(Not as) Big as a Barn: Upper Bounds on Dark Matter-Nucleus Cross Sections

Critical probes of dark matter come from tests of its elastic scattering with nuclei. The results are typically assumed to be model-independent, meaning that the form of the potential need not be specified and that the cross sections on different nuclear targets can be simply related to the cross section on nucleons. For point-like spin-independent scattering, the assumed scaling relation is $\sigma_{\chi A} \propto A^2 \mu_A^2 \sigma_{\chi N}\propto A^4 \sigma_{\chi N}$, where the $A^2$ comes from coherence and the $\mu_A^2\simeq A^2 m_N^2$ from kinematics for $m_\chi\gg m_A$. Here we calculate where model independence ends, i.e., where the cross section becomes so large that it violates its defining assumptions. We show that the assumed scaling relations generically fail for dark matter-nucleus cross sections $\sigma_{\chi A} \sim 10^{-32}-10^{-27}\;\text{cm}^2$, significantly below the geometric sizes of nuclei, and well within the regime probed by underground detectors. Last, we show on theoretical grounds, and in light of existing limits on light mediators, that point-like dark matter cannot have $\sigma_{\chi N}\gtrsim10^{-25}\;\text{cm}^2$, above which many claimed constraints originate from cosmology and astrophysics. The most viable way to have such large cross sections is composite dark matter, which introduces significant additional model dependence through the choice of form factor. All prior limits on dark matter with cross sections $\sigma_{\chi N}>10^{-32}\;\text{cm}^2$ with $m_\chi\gtrsim 1\;\text{GeV}$ must therefore be re-evaluated and reinterpreted.Comment: 17 pages, 7 figures, comments are welcom

Grasses and Legumes for Cellulosic Bioenergy

Human life has depended on renewable sources of bioenergy for many thousands of years, since the time humans fi rst learned to control fi re and utilize wood as the earliest source of bioenergy. The exploitation of forage crops constituted the next major technological breakthrough in renewable bioenergy, when our ancestors began to domesticate livestock about 6000 years ago. Horses, cattle, oxen, water buffalo, and camels have long been used as sources of mechanical and chemical energy. They perform tillage for crop production, provide leverage to collect and transport construction materials, supply transportation for trade and migratory routes, and create manure that is used to cook meals and heat homes. Forage crops—many of which form the basis of Grass: The 1948 Yearbook of Agriculture (Stefferud, 1948), as well as the other chapters of this volume—have composed the principal or only diet of these draft animals since the dawn of agriculture

The inner centromere is a biomolecular condensate scaffolded by the chromosomal passenger complex.

The inner centromere is a region on every mitotic chromosome that enables specific biochemical reactions that underlie properties, such as the maintenance of cohesion, the regulation of kinetochores and the assembly of specialized chromatin, that can resist microtubule pulling forces. The chromosomal passenger complex (CPC) is abundantly localized to the inner centromeres and it is unclear whether it is involved in non-kinase activities that contribute to the generation of these unique chromatin properties. We find that the borealin subunit of the CPC drives phase separation of the CPC in vitro at concentrations that are below those found on the inner centromere. We also provide strong evidence that the CPC exists in a phase-separated state at the inner centromere. CPC phase separation is required for its inner-centromere localization and function during mitosis. We suggest that the CPC combines phase separation, kinase and histone code-reading activities to enable the formation of a chromatin body with unique biochemical activities at the inner centromere

Hyperspectral phasor analysis enables multiplexed 5D in vivo imaging

Time-lapse imaging of multiple labels is challenging for biological imaging as noise, photobleaching and phototoxicity compromise signal quality, while throughput can be limited by processing time. Here, we report software called Hyper-Spectral Phasors (HySP) for denoising and unmixing multiple spectrally overlapping fluorophores in a low signal-to-noise regime with fast analysis. We show that HySP enables unmixing of seven signals in time-lapse imaging of living zebrafish embryos

Chemical diversity in a metal-organic framework revealed by fluorescence lifetime imaging

