Digital transformation in economy and law

This article gives an expansive overview of the changes in the economy, and in processes performed by both companies and the society at large, that are likely to accompany digitalization in light of the current COVID-19 crisis. In giving this broad overview, the article highlights the consequences for the structure of the economy that the growth of groups of companies profiting from digitalization will have, and the social issues that are linked to such growth. Also, the article offers thoughts on how different areas of activity, such as of companies as well as of education and science, are likely to change.

PRISONISASI DAN PENYEBARAN IDEOLOGI RADIKAL DI LEMBAGA PEMASYARAKAT

Penitentiary becomes a conducive tool for the spread of radical ideology, member recruitment and the development of extremist groups. This article explores the influence of prison life on prisoners, especially the process of spreading radical ideologies. Knowing the basics of radicalization in prisons and conditions of prisoners' vulnerability to radical ideology. Based on data from interviews with former terrorist inmates in East Java and Jakarta. This article concludes that Lapas has an impact on the continued development of radical ideology based on several conditions, namely: a life characterized by a loss of freedom, being unable to fulfill sexual desires, a loss of security. Based on environmental conditions in the prison process the radicalization process takes a number of forms such as religious conversion, the growth of groups or gangs, and the emergence of various forms of resistance or violence.Abstrak. Lembaga Pemasyarakatan (Lapas) menjadi sarana yang kondusif bagi penyebaran ideologi radikal, rekrutmen anggota dan perkembangan kelompok ekstremis. Artikel ini mengeksplorasi pengaruh kehidupan Lapas terhadap narapidana, khususnya proses penyebaran ideologi radikal. Mengetahui dasar-dasar radikalisasi di Lapas dan kondisi kerentanan napidana terhadap ideologi radikal. Berdasarkan data wawancara dengan mantan narapidana teroris di Jawa Timur dan Jakarta. Artikel ini menyimpulkan bahwa Lapas memberikan dampak bagi tetap berkembangnya ideologi radikal berdasarkan beberapa kondisi, yaitu: kehidupan yang ditandai dengan hilangnya kebebasan, tidak bisa memenuhi hasrat seksual, hilangnya rasa aman. Berdasakan kondisi lingkungan lapas proses radikalisasi mengambil berberapa bentuk seperti konversi agama, pertumbuhan kelompok atau geng, dan munculnya berbagai bentuk resistensi atau kekerasan.

Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts

Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s.Author summaryBoth the growth of groups of species and the spread of infectious diseases through populations can be modeled as birth-death processes. Birth events correspond either to speciation or infection, and death events to extinction or becoming noninfectious. The rates of birth and death may vary over time, and by examining this variation researchers can pinpoint important events in the history of life on Earth or in the course of an outbreak. Time-calibrated phylogenies track the relationships between a set of species (or infections) and the times of all speciation (or infection) events, and can thus be used to infer birth and death rates. We develop two phylogenetic birth-death models with the goal of discerning signal of rate variation from noise due to the stochastic nature of birth-death models. Using a variety of simulated datasets, we show that one of these models can accurately infer slow and rapid rate shifts without sacrificing precision. Using real data, we demonstrate that our new methodology can be used for simultaneous inference of phylogeny and rates through time.

Systems biology–based drug repositioning identifies digoxin as a potential therapy for groups 3 and 4 medulloblastoma

Medulloblastoma (MB) is the most common malignant brain tumor of childhood. Although outcomes have improved in recent decades, new treatments are still needed to improve survival and reduce treatment-related complications. The MB subtypes groups 3 and 4 represent a particular challenge due to their intragroup heterogeneity, which limits the options for “rational” targeted therapies. Here, we report a systems biology approach to drug repositioning that integrates a nonparametric, bootstrapping-based simulated annealing algorithm and a 3D drug functional network to characterize dysregulated driver signaling networks, thereby identifying potential drug candidates. From more than 1300 drug candidates studied, we identified five members of the cardiac glycoside family as potentially inhibiting the growth of groups 3 and 4 MB and subsequently confirmed this in vitro. Systemic in vivo treatment of orthotopic patient-derived xenograft (PDX) models of groups 3 and 4 MB with digoxin, a member of the cardiac glycoside family approved for the treatment of heart failure, prolonged animal survival at plasma concentrations known to be tolerated in humans. These results demonstrate the power of a systematic drug repositioning method in identifying a potential treatment for MB. Our strategy could potentially be used to accelerate the repositioning of treatments for other human cancers that lack clearly defined rational targets.