Corrigendum: Impact of a tobacco sales ban on the frequency of tobacco consumption in India during the COVID-19 pandemic.

[This corrects the article DOI: 10.18332/tid/161855.].

Biologically inspired robotic perception-action for soft fruit harvesting in vertical growing environments.

Multiple interlinked factors like demographics, migration patterns, and economics are presently leading to the critical shortage of labour available for low-skilled, physically demanding tasks like soft fruit harvesting. This paper presents a biomimetic robotic solution covering the full 'Perception-Action' loop targeting harvesting of strawberries in a state-of-the-art vertical growing environment. The novelty emerges from both dealing with crop/environment variance as well as configuring the robot action system to deal with a range of runtime task constraints. Unlike the commonly used deep neural networks, the proposed perception system uses conditional Generative Adversarial Networks to identify the ripe fruit using synthetic data. The network can effectively train the synthetic data using the image-to-image translation concept, thereby avoiding the tedious work of collecting and labelling the real dataset. Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, our platform's action system can coordinate the arm to reach/cut the stem using the Passive Motion Paradigm framework inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. While this article focuses on strawberry harvesting, ongoing research towards adaptation of the architecture to other crops such as tomatoes and sweet peppers is briefly described. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-023-10000-4.

Impact of a tobacco sales ban on the frequency of tobacco consumption in India during the COVID-19 pandemic.

INTRODUCTION: Measures to address the COVID-19 pandemic in India included a ban on the sale and use of tobacco products during 2020 when stay at home guidance (lockdown) was in place. In this study we examined the extent of reduction in frequency of tobacco consumption across all products. METHODS: Telephone survey was conducted between July and August 2020 across an existing cohort of tobacco users (n=801) residing in Delhi (55.4%) and Chennai (44.6%), India. The participants were recruited irrespective of their gender and use of any kind of tobacco product(s). The survey questionnaire was based on the STOP (Studying Tobacco users Of Pakistan) survey and adapted to the context of smoking and smokeless tobacco use in India. RESULTS: Cigarette consumption declined from a median value of 5.0 (IQR: 2-10) sticks in the pre-lockdown period to 2.0 (IQR: 0.4-5) sticks during the lockdown period. Reductions were reported in the daily use of bidis, from 8 (IQR: 4-12) sticks to 5 (IQR: 2-10) sticks and for smokeless tobacco users from 3.5 (IQR: 2-5) packs to 2 (IQR: 1-4) packs during the lockdown. Furthermore, the number of daily cigarette smokers in our cohort decreased from 32.6% (n=261) in the pre-lockdown period to 27.5% (n=220) during lockdown and smokeless tobacco users decreased from 35.8% (n=287) in pre-lockdown period to 30.3% (n=243) during the lockdown period. CONCLUSIONS: The decrease in tobacco use can be attributed to various societal and environmental factors. However, the pandemic-linked lockdown provided an opportune condition to reduce the use of tobacco products, which could be due to restricted access and increase in health awareness during the COVID-19 lockdown.

Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics.

Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to 'action' that do not cause any overt movement. Indeed for any complex body, human or embodied robot inhabiting unstructured environments, the dual processes of shaping motor output during action execution and providing the self with information related to feasibility, consequence and understanding of potential actions (of oneself/others) must seamlessly alternate during goal-oriented behaviors, social interactions. While prominent approaches like Optimal Control, Active Inference converge on the role of forward models, they diverge on the underlying computational basis. In this context, revisiting older ideas from motor control like the Equilibrium Point Hypothesis and synergy formation, this article offers an alternative perspective emphasizing the functional role of a 'plastic, configurable' internal representation of the body (body-schema) as a critical link enabling the seamless continuum between motor control and imagery. With the central proposition that both "real and imagined" actions are consequences of an internal simulation process achieved though passive goal-oriented animation of the body schema, the computational/neural basis of muscleless motor synergies (and ensuing simulated actions without movements) is explored. The rationale behind this perspective is articulated in the context of several interdisciplinary studies in motor neurosciences (for example, intracranial depth recordings from the parietal cortex, FMRI studies highlighting a shared cortical basis for action 'execution, imagination and understanding'), animal cognition (in particular, tool-use and neuro-rehabilitation experiments, revealing how coordinated tools are incorporated as an extension to the body schema) and pertinent challenges towards building cognitive robots that can seamlessly "act, interact, anticipate and understand" in unstructured natural living spaces.

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework.

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot's body and its peripersonal space. The internal models (of each robot's body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms.

Social Cognition for Human-Robot Symbiosis-Challenges and Building Blocks.

The next generation of robot companions or robot working partners will need to satisfy social requirements somehow similar to the famous laws of robotics envisaged by Isaac Asimov time ago (Asimov, 1942). The necessary technology has almost reached the required level, including sensors and actuators, but the cognitive organization is still in its infancy and is only partially supported by the current understanding of brain cognitive processes. The brain of symbiotic robots will certainly not be a "positronic" replica of the human brain: probably, the greatest part of it will be a set of interacting computational processes running in the cloud. In this article, we review the challenges that must be met in the design of a set of interacting computational processes as building blocks of a cognitive architecture that may give symbiotic capabilities to collaborative robots of the next decades: (1) an animated body-schema; (2) an imitation machinery; (3) a motor intentions machinery; (4) a set of physical interaction mechanisms; and (5) a shared memory system for incremental symbiotic development. We would like to stress that our approach is totally un-hierarchical: the five building blocks of the shared cognitive architecture are fully bi-directionally connected. For example, imitation and intentional processes require the "services" of the animated body schema which, on the other hand, can run its simulations if appropriately prompted by imitation and/or intention, with or without physical interaction. Successful experiences can leave a trace in the shared memory system and chunks of memory fragment may compete to participate to novel cooperative actions. And so on and so forth. At the heart of the system is lifelong training and learning but, different from the conventional learning paradigms in neural networks, where learning is somehow passively imposed by an external agent, in symbiotic robots there is an element of free choice of what is worth learning, driven by the interaction between the robot and the human partner. The proposed set of building blocks is certainly a rough approximation of what is needed by symbiotic robots but we believe it is a useful starting point for building a computational framework.

Early Intensified Insulin Therapy in Newly Diagnosed Type 2 Diabetes Leads to Sustained Improvement in Glycemic Control and Improved Beta Cell Function.

AIM: Type 2 diabetes (T2D) is a progressive disease characterized by relentless deterioration of pancreatic ß-cell function. Traditionally, insulin is used in later stages of T2DM. This study looks at use of insulin at time of diagnosis of T2DM and its effect on glycemic control and beta cell function. METHODS: This is a prospective observational study conducted in symptomatic newly diagnosed type 2 diabetes adults (>18 years) who presented with glycated hemoglobin (A1C) levels > 9%. For the initial 8 weeks, patients were treated with pre-mix insulin after which they were changed over to oral agents, and followed up for next three years. RESULTS: Amongst 122 study participants, who completed the study, 50% were female and 90% were from rural areas. Average age of participants was 51.4 ± 9.6 years. Baseline mean fasting plasma glucose (FPG), post prandial plasma glucose (PPPG) and A1C were 267 ± 76 mg/dl, 408 ± 101 mg/dl and 11.5 ± 1.4% respectively. At the end of insulin therapy (8 weeks), the mean FPG, PPG and A1C reduced to 107 ± 10 mg/dl, 145 ± 24 mg/dl and 7.3 ± 0.8% respectively all of which were highly significant. The mean post-prandial C-peptide significantly increased from 1.8± 0.6 to 2.8± 0.9 ng/dl. An average of 1.7 kg weight gain and 0.97 episodes of mild to moderate hypoglycemia were observed. At the end of study (156 weeks), the mean FPG, PPG and A1C were 99 ± 14 mg/dl, 152 ± 12 mg/dl and 6.7 ± 0.4%. CONCLUSION: Early insulin therapy in treatment naïve patients with type 2 diabetes results in rapid improvement of glycaemia thus helps to maintain long term normoglycemia and improves ß-cell function.

Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences.

Emerging studies indicate that several species such as corvids, apes and children solve 'The Crow and the Pitcher' task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause-effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended 'learning-prediction-abstraction' loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration.

Revisiting the body-schema concept in the context of whole-body postural-focal dynamics.

The body-schema concept is revisited in the context of embodied cognition, further developing the theory formulated by Marc Jeannerod that the motor system is part of a simulation network related to action, whose function is not only to shape the motor system for preparing an action (either overt or covert) but also to provide the self with information on the feasibility and the meaning of potential actions. The proposed computational formulation is based on a dynamical system approach, which is linked to an extension of the equilibrium-point hypothesis, called Passive Motor Paradigm: this dynamical system generates goal-oriented, spatio-temporal, sensorimotor patterns, integrating a direct and inverse internal model in a multi-referential framework. The purpose of such computational model is to operate at the same time as a general synergy formation machinery for planning whole-body actions in humanoid robots and/or for predicting coordinated sensory-motor patterns in human movements. In order to illustrate the computational approach, the integration of simultaneous, even partially conflicting tasks will be analyzed in some detail with regard to postural-focal dynamics, which can be defined as the fusion of a focal task, namely reaching a target with the whole-body, and a postural task, namely maintaining overall stability.

A neural framework for organization and flexible utilization of episodic memory in cumulatively learning baby humanoids.

