Search

Effects of cold temperatures on the early stages of maize (Zea mays L.). A review. Introduction. Maize is a crop of great economical importance. Despite its tropical origin and its high sensitivity to low temperatures, maize is now cultivated in a wide range of latitudes. To maintain competitive yields, many genetic and agricultural adaptations have been implemented. Literature. Plant responses to environmental stresses such as drought or cold have been extensively studied. This article reviews the progress and current knowledge regarding the effects of non-freezing cold temperatures on maize plants, from germination to floral transition. During the early stages of development, cold alters plant phenology and productivity, mainly because of its negative impact on photosynthesis. Plant growth is affected and secondary osmotic and oxidative stresses induce cell damage. Conclusion. A better understanding of the effects of cold will allow the development of new strategies for improving plant tolerance through the use of various physiological, genetic and molecular approaches.

Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot., (© 2023. Springer Nature Limited.)

Service robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity is to exploit compliant end-effectors to address the manipulation of deformable objects. However, the intrinsic compliance of such grippers results in increased difficulties in grasping control. Within the described context, this work addresses the problem of optimizing the grasping of deformable objects making use of a compliant, under-actuated, sensorless robotic hand. The main aim of the paper is, therefore, finding the best position and joint configuration for the mentioned robotic hand to grasp an unforeseen deformable object based on collected RGB image and partial point cloud. Due to the complex grasping dynamics, learning-from-simulations approaches (e.g., Reinforcement Learning) are not effective in the faced context. Thus, trial-and-error-based methodologies have to be exploited. In order to save resources, a samples-efficient approach has to be employed. Indeed, a Bayesian approach to address the optimization of the grasping strategy is proposed, enhancing it with transfer learning capabilities to exploit the acquired knowledge to grasp (partially) new objects. A PAL Robotics TIAGo (a mobile manipulator with a 7-degrees-of-freedom arm and an anthropomorphic underactuated compliant hand) has been used as a test platform, executing a pouring task while manipulating plastic (i.e., deformable) bottles. The sampling efficiency of the data-driven learning is shown, compared to an evenly spaced grid sampling of the input space. In addition, the generalization capability of the optimized model is tested (exploiting transfer learning) on a set of plastic bottles and other liquid containers, achieving a success rate of the 88%., (© 2022. The Author(s).)

Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients' status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) "high-level" training modalities, 2) "low-level" control strategies, and 3) "hardware-level" implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works., Competing Interests: SD, AP, FB, and MG hold shares in AGADE Srl, Milano, Italy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dalla Gasperina, Roveda, Pedrocchi, Braghin and Gandolla.)

Background: Several models have been developed to predict mortality in patients with COVID-19 pneumonia, but only a few have demonstrated enough discriminatory capacity. Machine learning algorithms represent a novel approach for the data-driven prediction of clinical outcomes with advantages over statistical modeling., Objective: We aimed to develop a machine learning-based score-the Piacenza score-for 30-day mortality prediction in patients with COVID-19 pneumonia., Methods: The study comprised 852 patients with COVID-19 pneumonia, admitted to the Guglielmo da Saliceto Hospital in Italy from February to November 2020. Patients' medical history, demographics, and clinical data were collected using an electronic health record. The overall patient data set was randomly split into derivation and test cohorts. The score was obtained through the naïve Bayes classifier and externally validated on 86 patients admitted to Centro Cardiologico Monzino (Italy) in February 2020. Using a forward-search algorithm, 6 features were identified: age, mean corpuscular hemoglobin concentration, PaO 2 /FiO 2 ratio, temperature, previous stroke, and gender. The Brier index was used to evaluate the ability of the machine learning model to stratify and predict the observed outcomes. A user-friendly website was designed and developed to enable fast and easy use of the tool by physicians. Regarding the customization properties of the Piacenza score, we added a tailored version of the algorithm to the website, which enables an optimized computation of the mortality risk score for a patient when some of the variables used by the Piacenza score are not available. In this case, the naïve Bayes classifier is retrained over the same derivation cohort but using a different set of patient characteristics. We also compared the Piacenza score with the 4C score and with a naïve Bayes algorithm with 14 features chosen a priori., Results: The Piacenza score exhibited an area under the receiver operating characteristic curve (AUC) of 0.78 (95% CI 0.74-0.84, Brier score=0.19) in the internal validation cohort and 0.79 (95% CI 0.68-0.89, Brier score=0.16) in the external validation cohort, showing a comparable accuracy with respect to the 4C score and to the naïve Bayes model with a priori chosen features; this achieved an AUC of 0.78 (95% CI 0.73-0.83, Brier score=0.26) and 0.80 (95% CI 0.75-0.86, Brier score=0.17), respectively., Conclusions: Our findings demonstrated that a customizable machine learning-based score with a purely data-driven selection of features is feasible and effective for the prediction of mortality among patients with COVID-19 pneumonia., (©Geza Halasz, Michela Sperti, Matteo Villani, Umberto Michelucci, Piergiuseppe Agostoni, Andrea Biagi, Luca Rossi, Andrea Botti, Chiara Mari, Marco Maccarini, Filippo Pura, Loris Roveda, Alessia Nardecchia, Emanuele Mottola, Massimo Nolli, Elisabetta Salvioni, Massimo Mapelli, Marco Agostino Deriu, Dario Piga, Massimo Piepoli. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2021.)

