Erectile Problems in a Non-clinical Sample: Remission rates and perceived factors in remission

One hundred and nine men (38% single, 48% married) participated in a study which investigated the prevalence and remission rate of erectile difficulty. A total of 25 men reported erectile difficulty at some time. Eight reported current erectile difficulty, the remaining 17 men reported remission of previous erectile difficulty, giving a remission rate of 68% for untreated erectile problems. Those men who had remitted did not differ from those still symptomatic in terms of age, marital status, socio-economic status or duration of the complaint. When asked for perceived reasons for their remission, 53% reported that they had stopped worrying about their erection and 47% believed that more opportunity for a sexual relationship had helped. Reasons for remission tended to be ‘psychological’ rather than ‘practical’, ‘physical’ or specifically ‘sexual’. Only two subjects had sought professional help but this was not specifically sex therapy. It is concluded that remission of erectile difficulty in those not seeking help would appear to be high and may be one reason why few men complain of sexual problems to their GP.     

The use of the twin model to investigate the genetics and epigenetics of skin diseases with genomic,transcriptomic and methylation data

Twins have always fascinated medical research even before the discovery of DNA and the understanding of the differences between identical and non‐identical twins. Dermatology with the benefit of being able to visualize phenotypes was one of the first specialities reporting on the fascinating concordance in identical (MZ) twins in the 1920’s. Over the last 20 years, the heritability of skin diseases using twins has been clearly demonstrated, across a wide variety of traits including melanoma, polymorphic light eruption, psoriasis, eczema and acne. Other rarer diseases have also been shown to have a significant genetic basis such as lupus, sarcoidosis and lichen sclerosus. Following evidence of heritability for many skin disease the next step was Genome‐Wide Association Studies (GWAS) which are uncovering new genes in large twin cohorts. The twin model is also ideal for the new field of epigenetics, investigating subtle differences in DNA methylation within discordant MZ pairs for a disease, as well as differences in CNVs. Twins are also valuable for examining differences in gene function via RNA expression in twins discordant for a skin trait or disease.     

Dialogue

Anemias in infancy and childhood constitute the major category of blood disorders seen by primary care physicians. Nonetheless, on many occasions childhood anemias are either overlooked or overstudied. Dr Monzon outlines a practical plan for delineating the nature of the anemia in order to determine the optimum management for each patient.     

V-region mutation in vitro,in vivo,and in silico reveal the importance of the enzymatic properties of AID and the sequence environment

The somatic hypermutation of Ig variable regions requires the activity of activation-induced cytidine deaminase (AID) which has previously been shown to preferentially deaminate WRC (W = A/T, R = A/G) motif hot spots in in vivo and in vitro assays. We compared mutation profiles of in vitro assays for the 3′ flanking intron of VhJ558-Jh4 region to previously reported in vivo profiles for the same region in the Msh2^−/−Ung^−/− mice that lack base excision and mismatch repair. We found that the in vitro and in vivo mutation profiles were highly correlated for the top (nontranscribed) strand, while for the bottom (transcribed) strand the correlation is far lower. We used an in silico model of AID activity to elucidate the relative importance of motif targeting in vivo. We found that the mutation process entails substantial complexity beyond motif targeting, a large part of which is captured in vitro. To elucidate the contribution of the sequence environment to the observed differences between the top and bottom strands, we analyzed intermutational distances. The bottom strand shows an approximately exponential distribution of distances in vivo and in vitro, as expected from a null model. However, the top strand deviates strongly from this distribution in that mutations approximately 50 nucleotides apart are greatly reduced, again both in vivo and in vitro, illustrating an important strand asymmetry. While we have confirmed that AID targeting of hot and cold spots is a key part of the mutation process, our results suggest that the sequence environment plays an equally important role.     

