The Syndemic Illness of HIV and Trauma: Implications for a Trauma-Informed Model of Care

Background: People living with HIV infection are disproportionately burdened by trauma and the resultant negative health consequences, making the combiin of HIV infection and trauma a syndemic illness. Despite the high co-occurrence and negative influence on health, trauma and posttrattmatic sequelae in people living with HIV infection often go unrecognized and untreated because of the current gaps in medical training and lack at practice guidelines, Objective: We set out to review the current literature on HIV infection and trauma and propose a trauma-informed model of care to target this,syndemic illness. Methods: We searched PubMed, PsycINFO, and Cochrane review databases for articles that contained the following search terms: HIV AND either trauma (specifically violent trauma), PTSD intimate partner violence (IPV) abuse, or trauma-informed care. Articles were limited to primary clinical research or metanalyses published in English, Articles were excluded if they referred to HIV-associated posttraumatic stress disorder or HIV-associated posttraumatic growth. Results: We confirm high, but variable, rates of trauma in people living with HIV infection demonstrated in multiple studies, ranging from 10%-90%. Trauma is associated with (1) increased HIV-risk behavior, contributing to transmission and acquisition of the virus; (2) negative internal and external mediators also associated with poor health and high-risk HIV behavior; (3) poor adherence to treatment; (4) poor HI V-related and other health outcomes; and (5) particularly vulnerable special populations. Conclusions: Clinicians should consider using a model of trauma-informed care in the treatment of people living with HIV infection. Its adoption in different settings needs to be matched to available resources

Mechanisms underlying dopamine-mediated reward bias in compulsive behaviors

SummaryPathological behaviors such as problem gambling or shopping are characterized by compulsive choice despite alternative options and negative costs. Reinforcement learning algorithms allow a computation of prediction error, a comparison of actual and expected outcomes, which updates our predictions and influences our subsequent choices. Using a reinforcement learning model, we show data consistent with the idea that dopamine agonists in susceptible individuals with Parkinson's disease increase the rate of learning from gain outcomes. Dopamine agonists also increase striatal prediction error activity, thus signifying a “better than expected” outcome. Thus, our findings are consistent with a model whereby a distorted estimation of the gain cue underpins a choice bias toward gains

Emotional stimuli and motor conversion disorder

Contains fulltext : 90300.pdf (publisher's version ) (Open Access)Conversion disorder is characterized by neurological signs and symptoms related to an underlying psychological issue. Amygdala activity to affective stimuli is well characterized in healthy volunteers with greater amygdala activity to both negative and positive stimuli relative to neutral stimuli, and greater activity to negative relative to positive stimuli. We investigated the relationship between conversion disorder and affect by assessing amygdala activity to affective stimuli. We conducted a functional magnetic resonance imaging study using a block design incidental affective task with fearful, happy and neutral face stimuli and compared valence contrasts between 16 patients with conversion disorder and 16 age- and gender-matched healthy volunteers. The patients with conversion disorder had positive movements such as tremor, dystonia or gait abnormalities. We also assessed functional connectivity between the amygdala and regions associated with motor preparation. A group by affect valence interaction was observed. Post hoc analyses revealed that whereas healthy volunteers had greater right amygdala activity to fearful versus neutral compared with happy versus neutral as expected, there were no valence differences in patients with conversion disorder. There were no group differences observed. The time course analysis also revealed greater right amygdala activity in patients with conversion disorder for happy stimuli (t = 2.96, P = 0.006) (with a trend for fearful stimuli, t = 1.81, P = 0.08) compared with healthy volunteers, with a pattern suggestive of impaired amygdala habituation even when controlling for depressive and anxiety symptoms. Using psychophysiological interaction analysis, patients with conversion disorder had greater functional connectivity between the right amygdala and the right supplementary motor area during both fearful versus neutral, and happy versus neutral 'stimuli' compared with healthy volunteers. These results were confirmed with Granger Causality Modelling analysis indicating a directional influence from the right amygdala to the right supplementary motor area to happy stimuli (P < 0.05) with a similar trend observed to fearful stimuli (P = 0.07). Our data provide a potential neural mechanism that may explain why psychological or physiological stressors can trigger or exacerbate conversion disorder symptoms in some patients. Greater functional connectivity of limbic regions influencing motor preparatory regions during states of arousal may underlie the pathophysiology of motor conversion symptoms.11 p

Faster pairing computations on curves with high-degree twists

Research on efficient pairing implementation has focussed on reducing the loop length and on using high-degree twists. Existence of twists of degree larger than 2 is a very restrictive criterion but luckily constructions for pairing-friendly elliptic curves with such twists exist. In fact, Freeman, Scott and Teske showed in their overview paper that often the best known methods of constructing pairing-friendly elliptic curves over fields of large prime characteristic produce curves that admit twists of degree 3, 4 or 6. A few papers have presented explicit formulas for the doubling and the addition step in Miller’s algorithm, but the optimizations were all done for the Tate pairing with degree-2 twists, so the main usage of the high- degree twists remained incompatible with more efficient formulas. In this paper we present efficient formulas for curves with twists of degree 2, 3, 4 or 6. These formulas are significantly faster than their predecessors. We show how these faster formulas can be applied to Tate and ate pairing variants, thereby speeding up all practical suggestions for efficient pairing implementations over fields of large characteristic

A short-list of pairing-friendly curves resistant to Special TNFS at the 128-bit security level

https://www.iacr.org/docs/pub_2013-16.htmlThis paper is the IACR version. It can be made freely available on the homepages of authors, on their employer's institutional page, and in non-commercial archival repositories such as the Cryptology ePrint Archive, ArXiv/CoRR, HAL, etc.International audienceThere have been notable improvements in discrete logarithm computations in finite fields since 2015 and the introduction of the Tower Number Field Sieve algorithm (TNFS) for extension fields. The Special TNFS is very efficient in finite fields that are target groups of pairings on elliptic curves, where the characteristic is special (e.g.~sparse). The key sizes for pairings should be increased, and alternative pairing-friendly curves can be considered.We revisit the Special variant of TNFS for pairing-friendly curves. In this case the characteristic is given by a polynomial of moderate degree (between 4 and 38) and tiny coefficients, evaluated at an integer (a seed). We present a polynomial selection with a new practical trade-off between degree and coefficient size. As a consequence, the security of curves computed by Barbulescu, El~Mrabet and Ghammam in 2019 should be revised: we obtain a smaller estimated cost of STNFS for all curves except BLS12 and BN.To obtain TNFS-secure curves, we reconsider the Brezing--Weng generic construction of families of pairing-friendly curves and estimate the cost of our new Special TNFS algorithm for these curves. This improves on the work of Fotiadis and Konstantinou, Fotiadis and Martindale, and Barbulescu, El~Mrabet and Ghammam. We obtain a short-list of interesting families of curves that are resistant to the Special TNFS algorithm, of embedding degrees 10 to 16 for the 128-bit security level. We conclude that at the 128-bit security level, BLS-12 and Fotiadis--Konstantinou--Martindale curves with $k=12$ over a 440 to 448-bit prime field seem to be the best choice for pairing efficiency. We also give hints at the 192-bit security level