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Cultivation and breeding of legumes as protein sources in the human or animal diet could benefit from accurate, rapid, and non-invasive measurements of protein content. A study was conducted into the feasibility of a fully non-invasive, in vivo protein measurement methodology applied to soybean (Glycine max. L.). The proposed methodology works by recording spectral images of the soybean pods in the visible and near-infrared (Vis-NIR), a rule-based segmentation approach, and partial least squares (PLS) regression to predict the crude protein content of the beans contained within the imaged pods. Using all 150 channels of the spectral camera, a model could be calibrated with a mean absolute precision error (MAPE) of 4.8 % (R2 = 0.92). Applying a tailored feature elimination approach to select only eight spectral bands and degrading the spectral resolution to 25 nm yields a model with a MAPE of 6.0 % (R2 = 0.88), indicating the potential for multispectral cameras in this application.

Greenhouses are complex systems whose size, shape, construction material, and equipment for climate control, lighting and heating can vary largely. The greenhouse design can, together with the outdoor weather conditions, have a large impact on the economic performance and the environmental consequences of the production. The aim of this study was to identify a greenhouse design out of several feasible designs that generated the highest net financial return (NFR) and lowest energy use for seasonal tomato production across Norway. A model-based greenhouse design method, which includes a module for greenhouse indoor climate, a crop growth module for yield prediction, and an economic module, was applied to predict the NFR and energy use. Observed indoor climate and tomato yield were predicted using the climate and growth modules in a commercial greenhouse in southwestern Norway (SW) with rail and grow heating pipes, glass cover, energy screens, and CO2-enrichment. Subsequently, the NFR and fossil fuel use of five combinations of these elements relevant to Norwegian conditions were determined for four locations: Kise in eastern Norway (E), Maere in midwestern Norway (MW), Orre in southwestern Norway (SW) and Tromso in northern Norway (N). Across designs and locations, the highest NFR was 47.6 NOK m−2 for the greenhouse design with a night energy screen. The greenhouse design with day and night energy screens, fogging and mechanical cooling and heating having the lowest fossil energy used per m2 in all locations had an NFR of −94.8 NOK m−2. The model can be adapted for different climatic conditions using a variation in the design elements. The study is useful at the practical and policy level since it combines the economic module with the environmental impact to measure CO2 emissions.

Introduction: The approval of 9-δ-tetrahydocannabinol (THC)+cannabidiol (CBD) oromucosal spray (Sativex®) in Italy as an add-on medication for the management of moderate to severe spasticity in multiple sclerosis (MS) has provided a new opportunity for MS patients with drug-resistant spasticity. We aimed to investigate the improvement of MS spasticity-related symptoms in a large cohort of patients with moderate to severe spasticity in daily clinical practice. Materials and methods: MS patients with drug-resistant spasticity were recruited from 30 Italian MS centers. All patients were eligible for THC:CBD treatment according to the approved label: ≥ 18 years of age, at least moderate spasticity (MS spasticity numerical rating scale [NRS] score ≥ 4) and not responding to the common antispastic drugs. Patients were evaluated at baseline (T0) and after 4 weeks of treatment (T1) with the spasticity NRS scale and were also asked about meaningful improvements in 6 key spasticity-related symptoms. Results: Out of 1615 enrolled patients, 1432 reached the end of the first month trial period (T1). Of these, 1010 patients (70.5%) reached a ≥ 20% NRS score reduction compared with baseline (initial responders; IR). We found that 627 (43.8% of 1432) patients showed an improvement in at least one spasticity-related symptom (SRSr group), 543 (86.6%) of them belonging to the IR group and 84 (13.4%) to the spasticity NRS non-responders group. Conclusion: Our study confirmed that the therapeutic benefit of cannabinoids may extend beyond spasticity, improving spasticity-related symptoms even in non-NRS responder patients. © 2020, Fondazione Società Italiana di Neurologia.