From the Cover: Convolutional networks for fast,energy-efficient neuromorphic computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.The human brain is capable of remarkable acts of perception while consuming very little energy. The dream of brain-inspired computing is to build machines that do the same, requiring high-accuracy algorithms and efficient hardware to run those algorithms. On the algorithm front, building on classic work on backpropagation (1), the neocognitron (2), and convolutional networks (3), deep learning has made great strides in achieving human-level performance on a wide range of recognition tasks (4). On the hardware front, building on foundational work on silicon neural systems (5), neuromorphic computing, using novel architectural primitives, has recently demonstrated hardware capable of running 1 million neurons and 256 million synapses for extremely low power (just 70 mW at real-time operation) (6). Bringing these approaches together holds the promise of a new generation of embedded, real-time systems, but first requires reconciling key differences in the structure and operation between contemporary algorithms and hardware. Here, we introduce and demonstrate an approach we call Eedn, energy-efficient deep neuromorphic networks, which creates convolutional networks whose connections, neurons, and weights have been adapted to run inference tasks on neuromorphic hardware.For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size (7).For operation, contemporary convolutional networks typically use high precision ( ≥ 32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for backpropagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to colocate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks). Here, we demonstrate that by introducing two constraints into the learning rule—binary-valued neurons with approximate derivatives and trinary-valued ({−1,0,1}) synapses—it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics. This approach draws inspiration from the spiking neurons and low-precision synapses of the brain (8) and builds on work showing that deep learning can create networks with constrained connectivity (9), low-precision synapses (10, 11), low-precision neurons (12–14), or both low-precision synapses and neurons (15, 16). For input data, we use a first layer to transform multivalued, multichannel input into binary channels using convolution filters that are learned via backpropagation (12, 16) and whose output can be sent on chip in the form of spikes. These binary channels, intuitively akin to independent components (17) learned with supervision, provide a parallel distributed representation to carry out high-fidelity computation without the need for high-precision representation.Critically, we demonstrate that bringing the above innovations together allows us to create networks that approach state-of-the-art accuracy performing inference on eight standard datasets, running on a neuromorphic chip at between 1,200 and 2,600 frames/s (FPS), using between 25 and 275 mW. We further explore how our approach scales by simulating multichip configurations. Ease-of-use is achieved using training tools built from existing, optimized deep learning frameworks (18), with learned parameters mapped to hardware using a high-level deployment language (19). Although we choose the IBM TrueNorth chip (6) for our example deployment platform, the essence of our constructions can apply to other emerging neuromorphic approaches (20–23) and may lead to new architectures that incorporate deep learning and efficient hardware primitives from the ground up.     

Potential effect of HIV type 1 antiretroviral and herpes simplex virus type 2 antiviral therapy on transmission and acquisition of HIV type 1 infection

Biological strategies for interrupting transmission of human immunodeficiency virus (HIV) type 1 should be directed at reducing infectiousness of and susceptibility to HIV-1. Potential antiretroviral interventions include reducing the likelihood of transmission of HIV-1 by reducing HIV-1 load in the blood and genital tract of HIV-1--infected person, prophylaxis after high-risk exposure, and pre-exposure prophylaxis for very high risk populations. Antiviral treatment of herpes simplex virus (HSV) type 2, the most common cause of genital ulcers, should be evaluated as a strategy for HIV-1 infection prevention by reducing infectiousness of and susceptibility to HIV-1, on the basis of biological and epidemiological data indicating that HSV-2 facilitates transmission and acquisition of HIV-1. The rationale for antiretroviral and HSV-2-specific interventions and studies to test these strategies are described.     

Sympathetic nervous system function as measured by I-123 metaiodobenzylguanidine predicts transplant-free survival in heart failure patients with idiopathic dilated cardiomyopathy

BACKGROUND: Heightened activity of the sympathetic nervous system in heart failure patients is a major contributor to disease progression and death. I-123 metaiodobenzylguanidine (MIBG) provides an accurate, noninvasive method to assess cardiac sympathetic nerve activity. METHODS: Thirty-seven patients with New York Heart Association class II, III, or IV heart failure underwent baseline measurement of I-123 MIBG heart-to-mediastinum ratios, maximum oxygen consumption, radionuclide left ventricular ejection fraction, and plasma norepinephrine levels. Patients were followed 48.8+/-8.6 months to endpoints of cardiac death or transplantation. The heart-to-mediastinum ratio of I-123 MIBG activity measured 15 minutes after injection was the only independent predictor of transplant-free survival (P<.0001). I-123 MIBG imaging at 15 minutes identified patients with subsequent cardiac transplantation or death with a sensitivity of 92% and specificity of 72%, whereas the corresponding values for maximum oxygen consumption were 75% and 56%. By Kaplan-Meier survival analysis, the time to a cardiac endpoint was significantly shorter in patients with a 15-minute I-123 MIBG heart-to-mediastinum ratio below the group mean ratio of 1.536, compared with patients with a preserved I-123 MIBG ratio. Maximum oxygen consumption was not predictive of time to cardiac transplant or death. CONCLUSIONS: In this study of patients with congestive heart failure resulting from dilated cardiomyopathy, a 15-minute heart-to-mediastinum ratio of I-123 MIBG activity provided more accurate prediction of cardiac transplantation or death than other standard clinical tests.