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Objectives To summarise the available data regarding the effect of hormone replacement therapy (HRT) on cognition and mood in women. Background Complaints of impaired cognition and mood are common in the peri-menopausal and menopausal period. There is debate as to whether HRT can ameliorate this phenomenon. Design A literature search of studies using electronic databases was conducted. Both randomised control trials and observational studies were included. Patients Perimenopausal and menopausal women. Results Due to the heterogenicity of results it is challenging to draw firm conclusions. The preparations used in many of the studies are older regimes no longer routinely used clinically. The notion of a ‘critical window’ for HRT is compelling, suggesting HRT has a positive impact on cognition when administered in the peri-menopausal or early postmenopausal period but may have negative effects on cognition in the older, postmenopausal woman. The evidence would seem to suggest importance of hormonal replacement in woman undergoing a surgical menopause, especially when young. It remains unclear for how long they ought to continue HRT though until at least the natural age of the menopause seems reasonable. Evidence for a positive effect of HRT on mood is more convincing, though possibly more efficacious in the younger age group. The effect of HRT on anxiety is less clear. Conclusions Further study, particularly focusing on the more contemporaneous HRT preparations, is warranted before evidence-based conclusions can be drawn.

Anabolic-androgenic steroid (AAS) have widespread and growing illicit use as image and performance enhancing drugs (IPED), predominantly in young men. Users trying to stop AAS are prone to distressing withdrawal symptoms which may trigger relapse in use. It is important to develop therapies to support AAS withdrawal. The illicit nature of AAS use has impeded the robust characterisation of its clinical withdrawal syndrome within any single study. Therefore, we conducted a systematic review summarising the available clinical studies describing symptoms associated with non-medically indicated AAS use, and AAS withdrawal. Reported clinical features of AAS withdrawal include headache, fatigue, myalgia, restlessness, insomnia, low mood and libido, anorexia, suicidal ideation, body image dissatisfaction, and steroid cravings; novel therapies for AAS withdrawal would need evaluation against these symptoms.

Human attention tends to get focused on the most prominent components of a scene which are in sharp contrast with the background. These are termed as salient regions. The human brain perceives an object to be salient based on various features like the relative intensity, spread of the region, color contrast with the background, size and position within an image. Since these features vary widely, no crisp thresholds can be specified for an automatic salient region detector. In this paper we present a rule based system which uses a set of fuzzy features to mark out the salient region in an image. A genetic algorithm based evolutionary system is used to learn the rules from the training images. Extensive comparisons with the state-of-the-art methods in terms of precision, recall and F-measure are made on two different publicly available datasets to prove the effectiveness of this approach. The application of the proposed salient object detection approach is shown in non-photorealistic rendering, perception based image compression and context aware retargeting applications with promising results.

In this paper we explore detection of unusual activities using Markov Logic Network (MLN) based approach. Any human activity which is in variance from a defined usual set attracts human attention and is considered unusual. Such activities include anomaly detection in crowds, some repetition or omission of subactivities in a given sequence of activities in Ambient Assisted Living environments or an outlier in case of surveillance. In this paper, we target the unusual activities occurring in workplaces. Typical usual activities considered are: entering a room, walking, sitting down and working. We define activities with unlabeled actions as well as outliers as unusual activities. The outliers include activities with repeated actions and activities with certain actions omitted. We use Markov Logic Network because it allows us to create common sense rules defining the relationship between different actions and activities. We validate our results on a noisy data set.

Financial inclusion is an equalizer that enables all citizens to contribute to economic growth and to gain from it. India was ahead of its times when it first ushered in financial inclusion by nationalizing its banks in mid-1969 and then coming up with a slew of policies to operationalize it. The track record is a mix of successes and failures. This paper tries to capture some of these and visualize where India will gain and what it may lose over the next three or four decades. Considering India's growing economic importance and the projections that the Indian economy will be as big as the United States economy around 2050, the paper tries to understand how the economic upswing will mesh with India's socio-political and environmental processes that also influence financial inclusion; and what the different scenarios might be in 2050. This paper discusses what role financial inclusion will play in India's economy in the year 2050. It suggests some possible scenarios and policy measures to move towards a desirable scenario.

Globalization has accelerated the growth of the informal sector worldwide. It now comprises majority workers in many countries, especially the developing world. The trade union movement should ideally have come forward to organize informal labour, both to legitimise itself and to help the exploited masses uplift themselves. Unfortunately, examples of this are few. Non-governmental organizations have stepped in to a limited extent to help organize informal labour ‘bottoms–up’ and from `above’—urging the corporate world to cleanse its supply chains and consumers to buy `fair trade’ products. In some instances, new or alternative unions have emerged. The Self-Employment Women's Association (SEWA), a trade union of nearly 700,000 poor women in the informal sector in India, can be seen as a pioneer of this trend though it first emerged as far back as in 1972 in the textile town of Ahmedabad, Gujarat. An analysis of SEWA's way of functioning, its holistic approach encompassing socio-economic and political rights and its widespread impact offers lessons for organising informal labour to give these workers a brighter future. This paper elaborates these lessons based on the SEWA experience.