Human-robot cooperation is increasingly demanded in industrial applications. Many tasks require the robot to enhance the capabilities of humans. In this scenario, safety also plays an important role in avoiding any accident involving humans, robots, and the environment. With this aim, the paper proposes a cooperative fuzzy-impedance control with embedded safety rules to assist human operators in heavy industrial applications while manipulating unknown weight parts. The proposed methodology is composed by four main components: (i) an inner Cartesian impedance controller (to achieve the compliant robot behavior), (ii) an outer fuzzy controller (to provide the assistance to the human operator), (iii) embedded safety rules (to limit force/velocity during the human-robot interaction enhancing safety), and (iv) a neural network approach (to optimize the control parameters for the human-robot collaboration on the basis of the target indexes of assistance performance defined for this purpose). The main achieved result refers to the capability of the controller to deal with uncertain payloads while assisting and empowering the human operator, both embedding in the controller safety features at force and velocity levels and minimizing the proposed performance indexes. The effectiveness of the proposed approach is verified with a KUKA iiwa 14 R820 manipulator in an experimental procedure where human subjects evaluate the robot performance in a collaborative lifting task of a 10 kg part., (Copyright © 2019 Roveda, Haghshenas, Caimmi, Pedrocchi and Molinari Tosatti.)

Background: European Flint maize inbred lines are used as a source of adaptation to cold in most breeding programs in Northern Europe. A deep understanding of their adaptation strategy could thus provide valuable clues for further improvement, which is required in the current context of climate change. We therefore compared six inbreds and two derived Flint x Dent hybrids for their response to one-week at low temperature (10 °C day/7 or 4 °C night) during steady-state vegetative growth., Results: Leaf growth was arrested during chilling treatment but recovered fast upon return to warm temperature, so that no negative effect on shoot biomass was measured. Gene expression analyses of the emerging leaf in the hybrids suggest that plants maintained a 'ready-to-grow' state during chilling since cell cycle genes were not differentially expressed in the division zone and genes coding for expansins were on the opposite up-regulated in the elongation zone. In photosynthetic tissues, a strong reduction in PSII efficiency was measured. Chilling repressed chlorophyll biosynthesis; we detected accumulation of the precursor geranylgeranyl chlorophyll a and down-regulation of GERANYLGERANYL REDUCTASE (GGR) in mature leaf tissues. Excess light energy was mostly dissipated through fluorescence and constitutive thermal dissipation processes, rather than by light-regulated thermal dissipation. Consistently, only weak clues of xanthophyll cycle activation were found. CO 2 assimilation was reduced by chilling, as well as the expression levels of genes encoding phosphoenolpyruvate carboxylase (PEPC), pyruvate orthophosphate dikinase (PPDK), and the small subunit of Rubisco. Accumulation of sugars was correlated with a strong decrease of the specific leaf area (SLA)., Conclusions: Altogether, our study reveals good tolerance of the photosynthetic machinery of Northern European maize to chilling and suggests that growth arrest might be their strategy for fast recovery after a mild stress.

