Neutrino Spin Transitions and the Violation of the Equivalence Principle

The violation of the equivalence principle (VEP) causing neutrino oscillations is of current interest. We study here the possibility of not only flavor oscillation but spin flavor oscillation of ultra high energy ($\sim$ 1 PeV) neutrinos emanating from AGN due to VEP and due to the presence of a large magnetic field ($\sim$ 1 Tesla) in AGN. In particular we look at the resonance spin flavor conversion driven by the AGN potential. Interesting bounds on the transition magnetic moment of neutrinos may therefore be obtained.Comment: Latex, 12 pages, no figures. To appear in Journal of Physics G: Nuclear and Particle Physics. Two references adde

Majorana Neutrinos and Gravitational Oscillation

We analyze the possibility of encountering resonant transitions of high energy Majorana neutrinos produced in Active Galactic Nuclei (AGN). We consider gravitational, electromagnetic and matter effects and show that the latter are ignorable. Resonant oscillations due to the gravitational interactions are shown to occur at energies in the PeV range for magnetic moments in the $10^{-17} \mu_B$ range. Coherent precession will dominate for larger magnetic moments. The alllowed regions for gravitational resonant transitions are obtained.Comment: 11 pages, 8 figures, Latex; requires revtex and epsf.tex submitted to Physical Review

A Critical Evaluation of River Management Models in Malaysia

Rivers are important natural resources of a country as they provide a wide range of ecosystem services. However, the problem is that rivers in Malaysia are badly degraded due to mismanagement, neglect, pollution and abuse. River management in Malaysia is largely based on the government-centric top-down model which is sectoral-based. This model is ineffective as it has no private sector, NGO and public engagement and support. This paper aims to examine various types of river management models to identify the ones that can be effective in Malaysia. The methodology used a mixture of literature review of existing river management models, secondary data on published journal papers, reports and books on river management. Results of selected river management conferences are also studied, examined and findings synthesized. Primary data is also compiled with selected in-depth qualitative interviews with key government officers, managers of private companies, NGO officers and village heads. Results show that the government (various levels) is traditionally the responsible party in managing rivers, but increasingly, the public, NGOs, businesses and other stakeholders are actively involved. Results also show that holistic river management with active engagement of all stakeholders is necessary. In Malaysia, rivers are found to be intricately intertwined with all aspects of development. It was concluded that the Public-Private Partnership (PPP) model is an effective river management model in Malaysia as it conserves the river and its environment, and brings together all parties for building their capacities in river management towards achieving many Sustainable Development Goals (SDGs)

QTL Mapping of Combining Ability and Heterosis of Agronomic Traits in Rice Backcross Recombinant Inbred Lines and Hybrid Crosses

BACKGROUND: Combining ability effects are very effective genetic parameters in deciding the next phase of breeding programs. Although some breeding strategies on the basis of evaluating combining ability have been utilized extensively in hybrid breeding, little is known about the genetic basis of combining ability. Combining ability is a complex trait that is controlled by polygenes. With the advent and development of molecular markers, it is feasible to evaluate the genetic bases of combining ability and heterosis of elite rice hybrids through QTL analysis. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we first developed a QTL-mapping method for dissecting combining ability and heterosis of agronomic traits. With three testcross populations and a BCRIL population in rice, biometric and QTL analyses were conducted for ten agronomic traits. The significance of general combining ability and special combining ability for most of the traits indicated the importance of both additive and non-additive effects on expression levels. A large number of additive effect QTLs associated with performance per se of BCRIL and general combining ability, and dominant effect QTLs associated with special combining ability and heterosis were identified for the ten traits. CONCLUSIONS/SIGNIFICANCE: The combining ability of agronomic traits could be analyzed by the QTL mapping method. The characteristics revealed by the QTLs for combining ability of agronomic traits were similar with those by multitudinous QTLs for agronomic traits with performance per se of BCRIL. Several QTLs (1-6 in this study) were identified for each trait for combining ability. It demonstrated that some of the QTLs were pleiotropic or linked tightly with each other. The identification of QTLs responsible for combining ability and heterosis in the present study provides valuable information for dissecting genetic basis of combining ability

Far-Infrared Therapy Induces the Nuclear Translocation of PLZF Which Inhibits VEGF-Induced Proliferation in Human Umbilical Vein Endothelial Cells

Many studies suggest that far-infrared (FIR) therapy can reduce the frequency of some vascular-related diseases. The non-thermal effect of FIR was recently found to play a role in the long-term protective effect on vascular function, but its molecular mechanism is still unknown. In the present study, we evaluated the biological effect of FIR on vascular endothelial growth factor (VEGF)-induced proliferation in human umbilical vein endothelial cells (HUVECs). We found that FIR ranging 3∼10 µm significantly inhibited VEGF-induced proliferation in HUVECs. According to intensity and time course analyses, the inhibitory effect of FIR peaked at an effective intensity of 0.13 mW/cm2 at 30 min. On the other hand, a thermal effect did not inhibit VEGF-induced proliferation in HUVECs. FIR exposure also inhibited the VEGF-induced phosphorylation of extracellular signal-regulated kinases in HUVECs. FIR exposure further induced the phosphorylation of endothelial nitric oxide (NO) synthase (eNOS) and NO generation in VEGF-treated HUVECs. Both VEGF-induced NO and reactive oxygen species generation was involved in the inhibitory effect of FIR. Nitrotyrosine formation significantly increased in HUVECs treated with VEGF and FIR together. Inhibition of phosphoinositide 3-kinase (PI3K) by wortmannin abolished the FIR-induced phosphorylation of eNOS and Akt in HUVECs. FIR exposure upregulated the expression of PI3K p85 at the transcriptional level. We further found that FIR exposure induced the nuclear translocation of promyelocytic leukemia zinc finger protein (PLZF) in HUVECs. This induction was independent of a thermal effect. The small interfering RNA transfection of PLZF blocked FIR-increased PI3K levels and the inhibitory effect of FIR. These data suggest that FIR induces the nuclear translocation of PLZF which inhibits VEGF-induced proliferation in HUVECs

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License