Tomato greenhouses at high latitudes (≥58°North) require supplemental light to enable high yields and year-round production. Supplemental light systems can differ in lamp type, high-pressure sodium (HPS) or light emitting diode (LED), and also vary in lamp capacity. Based on a combined greenhouse climate, tomato yield, and greenhouse economics model, a methodology was developed, for determining the optimal supplemental light system, dependent on local climate and economic conditions. Two optimisation objectives were considered separately, maximal energy use efficiency (EUE) and maximal net financial result (NFR). The developed methodology was applied to four different greenhouse locations in Norway. At each location, both optimisation objectives were reached with LEDs. The optimal lamp capacities range from 256 to 341 μmol m−2 s−1 (maximal EUE) and 302–323 μmol m−2 s−1 (maximal NFR). The economically optimal lamp capacity is little sensitive to climate conditions. At the lamp type respective NFR maxima, LEDs resulted, on average, in 10% higher tomato yield, 102.2 NOK m−2 year−1 higher NFR, and 35% higher EUE. Consequently, switching from HPS lamps to LEDs enables increasing productivity, energy efficiency and profitability of greenhouse tomato production. Furthermore, the difference between EUE and NFR optima was, on average, 24% lower in terms of EUE and 56% lower in terms of NFR, when using LEDs instead of HPS lamps. On farm-scale, the proposed methodology can be used as decision-support-tool for selecting an efficient and profitable supplemental light system for greenhouse tomato production, dependent on local climate and economic conditions.

The world population is increasing rapidly together with the demand for healthy fresh food. Greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) reaches breakthroughs in several areas, however, not yet in horticulture. An international competition on “autonomous greenhouses” aims in combining horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams with different background in horticulture and AI participated in a growing experiment. Each team had a 96-m2 modern greenhouse compartment at WUR to grow remotely a cucumber crop ('Hi-Power') during a 4-months period. Each compartment was equipped with standard actuators and sensors, heating, ventilation, screening, artificial lighting, fogging, CO2 supply and irrigation. Climate control set points were remotely determined by teams using own AI algorithms, actuators were operated by a climate and irrigation process computer. Additionally, teams sent instructions for the crop pruning strategy. Measurements and set points were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a reference compartment, operated manually by three commercial growers. Teams used additional individually chosen sensors such as RGB or thermal cameras, temperature-humidity-light-sensor-networks, root-zone sensors, sap-flow meters or crop-weighing sensors. Teams' strategies for remote control ranged from supervised, unsupervised and reinforcement machine learning. Teams were judged and received points on several aspects: 50% for net profit, 20% for sustainability indicators, energy- and water-use-efficiency and CO2 consumption, 30% for their artificial intelligence algorithms, novelty, level of autonomous control, robustness and scalability. The results obtained by different teams in terms of climate and growing strategies, different resource use efficiency and net profit are presented in this paper. One of the AI-controlled compartments achieved overall better results than the manual grown reference compartment.

A greenhouse climate-crop yield model was adapted to include additional climate modification techniques suitable for enabling sustainable greenhouse management at high latitudes. Additions to the model were supplementary lighting, secondary heating and heat harvesting technologies. The model: 1) included the impact of different light sources on greenhouse air temperature and tomato production 2) included a secondary heating system 3) calculated the amount of harvested heat whilst lighting was used. The crop yield model was not modified but it was validated for growing tomato in a semi-closed greenhouse equipped with HPS lamps (top-lights) and LED (inter-lights) in Norway. The combined climate-yield model was validated with data from a commercial greenhouse in Norway. The results showed that the model was able to predict the air temperature with sufficient accuracy during the validation periods with Relative Root Mean Square Error

