Capital and Punishment: Resource Scarcity Increases Endorsement of the Death Penalty

Faced with punishing severe offenders, why do some prefer imprisonment whereas others impose death? Previous research exploring death penalty attitudes has primarily focused on individual and cultural factors. Adopting a functional perspective, we propose that environmental features may also shape our punishment strategies. Individuals are attuned to the availability of resources within their environments. Due to heightened concerns with the costliness of repeated offending, we hypothesize that individuals tend toward elimination-focused punishments during times of perceived scarcity. Using global and United States data sets (studies 1 and 2), we find that indicators of resource scarcity predict the presence of capital punishment. In two experiments (studies 3 and 4), we find that activating concerns about scarcity causes people to increase their endorsement for capital punishment, and this effect is statistically mediated by a reduced willingness to risk repeated offenses. Perceived resource scarcity shapes our punishment preferences, with important policy implications

Monitoring SO2 emission at the Soufriere Hills Volcano: implications for changes in erruptive conditions

FLWINinfo:eu-repo/semantics/publishe

Explosive Dome Eruptions Modulated by Periodic Gas-Driven Inflation

Volcan Santiaguito (Guatemala) “breathes” with extraordinary regularity as the edifice\u27s conduit system accumulates free gas, which periodically vents to the atmosphere. Periodic pressurization controls explosion timing, which nearly always occurs at peak inflation, as detected with tiltmeters. Tilt cycles in January 2012 reveal regular 26 ± 6 min inflation/deflation cycles corresponding to at least ~101 kg/s of gas fluxing the system. Very long period (VLP) earthquakes presage explosions and occur during cycles when inflation rates are most rapid. VLPs locate ~300 m below the vent and indicate mobilization of volatiles, which ascend at ~50 m/s. Rapid gas ascent feeds pyroclast-laden eruptions lasting several minutes and rising to ~1 km. VLPs are not observed during less rapid inflation episodes; instead, gas vents passively through the conduit producing no infrasound and no explosion. These observations intimate that steady gas exsolution and accumulation in shallow reservoirs may drive inflation cycles at open-vent silicic volcanoes

The impact of Corynebacterium glucuronolyticumon semen parameters: a prospective pre-post-treatment study

Corynebacterium glucuronolyticum (C. glucuronolyticum) is a rare isolate that is only recently being acknowledged as a potential urogenital pathogen. The bibliographical references on this bacterial species are scarce, and its influence on all semen parameters was hitherto unknown - therefore, the aim of this study was to evaluate its effects on a range of sperm quality parameters. A prospective approach to compare semen parameters before and after treatment was used in this study. C. glucuronolyticum in semen specimens was identified using analytical profile index biotyping system (API Coryne) and additionally confirmed by matrix-assisted laser desorption/ionization time-of-flight mass-spectrometry (MALDI-TOF MS), with the determination of antimicrobial susceptibility by Kirby-Bauer method. Semen analysis was performed according to the criteria from the World Health Organization (with the use of Tygerberg method of sperm morphology categorization). Very strict inclusion criteria for participants also included detailed medical history and urological evaluation. From a total of 2169 screened semen specimens, the inclusion rate for participants with C. glucuronolyticum that satisfied all the criteria was 1.01%. Antibiogram-guided treatment of the infection with ensuing microbiological clearance has shown that the resolution of the infection correlates with statistically significant improvement in the vitality of spermatozoa, but also with a lower number of neck and mid-piece defects. Parameters such as sperm count, motility and normal morphology were not affected. In addition, susceptibility testing revealed a trend towards ciprofloxacin resistance, which is something that should be considered when selecting an optimal treatment approach. Albeit it is rarely encountered as a monoisolate in significant quantities, C. glucuronolyticum may negatively influence certain sperm parameters; therefore, it has to be taken into account in the microbiological analysis of urogenital samples

Polydiacetylenic nanofibers as new siRNA vehicles for in vitro and in vivo delivery

Polydiacetylenic nanofibers (PDA-Nfs) obtained by photopolymerization of surfactant 1 were optimized for intracellular delivery of small interfering RNAs (siRNAs). PDA-Nfs/siRNA complexes efficiently silenced the oncogene Lim-1 in the renal cancer cells 786-O in vitro. Intraperitoneal injection of PDA-Nfs/siLim1 downregulated Lim-1 in subcutaneous tumor xenografts obtained with 786-O cells in nude mice. Thus, PDA-Nfs represent an innovative system for in vivo delivery of siRNAs

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard heliophysics space missions provide a pool of information about the Sun's activity via the measurement of its magnetic field and the emission of light from the multi-layered, multi-thermal, and dynamic solar atmosphere. Extreme UV (EUV) wavelength observations from space help in understanding the subtleties of the outer layers of the Sun, namely the chromosphere and the corona. Unfortunately, such instruments, like the Atmospheric Imaging Assembly (AIA) onboard NASA's Solar Dynamics Observatory (SDO), suffer from time-dependent degradation, reducing their sensitivity. Current state-of-the-art calibration techniques rely on periodic sounding rockets, which can be infrequent and rather unfeasible for deep-space missions. We present an alternative calibration approach based on convolutional neural networks (CNNs). We use SDO-AIA data for our analysis. Our results show that CNN-based models could comprehensively reproduce the sounding rocket experiments' outcomes within a reasonable degree of accuracy, indicating that it performs equally well compared with the current techniques. Furthermore, a comparison with a standard "astronomer's technique" baseline model reveals that the CNN approach significantly outperforms this baseline. Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.Comment: 12 pages, 7 figures, 8 tables. This is a pre-print of an article submitted and accepted by A&A Journa