Discovery of KT-474─a Potent, Selective, and Orally Bioavailable IRAK4 Degrader for the Treatment of Autoimmune Diseases.

Interleukin-1 receptor associated kinase 4 (IRAK4) is an essential mediator of the IL-1R and TLR signaling pathways, both of which have been implicated in multiple autoimmune conditions. Hence, blocking the activity of IRAK4 represents an attractive approach for the treatment of autoimmune diseases. The activity of this serine/threonine kinase is dependent on its kinase and scaffolding activities; thus, degradation represents a potentially superior approach to inhibition. Herein, we detail the exploration of structure-activity relationships that ultimately led to the identification of KT-474, a potent, selective, and orally bioavailable heterobifunctional IRAK4 degrader. This represents the first heterobifunctional degrader evaluated in a nononcology indication and dosed to healthy human volunteers. This molecule successfully completed phase I studies in healthy adult volunteers and patients with atopic dermatitis or hidradenitis suppurativa. Phase II clinical trials in both of these indications have been initiated.

Discovery of KT-413, a Targeted Protein Degrader of IRAK4 and IMiD Substrates Targeting MYD88 Mutant Diffuse Large B-Cell Lymphoma.

Developing therapies for the activated B-cell like (ABC) subtype of diffuse large B-cell lymphomas (DLBCL) remains an area of unmet medical need. A subset of ABC DLBCL tumors is driven by activating mutations in myeloid differentiation primary response protein 88 (MYD88), which lead to constitutive activation of interleukin-1 receptor associated kinase 4 (IRAK4) and cellular proliferation. IRAK4 signaling is driven by its catalytic and scaffolding functions, necessitating complete removal of this protein and its escape mechanisms for complete therapeutic suppression. Herein, we describe the identification and characterization of a dual-functioning molecule, KT-413 and show it efficiently degrades IRAK4 and the transcription factors Ikaros and Aiolos. KT-413 achieves concurrent degradation of these proteins by functioning as both a heterobifunctional degrader and a molecular glue. Based on the demonstrated activity and safety of KT-413 in preclinical studies, a phase 1 clinical trial in B-cell lymphomas, including MYD88 mutant ABC DLBCL, is currently underway.

Leverage zones in Responsible AI: towards a systems thinking conceptualization.

There is a growing debate amongst academics and practitioners on whether interventions made, thus far, towards Responsible AI have been enough to engage with the root causes of AI problems. Failure to effect meaningful changes in this system could see these initiatives not reach their potential and lead to the concept becoming another buzzword for companies to use in their marketing campaigns. Systems thinking is often touted as a methodology to manage and effect change; however, there is little practical advice available for decision-makers to include systems thinking insights to work towards Responsible AI. Using the notion of 'leverage zones' adapted from the systems thinking literature, we suggest a novel approach to plan for and experiment with potential initiatives and interventions. This paper presents a conceptual framework called the Five Ps to help practitioners construct and identify holistic interventions that may work towards Responsible AI, from lower-order interventions such as short-term fixes, tweaking algorithms and updating parameters, through to higher-order interventions such as redefining the system's foundational structures that govern those parameters, or challenging the underlying purpose upon which those structures are built and developed in the first place. Finally, we reflect on the framework as a scaffold for transdisciplinary question-asking to improve outcomes towards Responsible AI.

Multivariate random forest prediction of poverty and malnutrition prevalence.

Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies' programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods.

Human health and climate change: leverage points for adaptation in urban environments.

The design of adaptation strategies that promote urban health and well-being in the face of climate change requires an understanding of the feedback interactions that take place between the dynamical state of a city, the health of its people, and the state of the planet. Complexity, contingency and uncertainty combine to impede the growth of such systemic understandings. In this paper we suggest that the collaborative development of conceptual models can help a group to identify potential leverage points for effective adaptation. We describe a three-step procedure that leads from the development of a high-level system template, through the selection of a problem space that contains one or more of the group's adaptive challenges, to a specific conceptual model of a sub-system of importance to the group. This procedure is illustrated by a case study of urban dwellers' maladaptive dependence on private motor vehicles. We conclude that a system dynamics approach, revolving around the collaborative construction of a set of conceptual models, can help communities to improve their adaptive capacity, and so better meet the challenge of maintaining, and even improving, urban health in the face of climate change.

Evaluation of mobile learning: students' experiences in a new rural-based medical school.

BACKGROUND: Mobile learning (ML) is an emerging educational method with success dependent on many factors including the ML device, physical infrastructure and user characteristics. At Gippsland Medical School (GMS), students are given a laptop at the commencement of their four-year degree. We evaluated the educational impact of the ML program from students' perspectives. METHODS: Questionnaires and individual interviews explored students' experiences of ML. All students were invited to complete questionnaires. Convenience sampling was used for interviews. Quantitative data was entered to SPSS 17.0 and descriptive statistics computed. Free text comments from questionnaires and transcriptions of interviews were thematically analysed. RESULTS: Fifty students completed the questionnaire (response rate 88%). Six students participated in interviews. More than half the students owned a laptop prior to commencing studies, would recommend the laptop and took the laptop to GMS daily. Modal daily use of laptops was four hours. Most frequent use was for access to the internet and email while the most frequently used applications were Microsoft Word and PowerPoint. Students appreciated the laptops for several reasons. The reduced financial burden was valued. Students were largely satisfied with the laptop specifications. Design elements of teaching spaces limited functionality. Although students valued aspects of the virtual learning environment (VLE), they also made many suggestions for improvement. CONCLUSIONS: Students reported many educational benefits from school provision of laptops. In particular, the quick and easy access to electronic educational resources as and when they were needed. Improved design of physical facilities would enhance laptop use together with a more logical layout of the VLE, new computer-based resources and activities promoting interaction.

The genetic architecture of maize flowering time.

Flowering time is a complex trait that controls adaptation of plants to their local environment in the outcrossing species Zea mays (maize). We dissected variation for flowering time with a set of 5000 recombinant inbred lines (maize Nested Association Mapping population, NAM). Nearly a million plants were assayed in eight environments but showed no evidence for any single large-effect quantitative trait loci (QTLs). Instead, we identified evidence for numerous small-effect QTLs shared among families; however, allelic effects differ across founder lines. We identified no individual QTLs at which allelic effects are determined by geographic origin or large effects for epistasis or environmental interactions. Thus, a simple additive model accurately predicts flowering time for maize, in contrast to the genetic architecture observed in the selfing plant species rice and Arabidopsis.

Genetic properties of the maize nested association mapping population.

Maize genetic diversity has been used to understand the molecular basis of phenotypic variation and to improve agricultural efficiency and sustainability. We crossed 25 diverse inbred maize lines to the B73 reference line, capturing a total of 136,000 recombination events. Variation for recombination frequencies was observed among families, influenced by local (cis) genetic variation. We identified evidence for numerous minor single-locus effects but little two-locus linkage disequilibrium or segregation distortion, which indicated a limited role for genes with large effects and epistatic interactions on fitness. We observed excess residual heterozygosity in pericentromeric regions, which suggested that selection in inbred lines has been less efficient in these regions because of reduced recombination frequency. This implies that pericentromeric regions may contribute disproportionally to heterosis.

Consciousness and neural cognizers: a review of some recent approaches.

This paper synthesises three diverse approaches to the study of consciousness in a description of an existing program of work in Artificial Neuroconsciousness. The three approaches are drawn from automata theory ([Aleksander, 1995][Aleksander, 1996]), psychology ([Karmiloff-Smith, 1992]; [Clark Karmiloff-Smith, 1993]) and philosophy ([Searle, 1992]).Previous work on bottom-level sensory-motor tasks from the program is described as a background to the current work on generating higher-level, abstract concepts which are an essential part of mental life. The entire program of work postulates automata theory as an appropriate framework for the study of cognition. It is demonstrated how both the bottom-level sensory-motor tasks and abstract knowledge representations can be tackled by a single neural state machine architecture. The resulting state space representations are then reconciled with both the psychological and philosophical theories, suggesting the appropriateness of taking an automata theory approach to consciousness.