Real-World Gait Detection Using a Wrist-Worn Inertial Sensor: Validation Study.

BACKGROUND: Wrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. OBJECTIVE: The aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back-worn inertial sensors. METHODS: Participants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back-worn inertial sensors. RESULTS: The best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. CONCLUSIONS: Algorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. TRIAL REGISTRATION: ISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-050785.

Acute otitis media diagnosis in childhood: still a problem in 2023?

BACKGROUND: Diagnosis of acute otitis media (AOM) in children can be challenging, given that symptoms are often non-specific or absent, and that the direct observation of the tympanic membrane in its entirety through otoscopy can sometimes be difficult. The aim of this study is to assess the diagnostic concordance in detection of AOM episodes between primary care paediatricians and physicians especially trained in paediatric otoscopy, and to characterize the most misleading elements in diagnostic failure. METHODS: Consecutive clinical charts of children regularly followed for recurrent AOM (RAOM, i.e.: >3 episodes in 6 months or > 4 episodes in 1 year) at our Otitis Media paediatric outpatient clinic were retrospectively screened, in order to collect any diagnosis of AOM episode (and the related clinical findings/middle ear complaints) performed by primary care paediatricians/emergency room paediatricians. Diagnosis of AOM episode was validated by the same experienced physician (FF) in case of otoscopic relief of a bulging eardrum with at least one of the following: hyperaemia or yellow-like colour. The diagnostic concordance in detection of AOM episodes between primary care/emergency room paediatricians and our internal validator was expressed as the percentage of matching diagnosis. RESULTS: One hundred and thirty-four single AOM episodes occurring in 87 children (mean age: 26.9 +/- 18.9 months) were included in the analysis. Diagnostic concordance in detection of AOM episodes between primary care/emergency room paediatricians and our internal validator was reported in 72.4% of cases. The most common pitfall found in our study was the misleading diagnosis of AOM in case of hyperaemic tympanic membrane without bulging (32/37 out of non-validated diagnoses). CONCLUSIONS: AOM diagnosis still represents a relevant issue among paediatricians in our country, and the presence of tympanic membrane hyperaemia without concomitant bulging can be confusing.

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987.

The effect of optical degradation from cataract using a new Deep Learning optical coherence tomography segmentation algorithm.

PURPOSE: To assess the validity of the results of a freely available online Deep Learning segmentation tool and its sensitivity to noise introduced by cataract. METHODS: The OCT images were collected with a Spectralis SD-OCT (Heidelberg Engineering, Heidelberg, Germany) as part of normal clinical practice. Data were segmented using a freely available online tool called Relayer ( https://www.relayer.online/ ), based on a cross-platform Deep Learning segmentation architecture specifically adapted for retinal OCT images. The segmentations were read into MATLAB (The MathWorks, Natick, MA, USA) and analyzed. RESULTS: There was an excellent agreement between the ETDRS measurements obtained from the two algorithms. Upon visual inspection, the segmentation based on Deep Learning obtained with Relayer appeared more accurate except in one case of apparent good quality image showing interrupted segmentations in some of the B-scans. CONCLUSION: A freely available online Deep Learning segmentation tool showed good and promising performance in healthy retinas before and after cataract surgery, proving robust to optical degradation of the image from media opacities.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases.

Introduction: The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods: Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion: The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

BACKGROUND: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. METHODS: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. RESULTS: We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

A multi-sensor wearable system for the assessment of diseased gait in real-world conditions.

Introduction: Accurately assessing people's gait, especially in real-world conditions and in case of impaired mobility, is still a challenge due to intrinsic and extrinsic factors resulting in gait complexity. To improve the estimation of gait-related digital mobility outcomes (DMOs) in real-world scenarios, this study presents a wearable multi-sensor system (INDIP), integrating complementary sensing approaches (two plantar pressure insoles, three inertial units and two distance sensors). Methods: The INDIP technical validity was assessed against stereophotogrammetry during a laboratory experimental protocol comprising structured tests (including continuous curvilinear and rectilinear walking and steps) and a simulation of daily-life activities (including intermittent gait and short walking bouts). To evaluate its performance on various gait patterns, data were collected on 128 participants from seven cohorts: healthy young and older adults, patients with Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease, congestive heart failure, and proximal femur fracture. Moreover, INDIP usability was evaluated by recording 2.5-h of real-world unsupervised activity. Results and discussion: Excellent absolute agreement (ICC >0.95) and very limited mean absolute errors were observed for all cohorts and digital mobility outcomes (cadence ≤0.61 steps/min, stride length ≤0.02 m, walking speed ≤0.02 m/s) in the structured tests. Larger, but limited, errors were observed during the daily-life simulation (cadence 2.72-4.87 steps/min, stride length 0.04-0.06 m, walking speed 0.03-0.05 m/s). Neither major technical nor usability issues were declared during the 2.5-h acquisitions. Therefore, the INDIP system can be considered a valid and feasible solution to collect reference data for analyzing gait in real-world conditions.

