A 3D printed plant model for accurate and reliable 3D plant phenotyping.

BACKGROUND: This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. RESULTS: Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from -10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. CONCLUSION: Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.

Gravity-Dependent Modulation of Downbeat Nystagmus and Subjective Visual Vertical in the Roll Plane.

Downbeat nystagmus (DBN) is the most common form of acquired central vestibular nystagmus. Gravity perception in patients with DBN has previously been investigated by means of subjective visual straight ahead (SVA) and subjective visual vertical (SVV) in the pitch and roll planes only during whole-body tilts. To our knowledge, the effect of head tilt in the roll plane on the SVV and on DBN has not yet been systematically studied in patients. In this study, we investigated static and dynamic graviceptive function in the roll-plane in patients with DBN (patients) and healthy-controls (controls) by assessment of the Subjective Visual Vertical (SVV) and the modulation of slow-phase-velocity (SPV) of DBN. SPV of DBN and SVV were tested at different head-on trunk-tilt positions in the roll-plane (0°,30° clockwise (cw) and 30° counterclockwise (ccw)) in 26 patients suffering from DBN and 13 controls. In patients, SPV of DBN did not show significant modulations at different head-tilt angles in the roll-plane. SVV ratings did not differ significantly between DBN patients vs. controls, however patients with DBN exhibited a higher variability in mean SVV estimates than controls. Our results show that the DBN does not exhibit any modulation in the roll-plane, in contrast to the pitch-plane. Furthermore, patients with DBN show a higher uncertainty in the perception of verticality in the roll-plane in form of a higher variability of responses.

Paramagnetic rim lesions lead to pronounced diffuse periplaque white matter damage in multiple sclerosis.

BACKGROUND: Paramagnetic rim lesions (PRLs) are an imaging biomarker in multiple sclerosis (MS), associated with a more severe disease. OBJECTIVES: To determine quantitative magnetic resonance imaging (MRI) metrics of PRLs, lesions with diffuse susceptibility-weighted imaging (SWI)-hypointense signal (DSHLs) and SWI-isointense lesions (SILs), their surrounding periplaque area (PPA) and the normal-appearing white matter (NAWM). METHODS: In a cross-sectional study, quantitative MRI metrics were measured in people with multiple sclerosis (pwMS) using the multi-dynamic multi-echo (MDME) sequence post-processing software "SyMRI." RESULTS: In 30 pwMS, 59 PRLs, 74 DSHLs, and 107 SILs were identified. Beside longer T1 relaxation times of PRLs compared to DSHLs and SILs (2030.5 (1519-2540) vs 1615.8 (1403.3-1953.5) vs 1199.5 (1089.6-1334.6), both p < 0.001), longer T1 relaxation times were observed in the PRL PPA compared to the SIL PPA and the NAWM but not the DSHL PPA. Patients with secondary progressive multiple sclerosis (SPMS) had longer T1 relaxation times in PRLs compared to patients with late relapsing multiple sclerosis (lRMS) (2394.5 (2030.5-3040) vs 1869.3 (1491.4-2451.3), p = 0.015) and also in the PRL PPA compared to patients with early relapsing multiple sclerosis (eRMS) (982 (927-1093.5) vs 904.3 (793.3-958.5), p = 0.013). CONCLUSION: PRLs are more destructive than SILs, leading to diffuse periplaque white matter (WM) damage. The quantitative MRI-based evaluation of the PRL PPA could be a marker for silent progression in pwMS.

Interferon beta-1a sc at 25 years: a mainstay in the treatment of multiple sclerosis over the period of one generation.

INTRODUCTION: Interferon beta (IFN beta) preparations are an established group of drugs used for immunomodulation in patients with multiple sclerosis (MS). Subcutaneously (sc) applied interferon beta-1a (IFN beta-1a sc) has been in continuous clinical use for 25 years as a disease-modifying treatment. AREAS COVERED: Based on data published since 2018, we discuss recent insights from analyses of the pivotal trial PRISMS and its long-term extension as well as from newer randomized studies with IFN beta-1a sc as the reference treatment, the use of IFN beta-1a sc across the patient life span and as a bridging therapy, recent data regarding the mechanisms of action, and potential benefits of IFN beta-1a sc regarding vaccine responses. EXPERT OPINION: IFN beta-1a sc paved the way to effective immunomodulatory treatment of MS, enabled meaningful insights into the disease process, and remains a valid therapeutic option in selected vulnerable MS patient groups.