The presence and variation of chemical functionality and defects in crystalline materials, such as metal–organic frameworks (MOFs), have tremendous impact on their properties. Finding a means of identifying and characterizing this chemical diversity is an important ongoing challenge. This task is complicated by the characteristic problem of bulk measurements only giving a statistical average over an entire sample, leaving uncharacterized any diversity that might exist between crystallites or even within individual crystals. Here we show that by using fluorescence imaging and lifetime analysis, both the spatial arrangement of functionalities and the level of defects within a multivariable MOF crystal can be determined for the bulk as well as for the individual constituent crystals. We apply these methods to UiO-67, to study the incorporation of functional groups and their consequences on the structural features. We believe that the potential of the techniques presented here in uncovering chemical diversity in what is generally assumed to be homogeneous systems can provide a new level of understanding of materials properties

Five-factor model personality traits in opioid dependence

<p>Abstract</p> <p>Background</p> <p>Personality traits may form a part of the aetiology of opioid dependence. For instance, opioid dependence may result from self-medication in emotionally unstable individuals, or from experimenting with drugs in sensation seekers. The five factor model (FFM) has obtained a central position in contemporary personality trait theory. The five factors are: Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness. Few studies have examined whether there is a distinct personality pattern associated with opioid dependence.</p> <p>Methods</p> <p>We compared FFM personality traits in 65 opioid dependent persons (mean age 27 years, 34% females) in outpatient counselling after a minimum of 5 weeks in buprenorphine replacement therapy, with those in a non-clinical, age- and sex-matched sample selected from a national database. Personality traits were assessed by a Norwegian version of the Revised NEO Personality Inventory (NEO PI-R), a 240-item self-report questionnaire. Cohen's d effect sizes were calculated for the differences in personality trait scores.</p> <p>Results</p> <p>The opioid-dependent sample scored higher on Neuroticism, lower on Extraversion and lower on Conscientiousness (d = -1.7, 1.2 and 1.7, respectively) than the controls. Effects sizes were small for the difference between the groups in Openness to experience scores and Agreeableness scores.</p> <p>Conclusion</p> <p>We found differences of medium and large effect sizes between the opioid dependent group and the matched comparison group, suggesting that the personality traits of people with opioid dependence are in fact different from those of non-clinical peers.</p

A Bayesian method for inferring quantitative information from FRET data

<p>Abstract</p> <p>Background</p> <p>Understanding biological networks requires identifying their elementary protein interactions and establishing the timing and strength of those interactions. Fluorescence microscopy and Förster resonance energy transfer (FRET) have the potential to reveal such information because they allow molecular interactions to be monitored in living cells, but it is unclear how best to analyze FRET data. Existing techniques differ in assumptions, manipulations of data and the quantities they derive. To address this variation, we have developed a versatile Bayesian analysis based on clear assumptions and systematic statistics.</p> <p>Results</p> <p>Our algorithm infers values of the FRET efficiency and dissociation constant, <it>K_d</it>, between a pair of fluorescently tagged proteins. It gives a posterior probability distribution for these parameters, conveying more extensive information than single-value estimates can. The width and shape of the distribution reflects the reliability of the estimate and we used simulated data to determine how measurement noise, data quantity and fluorophore concentrations affect the inference. We are able to show why varying concentrations of donors and acceptors is necessary for estimating <it>K_d</it>. We further demonstrate that the inference improves if additional knowledge is available, for example of the FRET efficiency, which could be obtained from separate fluorescence lifetime measurements.</p> <p>Conclusions</p> <p>We present a general, systematic approach for extracting quantitative information on molecular interactions from FRET data. Our method yields both an estimate of the dissociation constant and the uncertainty associated with that estimate. The information produced by our algorithm can help design optimal experiments and is fundamental for developing mathematical models of biochemical networks.</p

The Amsterdam Studies of Acute Psychiatry I (ASAP-I); A prospective cohort study of determinants and outcome of coercive versus voluntary treatment interventions in a metropolitan area