Base of Support, Step Length and Stride Width Estimation during Walking Using an Inertial and Infrared Wearable System.

The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground and it is closely related to additional parameters such as step length and stride width. These parameters may be determined in the laboratory using either a stereophotogrammetric system or an instrumented mat. Unfortunately, their estimation in the real world is still an unaccomplished goal. This study aims at proposing a novel, compact wearable system, including a magneto-inertial measurement unit and two time-of-flight proximity sensors, suitable for the estimation of the base of support parameters. The wearable system was tested and validated on thirteen healthy adults walking at three self-selected speeds (slow, comfortable, and fast). Results were compared with the concurrent stereophotogrammetric data, used as the gold standard. The root mean square errors for the step length, stride width and base of support area varied from slow to high speed between 10-46 mm, 14-18 mm, and 39-52 cm2, respectively. The mean overlap of the base of support area as obtained with the wearable system and with the stereophotogrammetric system ranged between 70% and 89%. Thus, this study suggested that the proposed wearable solution is a valid tool for the estimation of the base of support parameters out of the laboratory.

Running speed changes the distribution of excitation within the biceps femoris muscle in 80 m sprints.

Predictors and mitigators of strain injuries have been studied in sprint-related sports. While the rate of axial strain, and thus running speed, may determine the site of muscle failure, muscle excitation seemingly offers protection against failure. It seems therefore plausible to ask whether running at different speeds changes the distribution of excitation within muscles. Technical limitations undermine, however, the possibility of addressing this issue in high-speed, ecological conditions. Here, we circumvent these limitations with a miniaturized, wireless, multi-channel amplifier, suited for collecting spatio-temporal data and high-density surface electromyograms (EMGs) during overground running. We segmented running cycles while 8 experienced sprinters ran at speeds close to (70% and 85%) and at (100%) their maximum, over an 80 m running track. Then, we assessed the effect of running speed on the distribution of excitation within biceps femoris (BF) and gastrocnemius medialis (GM). Statistical parametric mapping (SPM) revealed a significant effect of running speed on the amplitude of EMGs for both muscles, during late swing and early stance. Paired SPM revealed greater EMG amplitude when comparing 100% with 70% running speed for BF and GM. Regional differences in excitation were observed only for BF, however. As running speed increased from 70% to 100% of the maximum, a greater degree of excitation was observed at more proximal BF regions (from 2% to 10% of the thigh length) during late swing. We discuss how these results, in the context of the literature, support the protective role of pre-excitation against muscle failure, suggesting the site of BF muscle failure may depend on running speed.

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization.

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.

A large, prospective, multicentre study of left main PCI using a latest-generation zotarolimus-eluting stent: the ROLEX study.

BACKGROUND: Data on left main (LM) percutaneous coronary interventions (PCI) have mostly been obtained in studies using drug-eluting stent (DES) platforms without dedicated large-vessel devices and with limited expansion capability. AIMS: Our study aimed to investigate the safety and efficacy of LM PCI with the latest-generation Resolute Onyx DES. METHODS: ROLEX (Revascularization Of LEft main with resolute onyX) is a prospective, multicentre study (ClinicalTrials.gov: NCT03316833) enrolling patients with unprotected LM coronary artery disease and a SYNTAX score <33 undergoing PCI with the Resolute Onyx zotarolimus-eluting coronary stent, that includes dedicated extra-large vessel platforms. The primary endpoint (EP) was target lesion failure (TLF): a composite of cardiac death, target vessel myocardial infarction (TVMI) and ischaemia-driven target lesion revascularisation (ID-TLR), at 1 year. All events were adjudicated by an independent clinical event committee. An independent core lab analysed all procedural angiograms. RESULTS: A total of 450 patients (mean age 71.8 years, SYNTAX score 24.5±7.2, acute coronary syndrome in 53%) were enrolled in 26 centres. Of these, 77% of subjects underwent PCI with a single-stent and 23% with a 2-stent technique (8% double kissing [DK] crush, 6% culotte, 9% T/T and small protrusion [TAP] stenting). Intravascular imaging guidance was used in 45% (42% intravascular ultrasound [IVUS], 3% optical coherence tomography [OCT]). At 1 year, the primary EP incidence was 5.1% (cardiac death 2.7%, TVMI 2.7%, ID-TLR 2.0%). The definite/probable stent thrombosis rate was 1.1%. In a prespecified adjusted subanalysis, the primary EP incidence was significantly lower in patients undergoing IVUS/OCT-guided versus angio-guided PCI (2.0 vs 7.6%; hazard ratio [HR] 0.28, 95% confidence interval [CI]: 0.13-0.58; p<0.001). CONCLUSIONS: In this large, multicentre, prospective registry, LM PCI with the Resolute Onyx DES showed good safety and efficacy at 1 year, particularly when guided by intracoronary imaging.