Background and purpose: Tumefactive multiple sclerosis (TuMS) (i.e., MS onset presenting with tumefactive demyelinating lesions [TDLs]) is a diagnostic and therapeutic challenge. We performed a multicentre retrospective study to describe the clinical characteristics and the prognostic factors of TuMS. Methods: One hundred two TuMS patients were included in this retrospective study. Demographic, clinical, magnetic resonance imaging (MRI), laboratory data and treatment choices were collected. Results: TuMS was found to affect women more than men (female:male: 2.4), with a young adulthood onset (median age: 29.5years, range: 11–68 years, interquartile range [IQR]: 38 years). At onset, 52% of TuMS patients presented with the involvement of more than one functional system and 24.5% of them with multiple TDLs. TDLs most frequently presented with an infiltrative MRI pattern (38.7%). Cerebrospinal fluid immunoglobulin G oligoclonal bands were often demonstrated (76.6%). In 25.3% of the cases, more than one acute-phase treatment was administered, and almost one-half of the patients (46.6%) were treated with high-efficacy treatments. After a median follow-up of 2.3years (range: 0.1–10.7 years, IQR: 3.4 years), the median Expanded Disability Status Scale (EDSS) score was 1.5 (range: 0–7, IQR: 2). Independent risk factors for reaching an EDSS score ≥3 were a higher age at onset (odds ratio [OR]: 1.08, 95% confidence interval [CI]: 1.03–1.14, p&nbsp

Attention to urban agriculture (UA) has recently grown among practitioners, scientists, and the public, resulting in several initiatives worldwide. Despite the positive perception of modern UA and locally grown, fresh produce, the potential food safety risks connected to these practices may be underestimated, leading to regulatory gaps. Thus, there is a need for assessment tools to evaluate the food safety risks connected to specific UA initiatives, to assist practitioners in self-evaluation and control, and to provide policy makers and scholars a means to pursue and assess food safety in city regions, avoiding either a lack or an excess of regulation that could ultimately hinder the sector. To address this aim, this paper reviews the most recent and relevant literature on UA food safety assessments. Food safety indicators were identified first. Then, a food safety assessment framework for UA initiatives was developed. The framework uses business surveys and food analyses (if available) as a data source for calculating a food safety index for single UA businesses and the whole UA landscape of a given city region. The proposed framework was designed to allow its integration into the CRFS (City Region Food System) toolkit developed by FAO (Food and Agriculture Organization of the United Nations), RUAF foundation (Resource Centres on Urban Agriculture and Food Security) and Wilfrid Laurier University., Highlights • Connection of several biological and chemical food safety risks to UA techniques. • Identifiable food safety risk factors for diverse UA practices. • Framework for the assessment of food safety levels of UA initiatives. • Development of a risk-based assessment that can be integrated into the FAO CRFS framework.

Unidad de excelencia María de Maeztu CEX2019-000940-M The COVID-19 pandemic unveiled the fragility of food sovereignty in cities and confirmed the close connection urban dwellers have with food. Although the pandemic was not responsible for a systemic failure, it suggested how citizens would accept and indeed support a transition toward more localized food production systems. As this attitudinal shift is aligned with the sustainability literature, this work aims to explore the tools and actions needed for a policy framework transformation that recognizes the multiple benefits of food systems, while considering local needs and circumstances. This perspective paper reviews the trends in production and consumption, and systematizes several impacts emerged across European food systems in response to the first wave of pandemic emergency, with the final aim of identifying challenges and future strategies for research and innovation toward the creation of resilient and sustainable city/region food systems. The proposal does not support a return to traditional small-scale economies that might not cope with the growing global population. It instead stands to reconstruct and upscale such connections using a "think globally act locally" mind-set, engaging local communities, and making existing and future citizen-led food system initiatives more sustainable. The work outlines a set of recommended actions for policy-makers: support innovative and localized food production, training and use of information and communication technology for food production and distribution; promote cross-pollination among city/region food systems; empower schools as agents of change in food provision and education about food systems; and support the development of assessment methodologies and the application of policy tools to ensure that the different sustainability dimensions of the food chain are considered.

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second &ldquo, Autonomous Greenhouse Challenge&rdquo, In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.