Background The overall number of involuntary admissions is increasing in many European countries. Patients with severe mental illnesses more often progress to stages in which acute, coercive treatment is warranted. The number of studies that have examined this development and possible consequences in terms of optimizing health care delivery in emergency psychiatry is small and have a number of methodological shortcomings. The current study seeks to examine factors associated with compulsory admissions in the Amsterdam region, taking into account a comprehensive model with four groups of predictors: patient vulnerability, social support, responsiveness of the health care system and treatment adherence. Methods/Design This paper describes the design of the Amsterdam Study of Acute Psychiatry-I (ASAP-I). The study is a prospective cohort study, with one and two-year follow-up, comparing patients with and without forced admission by means of a selected nested case-control design. An estimated total number of 4,600 patients, aged 18 years and over, consecutively coming into contact with the Psychiatric Emergency Service Amsterdam (PESA) are included in the study. From this cohort, a randomly selected group of 125 involuntary admitted subjects and 125 subjects receiving non-coercive treatment are selected for further evaluation and comparison. First, socio-demographic, psychopathological and network characteristics, and prior use of health services will be described for all patients who come into contact with PESA. Second, the in-depth study of compulsory versus voluntary patients will examine which patient characteristics are associated with acute compulsory admission, also taking into account social network and healthcare variables. The third focus of the study is on the associations between patient vulnerability, social support, healthcare characteristics and treatment adherence in a two-year follow-up for patients with or without involuntarily admittance at the index consultation. Discussion The current study seeks to establish a picture of the determinants of acute compulsory admissions in the Netherlands and tries to gain a better understanding of the association with the course of illness and patient's perception of services and treatment adherence. The final aim is to find specific patient and health care factors that can be influenced by adjusting treatment programs in order to reduce the number of involuntary admissions

The potential of optical proteomic technologies to individualize prognosis and guide rational treatment for cancer patients

Genomics and proteomics will improve outcome prediction in cancer and have great potential to help in the discovery of unknown mechanisms of metastasis, ripe for therapeutic exploitation. Current methods of prognosis estimation rely on clinical data, anatomical staging and histopathological features. It is hoped that translational genomic and proteomic research will discriminate more accurately than is possible at present between patients with a good prognosis and those who carry a high risk of recurrence. Rational treatments, targeted to the specific molecular pathways of an individual’s high-risk tumor, are at the core of tailored therapy. The aim of targeted oncology is to select the right patient for the right drug at precisely the right point in their cancer journey. Optical proteomics uses advanced optical imaging technologies to quantify the activity states of and associations between signaling proteins by measuring energy transfer between fluorophores attached to specific proteins. Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM) assays are suitable for use in cell line models of cancer, fresh human tissues and formalin-fixed paraffin-embedded tissue (FFPE). In animal models, dynamic deep tissue FLIM/FRET imaging of cancer cells in vivo is now also feasible. Analysis of protein expression and post-translational modifications such as phosphorylation and ubiquitination can be performed in cell lines and are remarkably efficiently in cancer tissue samples using tissue microarrays (TMAs). FRET assays can be performed to quantify protein-protein interactions within FFPE tissue, far beyond the spatial resolution conventionally associated with light or confocal laser microscopy. Multivariate optical parameters can be correlated with disease relapse for individual patients. FRET-FLIM assays allow rapid screening of target modifiers using high content drug screens. Specific protein-protein interactions conferring a poor prognosis identified by high content tissue screening will be perturbed with targeted therapeutics. Future targeted drugs will be identified using high content/throughput drug screens that are based on multivariate proteomic assays. Response to therapy at a molecular level can be monitored using these assays while the patient receives treatment: utilizing re-biopsy tumor tissue samples in the neoadjuvant setting or by examining surrogate tissues. These technologies will prove to be both prognostic of risk for individuals when applied to tumor tissue at first diagnosis and predictive of response to specifically selected targeted anticancer drugs. Advanced optical assays have great potential to be translated into real-life benefit for cancer patients