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Advanced microelectronics in the future may require semiconducting channel materials beyond silicon. Two-dimensional (2D) semiconductors, with their atomically thin thickness, hold great promise for future electronic devices. One challenge to achieving high-performance 2D semiconductor field effect transistors (FET) is the high contact resistance at the metal-semiconductor interface. In this study, we develop a charge-transfer doping strategy with WSe2/α-RuCl3 heterostructures to achieve low-resistance ohmic contact for p-type monolayer WSe2 transistors. We show that hole doping as high as 3 × 1013 cm-2 can be achieved in the WSe2/α-RuCl3 heterostructure due to its type-III band alignment, resulting in an ohmic contact with resistance of 4 kΩ μm. Based on that, we demonstrate p-type WSe2 transistors with an on-current of 35 μA·μm-1 and an ION/IOFF ratio exceeding 109 at room temperature.

Electron superlattices allow the engineering of correlated and topological quantum phenomena. The recent emergence of moiré superlattices in two-dimensional heterostructures has led to exciting discoveries related to quantum phenomena. However, the requirement for the moiré pattern poses stringent limitations, and its potential cannot be switched on and off. Here, we demonstrate remote engineering and on/off switching of correlated states in bilayer graphene. Employing a remote Coulomb superlattice realized by localized electrons in twisted bilayer WS2, we impose a Coulomb superlattice in the bilayer graphene with period and strength determined by the twisted bilayer WS2. When the remote superlattice is turned off, the two-dimensional electron gas in the bilayer graphene is described by a Fermi liquid. When it is turned on, correlated insulating states at both integer and fractional filling factors emerge. This approach enables in situ control of correlated quantum phenomena in two-dimensional materials hosting a two-dimensional electron gas.

There has been considerable interest exploring how the utility of an outcome impacts the probability with which it is mentally simulated. Earlier studies using varying methodologies have yielded divergent conclusions with different directions of the influence. To directly examine such mental process, we employed a random generation paradigm in which all the outcomes were either equally (i.e., followed a uniform distribution) or unequally (i.e., a binomial distribution) probable. While our results revealed individual differences in how the utility influenced responses, the overall findings suggested that it is the outcomes' probabilities, not their utilities, that guide this process. Notably, an initial utility-independent bias emerged, with individuals displaying a tendency to start with smaller values when all outcomes are equally likely. Our findings offer insights into the benefits of studying the mental sampling processes and provide empirical support for particular sampling models in this domain.

The mechanisms of learning stimulus-stimulus relationships are a longstanding research subject in psychology and neuroscience. Although traditional computational models provide valuable insights into learning processes, they often focus on the average behavior of a population. Individual learning trajectories, however, exhibit a diverse range of behaviors not captured by these models. In this paper, we compare sampling-based process-level models (i.e., particle filters) to representative associative and causal models (i.e., augmented Rescorla-Wagner and PowerPC) in their ability to capture individual learning behavior. We use likelihood-free inference incorporating machine-learned summary statistics for model estimation. We conduct a simulation study to demonstrate high model identifiability and test the models on an existing dataset and a newly conducted experiment which replicates and extends previous studies. We find that most participants are best explained by a particle filtering account, but more targeted experimental designs are required to estimate the best-fitting sub-type of these particle filter models.

The mechanisms underpinning probability matching remain a disputed topic. Among common explanations of the effect is that people employ a win-stay, lose-shift (WSLS) strategy. We suggest an alternative framing of probability matching as the result of a Bayesian sampling process involving simulating a mental sequence of possible outcomes. In three within-subject tasks, we presented people with information about a six-sided die with four sides of one colour and two of another. Two of them involved predicting the next outcome in a series of die rolls, with and without feedback. The third explicitly asked participants to mentally generate sequences of rolls from the die. The patterns of autocorrelations in responses, the absence of an effect of feedback on the next response, and the elevated proportion of maximising responses on the first trial in all conditions are all consistent with a Bayesian sampling model but contradict the WSLS account of probability matching.

Recent decision making theories have explained behaviour using mental sampling mechanisms where people imagine possible outcomes to guide their choices. Simultaneously, work in other domains has found evidence of particular mental sampling patterns, such as autocorrelations between samples and moderation by prior assumptions, which current decision making theories do not generally consider. Here, we seek to unify this work, developing a new sampling model of preferential choice incorporating these findings in other domains. Our model, based on the Autocorrelated Bayesian Sampler, predicts choice, reaction time, confidence and valuation from a common underlying process. We find a strong correspondence between our model's predictions and empirical choice data, though performance remains below leading explanations for such tasks. Our model does however cover a broader set of response types than existing theories, suggesting the advantages of considering of a wider range of behaviours than are commonly examined in current decision making studies.

People's mental representations of complex stimuli, such as images of facial affect, are difficult to elicit. To address this challenge, methods such as Markov Chain Monte Carlo with People (MCMCP), integrate human agents into computer-based sampling algorithms. However, such methods suffer from slow convergence, making them impractical for recovering the representations of individuals. Here, we extended MCMCP by introducing an adapted Variational Auto-Encoder (VAE) with domain knowledge as an auxiliary agent, guiding the sampling process away from less useful experimental trials. To test this approach, we ran a new experiment comparing such a VAE-guided MCMCP against baseline MCMCP in terms of convergence speed and quality of recovering human representations of facial affect. Preliminary results demonstrated that most guided chains converged on an individual's facial affect representation within a single experimental session, faster than the baseline methods, and results showed the extent of individual differences in facial affect representations. Thus, VAE-guided MCMCP provides a promising framework for interfacing machine intelligence with psychological experiments to enhance our understanding of human cognition.

Individuals make biased and variable probability judgements. Recent models such as the Bayesian Sampler (Zhu, et al., 2020), Probability Theory Plus Noise (Costello & Watts, 2014), and the Quantum Sequential Sampler (Huang et al., 2023) capture a wide range of effects by assuming people are biased towards indifference (i.e., 0.5). However, in some experiments participants instead showed asymmetric bias, defined as a pull toward non-0.5 values. We investigated asymmetric bias in 5 experiments, where participants judged the probabilities of dice rolls. While participants' judgements were independent of whether they were in a high or low probability environment or the number of alternative options displayed, participants showed a bias toward low (

People systematically deviate from the rational Bayesian updating of beliefs, as notably evidenced by conservatism and base-rate neglect. The primary cognitive models that explain these biases include simple heuristics (Woike et al., 2023, https://doi.org/10.1016/j.cogpsych.2023.101564) and stochastic sampling approximations of the Bayesian solution, like the Bayesian Sampler (Zhu et al., 2020, https://doi.org/10.1037/rev0000190). However, neither type of explanation appears entirely complete, as the data fall between the two; only about half of participants' responses align with heuristics. Could these results be explained by a new class of models that blend heuristics with Bayesian models? We test both simple mixtures of heuristics and the Bayesian Sampler, as well as a hybrid model in which heuristics are used to set a prior that improves estimates based on stochastic samples. Our analysis indicates that neither heuristics nor the Bayesian Sampler alone are sufficient to explain the data.

When making financial forecasts, individuals often overreact to recent information, as consistently observed in both laboratory studies with naïve participants and professional consensus real-world forecasting. Current models attribute this overreaction to either an overestimation of recent information or memory constraints favoring more accessible information. An alternative explanation suggests individuals accurately integrate all available information into their mental posterior probability distribution for forecasting, but are unable to directly access this distribution, leading to dependence on approximation methods such as sampling. Local sampling algorithms have received recent support in other forecasting contexts and may introduce overreaction as a consequence of a starting point bias. By reanalyzing existing data from a price prediction task with a random walk price series, we observe increasing variability in predicted values and forecast errors as the horizon expands. Employing this heightened variability and overreaction, we differentiate between competing explanations for the observed forecasting behavior.

Random generation studies have shown that people struggle to be unpredictable – they slowly and effortfully produce autocorrelated sequences instead. However, true random processes (such as radioactive decay) are also not instantaneous. In this project we explore how long it takes people in a random generation task to be random. We do so in two experiments asking people to draw samples from naturalistic domains (lifespans and heights), manipulating either the rate of generation or the requirement to be random (within participants). Irrespective of pace or instructions, we find that people can produce a random sample every four to five seconds. Additionally, the time a person needs to produce random samples is consistent across conditions, but varies widely between people. Following previous literature, we model random generation performance as an autocorrelated sampling algorithm, giving a process level account of how people do these tasks and why they need time to be random.

Societal expectations have been found to determine which social roles (e.g., jobs) people should occupy. Previously, however, these beliefs have been mainly explored using implicit measures such as sequential priming tasks where responding to expected (vs. conflicting) information is facilitated. We applied a random generation paradigm where participants said aloud the first names of hypothetical people working in various professions. This revealed that more female (male) first names were uttered for the female-typical (male-typical) occupations reflecting the societal gender stereotypes and the environmental statistics. Furthermore, the proportion of female and male names generated for each profession predicted participants' performance in a sequential priming task (prime = professions from random generation task, target = female vs. male face) better than the environmental statistics or participants' explicit gender ratio estimates of these jobs. Collectively, these findings offer a new method for exploring the internal representations elicited by cultural expectations.

BackgroundEsophageal squamous cell carcinoma (ESCC) comprises 90% of all esophageal cancer cases globally and is the most common histology in low-resource settings. Eastern Africa has a disproportionately high incidence of ESCC.MethodsWe describe the genomic profiles of 61 ESCC cases from Tanzania and compare them to profiles from an existing cohort of ESCC cases from Malawi. We also provide a comparison to ESCC tumors in The Cancer Genome Atlas (TCGA).ResultsWe observed substantial transcriptional overlap with other squamous histologies via comparison with TCGA PanCan dataset. DNA analysis revealed known mutational patterns, both genome-wide as well as in genes known to be commonly mutated in ESCC. TP53 mutations were the most common somatic mutation in tumors from both Tanzania and Malawi but were detected at lower frequencies than previously reported in ESCC cases from other settings. In a combined analysis, two unique transcriptional clusters were identified: a proliferative/epithelial cluster and an invasive/migrative/mesenchymal cluster. Mutational signature analysis of the Tanzanian cohort revealed common signatures associated with aging and cytidine deaminase activity (APOBEC) and an absence of signature 29, which was previously reported in the Malawi cohort.ConclusionsThis study defines the molecular characteristics of ESCC in Tanzania, and enriches the Eastern African dataset, with findings of overall similarities but also some heterogeneity across two unique sites.ImpactDespite a high burden of ESCC in Eastern Africa, investigations into the genomics in this region are nascent. This represents the largest comprehensive genomic analysis ESCC from sub-Saharan Africa to date.

Patients with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) often develop resistance to current standard third-generation EGFR tyrosine kinase inhibitors (TKIs); no targeted treatments are approved in the osimertinib-relapsed setting. In this open-label, dose-escalation and dose-expansion phase 1 trial, the potential for improved anti-tumor activity by combining amivantamab, an EGFR-MET bispecific antibody, with lazertinib, a third-generation EGFR TKI, was evaluated in patients with EGFR-mutant NSCLC whose disease progressed on third-generation TKI monotherapy but were chemotherapy naive (CHRYSALIS cohort E). In the dose-escalation phase, the recommended phase 2 combination dose was established; in the dose-expansion phase, the primary endpoints were safety and overall response rate, and key secondary endpoints included progression-free survival and overall survival. The safety profile of amivantamab and lazertinib was generally consistent with previous experience of each agent alone, with 4% experiencing grade ≥3 events; no new safety signals were identified. In an exploratory cohort of 45 patients who were enrolled without biomarker selection, the primary endpoint of investigator-assessed overall response rate was 36% (95% confidence interval, 22-51). The median duration of response was 9.6 months, and the median progression-free survival was 4.9 months. Next-generation sequencing and immunohistochemistry analyses identified high EGFR and/or MET expression as potential predictive biomarkers of response, which will need to be validated with prospective assessment. ClinicalTrials.gov identifier: NCT02609776 .

