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Background and ObjectivesMultiple system atrophy (MSA) is a progressive neurodegenerative disorder caused by the abnormal accumulation of α-synuclein in the nervous system. Clinical features include autonomic and motor dysfunction, which overlap with those of Parkinson disease (PD), particularly at early disease stages. There is an unmet need for accurate diagnostic and prognostic biomarkers for MSA and, specifically, a critical need to distinguish MSA from other synucleinopathies, particularly PD. The purpose of the study was to develop a unique cutaneous pathologic signature of phosphorylated α-synuclein that could distinguish patients with MSA from patients with PD and healthy controls.MethodsWe studied 31 patients with MSA and 54 patients with PD diagnosed according to current clinical consensus criteria. We also included 24 matched controls. All participants underwent neurologic examinations, autonomic testing, and skin biopsies at 3 locations. The density of intraepidermal, sudomotor, and pilomotor nerve fibers was measured. The deposition of phosphorylated α-synuclein was quantified. Results were compared with clinical rating assessments and autonomic function test results.ResultsPatients with PD had reduced nerve fiber densities compared with patients with MSA (p< 0.05, all fiber types). All patients with MSA and 51/54 with PD had evidence of phosphorylated α-synuclein in at least one skin biopsy. No phosphorylated α-synuclein was detected in controls. Patients with MSA had greater phosphorylated α-synuclein deposition (p< 0.0001) and more widespread peripheral distribution (p< 0.0001) than patients with PD. These results provided >90% sensitivity and specificity in distinguishing between the 2 disorders.Discussionα-synuclein is present in the peripheral autonomic nerves of patients with MSA and when combined with synuclein distribution accurately distinguishes MSA from PD.Classification of EvidenceThis study provides Class II evidence that measurement of phosphorylated α-synuclein in skin biopsies can differentiate patients with MSA from those with PD.

Verticillium dahliae (V. dahliae) is a fungal pathogen that causes Verticillium wilt (VW), which seriously reduces the yield of cotton owing to biological stress. The mechanism underlying the resistance of cotton to VW is highly complex, and the resistance breeding of cotton is consequently limited by the lack of in-depth research. Using QTL mapping, we previously identified a novel cytochrome P450 (CYP) gene on chromosome D4 of Gossypium barbadense that is associated with resistance to the non-defoliated strain of V. dahliae. In this study, the CYP gene on chromosome D4 was cloned together with its homologous gene on chromosome A4, and were denoted as GbCYP72A1d and GbCYP72A1a, respectively, according to their genomic location and protein subfamily classification. The two GbCYP72A1 genes were induced by V. dahliae and phytohormone treatment, and the findings revealed that the VW resistance of the lines with silenced GbCYP72A1 genes decreased significantly. Transcriptome sequencing and pathway enrichment analyses revealed that the GbCYP72A1 genes primarily affected disease resistance via the plant hormone signal transduction, plant-pathogen interaction, and mitogen-activated protein kinase (MAPK) signaling pathways. Interestingly, the findings revealed that although GbCYP72A1d and GbCYP72A1a had high sequence similarity and both genes enhanced the disease resistance of transgenic Arabidopsis sp., there was a difference between their disease resistance abilities. Protein structure analysis revealed that this difference was potentially attributed to the presence of a synaptic structure in the GbCYP72A1d protein. Altogether, the findings suggested that the GbCYP72A1 genes play an important role in plant response and resistance to VW.

This article proposes a novel integral geometric control attitude tracking scheme, utilizing a coordinate-free representation of attitude on the Lie group of rigid body rotations, SO(3). This scheme exhibits almost global asymptotic stability in tracking a reference attitude profile. The stability and robustness properties of this integral tracking control scheme are shown using Lyapunov stability analysis. A numerical simulation study, utilizing a Lie Group Variational Integrator (LGVI), verifies the stability of this tracking control scheme, as well as its robustness to a disturbance torque. In addition, a numerical comparison study shows the effectiveness of the proposed geometric integral term, when compared to other state-of-the-art attitude controllers. In addition, software-in-the-loop (SITL) simulations show the advantages of utilizing the proposed attitude controller in PX4 autopilot compared to using PX4’s original attitude controller.