Parcellation of the human sensorimotor cortex: a resting-state fMRI study

The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Transcriptome analysis reveals the molecular mechanisms of rubber biosynthesis and laticifer differentiation during rubber seed germination

The molecular mechanisms underlying the initiation of natural rubber synthesis and laticifer differentiation have not been fully elucidated. In this study, we conducted a time-series transcriptome analysis of five rubber tree tissues at four stages of seed germination. A total of 161,199 DEGs were identified between the two groups, including most 16,673 DEGs (A3 vs B3 and A3 vs C3) and lest 1,210 DEGs (C2 vs D2). We found that the maturation of the seed is accompanied by the formation of laticifer cells in cotyledon. Meanwhile, the analysis of hormones related genes expression may provide effective clues for us to promote the differentiation of laticifer cells in seeds by hormones in the future. In this study, hormone-related gene enrichment analyses revealed that IAA, GA, and CTK were activated in laticifer containing tissues. Similarly, GO and GEGG analysis showed that hormone pathways, especially the auxin pathway, are enriched. Gene expression clustering was analyzed using the short time-series expression miner (STEM), and the analysis revealed four distinct trends in the gene expression profiles. Moreover, we enriched transcription factor (TF) enrichment in cotyledon and embryonic axis tissues, and the MYB type exhibited the most significant difference. Furthermore, our findings revealed that genes related to rubber synthesis exhibited tissue-specific expression patterns during seed germination. Notably, key genes associated with rubber biosynthesis, specifically small rubber particle protein (SRPP) and cis-prenyltransferase (CPT), exhibited significant changes in expression in cotyledon and embryonic axis tissues, suggesting synchronous rubber synthesis with seed germination. Our staining results reveled that laticifer cells were exits in the cotyledon before seed imbibition stage. In conclusion, these results lay the foundation for exploring the molecular mechanisms underlying laticifer differentiation and rubber synthesis during seed germination, deepening our understanding of the initiation stages of rubber biosynthesis and laticifer differentiation

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback. Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations. However, the number of negative feedback is much larger than that of positive one; thus incorporating them simultaneously is challenging since positive feedback could be buried by negative one. In this paper, we develop a novel approach to incorporate them into the proposed deep recommender system (DEERS) framework. The experimental results based on real-world e-commerce data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of both positive and negative feedback in recommendations.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0020

ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning

In recent times, learning-based methods for video deraining have demonstrated commendable results. However, there are two critical challenges that these methods are yet to address: exploiting temporal correlations among adjacent frames and ensuring adaptability to unknown real-world scenarios. To overcome these challenges, we explore video deraining from a paradigm design perspective to learning strategy construction. Specifically, we propose a new computational paradigm, Alignment-Shift-Fusion Network (ASF-Net), which incorporates a temporal shift module. This module is novel to this field and provides deeper exploration of temporal information by facilitating the exchange of channel-level information within the feature space. To fully discharge the model's characterization capability, we further construct a LArge-scale RAiny video dataset (LARA) which also supports the development of this community. On the basis of the newly-constructed dataset, we explore the parameters learning process by developing an innovative re-degraded learning strategy. This strategy bridges the gap between synthetic and real-world scenes, resulting in stronger scene adaptability. Our proposed approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios, underscoring its efficacy. The code is available at https://github.com/vis-opt-group/ASF-Net