A more-Comers populAtion trEated with an ultrathin struts polimer-free Sirolimus stent: an Italian post-maRketing study (the CAESAR registry).

Introduction: The use of contemporary drug-eluting stents (DES) has significantly improved outcomes of patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI). However, concerns exist regarding the long-term proinflammatory effects of durable polymer coatings used in most DES, potentially leading to long-term adverse events. First-generation polymer-free stent technologies, such as sirolimus- and probucol-eluting stents (PF-SES), have shown an excellent safety and efficacy profile. The aim of this study was to evaluate the safety and efficacy of the new ultrathin Coroflex ISAR NEO PF-SES, in a more-comers PCI population. Methods: The CAESAR (a more-Comers populAtion trEated with an ultrathin struts polimer-free Sirolimus stent: An Italian post-maRketing study) registry is a multicenter, prospective study conducted in Italy, enrolling more-comers CAD patients undergoing PCI with the Coroflex ISAR NEO stent. Patients with left main (LM) disease, cardiogenic shock (CS), or severely reduced left-ventricular ejection fraction (LVEF) were excluded. The primary endpoint was target-lesion revascularization (TLR) at 1 year. Results: A total of 425 patients were enrolled at 13 centers (mean age 66.9 ± 11.6 years, Diabetes mellitus 29%, acute coronary syndrome 67%, chronic total occlusion 9%). Of these, 40.9% had multivessel disease (MVD) and in 3.3% cases, the target lesion was in-stent restenosis (ISR). Clinical device success was reached in 422 (99.6%) cases. At 1 year, only two (0.5%) subjects presented ischemia-driven TLR. The 1-year rates of target vessel revascularization and MACE were 0.5% and 5.1%, respectively. Major bleeding was observed in four (1.0%) patients. Conclusion: In this multicenter, prospective registry, the use of a new ultrathin Coroflex ISAR NEO PF-SES in a more-comers PCI population showed good safety and efficacy at 1 year.

A dual sgRNA-directed CRISPR/Cas9 construct for editing the fruit-specific ß-cyclase 2 gene in pigmented citrus fruits.

CRISPR/Cas9 genome editing is a modern biotechnological approach used to improve plant varieties, modifying only one or a few traits of a specific variety. However, this technology cannot be easily used to improve fruit quality traits in citrus, due to the lack of knowledge of key genes, long juvenile stage, and the difficulty regenerating whole plants of specific varieties. Here, we introduce a genome editing approach with the aim of producing citrus plantlets whose fruits contain both lycopene and anthocyanins. Our method employs a dual single guide RNA (sgRNA)-directed genome editing approach to knockout the fruit-specific ß-cyclase 2 gene, responsible for the conversion of lycopene to beta-carotene. The gene is targeted by two sgRNAs simultaneously to create a large deletion, as well as to induce point mutations in both sgRNA targets. The EHA105 strain of Agrobacterium tumefaciens was used to transform five different anthocyanin-pigmented sweet oranges, belonging to the Tarocco and Sanguigno varietal groups, and 'Carrizo' citrange, a citrus rootstock as a model for citrus transformation. Among 58 plantlets sequenced in the target region, 86% of them were successfully edited. The most frequent mutations were deletions (from -1 to -74 nucleotides) and insertions (+1 nucleotide). Moreover, a novel event was identified in six plantlets, consisting of the inversion of the region between the two sgRNAs. For 20 plantlets in which a single mutation occurred, we excluded chimeric events. Plantlets did not show an altered phenotype in vegetative tissues. To the best of our knowledge, this work represents the first example of the use of a genome editing approach to potentially improve qualitative traits of citrus fruit.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

BACKGROUND: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION: ISRCTN-12246987.