Human attention tends to get focused on the most prominent components of a scene which are in sharp contrast with the background. These are termed as salient regions. Saliency is defined in terms of local and global feature contrasts. The human brain perceives an object of salient type based on its difference with the surroundings in terms of color and texture. There have been many color based approaches in the past for salient object detection. In this paper, we define the uncertainty of a window being salient or background in terms of information extracted from different color components. The uncertainty associated with the elements of a fuzzy set is described by a membership function, which gives the degree of association of each element to the set. The overall uncertainty is sought to be quantified by an entropy function. To locate the salient parts of the image, we make use of the entropy to compute a new set of features from color and luminance components of the image. Extensive comparisons with the state-of-the-art methods in terms of precision, recall and F-Measure are made on a publicly available dataset to prove the effectiveness of this approach.

In this paper we propose to summarize videos based on key frames. We improve upon the histogram and pixel difference based approach with fuzzy rule based approach and also give a new approach which reduces the computation of framewise differences. We test our methods using fidelity ratio and compression ratio on videos of sports from YouTube and UCF sports dataset, videos of commercials and sitcoms. The results of our methods are seen to be comparable to other state of the art approaches.

Background: Niche plays an important role in deciding the fate of the cell. Three dimensional (3-D) cell culture systems can provide conditions for the cells to revert back to de-differentiated state. Here, egg white is used as a scaffold as well as nutritive medium for the 3-D culture, as it provides nutrients for the cells to grow without extra media. In order to compare the growth of spheroids, another model was also taken into account that is gelatin coated plate. The difference between these two models was that the egg white acted as scaffold and provided additional nutrients for the growth of spheroids, while gelatin provided conditions for low attachment only. Method: Low attachment plates were prepared using egg white and gelatin for the spheroids to grow on it. RNA from cells grown on both 2D and 3D culture condition were extracted using trizol method. The concentration of RNA was determined by repeated OD measurements of aliquots at a wavelength of 260 nm and c-DNA was constructed using Fermentas kit. Finally, gene expressions were checked by PCR using thermo cycler. Conclusion: The cells had grown into spheroids on egg white based plate rather than gelatin coated plate. Gene expression was found to be upregulated in cells grown on egg white. Thus, it was proved that the niche provided by the egg white has induced de-differentiation, which leads to the acquisition of a stem cell like properties.

Color is the most dominant feature used by the human brain to perceive an image region to be salient. Since size, shape, location and colors of salient objects vary widely, a fuzzy rule based system is proposed in this paper which uses color proximity, color spread, connected components and presence of a face as linguistic variables. These rules are learned using a genetic algorithm. The colorspace used is the CIELab colorspace which closely conforms with human perception of colors. A publicly available image dataset is used for training and testing the system. Comparisons with existing state-of-the-art methods in terms of precision, recall and F-Measure have been presented.

Alkaliphiles are interesting groups of extremophilic organisms that thrive at pH of 9.0 and above. Many of their products, in particular enzymes, have found widespread applications in industry, primarily in the detergent and laundry industries. While the enzymes have been a runaway success from the industrial point of view, many more products have been reported from alkaliphiles such as antibiotics and carotenoids. Less known are their potential for degradation of xenobiotics. They also play a key role in biogeocycling of important inorganic compounds. This review provides an insight into the huge diversity of alkaliphilic bacteria, the varied products obtained from them, and the need for further investigations on these interesting bacteria.

Automatic salient region detection in an image is useful for image compression, image cropping for resizing on smaller displays, object recognition and tracking. In this paper case-based reasoning is used to make a system learn the relevant attributes of a salient region, based on global and local color contrast. CIELab colorspace is used as it is perceptually uniform and matches the human visual perception. The parameters used are background colors at the boundary, color distance, spatial variance, size of the connected components of salient color and dominant colors. Intensity values are used to deal with images containing black and white shades. Exemplar images are presented to the system to categorize the cases. The method is tested on a large image database.

Cancer stem cells are a small population of cells in a tumor. They have the ability to self-renew and maintain the tumor. The most apt and accepted hypothesis for tumor development is Cancer Stem Cells. This review focuses on this concept of cancer stem cells, serving their purpose and leading to the development of tumor. There are many cell biomarkers which have been described for the identification and characterization of cancer stem cells. The most prominent of the cellular markers for the detection of cancer stem cells; CD133, CD44, ALDH-1 along with some others have been discussed in detail in this review.

In the era of climate change, farmers to adapt to the changing climate, the policy makers need to understand this in a broader context of ecological, economical and sociopolitical processes and build support systems to facilitate adaptation. One of the key issue is bringing village-level weather variations centre-stage because climate variations are highly localized. Prioritize on ecological farming practices as they reduce the contribution to climate change and help in adopting to the change. Understanding, the weather variations and adaptation with a gender lens is very important as the impact of the variations is high on them.