Objective: To test the hypothesis that the N10 far field potential in median nerve somatosensory evoked potentials is generated by the motor axons by examini ng patients with amyotrophic lateral sclerosis (ALS). Metho...Objective: To test the hypothesis that the N10 far field potential in median nerve somatosensory evoked potentials is generated by the motor axons by examini ng patients with amyotrophic lateral sclerosis (ALS). Methods: Subjects were 5 A LS patients showing pronounced or complete denervation of median inner vated s mall hand muscles. We evaluated N10 over scalp, and proximal plexus volleys (PPV s) at lateral or anterior cervical electrode. Results: N10 and PPVs were definit ely preserved for every ALS subject. N10 amplitudes of ALS subjects were even si gnificantly larger than control subjects. In one ALS patient completely lacking motor axons, N10 was larger than the largest one among control subjects. Conclus ions: Present results clearly indicate that N10 is not predominantly generated b y motor axons but by the whole median nerve dominated by sensory axons. We propo se a theory that N10 is a junctional potential generated by the entrance of the median nerve into bone at the intervertebral foramen, producing a positive pole at the non cephalic reference electrode. Significantly larger N10 in ALS subjec ts may be due to the lack of cancellation by slower motor axons. Significance: T he hypothesis that N10 is generated by motor axons is refuted, and a new theory of its generation is presented.展开更多
[目的/意义]为解决番茄叶片病虫害检测中面临的环境复杂、目标小、精度低、参数冗余及计算复杂度高等问题,提出了一种新型轻量化、高精度、实时的检测模型——YOLOv10n-YS (You Only Look Once Version10-YS)。[方法]首先,采用C2f_RepVi...[目的/意义]为解决番茄叶片病虫害检测中面临的环境复杂、目标小、精度低、参数冗余及计算复杂度高等问题,提出了一种新型轻量化、高精度、实时的检测模型——YOLOv10n-YS (You Only Look Once Version10-YS)。[方法]首先,采用C2f_RepViTBlock模块替换主干网络的C2f,减少了模型的计算量和参数量。其次,加入带切片操作的注意力机制SimAM,结合原有卷积形成Conv_SWS模块,提升了小目标的特征提取能力。另外,在颈部网络中使用DySample轻量动态上采样模块,使采样点集中在目标区域而不会关注背景部分,实现病虫害的有效识别。最后,将跨通道交互的高效率通道注意力(Efficient Channel Attention with Cross-Channel Interaction,EMCA)替换主干网络的金字塔空间注意力机制(Pyramid Spatial Attention, PSA),进一步提高了主干网络的特征提取能力。[结果与讨论]实验结果显示,YOLOv10n-YS模型在番茄病虫害数据集上展现出了卓越的性能。其平均识别精度、检测准确率和召回率分别达到了92.1%、89.2%和82.1%,相较于原模型,这些指标分别提升了3.8、3.3和4.2个百分点。同时,模型在参数量和计算量上也实现了显著的优化,分别减少了13.8%和8.5%。[结论]这些改进不仅提升了模型的性能,还保持了其轻量化特性,对番茄叶片病虫害的检测具有重要参考价值。展开更多
文摘Objective: To test the hypothesis that the N10 far field potential in median nerve somatosensory evoked potentials is generated by the motor axons by examini ng patients with amyotrophic lateral sclerosis (ALS). Methods: Subjects were 5 A LS patients showing pronounced or complete denervation of median inner vated s mall hand muscles. We evaluated N10 over scalp, and proximal plexus volleys (PPV s) at lateral or anterior cervical electrode. Results: N10 and PPVs were definit ely preserved for every ALS subject. N10 amplitudes of ALS subjects were even si gnificantly larger than control subjects. In one ALS patient completely lacking motor axons, N10 was larger than the largest one among control subjects. Conclus ions: Present results clearly indicate that N10 is not predominantly generated b y motor axons but by the whole median nerve dominated by sensory axons. We propo se a theory that N10 is a junctional potential generated by the entrance of the median nerve into bone at the intervertebral foramen, producing a positive pole at the non cephalic reference electrode. Significantly larger N10 in ALS subjec ts may be due to the lack of cancellation by slower motor axons. Significance: T he hypothesis that N10 is generated by motor axons is refuted, and a new theory of its generation is presented.
文摘[目的/意义]为解决番茄叶片病虫害检测中面临的环境复杂、目标小、精度低、参数冗余及计算复杂度高等问题,提出了一种新型轻量化、高精度、实时的检测模型——YOLOv10n-YS (You Only Look Once Version10-YS)。[方法]首先,采用C2f_RepViTBlock模块替换主干网络的C2f,减少了模型的计算量和参数量。其次,加入带切片操作的注意力机制SimAM,结合原有卷积形成Conv_SWS模块,提升了小目标的特征提取能力。另外,在颈部网络中使用DySample轻量动态上采样模块,使采样点集中在目标区域而不会关注背景部分,实现病虫害的有效识别。最后,将跨通道交互的高效率通道注意力(Efficient Channel Attention with Cross-Channel Interaction,EMCA)替换主干网络的金字塔空间注意力机制(Pyramid Spatial Attention, PSA),进一步提高了主干网络的特征提取能力。[结果与讨论]实验结果显示,YOLOv10n-YS模型在番茄病虫害数据集上展现出了卓越的性能。其平均识别精度、检测准确率和召回率分别达到了92.1%、89.2%和82.1%,相较于原模型,这些指标分别提升了3.8、3.3和4.2个百分点。同时,模型在参数量和计算量上也实现了显著的优化,分别减少了13.8%和8.5%。[结论]这些改进不仅提升了模型的性能,还保持了其轻量化特性,对番茄叶片病虫害的检测具有重要参考价值。