Design of the PERSEO Registry on the management of patients treated with oral anticoagulants and coronary stent.

AIM: Percutaneous coronary intervention with stent implantation (PCI-S) in patients requiring chronic oral anticoagulant therapy (OAC) is associated with an increased risk of bleeding and ischemic complications. Different randomized studies showed a significant advantage of a double antithrombotic therapy and superiority of direct oral anticoagulant (DOAC) compared with warfarin, but real-world data are limited. Aim is to evaluate the antithrombotic management and clinical outcome of patients with an indication for OAC who undergo PCI-S in a 'real-world' setting. METHODS: The multicentre prospective observational PERSEO (PERcutaneouS coronary intErventions in patients treated with Oral anticoagulant therapy) Registry (ClinicalTrials.gov Identifier: NCT03392948) has been designed to enrol patients requiring OAC treated by PCI-S in 25 Italian centres. A target of at least 1080 patients will be followed for 1 year and data on thromboembolic and bleeding events and changes in antithrombotic therapy will be registered. The primary end point is a combined measure of efficacy and safety outcome (NACE), including major bleeding events and major adverse cardiac and cerebral events at 1-year follow-up in patients treated with DOAC (and dual or triple antiplatelet therapy) compared with the corresponding strategies with vitamin K antagonists. A secondary prespecified analysis has been defined to evaluate NACE in dual versus triple antithrombotic therapy after hospital discharge at 1-year follow-up. CONCLUSION: The PERSEO Registry will investigate in a 'real world' setting the safety and efficacy of DOAC versus warfarin and dual versus triple antithrombotic therapy in patients with indication for oral anticoagulant therapy who undergo PCI-S.

Expression of Clementine Asp-Rich Proteins (CcASP-RICH) in Tobacco Plants Interferes with the Mechanism of Pollen Tube Growth.

Low-molecular-weight, aspartic-acid-rich proteins (ASP-RICH) have been assumed to be involved in the self-incompatibility process of clementine. The role of ASP-RICH is not known, but hypothetically they could sequester calcium ions (Ca2+) and affect Ca2+-dependent mechanisms. In this article, we analyzed the effects induced by clementine ASP-RICH proteins (CcASP-RICH) when expressed in the tobacco heterologous system, focusing on the male gametophyte. The aim was to gain insight into the mechanism of action of ASP-RICH in a well-known cellular system, i.e., the pollen tube. Pollen tubes of tobacco transgenic lines expressing CcASP-RICH were analyzed for Ca2+ distribution, ROS, proton gradient, as well as cytoskeleton and cell wall. CcASP-RICH modulated Ca2+ content and consequently affected cytoskeleton organization and the deposition of cell wall components. In turn, this affected the growth pattern of pollen tubes. Although the expression of CcASP-RICH did not exert a remarkable effect on the growth rate of pollen tubes, effects at the level of growth pattern suggest that the expression of ASP-RICH may exert a regulatory action on the mechanism of plant cell growth.

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson's disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient ( I C C 2,1 )] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found ( I C C 2,1 = 0.817 -   0.999 ). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments.

Transcriptome Analysis of Plenodomus tracheiphilus Infecting Rough Lemon (Citrus jambhiri Lush.) Indicates a Multifaceted Strategy during Host Pathogenesis.

The causal agent of mal secco disease is the fungus Plenodomus tracheiphilus, mainly affecting lemon tree survival in the Mediterranean area. Using a fully compatible host-pathogen interaction, the aim of our work was to retrieve the fungus transcriptome by an RNA seq approach during infection of rough lemon (Citrus jambhiri Lush.) to identify crucial transcripts for pathogenesis establishment and progression. A total of 2438 clusters belonging to P. tracheiphilus were retrieved and classified into the GO and KEGG categories. Transcripts were categorized mainly within the "membrane", "catalytic activity", and "primary metabolic process" GO terms. Moreover, most of the transcripts are included in the "ribosome", "carbon metabolism", and "oxidative phosphorylation" KEGG categories. By focusing our attention on transcripts with FPKM values higher than the median, we were able to identify four main transcript groups functioning in (a) fungus cell wall remodeling and protection, (b) destroying plant defensive secondary metabolites, (c) optimizing fungus development and pathogenesis, and (d) toxin biosynthesis, thus indicating that a multifaceted strategy to subdue the host was executed.