Background: Circulating tumor cells (CTCs) represent one of the most interesting target in improving diagnosis, prognosis and treatment. Herein we evaluate the possibility of using an emo-cytometric approach on the evaluation of the heterogeneous population of CTCs to improve personalized metastatic risk assessment. We benchmarked ex vivo behavior of distinct subsets of circulating colon tumor cells with correspondent clinical behavior of patients from which we isolated CTCs., Methods: Isolation and CTC expansion were performed by a gradient protocol. In vitro characterization was determined by flow cytometry, immunofluorescence, western blotting and proteomic profiling. Cell sorter was performed with immunomagnetic beads. Confocal microscopy was used to evaluate tissue sections. Kaplan Mayer curves was cared for through Medcalc program., Results: We collected heterogeneous CTCs, derived from the whole blood of seven patients affected by colon cancer, expressing CD133(pos)CD45(neg) (5 ± 1) and (2 ± 1) and CK20(pos)CD45(neg) of (29 ± 3) (11 ± 1) cells/ml in Dukes D and A stage respectively. Proliferation rate of 57 ± 16 %, expression for CXCR4(pos) of 18 ± 7 % and detectable levels of IL-6, IL-8 and SDF-1 cytokines in conditioned culture medium characterized short-time expanded-CTCs (eCTCs). ECTCs organized in tumor sphere were CD45(neg)CD133(pos) while in adhesion were CXCR4(pos)CK20(pos). These two subsets were separately injected in mice. The first group of xenografts developed superficial lesions within 2 weeks. In the second group, in absence of growing tumour, the survival of injected eCTCs was monitored through SDF-1 serum levels detection. The detection of human cancer cells expressing CK20, in mice tissues sections, suggested a different biological behaviour of injected eCTC-subsets: tumorigenic for the first and disseminating for the second. The benchmarking of the experimental data with the clinical course highlights that patients with prevalence of circulating cancer stem cells (CD45(neg)CD133(pos)) have a lower overall survival. Conversely, patients with prevalence of circulating differentiated cells (CXCR4(pos)CK20(pos)) have a low disease-free survival., Conclusion: On the basis of the heterogeneous composition and despite the low number of CTCs, it was possible to distinguish two subgroups of CTCs, suggesting a different clinical outcome. CTC-subsets detailing is useful to better define the metastatic-risk personalized score thus improving disease management and reducing patient care cost.

Nasopharyngeal carcinoma (NPC) is a rare neoplasm which is associated with Epstein-Barr virus (EBV). First of all, we reviewed the literature on NPC treatment. Radio/chemotherapy is currently the gold standard but unfortunately is affected by rates of failure ranging from 7% up to 58%. Because NPC development is promoted by the EBV latent life cycle, EBV-targeted treatments were investigated. Firstly, forcing cytolytic virus activation through administration of gemcitabine and/or valproic acid before administration of a nucleoside analogue showed anti-tumoral activity in vitro as well as in murine model and it was also well tolerated in humans. Secondly, the association of autologous EBV-specific cytotoxic T lymphocytes with chemotherapy correlated with an improved median survival and was safe but not effective versus metastatic lesions. Thirdly, suppression of late membrane protein-1 in the clinic proved controversial because it gave resistance to chemotherapy and, on the other hand, increased radiosensitivity. Finally, we suggest future perspectives for clinical research which should include both prospective and observational cohort studies to assess the role of different risk factors in the development of NPC and the effectiveness of new investigational treatments.

