Biosynthesis and Characterization of Silver Nanoparticles by Aspergillus Species

Currently, researchers turn to natural processes such as using biological microorganisms in order to develop reliable and ecofriendly methods for the synthesis of metallic nanoparticles. In this study, we have investigated extracellular biosynthesis of silver nanoparticles using four Aspergillus species including A. fumigatus, A. clavatus, A. niger, and A. flavus. We have also analyzed nitrate reductase activity in the studied species in order to determine the probable role of this enzyme in the biosynthesis of silver nanoparticles. The formation of silver nanoparticles in the cell filtrates was confirmed by the passage of laser light, change in the color of cell filtrates, absorption peak at 430 nm in UV-Vis spectra, and atomic force microscopy (AFM). There was a logical relationship between the efficiencies of studied Aspergillus species in the production of silver nanoparticles and their nitrate reductase activity. A. fumigatus as the most efficient species showed the highest nitrate reductase activity among the studied species while A. flavus exhibited the lowest capacity in the biosynthesis of silver nanoparticles which was in accord with its low nitrate reductase activity. The present study showed that Aspergillus species had potential for the biosynthesis of silver nanoparticles depending on their nitrate reductase activity

Validation of a method for identifying nursing home admissions using administrative claims

<p>Abstract</p> <p>Background</p> <p>Currently there is no standard algorithm to identify whether a subject is residing in a nursing home from administrative claims. Our objective was to develop and validate an algorithm that identifies nursing home admissions at the resident-month level using the MarketScan Medicare Supplemental and Coordination of Benefit (COB) database.</p> <p>Methods</p> <p>The computer algorithms for identifying nursing home admissions were created by using provider type, place of service, and procedure codes from the 2000 – 2002 MarketScan Medicare COB database. After the algorithms were reviewed and refined, they were compared with a detailed claims review by an expert reviewer. A random sample of 150 subjects from the claims was selected and used for the validity analysis of the algorithms. Contingency table analysis, comparison of mean differences, correlations, and t-test analyses were performed. Percentage agreement, sensitivity, specificity, and Kappa statistics were analyzed.</p> <p>Results</p> <p>The computer algorithm showed strong agreement with the expert review (99.9%) for identification of the first month of nursing home residence, with high sensitivity (96.7%), specificity (100%) and a Kappa statistic of 0.97. Weighted Pearson correlation coefficient between the algorithm and the expert review was 0.97 (<it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>A reliable algorithm indicating evidence of nursing home admission was developed and validated from administrative claims data. Our algorithm can be a useful tool to identify patient transitions from and to nursing homes, as well as to screen and monitor for factors associated with nursing home admission and nursing home discharge.</p

Energy-scaling behavior of intrinsic transverse-momentum parameters in Drell-Yan simulation

Data Availability: Release and preservation of data used by the CMS Collaboration as the basis for publications is guided by the CMS data preservation, re-use, and open access policy https://dx.doi.org/10.7483/OPENDATA.CMS.7347.JDWH .A preprint version of the article is available on arXiv, arXiv:2409.17770v2 [hep-ph] (https://arxiv.org/abs/2409.17770). [v2] Tue, 8 Apr 2025 23:23:48 UTC (450 KB). Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/GEN-22-001 (CMS Public Pages). Subjects: High Energy Physics - Phenomenology (hep-ph); High Energy Physics - Experiment (hep-ex). Report numbers: CMS-GEN-22-001, CERN-EP-2024-216An analysis is presented based on models of the intrinsic transverse momentum (intrinsic ) of partons in nucleons by studying the dilepton transverse momentum in Drell-Yan events. Using parameter tuning in event generators and existing data from fixed-target experiments and from hadron colliders, our investigation spans 3 orders of magnitude in center-of-mass energy and 2 orders of magnitude in dilepton invariant mass. The results show an energy-scaling behavior of the intrinsic parameters, independent of the dilepton invariant mass at a given center-of-mass energy.We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the CMS detector, and the supporting computing infrastructure provided by the following funding agencies: SC (Armenia), BMBWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, FAPERGS, and FAPESP (Brazil); MES and BNSF (Bulgaria); CERN; CAS, MoST, and NSFC (China); MINCIENCIAS (Colombia); MSES and CSF (Croatia); RIF (Cyprus); SENESCYT (Ecuador); ERC PRG, RVTT3 and MoER TK202 (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); SRNSF (Georgia); BMBF, DFG, and HGF (Germany); GSRI (Greece); NKFIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); MSIP and NRF (Republic of Korea); MES (Latvia); LMTLT (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLP-FAI (Mexico); MOS (Montenegro); MBIE (New Zealand); PAEC (Pakistan); MES and NSC (Poland); FCT (Portugal); MESTD (Serbia); MCIN/AEI and PCTI (Spain); MOSTR (Sri Lanka); Swiss Funding Agencies (Switzerland); MST (Taipei); MHESI and NSTDA (Thailand); TUBITAK and TENMAK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA)