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Due to environmental interference and operational errors, problems such as incomplete and random missing logging data have occurred during the geophysical logging data collection process. Since it is difficult to establish a geophysical model based on logging data and geological information, the data complementation effect of conventional methods is not very satisfactory. In this paper, we propose an MC-GAN-BiLSTM model based on spatiotemporal sequence prediction. In the model, we adopt a generative adversarial network (GAN) as a network framework, and a long short-term memory (LSTM) neural network and a bi-directional long short-term memory (Bi-LSTM) as the basic modules. We use the LSTM instead of a fully-connected layer in the GAN to extract the potential information in the logging data depth domain. We complete the logging data missing values through an encoding-decoding structure that includes the Bi-LSTM. In addition, the generator module also uses multiscale convolution to fully extract the logging data features. We use logging data random missing values and consecutive missing values to simulate a field data acquisition environment and threshold control to simulate a laboratory processing environment for experiments. The experimental results show that the coefficient of determination (R2) of the GAN-LSTM model reaches 0.906 when 30% of random logging data are missing and 0.851 when 30% of consecutive logging data are missing. The effect of the model proposed in this paper is significantly higher than the commonly used random forest (RF), sequence to sequence (seq2seq) and generative adversarial interpolation network (GAIN) models.

Large sample size (N) is seen as a key criterion in judging the replicability of psychological research, a phenomenon we refer to as the N-Heuristic. This heuristic has led to the incentivization of fast, online, non-behavioral studies—to the potential detriment of psychological science. While large N should in principle increase statistical power and thus the replicability of effects, in practice it may not. Large-N studies may have other attributes that undercut their power or validity. Consolidating data from all systematic, large-scale attempts at replication (N = 307 original-replication study pairs), we find that the original study's sample size did not predict its likelihood of being replicated (rs = -0.02, p = 0.741), even with study design and research area controlled. By contrast, effect size emerged as a substantial predictor (rs = 0.21, p < 0.001), which held regardless of the study's sample size. N may be a poor predictor of replicability because studies with larger N investigated smaller effects (rs = -0.49, p < 0.001). Contrary to these results, a survey of 215 professional psychologists, presenting them with a comprehensive list of methodological criteria, found sample size to be rated as the most important criterion in judging a study's replicability. Our findings strike a cautionary note with respect to the prioritization of large N in judging the replicability of psychological science. [ABSTRACT FROM AUTHOR]

SH2B1 mutations are associated with obesity, type 2 diabetes, and metabolic dysfunction‐associated steatotic liver disease (MASLD) in humans. Global deletion of Sh2b1 results in severe obesity, type 2 diabetes, and MASLD in mice. Neuron‐specific restoration of SH2B1 rescues the obesity phenotype of Sh2b1‐null mice, indicating that the brain is a main SH2B1 target. However, SH2B1 neurocircuits remain elusive. SH2B1‐expressing neurons in the paraventricular hypothalamus (PVHSH2B1) and a PVHSH2B1→dorsal raphe nucleus (DRN) neurocircuit are identified here. PVHSH2B1 axons monosynaptically innervate DRN neurons. Optogenetic stimulation of PVHSH2B1 axonal fibers in the DRN suppresses food intake. Chronic inhibition of PVHSH2B1 neurons causes obesity. In male and female mice, either embryonic‐onset or adult‐onset deletion of Sh2b1 in PVH neurons causes energy imbalance, obesity, insulin resistance, glucose intolerance, and MASLD. Ablation of Sh2b1 in the DRN‐projecting PVHSH2B1 subpopulation also causes energy imbalance, obesity, and metabolic disorders. Conversely, SH2B1 overexpression in either total or DRN‐projecting PVHSH2B1 neurons protects against diet‐induced obesity. SH2B1 binds to TrkB and enhances brain‐derived neurotrophic factor (BDNF) signaling. Ablation of Sh2b1 in PVHSH2B1 neurons induces BDNF resistance in the PVH, contributing to obesity. In conclusion, these results unveil a previously unrecognized PVHSH2B1→DRN neurocircuit through which SH2B1 defends against obesity by enhancing BDNF/TrkB signaling. [ABSTRACT FROM AUTHOR]

The sliding process of liquefied submarine landslides is generally regarded as being induced by the coupling of excess pore pressure accumulation and shear stress under surface wave action. However, the significant role of interfacial waves formed over the seabed surface upon liquefaction has been largely ignored. The characteristics of interfacial waves and their effect on the development of a seabed sliding zone are poorly understood. Wave flume experiments were conducted to observe the occurrence and evolution of the interfacial wave and sliding zone, combined with image analysis to extract interfacial wave parameters. The results show that the shear action of interfacial waves can cause progressive liquefaction sliding of the seabed and the formation of a sliding zone. The specific location and thickness of the sliding zone are always dynamically changing during the liquefaction development process and are consistent with the liquefaction depth. The wave height of liquefaction interfacial waves increases with liquefaction depth, and the maximum ratio of interfacial wave height to surface wave height can reach 0.175, corresponding to a maximum longitudinal width ratio of the sliding zone of 0.25. The continuously developing interfacial waves transfer the energy of surface waves to deeper areas, expanding the limit depth of sliding zone evolution. This study can provide theoretical guidance for the prevention and control of seabed instability and sliding disasters under extreme storm conditions. [ABSTRACT FROM AUTHOR]