Grand Unification as a Bridge Between String Theory and Phenomenology

In the first part of the talk, I explain what empirical evidence points to the need for having an effective grand unification-like symmetry possessing the symmetry SU(4)-color in 4D. If one assumes the premises of a future predictive theory including gravity--be it string/M theory or a reincarnation--this evidence then suggests that such a theory should lead to an effective grand unification-like symmetry as above in 4D, near the string-GUT-scale, rather than the standard model symmetry. Advantages of an effective supersymmetric G(224) = SU(2)$_L \times$ SU(2)$_R \times$ SU(4)$^c$ or SO(10) symmetry in 4D in explaining (i) observed neutrino oscillations, (ii) baryogenesis via leptogenesis, and (iii) certain fermion mass-relations are noted. And certain distinguishing tests of a SUSY G(224) or SO(10)-framework involving CP and flavor violations (as in $\mu \to e\gamma$, $\tau \to\mu\gamma$, edm's of the neutron and the electron) as well as proton decay are briefly mentioned. Recalling some of the successes we have had in our understanding of nature so far, and the current difficulties of string/M theory as regards the large multiplicity of string vacua, some comments are made on the traditional goal of understanding {\em vis a vis} the recently evolved view of landscape and anthropism.Comment: A chart showing some insights gained in the world of the very small and that of the very large is included. A few relevant references are added. Some clarification is made in the last section as regards the question of understanding versus landscape and anthropis

Impact of basic psychological support on stigma and mental well-being of people with disabilities due to leprosy and lymphatic filariasis:a proof-of-concept study

Background: People with leprosy and lymphatic filariasis (LF)-related disabilities experience higher levels of poor mental well-being compared with the general community. Mental health services are often not available. This study was conducted to provide proof of concept that basic psychological support for people affected by neglected tropical diseases (BPS-N) can be given by peer supporters to reduce stigma, improve mental well-being and participation among clients. Methods: The BPS-N approach was tested in a quasi-experimental design using mixed methods. To provide psychological support using the BPS-N, peer supporters were selected and trained. They supported people with leprosy- and LF-related disabilities. Preintervention and postintervention, stigma, mental well-being, depression and participation were measured through standard scales within 4 wk of the intervention; differences were tested using standard tests of significance. Results: After 3 mo of intervention, the mean level of stigma had decreased (30.3 to 24, p&lt;0.001); high mental well-being increased (0% to 13.3%, p&lt;0.001); and moderate to severe depression decreased (88% to 47%, p&lt;0.001). No significant change occurred in participation restrictions (87% to 92%, p=0.497). Conclusions: Psychological peer support using the BPS-N guideline appears effective in reducing stigma and improving mental well-being and can be operationalised. However, this should be confirmed through a randomised controlled trial. </p

In-orbit Performance of UVIT on ASTROSAT

We present the in-orbit performance and the first results from the ultra-violet Imaging telescope (UVIT) on ASTROSAT. UVIT consists of two identical 38cm coaligned telescopes, one for the FUV channel (130-180nm) and the other for the NUV (200-300nm) and VIS (320-550nm) channels, with a field of view of 28 $arcmin$. The FUV and the NUV detectors are operated in the high gain photon counting mode whereas the VIS detector is operated in the low gain integration mode. The FUV and NUV channels have filters and gratings, whereas the VIS channel has filters. The ASTROSAT was launched on 28th September 2015. The performance verification of UVIT was carried out after the opening of the UVIT doors on 30th November 2015, till the end of March 2016 within the allotted time of 50 days for calibration. All the on-board systems were found to be working satisfactorily. During the PV phase, the UVIT observed several calibration sources to characterise the instrument and a few objects to demonstrate the capability of the UVIT. The resolution of the UVIT was found to be about 1.4 - 1.7 $arcsec$ in the FUV and NUV. The sensitivity in various filters were calibrated using standard stars (white dwarfs), to estimate the zero-point magnitudes as well as the flux conversion factor. The gratings were also calibrated to estimate their resolution as well as effective area. The sensitivity of the filters were found to be reduced up to 15\% with respect to the ground calibrations. The sensitivity variation is monitored on a monthly basis. UVIT is all set to roll out science results with its imaging capability with good resolution and large field of view, capability to sample the UV spectral region using different filters and capability to perform variability studies in the UV.Comment: 10 pages, To appear in SPIE conference proceedings, SPIE conference paper, 201

Search for a W' boson decaying to a bottom quark and a top quark in pp collisions at sqrt(s) = 7 TeV

Results are presented from a search for a W' boson using a dataset corresponding to 5.0 inverse femtobarns of integrated luminosity collected during 2011 by the CMS experiment at the LHC in pp collisions at sqrt(s)=7 TeV. The W' boson is modeled as a heavy W boson, but different scenarios for the couplings to fermions are considered, involving both left-handed and right-handed chiral projections of the fermions, as well as an arbitrary mixture of the two. The search is performed in the decay channel W' to t b, leading to a final state signature with a single lepton (e, mu), missing transverse energy, and jets, at least one of which is tagged as a b-jet. A W' boson that couples to fermions with the same coupling constant as the W, but to the right-handed rather than left-handed chiral projections, is excluded for masses below 1.85 TeV at the 95% confidence level. For the first time using LHC data, constraints on the W' gauge coupling for a set of left- and right-handed coupling combinations have been placed. These results represent a significant improvement over previously published limits.Comment: Submitted to Physics Letters B. Replaced with version publishe

Search for excited leptons in pp collisions at √s=7 TeV

This is the pre-print version of the final published paper that is available from the link belowResults are presented of a search for compositeness in electrons and muons using a data sample of pp collisions at a center-of-mass energy √s=7 TeV collected with the CMS detector at the LHC and corresponding to an integrated luminosity of 5.0 fb−15.0 fb−1. Excited leptons (ℓ⁎) are assumed to be produced via contact interactions in conjunction with a standard model lepton and to decay via ℓ⁎→ℓγ, yielding a final state with two energetic leptons and a photon. The number of events observed in data is consistent with that expected from the standard model. The 95% confidence upper limits for the cross section for the production and decay of excited electrons (muons), with masses ranging from 0.6 to 2 TeV, are 1.48 to 1.24 fb (1.31 to 1.11 fb). Excited leptons with masses below 1.9 TeV are excluded for the case where the contact interaction scale equals the excited lepton mass. The limits on the cross sections are the most stringent ones published to date

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing