Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.

Abnormalities of the immune function in depression and suicide are based in part on the observation of increased levels of proinflammatory cytokines in the serum and postmortem brain of depressed and suicidal patients. We have examined if abnormalities of the innate immune receptors, known as Toll-like receptors (TLRs), in the brain are associated with depression and suicide, since the activation of these receptors results in production of cytokines. Of all the TLRs shown to be present in humans, TLR3 and TLR4 appear to be unique and important in brain function. We have determined the protein (by ELISA method) and mRNA expression (using qPCR) of TLR3 and TLR4 in the postmortem brain (dorsolateral prefrontal cortex [DLPFC]) of 22 depressed suicide victims, 11 non-depressed suicide victims, 12 depressed non-suicide subjects and 20 normal control subjects. We found that the mRNA expression of TLR3 and TLR4 was significantly increased in DLPFC of depressed suicide victims and in depressed non-suicide subjects, compared with controls. However, the protein expression of TLR3 and TLR4 was significantly increased in depressed suicide victims, but not in depressed non-suicide subjects compared with controls. The observed abnormalities of proinflammatory cytokines in the brain of suicide victims may be related to an abnormality of TLR3 and TLR4 over-expression. To our knowledge, this is the first study of TLRs in the brain of psychiatric subjects.

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system's recommendations by injecting biased profiles into it. In this paper, we examine attacks that concentrate on a targeted set of users with similar tastes, biasing the system's responses to these users. We show that such attacks are both pragmatically reasonable and also highly effective against both user-based and itembased algorithms. As a result, an attacker can mount such a "segmented" attack with little knowledge of the specific system being targeted and with strong likelihood of success.

Background: Depression and stress are major risk factors for suicidal behaviour, and some studies show abnormalities of proinflamma tory cytokines in the serum and cerebrospinal fluid (CSF) of depressed and suicidal patients. However, it is not clear if similar abnormal ities of cytokines are present in the brain of suicidal and depressed patients. Methods: We therefore determined the mRNA (using real time polymerase chain reaction) and protein (using enzymelinked immunosorbent assay and Western Blot) expression levels of interleukin (IL)1β, IL6, tumour necrosis factor (TNF)α, lymphotoxin A, lymphotoxin B, IL8, IL10 and IL13 in the prefrontal cortex (PFC) obtained from 24 depressed individuals who died by suicide and 24 nonpsychiatric controls. Results: We observed that the mRNA and protein levels of IL1β, IL6, TNFα, and lymphotoxin A were significantly increased, and levels of antiinflammatory cytokine IL10, and of IL1 receptor antagonist (IL1RA) were significantly decreased in the PFC of depressed individuals who died by suicide compared with controls. There were no significant differences in the protein and mRNA levels of IL8 and IL13 in the PFC. Limitations:The main limitation of this study is that some of the suicide group had been taking antidepressant medication at the time of death. Conclusion: Our results suggest that alterations of cytokines may be associated with the pathophysiology of depressed suicide and there may be an imbalance between pro and antiinflammatory cytokines in people who die by suicide. The causes of these increases in the brain of people who die by suicide, therefore, need to be investigated further.

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.