Kwapa: Gente del río. Estrategias transmedia de impacto social

El PAP Alter Código, período Primavera 2023, trabajó con los dos proyectos que se han venido trabajando en semestres anteriores: el videojuego A Orillas del río y el documental Déjennos pescar. Ambos proyectos parten de la metodología interdisciplinaria y colaborativa con miembros de la comunidad Cucapá para crear representaciones audiovisuales no estigmatizantes, que detonen el sentido de comunidad y refuercen su acervo cultural. El videojuego A Orillas del río es del género point and click, de vista isométrica, el cual está inspirado en el cuento oral tradicional El zorro y el coyote, que busca ser una herramienta lúdica de aprendizaje para reforzar el aprendizaje de la lengua Cucapá en los niños. Los resultados obtenidos fueron el demo del nivel uno (escenas uno y dos); colorimetría, arcos de personajes principales; programación de minijuegos. Dentro del documental ‘Déjennos pescar’ los resultados fueron un montaje, una clasificación del material grabado con transcripciones de audio, mientras que en la parte de estrategia de impacto se creó un manual de uso de redes sociales con colorimetría, tipografía, estilo de voz, tipo de contenido según la red social, para los futuros integrantes del equipo.ITESO, A.C

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

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

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Transverse momentum spectra of charged particles in proton–proton collisions at √s=900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton–proton collisions at s=900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (|η|<0.8) over the transverse momentum range 0.15<pT<10 GeV/c. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for |η|<0.8 is 〈pT〉INEL=0.483±0.001 (stat.)±0.007 (syst.) GeV/c and 〈pT〉NSD=0.489±0.001 (stat.)±0.007 (syst.) GeV/c, respectively. The data exhibit a slightly larger 〈pT〉 than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET

Suppression of charged particle production at large transverse momentum in central Pb–Pb collisions at √sNN=2.76 TeV

Inclusive transverse momentum spectra of primary charged particles in Pb–Pb collisions at √sNN=2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0–5% and 70–80% of the hadronic Pb–Pb cross section. The measured charged particle spectra in |η|<0.8 and 0.3<pT<20 GeV/c are compared to the expectation in pp collisions at the same sNN, scaled by the number of underlying nucleon–nucleon collisions. The comparison is expressed in terms of the nuclear modification factor RAA. The result indicates only weak medium effects (RAA≈0.7) in peripheral collisions. In central collisions, RAA reaches a minimum of about 0.14 at pT=6–7 GeV/c and increases significantly at larger pT. The measured suppression of high-pT particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb–Pb collisions at the LHC

Two-pion Bose–Einstein correlations in central Pb–Pb collisions at √sNN=2.76 TeV

The first measurement of two-pion Bose–Einstein correlations in central Pb–Pb collisions at √sNN=2.76 TeV at the Large Hadron Collider is presented. We observe a growing trend with energy now not only for the longitudinal and the outward but also for the sideward pion source radius. The pion homogeneity volume and the decoupling time are significantly larger than those measured at RHIC

Rapidity and transverse momentum dependence of inclusive J/ψ production in pp collisions at √s=7 TeV

The ALICE experiment at the LHC has studied inclusive J/ψ production at central and forward rapidities in pp collisions at √s=7 TeV. In this Letter, we report on the first results obtained detecting the J/ψ through the dilepton decay into e+e− and μ+μ− pairs in the rapidity ranges |y|<0.9 and 2.5<y<4, respectively, and with acceptance down to zero pT. In the dielectron channel the analysis was carried out on a data sample corresponding to an integrated luminosity Lint=5.6 nb−1 and the number of signal events is NJ/ψ=352±32(stat.)±28(syst.); the corresponding figures in the dimuon channel are Lint=15.6 nb−1 and NJ/ψ=1924±77(stat.)±144(syst.). The measured production cross sections are σJ/ψ(|y|<0.9)=10.7±1.0(stat.)±1.6(syst.)−2.3+1.6(syst.pol.)μb and σJ/ψ(2.5<y<4)=6.31±0.25(stat.)±0.76(syst.)−1.96+0.95(syst.pol.)μb. The differential cross sections, in transverse momentum and rapidity, of the J/ψ were also measured

Multi-strange baryon production in pp collisions at √s=7 TeV with ALICE

A measurement of the multi-strange Ξ− and Ω− baryons and their antiparticles by the ALICE experiment at the CERN Large Hadron Collider (LHC) is presented for inelastic proton–proton collisions at a centre-of-mass energy of 7 TeV. The transverse momentum (pT) distributions were studied at mid-rapidity (|y|6.0 GeV/c. We also illustrate the difference between the experimental data and model by comparing the corresponding ratios of (Ω−+Ω¯+)/(Ξ−+Ξ¯+) as a function of transverse mass