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This paper proposes a two-stage phase I-II clinical trial design to optimize doseschedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decisionmaking is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design’s performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

Predatory journals are easy to please. They seem to accept papers with little regard for quality, at a fraction of the cost charged by mainstream open-access journals. These supposedly scholarly publishing entities are murky operations, making money by collecting fees while failing to deliver on their claims of being open access and failing to provide services such as peer review and archiving.

This study examines the impact of artificial intelligence (AI) on loss in decision-making, laziness, and privacy concerns among university students in Pakistan and China. Like other sectors, education also adopts AI technologies to address modern-day challenges. AI investment will grow to USD 253.82 million from 2021 to 2025. However, worryingly, researchers and institutions across the globe are praising the positive role of AI but ignoring its concerns. This study is based on qualitative methodology using PLS-Smart for the data analysis. Primary data was collected from 285 students from different universities in Pakistan and China. The purposive Sampling technique was used to draw the sample from the population. The data analysis findings show that AI significantly impacts the loss of human decision-making and makes humans lazy. It also impacts security and privacy. The findings show that 68.9% of laziness in humans, 68.6% in personal privacy and security issues, and 27.7% in the loss of decision-making are due to the impact of artificial intelligence in Pakistani and Chinese society. From this, it was observed that human laziness is the most affected area due to AI. However, this study argues that significant preventive measures are necessary before implementing AI technology in education. Accepting AI without addressing the major human concerns would be like summoning the devils. Concentrating on justified designing and deploying and using AI for education is recommended to address the issue.

Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the “curse of dimensionality.” This paper introduces a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black–Scholes equation, the Hamilton–Jacobi–Bellman equation, and the Allen–Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and cost. This opens up possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their interrelationships.

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11-29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

In arterial myocytes, the canonical function of voltage-gated Ca V 1.2 and K V 2.1 channels is to induce myocyte contraction and relaxation through their responses to membrane depolarization, respectively. Paradoxically, K V 2.1 also plays a sex-specific role by promoting the clustering and activity of Ca V 1.2 channels. However, the impact of K V 2.1 protein organization on Ca V 1.2 function remains poorly understood. We discovered that K V 2.1 forms micro-clusters, which can transform into large macro-clusters when a critical clustering site (S590) in the channel is phosphorylated in arterial myocytes. Notably, female myocytes exhibit greater phosphorylation of S590, and macro-cluster formation compared to males. Contrary to current models, the activity of K V 2.1 channels seems unrelated to density or macro-clustering in arterial myocytes. Disrupting the K V 2.1 clustering site (K V 2.1 S590A ) eliminated K V 2.1 macro-clustering and sex-specific differences in Ca V 1.2 cluster size and activity. We propose that the degree of K V 2.1 clustering tunes Ca V 1.2 channel function in a sex-specific manner in arterial myocytes. Advanced imaging and electrophysiology show that phosphorylation boosts the size of K V 2.1 clusters, which in turn modulates dihydropyridine-sensitive Ca V 1.2 channel organization and function in arterial smooth muscle, with variations based on sex.

The microbiota modulates gut immune homeostasis. Bacteria influence the development and function of host immune cells, including T helper cells expressing interleukin-17A (TH17 cells). We previously reported that the bile acid (BA) metabolite 3-oxolithocholic acid (3-oxoLCA) inhibits TH17 cell differentiation1. While it was suggested that gut-residing bacteria produce 3-oxoLCA, the identity of such bacteria was unknown, and it was unclear whether 3-oxoLCA and other immunomodulatory BAs are associated with inflammatory pathologies in humans. Here, we identify human gut bacteria and corresponding enzymes that convert the secondary BA lithocholic acid into 3-oxoLCA as well as the abundant gut metabolite isolithocholic acid (isoLCA). Like 3-oxoLCA, isoLCA suppressed TH17 differentiation by inhibiting RORγt (retinoic acid receptor-related orphan nuclear receptor γt), a key TH17 cell-promoting transcription factor. Levels of both 3-oxoLCA and isoLCA and the 3α-hydroxysteroid dehydrogenase (3α-HSDH) genes required for their biosynthesis were significantly reduced in inflammatory bowel disease (IBD) patients. Moreover, levels of these BAs were inversely correlated with expression of TH17 cell-associated genes. Overall, our data suggest that bacterially produced BAs inhibit TH17 cell function, an activity that may be relevant to the pathophysiology of inflammatory disorders such as IBD.

High-entropy alloys (HEAs) are an intriguing new class of metallic materials due to their unique mechanical behavior. Achieving a detailed understanding of structure–property relationships in these materials has been challenged by the compositional disorder that underlies their unique mechanical behavior. Accordingly, in this work, we employ first-principles calculations to investigate the nature of local chemical order and establish its relationship to the intrinsic and extrinsic stacking fault energy (SFE) in CrCoNi medium-entropy solid-solution alloys, whose combination of strength, ductility, and toughness properties approaches the best on record. We find that the average intrinsic and extrinsic SFE are both highly tunable, with values ranging from −43 to 30 mJ·m−2 and from −28 to 66 mJ·m−2, respectively, as the degree of local chemical order increases. The state of local ordering also strongly correlates with the energy difference between the face-centered cubic (fcc) and hexagonal close-packed (hcp) phases, which affects the occurrence of transformation-induced plasticity. This theoretical study demonstrates that chemical short-range order is thermodynamically favored in HEAs and can be tuned to affect the mechanical behavior of these alloys. It thus addresses the pressing need to establish robust processing–structure–property relationships to guide the science-based design of new HEAs with targeted mechanical behavior.

Addressing fake news requires a multidisciplinary effort The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.

Workplace mistreatment for women increases depression, anxiety, burnout, low self-esteem, low life satisfaction, and psychological distress, and decreases work productivity. Additionally, victims and bystanders of workplace mistreatment are likely to leave an organization. To fulfill the objective of documenting the current best practices that could assist counselors working with and advocating for US women experiencing workplace mistreatment, a systematic literature review (SLR) of materials published in the past 15 years was conducted. The 21 articles found resulted in two major themes. The first theme, Addressing Female Mistreatment in the Workplace , had three sub-themes. Four materials discussed Workplace Interventions , eight discussed Workplace Training , and three discussed the Reporting of Workplace Mistreatment . The second theme, Counseling Women Experiencing Workplace Mistreatment , was supported by 11 articles. When working with employers, counselors can encourage year-round improvements in workplace recruitment, orientation, and inclusion of culturally diverse employees; offer bystander training; and create a comprehensive program to report and resolve workplace mistreatment concerns. Counselors working directly with women experiencing workplace mistreatment will want to help the client focus on productive cognitive processes, obtain social support, directly confront the workplace mistreatment, and negotiate the unfortunate realities of workplace mistreatment.