Spatial navigation and multiscale representation by hippocampal place cells

Hippocampal lesions are known to impair success in navigation tasks. While such tasks could be solved by memorizing complete paths from a starting location to the goal, animals still perform successfully when placed in a novel starting position. We propose a navigation algorithm to solve the latter problem by exploiting two facts about hippocampal organization: (1) The size of the place fields of hippocampal place cells varies systematically along the dorsoventral axis, with dorsal place cells having smaller place fields than ventral (Kjelstrup et. al. 2008); and (2) the theta oscillation propagates as a traveling wave from dorsal to ventral hippocampus (Lubenov and Siapas, 2009). Taken together, these observations imply that the hippocampal representation of space progresses from fine- to coarse-grained within every theta cycle. 

The algorithm assumes that place cells can be activated by the animal's imagining a goal location, in addition to physically standing in the appropriate location. In the proposed algorithm, place cell activation propagates from small scale to large scale until place cells are found which respond strongly to both the physical location and the goal location. These place fields have their centers aligned roughly in the direction of the goal, providing a crude estimate of which direction the animal should step to approach the goal. Fine-grained directional information is contained in the smaller scale place fields within these large ones. Our algorithm therefore identifies a sequence of place cells, one from each scale, whose centers lie roughly along the line to the goal. 

Simulations reveal successful navigation to the goal, even around obstacles. By minimizing the number of steps the animal takes to reach the goal, we predict the organization of the optimal place field "map"; specifically the fraction of place cells which should be allocated to each spatial scale. This prediction is, in principle, experimentally testable.

The set of place fields with centers lying along a line to the goal is used to compute a step direction by maximizing the probability that those cells will be active in the next time step, given that a particular step direction is chosen.

The proposed algorithm handles navigation around obstacles by including "border cells" (Solstad et. al. 2008) which inhibit place cells in proportion to the degree of overlap between the place field and the obstacle. Furthermore, including firing rate adaptation of place cells prevents the animal from getting stuck in one spot

Retinal adaptation to spatial correlations

The classical center-surround retinal ganglion cell receptive field is thought to remove the strong spatial correlations in natural scenes, enabling efficient use of limited bandwidth. While early studies with drifting gratings reported robust surrounds (Enroth-Cugell and Robson, 1966), recent measurements with white noise reveal weak surrounds (Chichilnisky and Kalmar, 2002). This might be evidence for dynamical weakening of the retinal surround in response to decreased spatial correlations, which would be predicted by efficient coding theory. Such adaptation is reported in LGN (Lesica et al., 2007), but whether the retina also adapts to correlations is unknown. 

We tested for adaptation by recording simultaneously from ~40 ganglion cells on a multi-electrode array while presenting white and exponentially correlated checkerboards and strips. Measuring from ~200 cells responding to 90 minutes each of white and correlated stimuli, we were able to extract precise spatiotemporal receptive fields (STRFs). We found that a difference-of-Gaussians was not a good fit and the surround was generally displaced from the center. Thus, to assess surround strength we found the center and surround regions and the total weight on the pixels in each region. The relative surround strength was then defined as the ratio of surround weight to center weight. Surprisingly, we found that the majority of recorded cells have a stronger surround under white noise than under correlated noise (p<.05), contrary to naive expectation from theory. The conclusion was robust to different methods of extracting STRFs and persisted with checkerboard and strip stimuli.

To test, without assuming a model, whether the retina decorrelates stimuli, we also measured the pairwise correlations between spike trains of simultaneously recorded neurons under three conditions: white checkerboard, exponentially correlated noise, and scale-free noise. The typical amount of pairwise correlation increased with extent of input correlation, in line with our STRF measurements