The clinical development of locally and advanced non-small cell lung cancer (NSCLC) suffers from a lack of biomarkers as a guide in the selection of optimal prognostic prediction. Circulating Tumour Cells (CTCs) are correlated to prognosis and show efficacy in cancer monitoring in patients. However, their enumeration alone might be inadequate; it might also be critical to understand the viability, the apoptotic state and the kinetics of these cells. Here, we report what we believe to be a new and selective approach to visually detect tumour specific CTCs. Firstly, using labelled human lung cancer cells, we detected a specific density interval in which NSCL-CTCs were concentrated. Secondly, to better characterize CTCs in respect to their heterogeneous composition and tumour reference, blood and tumour biopsy were performed on specimens taken from the same patient. The approach consisted in comparing phenotype profile of CTCs, and their progenitor Tumour Stem Cells, (TSCs). Moreover, NSCL-CTCs were cultivated in short-time human cultures to provide response to drug sensitivity. Our bimodal approach allowed to reveal two items. Firstly, that one part of a tumour, proximal to the bronchial structure, displays a predominance of CD133+. Secondly, specific NSCL-CTCs Epithelial Cell Adhesion Molecule (EpCAM)+CD29+ can be used as a negative prognostic factor as well the high expression of CTCs EpCAM+. These data were confirmed by drug-sensitivity tests, in vitro, and by the survival curves, in vivo.

The UN1 monoclonal antibody recognized the UN1 antigen as a heavily sialylated and O-glycosylated protein with the apparent molecular weight of 100-120 kDa; this antigen was peculiarly expressed in fetal tissues and several cancer tissues, including leukemic T cells, breast, and colon carcinomas. However, the lack of primary structure information has limited further investigation on the role of the UN1 antigen in neoplastic transformation. In this study, we have identified the UN1 antigen as CD43, a transmembrane sialoglycoprotein involved in cell adhesion, differentiation, and apoptosis. Indeed, mass spectrometry detected two tryptic peptides of the membrane-purified UN1 antigen that matched the amino acidic sequence of the CD43 intracellular domain. Immunological cross-reactivity, migration pattern in mono- and bi-dimensional electrophoresis, and CD43 gene-dependent expression proved the CD43 identity of the UN1 antigen. Moreover, the monosaccharide GalNAc-O-linked to the CD43 peptide core was identified as an essential component of the UN1 epitope by glycosidase digestion of specific glycan branches. UN1-type CD43 glycoforms were detected in colon, sigmoid colon, and breast carcinomas, whereas undetected in normal tissues from the same patients, confirming the cancer-association of the UN1 epitope. Our results highlight UN1 monoclonal antibody as a suitable tool for cancer immunophenotyping and analysis of CD43 glycosylation in tumorigenesis.

Background and Objective: Extracorporeal photochemotherapy (ECP) is a therapeutic approach based on the biological effects of ultraviolet light (UV) - A and psoralens on mononuclear cells collected by apheresis. Recently, ECP has been under investigation as an alternative treatment for various immune and autoimmune diseases. The aim of this study was to evaluate the safety and feasibility of a new three-step ECP technique, in terms of reproducibility, acceptance, tolerability, and short and long term side effects., Design and Methods: Seventeen patients affected by acute or chronic graft-versus-host disease (GvHD), pemphigus vulgaris, or interferon-resistant chronic hepatitis C and one patient being treated for prevention of heart transplant rejection underwent 240 ECP procedures. MNC collection and processing parameters were recorded, biological effects of UV-A/8 methoxy-psoralen (8-MOP) were evaluated, and short and long term side effects were monitored., Results: At a mean follow up of 7 months (range 2-19) 240 ECP had been completed, a mean of 7,136 mL (range 1,998-10,591) of whole blood having beenprocessed per procedure. The mean of total nucleated cells collected per procedure was 6.5x109 (range 0.65-23.8), with a mean MNC percentage of 85% (41. 4-98%) in a mean final volume of 115.5 mL (37-160). No severe side effects were documented and no infectious episodes occurred throughout the course of the treatment., Interpretation and Conclusions: The new ECP technique was highly reproducible as regards the collection and each processing step. Short and long term side effects were mild. No increase in infectious episodes was recorded. All patients willingly underwent ECP, demonstrating an excellent tolerability for the procedure even after several courses.