Background: Practice guidelines have recommended cognitive behavioral therapy (CBT) and interpersonal psychotherapy (IPT) as the treatment of choice for major depression disorder (MDD). However, whether one therapy is better than the other remains inconclusive. The aim of this study was to compare the treatment efficacy of the two treatment approaches for MDD. Methods: Using the terms "cognitive behavior therapy or cognitive therapy or CBT or CT or cognitive behavioral therapy" and "interpersonal psychotherapy or IPT," we systematically searched PubMed, Psyclnfo and Chinese National Knowledge Infrastructure databases up to February 2017. The language was restricted to be English and Chinese. Therapeutic outcomes, characteristics, and research quality were then extracted and analyzed independently. In accessing the included studies, we followed the criteria suggested by the Cochrane Handbook for Systematic Reviews of Interventions. Results: Data for 946 patients from 10 randomized controlled trials were included in the study. Methodological quality was not optimal in most trials. Meta-analysis showed a mean difference (MD) of −1.31, 95% confidence interval (CI) (−2.49, −0.12) (P < 0.05) in favor of CBT according to the Beck Depression Inventory (BDI), and however, we did not found any statistically significant difference between CBT and IPT on the Hamilton Rating Scale for depression (HRSD) (MD −0.90, 95% CI [−2.18, 0.38]). Subgroup analyses for the studies in which patients were treated only by psychotherapy (MD −1.26, 95% CI [−2.78, 0.35]) and for those which offered more sessions of therapies (MD −0.82, 95% CI [−2.23, 0.59]) showed there was no significant difference between CBT and IPT according to BDI. Conclusions: Differences in treatment efficacy seem to vary according to different outcome measures. CBT shows an advantage over IPT for MDD according to BDI, and there is no significant difference between the two according to HRSD. These results should be interpreted with caution.
Cognitive processes require working memory (WM) that involves a brief period of memory retention known as the delay period. Elevated delay-period activity in the medial prefrontal cortex (mPFC) has been observed, but its functional role in WM tasks remains unclear. We optogenetically suppressed or enhanced activity of pyramidal neurons in mouse mPFC during the delay period. Behavioral performance was impaired during the learning phase but not after the mice were well trained. Delay-period mPFC activity appeared to be more important in memory retention than in inhibitory control, decision-making, or motor selection. Furthermore, endogenous delay-period mPFC activity showed more prominent modulation that correlated with memory retention and behavioral performance. Thus, properly regulated mPFC delay-period activity is critical for information retention during learning of a WM task.
Objective The aim of this proof-of-concept study is to develop a predictive model based on deep learning algorithms to predict working alliances after the first therapeutic session and to provide a basis for clinical decisions.Methods Using a sample of 325 patients and 32 psychotherapists from three university counseling centers, a deep learning algorithm known as fully connected neural networks (FCNNs) was adopted to construct data-driven predictive models. The performance differences between the model including only patient indicators and the model including both patient and therapist indicators were compared. The optimal model was further tested in a general hospital sample of 85 patients and 8 therapists.Results The model incorporating both patient indicators and therapist-level indicators (R²: 0.30 ± 0.02) performed better than the model incorporating only patient indicators (R²: 0.11 ± 0.02). The performance of this model decreased when being transferred to the independent general hospital sample, but still retained some predictive value (R² = 0.11).Conclusion This study showed that the inclusion of therapist-level indicators can improve the performance of a predictive model in predicting working alliances. This model could assist clinical decisions on choosing psychotherapists for patients and may also initiate new possibilities for future research.
We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR handles temporal planning, while DM focuses on spatial generation in an asymmetric network design: i) a MAR-based planning model containing most of the parameters generates planning signals for each masked frame using low-resolution input; ii) a lightweight generation model uses these signals to produce high-resolution frames via diffusion de-noising. MarDini's MAR enables video generation conditioned on any number of masked frames at any frame positions: a single model can handle video interpolation (e.g., masking middle frames), image-to-video generation (e.g., masking from the second frame onward), and video expansion (e.g., masking half the frames). The efficient design allocates most of the computational resources to the low-resolution planning model, making computationally expensive but important spatio-temporal attention feasible at scale. MarDini sets a new state-of-the-art for video interpolation; meanwhile, within few inference steps, it efficiently generates videos on par with those of much more expensive advanced image-to-video models.
Cyclin-dependent kinases (CDKs) are a large family of proteins exerting different regulatory functions in eukaryotic cells, including control over the cell cycle and gene transcription. CDK1 controls the cell cycle and promotes cell proliferation by regulating the initiation and transition of the G2/M phase. Similar to mammals, CDK1 has also been characterized in arthropods; however, its functions and physiological importance in crustaceans remain unknown. Our pervious transcriptomic data indicated that CDK1 was a candidate cold-stress response gene in the Pacific white shrimp (Litopenaeus vannamei). Therefore, in the present study, the full-length sequence of CDK1 (LvCDK1) was obtained from L. vannamei containing a conserved PSTAIRE sequence and ATP-binding region. The expression of LvCDK1 is tissue-specific, with relatively higher in the hepatopancreas. The in situ hybridization (ISH) signals for LvCDK1 were present in the distal zone of hepatopancreas and mainly located in the cytoplasm and nucleus. The expression of LvCDK1 in hepatopancreatic was sensitive to environmental temperature. Furthermore, under cold stress, knockdown of LvCDK1 led to increased mortality in shrimp. Our study provides the first evidence of LvCDK1 responding to cold stress in shrimps. This mechanism for modifying the transcriptional roles of LvCDK1 may show new light on the molecular regulation of crustaceans in response to cold stress.
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