Transformation of Stimulus Correlations by the Retina

Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.</p

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

Plasminogen Alleles Influence Susceptibility to Invasive Aspergillosis

Invasive aspergillosis (IA) is a common and life-threatening infection in immunocompromised individuals. A number of environmental and epidemiologic risk factors for developing IA have been identified. However, genetic factors that affect risk for developing IA have not been clearly identified. We report that host genetic differences influence outcome following establishment of pulmonary aspergillosis in an exogenously immune suppressed mouse model. Computational haplotype-based genetic analysis indicated that genetic variation within the biologically plausible positional candidate gene plasminogen (Plg; Gene ID 18855) correlated with murine outcome. There was a single nonsynonymous coding change (Gly110Ser) where the minor allele was found in all of the susceptible strains, but not in the resistant strains. A nonsynonymous single nucleotide polymorphism (Asp472Asn) was also identified in the human homolog (PLG; Gene ID 5340). An association study within a cohort of 236 allogeneic hematopoietic stem cell transplant (HSCT) recipients revealed that alleles at this SNP significantly affected the risk of developing IA after HSCT. Furthermore, we demonstrated that plasminogen directly binds to Aspergillus fumigatus. We propose that genetic variation within the plasminogen pathway influences the pathogenesis of this invasive fungal infection

Klebsiella pneumoniae Multiresistance Plasmid pMET1: Similarity with the Yersinia pestis Plasmid pCRY and Integrative Conjugative Elements

Dissemination of antimicrobial resistance genes has become an important public health and biodefense threat. Plasmids are important contributors to the rapid acquisition of antibiotic resistance by pathogenic bacteria.The nucleotide sequence of the Klebsiella pneumoniae multiresistance plasmid pMET1 comprises 41,723 bp and includes Tn1331.2, a transposon that carries the bla(TEM-1) gene and a perfect duplication of a 3-kbp region including the aac(6')-Ib, aadA1, and bla(OXA-9) genes. The replication region of pMET1 has been identified. Replication is independent of DNA polymerase I, and the replication region is highly related to that of the cryptic Yersinia pestis 91001 plasmid pCRY. The potential partition region has the general organization known as the parFG locus. The self-transmissible pMET1 plasmid includes a type IV secretion system consisting of proteins that make up the mating pair formation complex (Mpf) and the DNA transfer (Dtr) system. The Mpf is highly related to those in the plasmid pCRY, the mobilizable high-pathogenicity island from E. coli ECOR31 (HPI(ECOR31)), which has been proposed to be an integrative conjugative element (ICE) progenitor of high-pathogenicity islands in other Enterobacteriaceae including Yersinia species, and ICE(Kp1), an ICE found in a K. pneumoniae strain causing primary liver abscess. The Dtr MobB and MobC proteins are highly related to those of pCRY, but the endonuclease is related to that of plasmid pK245 and has no significant homology with the protein of similar function in pCRY. The region upstream of mobB includes the putative oriT and shares 90% identity with the same region in the HPI(ECOR31).The comparative analyses of pMET1 with pCRY, HPI(ECOR31), and ICE(Kp1 )show a very active rate of genetic exchanges between Enterobacteriaceae including Yersinia species, which represents a high public health and biodefense threat due to transfer of multiple resistance genes to pathogenic Yersinia strains

Africa and the global carbon cycle

The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO(2). Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century

Imaging biomarker roadmap for cancer studies.

Imaging biomarkers (IBs) are integral to the routine management of patients with cancer. IBs used daily in oncology include clinical TNM stage, objective response and left ventricular ejection fraction. Other CT, MRI, PET and ultrasonography biomarkers are used extensively in cancer research and drug development. New IBs need to be established either as useful tools for testing research hypotheses in clinical trials and research studies, or as clinical decision-making tools for use in healthcare, by crossing 'translational gaps' through validation and qualification. Important differences exist between IBs and biospecimen-derived biomarkers and, therefore, the development of IBs requires a tailored 'roadmap'. Recognizing this need, Cancer Research UK (CRUK) and the European Organisation for Research and Treatment of Cancer (EORTC) assembled experts to review, debate and summarize the challenges of IB validation and qualification. This consensus group has produced 14 key recommendations for accelerating the clinical translation of IBs, which highlight the role of parallel (rather than sequential) tracks of technical (assay) validation, biological/clinical validation and assessment of cost-effectiveness; the need for IB standardization and accreditation systems; the need to continually revisit IB precision; an alternative framework for biological/clinical validation of IBs; and the essential requirements for multicentre studies to qualify IBs for clinical use.Development of this roadmap received support from Cancer Research UK and the Engineering and Physical Sciences Research Council (grant references A/15267, A/16463, A/16464, A/16465, A/16466 and A/18097), the EORTC Cancer Research Fund, and the Innovative Medicines Initiative Joint Undertaking (grant agreement number 115151), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in kind contribution

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection