A quadratic lower bound for Rocchio’s similarity-based relevance feedback algorithm with a fixed query updating factor

Rocchio’s similarity-based relevance feedback algorithm, one of the most important query reformation methods in information retrieval, is essentially an adaptive supervised learning algorithm from examples. In practice, Rocchio’s algorithm often uses a fixed query updating factor. When this is the case, we strengthen the linear Ω(n) lower bound obtained by Chen and Zhu (Inf. Retr. 5:61–86, 2002) and prove that Rocchio’s algorithm makes Ω(k(n−k)) mistakes in searching for a collection of documents represented by a monotone disjunction of k relevant features over the n-dimensional binary vector space {0,1}n, when the inner product similarity measure is used. A quadratic lower bound is obtained when k is linearly proportional to n. We also prove an O(k(n−k)3) upper bound for Rocchio’s algorithm with the inner product similarity measure in searching for such a collection of documents with a constant query updating factor and a zero classification threshold

Neutrino Background Flux from Sources of Ultrahigh-Energy Cosmic-Ray Nuclei

Motivated by Pierre Auger Observatory results favoring a heavy nuclear composition for ultrahigh-energy (UHE) cosmic rays, we investigate implications for the cumulative neutrino background. The requirement that nuclei not be photodisintegrated constrains their interactions in sources, therefore limiting neutrino production via photomeson interactions. Assuming a $dN_{\rm CR}/dE_{\rm CR} \propto E_{\rm CR}^{-2}$ injection spectrum and photodisintegration via the giant dipole resonance, the background flux of neutrinos is lower than $E_\nu^2 \Phi_\nu \sim {10}^{-9} {\rm GeV} {\rm cm}^{-2} {\rm s}^{-1} {\rm sr}^{-1}$ if UHE nuclei ubiquitously survive in their sources. This is smaller than the analogous Waxman-Bahcall flux for UHE protons by about one order of magnitude, and is below the projected IceCube sensitivity. If IceCube detects a neutrino background, it could be due to other sources, e.g., hadronuclear interactions of lower-energy cosmic rays; if it does not, this supports our strong restrictions on the properties of sources of UHE nuclei.Comment: 7 pages, 3 figure

Ozone changes under solar geoengineering:Implications for UV exposure and air quality

Various forms of geoengineering have been proposed to counter anthropogenic climate change. Methods which aim to modify the Earth's energy balance by reducing insolation are often subsumed under the term Solar Radiation Management (SRM). Here, we present results of a standard SRM modelling experiment in which the incoming solar irradiance is reduced to offset the global mean warming induced by a quadrupling of atmospheric carbon dioxide. For the first time in an atmosphere-ocean coupled climate model, we include atmospheric composition feedbacks such as ozone changes under this scenario. Including the composition changes, we find large reductions in surface UV-B irradiance, with implications for vitamin D production, and increases in surface ozone concentrations, both of which could be important for human health. We highlight that both tropospheric and stratospheric ozone changes should be considered in the assessment of any SRM scheme, due to their important roles in regulating UV exposure and air quality

Should we tweet this? Generative response modeling for predicting reception of public health messaging on Twitter

The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a predictive method which can be used to explore the potential reception of such messages. Specifically, we harness a generative model (GPT-2) to directly predict probable future responses and demonstrate how it can be used to optimize expected reception of important health guidance. Finally, we introduce a novel evaluation scheme with extensive statistical testing which allows us to conclude that our models capture the semantics and sentiment found in actual public health responses.Comment: Accepted at ACM WebSci 202

TO IDENTIFY, EVALUATE, AND ANALYZE THE POSSIBLE DRUG-DRUG INTERACTIONS IN PATIENTS DIAGNOSED AS TYPE 2 DIABETES MELLITUS WITH HYPERTENSION IN A TERTIARY CARE TEACHING HOSPITAL

Objective: To identify, evaluate, and analyze the possible drug-drug interactions (DDIs) in patients diagnosed as Type 2 diabetes mellitus withhypertension in a tertiary care teaching hospital Davangere.Methods: This prospective interventional study was conducted for a period of 6 months. Data were collected from patients who were prescribed withat least one antidiabetic drug and at least one antihypertensive drug at the same time. Data were analyzed for DDIs by using software Micromedex andother resources. Results were notified to the physicians for modification or alteration in the drug therapy.Results: A total of 150 patients were analyzed out of which, 60.67% were male, and the rest 39.33% were female. In terms of interactions present,95 (63.33%) prescriptions had one or more interactions. Antihypertensive drugs most frequently seen in prescriptions were diuretics (24.44%). Antidiabeticdrugs seen frequently prescribed are biguanides (34.36%). A total of 167 possible DDIs were obtained. Angiotensin-converting enzyme inhibitors weremost frequently involved antihypertensive drug in DDIs, with 60 of all possible DDIs identified. Insulin and biguanides were most frequently involvedantidiabetic drugs in DDIs, with 58 each of all possible DDIs identified. Most frequently interacting drug pair was insulin + metformin (n=19).Conclusion: For every possible DDIs found in the prescription, the appropriate intervention was advised from the investigator's part as well asprovision for a physician to review and initiate modification of his choice.Keywords: Hypertension, Possible drug-drug interaction, Type 2 diabetes mellitus.Â

Human GUCY2C-Targeted Chimeric Antigen Receptor (CAR)-Expressing T Cells Eliminate Colorectal Cancer Metastases.

One major hurdle to the success of adoptive T-cell therapy is the identification of antigens that permit effective targeting of tumors in the absence of toxicities to essential organs. Previous work has demonstrated that T cells engineered to express chimeric antigen receptors (CAR-T cells) targeting the murine homolog of the colorectal cancer antigen GUCY2C treat established colorectal cancer metastases, without toxicity to the normal GUCY2C-expressing intestinal epithelium, reflecting structural compartmentalization of endogenous GUCY2C to apical membranes comprising the intestinal lumen. Here, we examined the utility of a human-specific, GUCY2C-directed single-chain variable fragment as the basis for a CAR construct targeting human GUCY2C-expressing metastases. Human GUCY2C-targeted murine CAR-T cells promoted antigen-dependent T-cell activation quantified by activation marker upregulation, cytokine production, and killing of GUCY2C-expressing, but not GUCY2C-deficient, cancer cells in vitro. GUCY2C CAR-T cells provided long-term protection against lung metastases of murine colorectal cancer cells engineered to express human GUCY2C in a syngeneic mouse model. GUCY2C murine CAR-T cells recognized and killed human colorectal cancer cells endogenously expressing GUCY2C, providing durable survival in a human xenograft model in immunodeficient mice. Thus, we have identified a human GUCY2C-specific CAR-T cell therapy approach that may be developed for the treatment of GUCY2C-expressing metastatic colorectal cancer

Detecting Population III stars through observations of near-IR cosmic infrared background anisotropies

Following the successful mapping of the last scattering surface by WMAP and balloon experiments, the epoch of the first stars, when Population III stars formed, is emerging as the next cosmological frontier. It is not clear what these stars' properties were, when they formed or how long their era lasted before leading to the stars and galaxies we see today. We show that these questions can be answered with the current and future measurements of the near-IR cosmic infrared background (CIB). Theoretical arguments suggest that Population III stars were very massive and short-lived stars that formed at $z\sim 10-20$ at rare peaks of the density field in the cold-dark-matter Universe. Because Population III stars probably formed individually in small mini-halos, they are not directly accessible to current telescopic studies. We show that these stars left a strong and measurable signature via their contribution to the CIB anisotropies for a wide range of their formation scenarios. The excess in the recently measured near-IR CIB anisotropies over that from normal galaxies can be explained by contribution from early Population III stars. These results imply that Population III were indeed very massive stars and their epoch started at $z\sim 20$ and lasted past z\lsim 13. We show the importance of accurately measuring the CIB anisotropies produced by Population III with future space-based missions.Comment: Ap.J., in press. (Replaced with accepted version