Diagnostic Performance of Adding the Optic Nerve Region Assessed by Optical Coherence Tomography to the Diagnostic Criteria for Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: The optic nerve has been recommended as an additional region for demonstrating dissemination in space (DIS) in diagnostic criteria for multiple sclerosis (MS). The aim of this study was to investigate whether adding the optic nerve region as determined by optical coherence tomography (OCT) as part of the DIS criteria improves the 2017 diagnostic criteria. METHODS: From a prospective observational study, we included patients with a first demyelinating event who had complete information to assess DIS and a spectral domain OCT scan obtained within 180 days. Modified DIS criteria (DIS + OCT) were constructed by adding the optic nerve to the current DIS regions based on validated thresholds for OCT intereye differences. Time to second clinical attack was the primary endpoint. RESULTS: We analyzed 267 patients with MS (mean age 31.3 years [SD 8.1], 69% female) during a median observation period of 59 months (range: 13-98). Adding the optic nerve as a fifth region improved the diagnostic performance by increasing accuracy (DIS + OCT 81.2% vs DIS 65.6%) and sensitivity (DIS + OCT 84.2% vs DIS 77.9%) without lowering specificity (DIS + OCT 52.2% vs DIS 52.2%). Fulfilling DIS + OCT criteria (≥2 of 5 DIS + OCT regions involved) indicated a similar risk of a second clinical attack (hazard ratio [HR] 3.6, CI 1.4-14.5) compared with a 2.5-fold increased risk when fulfilling DIS criteria (HR 2.5, CI 1.2-11.8). When the analysis was conducted according to topography of the first demyelinating event, DIS + OCT criteria performed similarly in both optic neuritis and nonoptic neuritis. DISCUSSION: Addition of the optic nerve, assessed by OCT, as a fifth region in the current DIS criteria improves diagnostic performance by increasing sensitivity without lowering specificity. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that adding the optic nerve as determined by OCT as a fifth DIS criterion to the 2017 McDonald criteria improves diagnostic accuracy.

Phenotyping of a novel COL4A4 and novel GLA variant in a patient presenting with microhematuria and mildly impaired kidney function: a case report.

We describe the case of a 44-year-old male patient with a longstanding history of microhematuria and mildly impaired kidney function (CKD G2A1). The family history disclosed three females who also had microhematuria. Genetic testing by whole exome sequencing revealed two novel variants in COL4A4 (NM_000092.5: c.1181G>T, NP_000083.3: p.Gly394Val, heterozygous, likely pathogenic; Alport syndrome, OMIM# 141200, 203780) and GLA (NM_000169.3: c.460A>G, NP_000160.1: p.Ile154Val, hemizygous, variant of uncertain significance; Fabry disease, OMIM# 301500), respectively. Extensive phenotyping revealed no biochemical or clinical evidence for the presence of Fabry disease. Thus, the GLA c.460A>G, p.Ile154Val, is to be classified as a benign variant, whereas the COL4A4 c.1181G>T, p.Gly394Val confirms the diagnosis of autosomal dominant Alport syndrome in this patient.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning.

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Association of paramagnetic rim lesions and retinal layer thickness in patients with multiple sclerosis.

BACKGROUND: Paramagnetic rim lesions (PRLs) are chronic active lesions associated with a more severe disease course in multiple sclerosis (MS). Retinal layer thinning measured by optical coherence tomography (OCT) is a biomarker of neuroaxonal damage associated with disability progression in MS. OBJECTIVE: We aimed to determine a potential association between OCT parameters (peripapillary retinal nerve fiber layer (pRNFL) ganglion cell-inner plexiform layer (GCIPL), inner nuclear layer (INL) thickness), and PRLs in patients with MS (pwMS). METHODS: In this cross-sectional retrospective study, we included pwMS with both 3T brain MRI and an OCT scan. Regression models were calculated with OCT parameters (pRNFL, GCIPL, INL) as dependent variables, and the number of PRLs as an independent variable adjusted for covariates. RESULTS: We analyzed data from 107 pwMS (mean age 34.7 years (SD 10.9), 64.5% female, median disease duration 6 years (IQR 1-13), median EDSS 1.5 (range 0-6.5)). Higher number of PRLs was associated with lower pRNFL (ß = -0.18; 95% CI -0.98, -0.03; p = 0.038) and GCIPL thickness (ß = -0.21; 95% CI -0.58, -0.02; p = 0.039). CONCLUSION: The association between higher number of PRLs and lower pRNFL and GCIPL thicknesses provides additional evidence that pwMS with PRLs are affected by a more pronounced neurodegenerative process.

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites.

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.

Treatment of neuromyelitis optica spectrum disorder: revisiting the complement system and other aspects of pathogenesis.