IntroductionDelta-δ-tetrahydrocannabinol and cannabidiol (THC:CBD) oromucosal spray is used as an add-on therapy option for moderate to severe multiple sclerosis (MS) spasticity resistant to other medications. Aims of this study were to provide real-life data on long-term clinical outcomes in a large population of Italian patients treated with THC:CBD and to evaluate predictors of THC:CBD therapy continuation.Materials and methodsThis prospective observational multicentre Italian study screened all patients with MS consecutively included in the Agenzia Italiana del Farmaco e-registry at the start of THC:CBD treatment (baseline), after 4 weeks (T1), 12±3 weeks (T2), 24±3 weeks (T3), 48±3 weeks (T4) and 72±3 weeks (T5) from baseline.ResultsA total of 1845 patients were recruited from 32 MS Italian centres. At T1, 1502 (81.4%) of patients reached a Numerical Rating Scale (NRS) improvement of ≥20%, with an NRS reduction of 26.9% at T1 and of 34.4% at T5. At T5, 725 patients (48.3% of 1502) discontinued treatment with highest discontinuation rate at T2 and T3. Daily number of puffs was generally stable through the observation period. The multivariate analysis showed that higher NRS scores at baseline (OR 2.28, 95% CI 1.15 to 6.36, pDiscussionTHC:CBD effects were sustained for 18 months with a relatively stable number of puffs per day. About 50% of patients abandoned THC:CBD therapy for loss of efficacy or adverse events.

Definition of benign multiple sclerosis (BMS) remains controversial. Moreover, a sizeable proportion of classically defined BMS patients may be no longer benign (NLB) when re-assessed in the long-term. In a previous work, we found that after a five-year follow-up, a clinical score was able to identify patients at risk of losing their benign status.In this 12-year reappraisal of the same cohort, we aimed at assessing the predictive value of the same score in the long-term.After a mean follow-up of 12.6 + 0.4 years, patients still having an EDSS score 3.0 were classified as "still benign" (SB), whereas patients having an EDSS score ≥3.5 were defined as NLB. The predictive value of the mentioned score was re-assessed using survival analysis.By the end of the follow-up, 20 (32.8%) were classified as NLB. Patients were grouped on the basis of the above mentioned score. Patients with score 2-3 were at higher risk of NLB status at the follow-up (HR = 3.5; 95%CI 1.5-8.6; p = 0.005, accuracy = 70.5%).In patients with established BMS, prognostic prediction of longer-term disease course remains of critical value. In this study, a clinical score was able to predict disease evolution in the long term.

Background: The study of cognitive reserve (CR) in relationship with cognitive impairment (CI) in pediatric-onset multiple sclerosis (POMS) may provide cues to identifying subjects at higher risk of impairment and scope for therapeutic strategies. Objectives: To assess the potential impact of CR on cognition in a cohort of POMS patients. Methods: In all, 48 POMS patients were followed up for 4.7 ± 0.4 years. CI was defined as the failure of ⩾3 tests on an extensive neuropsychological battery. Change of neuropsychological performance was assessed through the Reliable Change Index (RCI) method. At baseline, CR was estimated by measuring the intelligence quotient (IQ). The relationships were assessed through multivariable regression analyses. Results: At baseline, CI was detected in 14/48 (29.2%) patients. Two out of 57 healthy control (HC; 3.5%) met the same criteria of CI ( p Conclusion: Our results suggest that higher CR in POMS patients may protect from CI, particularly in subjects with initial cognitive preservation, providing relevant implications for counseling and rehabilitation strategies.

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but encounters difficulties finding skilled staff to manage crop production. Artificial intelligence (AI) has reached breakthroughs in several areas, however, not yet in horticulture. An international competition on &ldquo, autonomous greenhouses&rdquo, aimed to combine horticultural expertise with AI to make breakthroughs in fresh food production with fewer resources. Five international teams, consisting of scientists, professionals, and students with different backgrounds in horticulture and AI, participated in a greenhouse growing experiment. Each team had a 96 m2 modern greenhouse compartment to grow a cucumber crop remotely during a 4-month-period. Each compartment was equipped with standard actuators (heating, ventilation, screening, lighting, fogging, CO2 supply, water and nutrient supply). Control setpoints were remotely determined by teams using their own AI algorithms. Actuators were operated by a process computer. Different sensors continuously collected measurements. Setpoints and measurements were exchanged via a digital interface. Achievements in AI-controlled compartments were compared with a manually operated reference. Detailed results on cucumber yield, resource use, and net profit obtained by teams are explained in this paper. We can conclude that in general AI performed well in controlling a greenhouse. One team outperformed the manually-grown reference.

Background: Few data are available on very long-term follow-up of pediatric multiple sclerosis (MS) patients treated with disease modifying treatments (DMTs). Objectives: To present a long-term follow-up of a cohort of Pediatric-MS patients starting injectable first-line agents. Methods: Data regarding treatments, annualized relapse rate (ARR), Expanded Disability Status Scale (EDSS) score, and serious adverse event were collected. Baseline characteristics were tested in multivariate analysis to identify predictors of disease evolution. Results: In total, 97 patients were followed for 12.5 ± 3.3 years. They started therapy at 13.9 ± 2.1 years, 88 with interferons and 9 with copaxone. During the whole follow-up, 82 patients changed therapy, switching to immunosuppressors/second-line treatment in 58% of cases. Compared to pre-treatment phase, the ARR was significantly reduced during the first treatment (from 3.2 ± 2.6 to 0.7 ± 1.5, p Conclusion: Pediatric-MS patients benefited from interferons/copaxone, but the majority had to switch to more powerful drugs. Starting therapy before 12 years of age could lead to a more favorable outcome.

Background: Recently, a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) has been developed as an international and standardized brief cognitive test, which is easily performed in everyday clinical practice for neuropsychological assessment in multiple sclerosis (MS). However, we need to gather more information about this tool compared to other neuropsychological batteries. The aim of our study is to compare the performance of BICAMS and Brief Repeatable Battery (BRB) in MS subjects. Methods: Tests of the BRB and BICAMS were administered to MS patients recruited from 11 Italian MS centres. Cognitive impairment (CI) was defined as the failure on at least two tests (scores below the fifth percentile) on the BRB and as the failure on at least one test of the BICAMS. The agreement between the performances on the two batteries was assessed through Cohen's K statistic. Finally we calculated the effects sizes for each test of the two batteries using Cohen's d. Results: The two batteries were administered to 192 MS patients (142 women, 50 men; mean age 41.4 ± 10.8 years, mean education 12.3 ± 3.5 years). Mean scores of patients were lower compared to those of healthy subjects in all the cognitive measures examined. Forty-six MS patients were identified as impaired and 48 as unimpaired on both of the batteries, when the Symbol Digit Modalities Test (SDMT) was included in the analysis. Cohen's K statistic was 0.46 which corresponds to a moderate accord. If the SDMT was excluded from the BRB, 37 MS patients were identified as impaired and 57 as unimpaired on both of the batteries. Cohen's K statistic was 0.3 which corresponds to a poor accord. The SDMT, the Paced Auditory Serial Addition Test (PASAT) 3 and 2 yielded higher d values (SDMT 0.83, PASAT 3 0.65, PASAT 2 0.84). Conclusions: This study confirms the feasibility of BICAMS in everyday clinical practice for the identification of CI and highlights the good psychometric properties of the SDMT.

We report the case of a 83-year-old woman suffering from Restless Legs Syndrome since 2002 and Multiple Sclerosis since 2007. She had been diagnosed as Restless Legs Syndrome five years before multiple sclerosis, with the support of a polysomnographic examination. The clinical diagnosis of multiple sclerosis took place at the age of 75, when she complained of walking difficulties and abnormal sensitivity in lower limbs, especially in the evening. Associated symptoms included dysesthesias on the left leg and arm and left emitrunk, visual acuity reduction, blurred vision and fatigue. The brain magnetic resonance showed multiple lesions in white matter, inconsistent with a vascular disease but suggestive for a demyelinating disease. She was admitted at the hospital, where the spinal fluid examination and a second magnetic resonance confirmed the diagnosis. Since that, the patient regularly performed medical examinations and magnetic resonance controls which didn't show any increase of lesions burden nor pathological enhancement, but a slow worsening of ambulation. Due to the patient’s age, a disease modifying therapy for multiple sclerosis was not established, the only drugs being represented by symptomatic agents.