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Human error is so often cited as a cause of accidents. There is perception of a 'human error problem'. Solutions are thought to lie in changing the people or their role. The label 'human error', however, is prejudicial and hides more than it reveals about how a system malfunctions. This book takes you behind the label. It explains how human error results from social and psychological judgments by the system's stakeholders that focus only on one facet of a set of interacting contributors.

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 have shoddy reporting and include papers from wealthy nations, find David Moher, Larissa Shamseer, Kelly Cobey and colleagues.

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.

Cytotoxic T cells and natural killer cells use several strategies to kill infected or transformed cells. One such pathway entails the delivery of a family of serine proteases called granzymes to target cells through perforin-mediated pores to induce a form of programmed cell death called apoptosis. Zhou et al. show that granzyme A cleaves and activates gasdermin B (GSDMB), a central player in the highly inflammatory cell death process known as pyroptosis (see the Perspective by Nicolai and Raulet). GSDMB expression was highly expressed in some tissues and could be up-regulated by interferon-γ. Enforced expression of GSDMB in cancer cells enhanced tumor clearance in a mouse model, suggesting that this pathway may be a target for future cancer immunotherapies. Science , this issue p. eaaz7548 ; see also p. 943 Cytotoxic lymphocytes use granzyme A to cleave gasdermin B and induce pyroptosis in target cells. Cytotoxic lymphocyte–mediated immunity relies on granzymes. Granzymes are thought to kill target cells by inducing apoptosis, although the underlying mechanisms are not fully understood. Here, we report that natural killer cells and cytotoxic T lymphocytes kill gasdermin B (GSDMB)–positive cells through pyroptosis, a form of proinflammatory cell death executed by the gasdermin family of pore-forming proteins. Killing results from the cleavage of GSDMB by lymphocyte-derived granzyme A (GZMA), which unleashes its pore-forming activity. Interferon-γ (IFN-γ) up-regulates GSDMB expression and promotes pyroptosis. GSDMB is highly expressed in certain tissues, particularly digestive tract epithelia, including derived tumors. Introducing GZMA-cleavable GSDMB into mouse cancer cells promotes tumor clearance in mice. This study establishes gasdermin-mediated pyroptosis as a cytotoxic lymphocyte–killing mechanism, which may enhance antitumor immunity.

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.