BACKGROUND: Procedural training is a required competency in internal medicine (IM) residency, yet limited data exist on residents experience of procedural training. OBJECTIVES: We sought to understand how gender impacts access to procedural training among IM residents. METHODS: A mixed-methods, explanatory sequential study was performed. Procedure volume for IM residents between 2016 and 2020 was assessed at two large academic residencies (Program A and Program B: 399 residents and 4,020 procedures). Procedural rates and actual versus expected procedure volume by gender were compared, with separate analyses by clinical environment (intensive care unit [ICU] or structured procedural service). Semistructured gender-congruent focus groups were conducted. Topics included identity formation as a proceduralist and the resident procedural learning experience, including perceived gender bias in procedure allocation. RESULTS: Compared with men, women residents performed disproportionately fewer ICU procedures per month at Program A (1.4 vs. 2.7; P

Descriptive and qualitative research was used to explore teacher training, teachers' perceptions, and instructional practice for writing in inclusive middle school science (n = 22) and social studies (n = 27) classrooms. Data sources for the two parallel studies included questionnaires and interviews with 49 participating teachers as well as four observations within each teacher's class during content-area writing instruction, resulting in a total of 196 class observations. Teachers reported during interviews that special education teacher (or paraprofessional) support for students needing small group or individualized instruction and adaptations was essential for enhancing writing for students with disabilities. Teachers' responses during interviews and classroom observations corroborate findings that tools including graphic organizers and guided notes were commonly used and are beneficial for all students in their inclusive content classrooms. Findings suggest a need for increased pre- and in-service teacher training and professional development for how to strategically teach and incorporate additional writing instruction, beyond writing notes and short responses, into science and social studies classroom instruction. This article concludes with considerations for future research, professional development, and teacher preparation to support knowledge development and content-area instruction in written expression.

While the medical field in the United States is renowned for research and innovation, with large numbers of world-class healthcare facilities and cutting-edge technologies, inequities in overall health outcomes demonstrate pronounced disparities in accessing care. This article addresses increasing provider diversity and cultural humility as critical steps in ensuring advancements are made to equitable care under the current confines of the American healthcare system. Increasing diversity amongst medical providers starts before students begin applying to medical school. Critical strategies are needed at the K-12 level to ensure early exposure to Science, Technology, Engineering, and Mathematics (STEM). Research demonstrates that early exposure to STEM can positively impact children's interest, confidence, and pursuit of STEM careers, including medicine. Early exposure to STEM can increase engagement and motivation in STEM subjects particularly for girls and racially marginalized groups.

The first records of Proarna inignis Distant, Dorisiana metcalfi Sanborn & Heath, Guyalna cuta (Walker), Quesada gigas (Olivier), Carineta crassicauda Torres, and Herrera umbraphila Sanborn & Heath are provided for the province of Jujuy. Included are the first provincial records for the genera Dorisiana Metcalf, Guyalna Boulard & Martinelli, and Quesada Distant. The additions increase the known cicada diversity 66.7% bringing the total known alpha diversity in Jujuy to 15 species representing 11 genera, five tribes, and three subfamilies.

Background: Preterm birth (birth at

Abstract In this review, we cover the current understanding of BRAF mutations and associated clinical characteristics in patients with metastatic NSCLC, approved and emerging treatment options, BRAF sequencing approaches, and unmet needs. The BRAFV600E mutation confers constitutive activity of the MAPK pathway, leading to enhanced growth, proliferation, and survival of tumor cells. Testing for BRAF mutations enables patients to be treated with therapies that directly target BRAFV600E and the MAPK pathway, but BRAF testing lags behind other oncogene testing in metastatic NSCLC. Additional therapies targeting BRAFV600E mutations provide options for patients with metastatic NSCLC. Emerging therapies and combinations under investigation could potentially overcome issues of resistance and target non-V600E mutations. Therefore, because targeted therapies with enhanced efficacy are on the horizon, being able to identify BRAF mutations in metastatic NSCLC may become even more important.

Recent work suggests that people produce random sequences by tapping into a general ability to derive probabilistic inferences through sampling an internal belief distribution. In contrast, previous work has proposed that participants monitor the production of inherently deterministic sequences, changing strategy when randomness appears low. Experiments exploring this process have highlighted the role of task requirements (e.g., production pace, cognitive load), finding that randomness decreased when demands increased.However, assuming random generation stems from sampling for inference, increasing production rates might affect sequential dependencies and the overall sample distribution independently. For example, increased production rates might result in more predictable sequences but leave the overall distribution of the sample intact. Here, we assessed the effects of production rates on sequential statistics and overall distributional properties in a preregistered experiment in which participants produced people's lifespans. We discuss our results in terms of computational models of sampling for inference.