Ask about this productRelated genes to: SIM2 antibody
- Gene:
- SIM2 NIH gene
- Name:
- SIM bHLH transcription factor 2
- Previous symbol:
- SIM
- Synonyms:
- MGC119447, bHLHe15
- Chromosome:
- 21q22.13
- Locus Type:
- gene with protein product
- Date approved:
- 1995-10-02
- Date modifiied:
- 2018-04-20
Related products to: SIM2 antibody
Related articles to: SIM2 antibody
- In this issue of Neuron, Walker, Lowenstein et al. identify PVH neurons as excitatory input that rapidly activates ARC hunger neurons. By tracking predicted food availability, these cells help the brain anticipate an energy deficit before the body registers one. - Source: PubMed
Lyu MoyuWilliams Kevin W - AgRP neurons cause hunger, the drive to seek and consume food. Their activation by fasting is key for survival and is thought to be triggered by feedback when energy stores are low. However, we know that environmental cues can also regulate AgRP neurons since cues that predict future food intake rapidly inhibit AgRP neurons, but is the converse true: can the prediction of future fasting rapidly activate AgRP neurons? Here, we show in mice that such rapid fasting activation of AgRP neurons does occur. This rapid activation is driven by excitatory input from paraventricular hypothalamic (PVH) neurons expressing Sim2, which are bidirectionally sensitive to predictions of future energy state. Thus, cognitively processed contextual information conveyed by PVH neurons strongly activates AgRP neurons. Lastly, chronic silencing of PVH neurons causes persistent hypophagia. This PVH-to-AgRP-neuron circuit, by anticipating and preventing negative energy balance, provides an important new dimension of hunger regulation. - Source: PubMed
Publication date: 2026/06/03
Walker Samuel JLowenstein Elijah DDouglass Amelia MThomas Callum M PMadara Joseph CKucukdereli HakanBarbosa-Meillon Eunice ATao JenkangResch Jon MLowell Bradford B - Recently, functional evaluation using 3D gait analysis (3DGA) proved to predict health-related quality-of-life (HRQOL) scores better than static radiographic evaluation in adult spinal deformity (ASD). However, 3DGA provides multiple parameters that can be a burden to interpret by nonexperts. A recent study showed that the dynamic pelvic tilt (dPT), the forward projection of the head and thorax (dODHA) and walking step length (SL) are the most representative gait kinematics in ASD patients. - Source: PubMed
Publication date: 2026/04/28
Mekhael ElioRachkidi RamiNassim NabilHaddad Georges ElBoutros MarcKaram Maria RWakim EmmanuelleAsmar MariaKaram MohamadMassaad AbirGhanem IsmatAssi Ayman - Collaborative perception allows each agent to enhance its perceptual abilities by exchanging messages with others. It inherently results in a trade-off between perception ability and communication costs. Previous works transmit complete full-frame high-dimensional feature maps among agents, resulting in substantial communication costs. To promote communication efficiency, we propose only transmitting the information needed for the collaborator's downstream task. This pragmatic communication strategy focuses on three key aspects: i) pragmatic message selection, which selects task-critical parts from the complete data, resulting in spatially and temporally sparse feature vectors; ii) pragmatic message representation, which achieves pragmatic approximation of high-dimensional feature vectors with a task-adaptive dictionary, enabling communicating with integer indices; iii) pragmatic collaborator selection, which identifies beneficial collaborators, pruning unnecessary communication links. Following this strategy, we first formulate a mathematical optimization framework for the perception-communication trade-off and then propose PragComm, a multi-agent collaborative perception system with two key components: i) single-agent detection and tracking and ii) pragmatic collaboration. The proposed PragComm promotes pragmatic communication and adapts to a wide range of communication conditions. We evaluate PragComm for both collaborative 3D object detection and tracking tasks in both real-world, V2V4Real, and simulation datasets, OPV2V and V2X-SIM2.0. PragComm consistently outperforms previous methods with more than 32.7 K× lower communication volume on OPV2V. - Source: PubMed
Hu YuePang XiangheQin XiaoqiEldar Yonina CChen SihengZhang PingZhang Wenjun - Esophageal squamous cell carcinoma (ESCC) remains a major health burden, particularly in Asia, with poor patient prognosis despite advancements in radiotherapy, chemotherapy, and immunotherapy. The marked interpatient and intratumor heterogeneity of ESCC underscores the need for molecularly informed diagnostic and therapeutic strategies. Recent high-throughput omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have substantially advanced our understanding of ESCC biology. Genomic profiling has revealed recurrent alterations such as TP53 and NOTCH1 mutations, as well as actionable targets including PIK3CA, FGFR1, and SOX2 amplifications, which provide new opportunities for precision therapy. Epigenomic and transcriptomic analyses have identified methylation-based early detection markers (e.g., PAX9, SIM2) and immune-related transcriptomic subtypes associated with prognosis and immunotherapy responsiveness. Proteomic and metabolomic studies have further uncovered cell cycle and spliceosome pathway activation and altered lactate metabolism, offering additional biomarker and therapeutic insights. In this review, we synthesize these multi-omics advances and highlight how they collectively inform improved diagnostic, prognostic, and therapeutic strategies for ESCC. Despite these developments, the clinical translation of multi-omics findings remains limited due to the lack of standardized analytical pipelines, insufficient multicenter validation, and the high cost and technical complexity of integrating multi-omics data into routine clinical workflows. Future research integrating artificial intelligence with multi-omics data holds promise for enhancing diagnostic accuracy and enabling more precise therapeutic decision-making in ESCC. - Source: PubMed
Zhao DengyunHe XinyuGuo YapingWei HuifangDong ZigangLiu Kangdong