Neuromyelitis optica spectrum disorder (NMOSD) represents a rare neuroimmunological disease causing recurrent attacks and accumulation of permanent disability in affected patients. The discovery of the pathogenic IgG­1 antibody targeting a water channel expressed in astrocytes, aquaporin 4, constitutes a milestone achievement. Subsequently, multiple pathophysiological aspects of this distinct disease entity have been investigated. Demyelinating lesions and axonal damage ensue from autoantibodies targeting an astroglial epitope. This conundrum has been addressed in the current disease model, where activation of the complement system as well as B cells and interleukin 6 (IL-6) emerged as key contributors. It is the aim of this review to address these factors in light of novel treatment compounds which reflect these pathophysiological concepts in aiming for attack prevention, thus reducing disease burden in patients with NMOSD.

Feasibility of a smartphone app to monitor patient reported outcomes in multiple sclerosis: The haMSter interventional trial.

Background: Monitoring of patient outcomes in multiple sclerosis (MS) is fundamental for individualized treatment decisions. So far, these decisions have been motivated by conventional outcomes, i.e., relapses or clinical disability supported by radiological disease activity. Complementing this concept, patient reported outcomes (PROs) assess individual health-related quality of life, among other constructs. Their inclusion in clinical routine, however, has been challenging as assessing them requires resources of time and personnel. Objective: This interventional feasibility study investigated the haMSter app, a mobile health solution for remote and longitudinal monitoring of PROs in a sample of people with MS (pwMS). Methods: The core feature of haMSter is the provision of three PRO questionnaires relevant to MS (anxiety/depression, MS-related quality of life, and fatigue) that patients can fill out once a month. For this feasibility trial, we offered 50 volunteers to use the haMSter app over six months and to take part in a haMSter study visit. This consultation concluded the study and participants had the opportunity to discuss their graphically plotted PRO results with their treating physician. Results: The main outcome was overall patient adherence to monthly completion of the PRO questionnaires, which remained high up to 4 months (98%) and dropped over time (months 5: 83% and 6: 66%). Exploratory outcomes included patient satisfaction as estimated on the Telemedicine Perception Questionnaire (TMPQ, 17-85 points). The mean TMPQ score was 64 (95%CI: 62-66) points, indicating a high degree of approval. Ancillary tests included subgroup analyses of participants with particularly high or low satisfaction and upper extremity disability as a potential obstacle to utility or acceptance. We found no distinct characteristics separating participants with high or low satisfaction. Conclusions: In this first feasibility trial, the haMSter app for longitudinal PRO monitoring was well received in terms of adherence and satisfaction. ClinicalTrials.gov identifier: NCT04555863.

Real-world applicability of glial fibrillary acidic protein and neurofilament light chain in Alzheimer's disease.

Background: Blood-based biomarkers may add a great benefit in detecting the earliest neuropathological changes in patients with Alzheimer's disease (AD). We examined the utility of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) regarding clinical diagnosis and differentiation between amyloid positive and negative patients. To evaluate the practical application of these biomarkers in a routine clinical setting, we conducted this study in a heterogeneous memory-clinic population. Methods: We included 167 patients in this retrospective cross-sectional study, 123 patients with an objective cognitive decline [mild cognitive impairment (MCI) due to AD, n = 63, and AD-dementia, n = 60] and 44 age-matched healthy controls (HC). Cerebrospinal fluid (CSF) and plasma concentrations of NfL and GFAP were measured with single molecule array (SIMOA®) technology using the Neurology 2-Plex B kit from Quanterix. To assess the discriminatory potential of different biomarkers, age- and sex-adjusted receiver operating characteristic (ROC) curves were calculated and the area under the curve (AUC) of each model was compared. Results: We constructed a panel combining plasma NfL and GFAP with known AD risk factors (Combination panel: age+sex+APOE4+GFAP+NfL). With an AUC of 91.6% (95%CI = 0.85-0.98) for HC vs. AD and 81.7% (95%CI = 0.73-0.90) for HC vs. MCI as well as an AUC of 87.5% (95%CI = 0.73-0.96) in terms of predicting amyloid positivity, this panel showed a promising discriminatory power to differentiate these populations. Conclusion: The combination of plasma GFAP and NfL with well-established risk factors discerns amyloid positive from negative patients and could potentially be applied to identify patients who would benefit from a more invasive assessment of amyloid pathology. In the future, improved prediction of amyloid positivity with a noninvasive test may decrease the number and costs of a more invasive or expensive diagnostic approach.

