Pulse-Shape discrimination with the Counting Test Facility

Pulse shape discrimination (PSD) is one of the most distinctive features of liquid scintillators. Since the introduction of the scintillation techniques in the field of particle detection, many studies have been carried out to characterize intrinsic properties of the most common liquid scintillator mixtures in this respect. Several application methods and algorithms able to achieve optimum discrimination performances have been developed. However, the vast majority of these studies have been performed on samples of small dimensions. The Counting Test Facility, prototype of the solar neutrino experiment Borexino, as a 4 ton spherical scintillation detector immersed in 1000 tons of shielding water, represents a unique opportunity to extend the small-sample PSD studies to a large-volume setup. Specifically, in this work we consider two different liquid scintillation mixtures employed in CTF, illustrating for both the PSD characterization results obtained either with the processing of the scintillation waveform through the optimum Gatti's method, or via a more conventional approach based on the charge content of the scintillation tail. The outcomes of this study, while interesting per se, are also of paramount importance in view of the expected Borexino detector performances, where PSD will be an essential tool in the framework of the background rejection strategy needed to achieve the required sensitivity to the solar neutrino signals.Comment: 39 pages, 17 figures, submitted to Nucl. Instr. Meth.

New limits on nucleon decays into invisible channels with the BOREXINO Counting Test Facility

The results of background measurements with the second version of the BOREXINO Counting Test Facility (CTF-II), installed in the Gran Sasso Underground Laboratory, were used to obtain limits on the instability of nucleons, bounded in nuclei, for decays into invisible channels ($inv$): disappearance, decays to neutrinos, etc. The approach consisted of a search for decays of unstable nuclides resulting from $N$ and $NN$ decays of parents $^{12}$C, $^{13}$C and $^{16}$O nuclei in the liquid scintillator and the water shield of the CTF. Due to the extremely low background and the large mass (4.2 ton) of the CTF detector, the most stringent (or competitive) up-to-date experimental bounds have been established: $\tau(n \to inv) > 1.8 \cdot 10^{25}$ y, $\tau(p \to inv) > 1.1 \cdot 10^{26}$ y, $\tau(nn \to inv) > 4.9 \cdot 10^{25}$ y and $\tau(pp \to inv) > 5.0 \cdot 10^{25}$ y, all at 90% C.L.Comment: 22 pages, 3 figures,submitted to Phys.Lett.

Measurements of extremely low radioactivity levels in BOREXINO

The techniques researched, developed and applied towards the measurement of radioisotope concentrations at ultra-low levels in the real-time solar neutrino experiment BOREXINO at Gran Sasso are presented and illustrated with specific results of widespread interest. We report the use of low-level germanium gamma spectrometry, low-level miniaturized gas proportional counters and low background scintillation detectors developed in solar neutrino research. Each now sets records in its field. We additionally describe our techniques of radiochemical ultra-pure, few atom manipulations and extractions. Forefront measurements also result from the powerful combination of neutron activation and low-level counting. Finally, with our techniques and commercially available mass spectrometry and atomic absorption spectroscopy, new low-level detection limits for isotopes of interest are obtained.Comment: 27 pages, 5 figures. Submitted to Astroparticle Physics (17 Sep 2001). Spokesperson of the Borexino Collaboration: G. Bellini. Corresponding author: W. Hampe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Genetic differentiation of Artemia franciscana (Kellogg, 1906) in Kenyan coastal saltworks

The nature of genetic divergence between the Artemia population native to San Francisco Bay, (SFB) USA and those from the introductions of SFB material in the Kenyan coast two decades ago were investigated using the mitochondrial DNA (mtDNA) and heat shock protein 70 (Hsp70) gene molecular markers. The DNA was extracted from 80 single Artemia cysts using the Chelex protocol. The 1,500 bp fragment of the 12S - 16S region of the mtDNA and a 1,935 bp fragment of the Hsp70 gene were amplified through Polymerase Chain Reaction (PCR) followed by Restriction Fragment Length Polymorphism (RFLP) digestion using appropriate endonucleases. The mtDNA analysis indicated higher haplotype diversity (0.76 ± 0.07) in Artemia from Fundisha saltworks while the rest of the samples were monomorphic. A private haplotype (AAABBA) in Fundisha samples confirmed a molecular evidence of a systematic genetic differentiation albeit in an insignificant manner (P > 0.05). There was molecular evidence of coexistence of SFB and GSL Artemia strains in Fundisha saltworks. The monomorphic DNA fingerprint in Kensalt Artemia cysts was probably caused by non-sequential Artemia culture system and limited mtDNA fragment size analysed. The Hsp70 gene RFLP fingerprint did not show any unique gene signatures in the Kenyan Artemia samples suggesting that other factors other than Hsp70 were involved in their superior thermotolerance. Further genetical studies based on the larger mtDNA fragment using robust genetic markers are recommended. Ecological studies of the heat shock protein family and the stress response would be more relevant than the qualitative RFLP technique

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost