When an attitude changes from A1 to A2, what happens to A1? Most theories assume, at least implicitly, that the new attitude replaces the former one. The authors argue that a new attitude can override, but not replace, the old one, resulting in dual attitudes. Dual attitudes are defined as different evaluations of the same attitude object: an automatic, implicit attitude and an explicit attitude. The attitude that people endorse depends on whether they have the cognitive capacity to retrieve the explicit attitude and whether this overrides their implicit attitude. A number of literatures consistent with these hypotheses are reviewed, and the implications of the dual-attitude model for attitude theory and measurement are discussed. For example, by including only explicit measures, previous studies may have exaggerated the ease with which people change their attitudes. Even if an explicit attitude changes, an implicit attitude can remain the same.

Because of personal motives and the architecture of the mind, it may be difficult for people to know themselves. People often attempt to block out unwanted thoughts and feelings through conscious suppression and perhaps through unconscious repression, though whether such attempts are successful is controversial. A more common source of self-knowledge failure is the inaccessibility of much of the mind to consciousness, including mental processes involved in perception, motor learning, personality, attitudes, and self-esteem. Introspection cannot provide a direct pipeline to these mental processes, though some types of introspection may help people construct beneficial personal narratives. Other ways of increasing self-knowledge include looking at ourselves through the eyes of others and observing our own behavior. These approaches can potentially promote self-knowledge, although major obstacles exist. It is not always advantageous to hold self-perceptions that correspond perfectly with reality, but increasing awareness of nonconscious motives and personality is generally beneficial.

Don't leave me alone with my thoughts: Nowadays, we enjoy any number of inexpensive and readily accessible stimuli, be they books, videos, or social media. We need never be alone, with no one to talk to and nothing to do. Wilson et al. explored the state of being alone with one's thoughts and found that it appears to be an unpleasant experience. In fact, many of the people studied, particularly the men, chose to give themselves a mild electric shock rather than be deprived of external sensory stimuli. Science , this issue p. 75

Recent studies reveal that airway epithelial cells are critical pulmonary circadian pacemaker cells, mediating rhythmic inflammatory responses. Using mouse models, we now identify the rhythmic circadian repressor REV-ERBα as essential to the mechanism coupling the pulmonary clock to innate immunity, involving both myeloid and bronchial epithelial cells in temporal gating and determining amplitude of response to inhaled endotoxin. Dual mutation of REV-ERBα and its paralog REV-ERBβ in bronchial epithelia further augmented inflammatory responses and chemokine activation, but also initiated a basal inflammatory state, revealing a critical homeostatic role for REV-ERB proteins in the suppression of the endogenous proinflammatory mechanism in unchallenged cells. However, REV-ERBα plays the dominant role, as deletion of REV-ERBβ alone had no impact on inflammatory responses. In turn, inflammatory challenges cause striking changes in stability and degradation of REV-ERBα protein, driven by SUMOylation and ubiquitination. We developed a novel selective oxazole-based inverse agonist of REV-ERB, which protects REV-ERBα protein from degradation, and used this to reveal how proinflammatory cytokines trigger rapid degradation of REV-ERBα in the elaboration of an inflammatory response. Thus, dynamic changes in stability of REV-ERBα protein couple the core clock to innate immunity.

Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches.

Concerted multidisciplinary efforts have led to the development of Cyclin-Dependent Kinase inhibitors (CDKi’s) as small molecule drugs and chemical probes of intracellular CDK function. However, conflicting data has been reported on the inhibitory potency of CDKi’s and a systematic characterization of affinity and selectivity against intracellular CDKs is lacking. We have developed a panel of cell-permeable energy transfer probes to quantify target occupancy for all 21 human CDKs in live cells, and present a comprehensive evaluation of intracellular isozyme potency and selectivity for a collection of 46 clinically-advanced CDKi’s and tool molecules. We observed unexpected intracellular activity profiles for a number of CDKi’s, offering avenues for repurposing of highly potent molecules as probes for previously unreported targets. Overall, we provide a broadly applicable method for evaluating the selectivity of CDK inhibitors in living cells, and present a refined set of tool molecules to study CDK function.