maintain recognizability of its contents. A novel rectangular partitioning based retargeting scheme is proposed in this paper to address this issue. Constrained growth of rectangular partitions containing important objects is major characteristic of this scheme. Subjective evaluation by a group of volunteers establishes usefulness of this scheme.

Pluripotent stem cells possess the unique property of differentiating into all other cell types of the human body. Further, the discovery of induced pluripotent stem cells (iPSCs) in 2006 has opened up new avenues in clinical medicine. In simple language, iPSCs are nothing but somatic cells reprogrammed genetically to exhibit pluripotent characteristics. This process utilizes retroviruses/lentiviruses/adenovirus/plasmids to incorporate candidate genes into somatic cells isolated from any part of the human body. It is also possible to develop disease-specific iPSCs which are most likely to revolutionize research in respect to the pathophysiology of most debilitating diseases, as these can be mimicked ex vivo in the laboratory. These models can also be used to study the safety and efficacy of known drugs or potential drug candidates for a particular diseased condition, limiting the need for animal studies and considerably reducing the time and money required to develop new drugs. Recently, functional neurons, cardiomyocytes, pancreatic islet cells, hepatocytes and retinal cells have been derived from human iPSCs, thus re-confirming the pluripotency and differentiation capacity of these cells. These findings further open up the possibility of using iPSCs in cell replacement therapy for various degenerative disorders. In this review we highlight the development of iPSCs by different methods, their biological characteristics and their prospective applications in regenerative medicine and drug screening. We further discuss some practical limitations pertaining to this technology and how they can be averted for the betterment of human life.

Evaluation is a key part while proposing a new model. To evaluate models of visual saliency, one needs to compare the model's output with salient locations in an image. This paper proposes an approach to find out the salient locations, i.e., groundtruth for experiments with visual saliency models. It is found that the proposed human hand-eye coordination based technique can be an alternative to costly human pupil-tracking based systems. Moreover, an evaluation metric is also proposed that suits the necessity of the saliency models.

In this paper, a new spatial domain adaptive image watermarking scheme is proposed which embeds watermark information to the least salient pixels of the image. Watermarked image thus produced has less perceptual error with respect to human visual system (HVS). In proposed scheme least salient pixels are determined using the well known Itti-Koch model. Experimental results reveal that proposed scheme has less per- ceptual error than existing spatial domain embedding scheme.