Retinal Layer Thinning After Optic Neuritis Is Associated With Future Relapse Remission in Relapsing Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: Remission of relapses is an important contributor to both short- and long-term prognosis in relapsing multiple sclerosis (RMS). In MS-associated acute optic neuritis (MS-ON), retinal layer thinning measured by optical coherence tomography (OCT) is a reliable biomarker of both functional recovery and the degree of neuroaxonal damage. However, prediction of non-ON relapse remission is challenging. We aimed to investigate whether retinal thinning after ON is associated with relapse remission after subsequent non-ON relapses. METHODS: For this longitudinal observational study from the Vienna MS database, we included patients with MS with (1) an episode of acute ON, (2) available spectral domain OCT scans within 12 months before ON onset (OCTbaseline), within 1 week after ON onset (OCTacute), and 3-6 months after ON (OCTfollow-up), and (3) at least 1 non-ON relapse after the ON episode. Subsequent non-ON relapses were classified as displaying either complete or incomplete remission based on change in the Expanded Disability Status Scale score assessed 6 months after relapse. Association of retinal thinning in the peripapillary retinal nerve fiber layer (ΔpRNFL) and macular ganglion cell and inner plexiform layer (ΔGCIPL) with incomplete remission was tested by multivariate logistic regression models adjusting for age, sex, disease duration, relapse severity, time to steroid treatment, and disease-modifying treatment status. RESULTS: We analyzed 167 patients with MS (mean age 36.5 years [SD 12.3], 71.3% women, mean disease duration 3.1 years [SD 4.5]) during a mean observation period of 3.4 years (SD 2.8) after the ON episode. In 61 patients (36.5%), at least 1 relapse showed incomplete remission. In the multivariable analyses, incomplete remission of non-ON relapse was associated with ΔGCIPL thinning both from OCTbaseline to OCTfollow-up and from OCTacute to OCTfollow-up (OR 2.4 per 5 µm, p < 0.001, respectively), independently explaining 29% and 27% of variance, respectively. ΔpRNFL was also associated with incomplete relapse remission when measured from OCTbaseline to OCTfollow-up (OR 1.9 per 10 µm, p < 0.001), independently accounting for 22% of variance, but not when measured from OCTacute to OCTfollow-up. DISCUSSION: Retinal layer thinning after optic neuritis may be useful as a marker of future relapse remission in RMS.

Remote visits for people with multiple sclerosis during the COVID-19 pandemic in Austria: The TELE MS randomized controlled trial.

Introduction: Continuous monitoring is the hallmark of managing chronic disease. Multiple sclerosis (MS), in particular, requires patients to visit their treating neurologists typically twice a year, at least. In that respect, the COVID-19 pandemic made us rethink our communication strategies. This study determined satisfaction with remote visits for people with MS (pwMS) by comparing non-inferiority to conventional visits. Methods: TELE MS was a randomized controlled trial that was open to any person with MS. We randomized a volunteer sample of 45 patients. We compared satisfaction with remote visits (via phone or via videochat) with conventional outpatient visits. The primary endpoint was patient satisfaction determined by the Telemedicine Perception Questionnaire (TMPQ, min: 17 and max: 85 points) with the hypothesis of non-inferiority of televisits to conventional visits. Physician satisfaction measured on the PPSM score (Patient and Physician Satisfaction with Monitoring, min: 5 and max: 25 points) was the secondary endpoint. Results: The trial met both endpoints. Mean (SD) TMPQ scores in the individual groups were 58 (6.7) points for conventional visits, 65 (7.5) points for phone visits, and 62 (5.5) points for video visits. Physician satisfaction over the whole cohort was similarly high. Median (range) PPSM scores were 23 (16-25) for the whole cohort, 19 (16-25) for conventional visits, 25 (17-25) for phone visits, and 25 (16-25) for video visits. Conclusions: Televisits in multiple sclerosis yield a high level of satisfaction for both patients and treating physicians. This concept for remote patient monitoring adopted during the current pandemic may be communicable to other chronic diseases as well. ClinicalTrials.gov identifier: NCT04838990.

Digital plant pathology: a foundation and guide to modern agriculture.

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Genetic, Environmental and Lifestyle Determinants of Accelerated Telomere Attrition as Contributors to Risk and Severity of Multiple Sclerosis.

Telomeres are protective structures at the ends of linear chromosomes. Shortened telomere lengths (TL) are an indicator of premature biological aging and have been associated with a wide spectrum of disorders, including multiple sclerosis (MS). MS is a chronic inflammatory, demyelinating and neurodegenerative disease of the central nervous system. The exact cause of MS is still unclear. Here, we provide an overview of genetic, environmental and lifestyle factors that have been described to influence TL and to contribute to susceptibility to MS and possibly disease severity. We show that several early-life factors are linked to both reduced TL and higher risk of MS, e.g., adolescent obesity, lack of physical activity, smoking and vitamin D deficiency. This suggests that the mechanisms underlying the disease are connected to cellular aging and senescence promoted by increased inflammation and oxidative stress. Additional prospective research is needed to clearly define the extent to which lifestyle changes can slow down disease progression and prevent accelerated telomere loss in individual patients. It is also important to further elucidate the interactions between shared determinants of TL and MS. In future, cell type-specific studies and advanced TL measurement methods could help to better understand how telomeres may be causally involved in disease processes and to uncover novel opportunities for improved biomarkers and therapeutic interventions in MS.

Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis.

Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.