Električni faktor oblika neutrona pomoću odbojne polarimetrije

Jefferson Lab experiment 93–038 is designed to measure the ratio of the electric to the magnetic form-factor of the neutron from the quasielastic 2H(~e, e ′~n)1H reaction.Za mjerenje omjera električnog i magnetskog faktora oblika neutrona pomoću kvazielastične reakcije 2H(~e, e ′~n)1H postavljen je eksperiment Jefferson Lab 93–038

Efficacy and safety of pharmacotherapies for smoking cessation in anxiety disorders: Subgroup analysis of the randomized, active- and placebo-controlled EAGLES trial.

BackgroundSmoking rates are high in adults with anxiety disorders (ADs), yet little is known about the safety and efficacy of smoking-cessation pharmacotherapies in this group.MethodsPost hoc analyses in 712 smokers with AD (posttraumatic stress disorder [PTSD], n = 192; generalized anxiety disorder [GAD], n = 243; panic disorder [PD], n = 277) and in a nonpsychiatric cohort (NPC; n = 4,028). Participants were randomly assigned to varenicline, bupropion, nicotine-replacement therapy (NRT), or placebo plus weekly smoking-cessation counseling for 12 weeks, with 12 weeks follow-up. General linear models were used to test the effects of treatment group, cohort, and their interaction on neuropsychiatric adverse events (NPSAEs), and continuous abstinence weeks 9-12 (treatment) and 9-24 (follow-up).ResultsNPSAE incidence for PTSD (6.9%), GAD (5.4%), and PD (6.2%) was higher versus NPC (2.1%), regardless of treatment. Across all treatments, smokers with PTSD (odds ratio [OR] = 0.58), GAD (OR = 0.72), and PD (OR = 0.53) had lower continuous abstinence rates weeks 9-12 (CAR9-12) versus NPC. Varenicline demonstrated superior efficacy to placebo in smokers with GAD and PD, respectively (OR = 4.53; 95% confidence interval [CI] = 1.20-17.10; and OR = 8.49; 95% CI = 1.57-45.78); NRT was superior to placebo in smokers with PD (OR = 7.42; 95% CI = 1.37-40.35). While there was no statistically significant effect of any treatment on CAR9-12 for smokers with PTSD, varenicline improved 7-day point prevalence abstinence at end of treatment in this subcohort.ConclusionIndividuals with ADs were more likely than those without psychiatric illness to experience moderate to severe NPSAEs during smoking-cessation attempts, regardless of treatment. While the study was not powered to evaluate abstinence outcomes with these subgroups of smokers with ADs, varenicline provided significant benefit for cessation in those with GAD and PD, while NRT provided significant benefit for those with PD

Developing a real time sensing system to monitor bacteria in wound dressings

Infection control is a key aspect of wound management strategies. Infection results in chemical imbalances and inflammation in the wound and may lead to prolonged healing times and degradation of the wound surface. Frequent changing of wound dressings may result in damage to healing tissues and an increased risk of infection. This paper presents the first results from a monitoring system that is being developed to detect presence and growth of bacteria in real time. It is based on impedance sensors that could be placed at the wound-dressing interface and potentially monitor bacterial growth in real time. As wounds can produce large volumes of exudate, the initial system reported here was developed to test for the presence of bacteria in suspension. Impedance was measured using disposable silver-silver chloride electrodes. The bacteria Staphylococcus aureus were chosen for the study as a species commonly isolated from wounds. The growth of bacteria was confirmed by plate counting methods and the impedance data were analysed for discernible differences in the impedance profiles to distinguish the absence and/or presence of bacteria. The main findings were that the impedance profiles obtained by silver-silver chloride sensors in bacterial suspensions could detect the presence of high cell densities. However, the presence of the silver-silver chloride electrodes tended to inhibit the growth of bacteria. These results indicate that there is potential to create a real time infection monitor for wounds based upon impedance sensing

Single-photon absorption of isolated collagen mimetic peptides and triple-helix models in the VUV-X energy range

Cartilage and tendons owe their special mechanical properties to the fibrous collagen structure. These strong fibrils are aggregates of a sub-unit consisting of three collagen proteins wound around each other in a triple helix. Even though collagen is the most abundant protein in the human body, the response of this protein complex to ionizing radiation has never been studied. In this work, we probe the direct effects of VUV and soft X-ray photons on isolated models of the collagen triple helix, by coupling a tandem mass spectrometer to a synchrotron beamline. Single-photon absorption is found to induce electronic excitation, ionization and conversion into internal energy leading to inter- and intra-molecular fragmentation, mainly due to Gly-Pro peptide bond cleavages. Our results indicate that increasing the photon energy from 14 to 22 eV reduces fragmentation. We explain this surprising behavior by a smooth transition from excitation to ionization occurring with increasing photon energy. Moreover, our data support the assumption of a stabilization of the triple helix models by proline hydroxylation via intra-complex stereoelectronic effects, instead of the influence of solvent

Improving Classification Performance With Human Feedback: Label a few, we label the rest

In the realm of artificial intelligence, where a vast majority of data is unstructured, obtaining substantial amounts of labeled data to train supervised machine learning models poses a significant challenge. To address this, we delve into few-shot and active learning, where are goal is to improve AI models with human feedback on a few labeled examples. This paper focuses on understanding how a continuous feedback loop can refine models, thereby enhancing their accuracy, recall, and precision through incremental human input. By employing Large Language Models (LLMs) such as GPT-3.5, BERT, and SetFit, we aim to analyze the efficacy of using a limited number of labeled examples to substantially improve model accuracy. We benchmark this approach on the Financial Phrasebank, Banking, Craigslist, Trec, Amazon Reviews datasets to prove that with just a few labeled examples, we are able to surpass the accuracy of zero shot large language models to provide enhanced text classification performance. We demonstrate that rather than needing to manually label millions of rows of data, we just need to label a few and the model can effectively predict the rest