Cumulatively developing robots offer a unique opportunity to reenact the constant interplay between neural mechanisms related to learning, memory, prospection, and abstraction from the perspective of an integrated system that acts, learns, remembers, reasons, and makes mistakes. Situated within such interplay lie some of the computationally elusive and fundamental aspects of cognitive behavior: the ability to recall and flexibly exploit diverse experiences of one's past in the context of the present to realize goals, simulate the future, and keep learning further. This article is an adventurous exploration in this direction using a simple engaging scenario of how the humanoid iCub learns to construct the tallest possible stack given an arbitrary set of objects to play with. The learning takes place cumulatively, with the robot interacting with different objects (some previously experienced, some novel) in an open-ended fashion. Since the solution itself depends on what objects are available in the "now," multiple episodes of past experiences have to be remembered and creatively integrated in the context of the present to be successful. Starting from zero, where the robot knows nothing, we explore the computational basis of organization episodic memory in a cumulatively learning humanoid and address (1) how relevant past experiences can be reconstructed based on the present context, (2) how multiple stored episodic memories compete to survive in the neural space and not be forgotten, (3) how remembered past experiences can be combined with explorative actions to learn something new, and (4) how multiple remembered experiences can be recombined to generate novel behaviors (without exploration). Through the resulting behaviors of the robot as it builds, breaks, learns, and remembers, we emphasize that mechanisms of episodic memory are fundamental design features necessary to enable the survival of autonomous robots in a real world where neither everything can be known nor can everything be experienced.

The ITALK project: a developmental robotics approach to the study of individual, social, and linguistic learning.

This article presents results from a multidisciplinary research project on the integration and transfer of language knowledge into robots as an empirical paradigm for the study of language development in both humans and humanoid robots. Within the framework of human linguistic and cognitive development, we focus on how three central types of learning interact and co-develop: individual learning about one's own embodiment and the environment, social learning (learning from others), and learning of linguistic capability. Our primary concern is how these capabilities can scaffold each other's development in a continuous feedback cycle as their interactions yield increasingly sophisticated competencies in the agent's capacity to interact with others and manipulate its world. Experimental results are summarized in relation to milestones in human linguistic and cognitive development and show that the mutual scaffolding of social learning, individual learning, and linguistic capabilities creates the context, conditions, and requisites for learning in each domain. Challenges and insights identified as a result of this research program are discussed with regard to possible and actual contributions to cognitive science and language ontogeny. In conclusion, directions for future work are suggested that continue to develop this approach toward an integrated framework for understanding these mutually scaffolding processes as a basis for language development in humans and robots.

Increased IL-12 and decreased IL-33 serum levels are associated with increased Th1 and suppressed Th2 cytokine profile in patients with diabetic nephropathy (CURES-134).

The role played by recently discovered novel cytokine IL-33 in controlling T-helper (Th)1 and Th2 cytokines under conditions of diabetic nephropathy (DN) is less well studied. In the present study, we estimated the levels of IL-33 along with both Th1 and Th2 cytokines in the serum of normal glucose tolerant (NGT), diabetic subjects with (DN) or without nephropathy (DM) and correlated it with the clinical risk factors of diabetes and nephropathy. 222 study subjects were recruited from the Chennai Urban Rural Epidemiology Study (CURES): 61 NGT, 79 DM and 82 DN. IL-33 level was estimated by ELISA while other Th1 (IL-12, IFN-gamma and IL-2) and Th2 (IL-4, IL-5 and IL-13) cytokines were measured using a Bio-plex bead assay. DM subjects showed a mixed Th1-Th2 profile (increased IFN-g, IL-12, IL-4 and IL-13 and decreased IL-33) while DN subjects showed enhanced Th1 profile (increased IFN-g, IL-2 and IL-12) with suppression of Th2 cytokine (decreased IL-33 and IL-13). The IL-33 levels showed a serial decline with increasing severity of insulin resistance and microalbuminuria. DN was associated with enhanced Th1 response and suppression of Th2 responses which might be due to inreased levels of IL-12 and decreased levels of IL-33 cytokines respectively.

A biomimetic framework for coordinating and controlling whole body movements in humanoid robots.

An integrated model for the coordination of whole body movements of a humanoid robot with a compliant ankle similar to the human case is described. It includes a synergy formation part, which takes into account the motor redundancy of the body model, and an intermittent controller, which stabilizes in a robust way postural sway movements, thus combining the hip strategy with ankle strategy.

Analysis & commentary: Global prevention and control of type 2 diabetes will require paradigm shifts in policies within and among countries.

Continued increases in the prevalence of and disproportionate health spending associated with type 2 diabetes argue for policies focused on preventing that condition and treating it appropriately, even as we strive to improve coordination of care for coexisting chronic diseases. This article argues that four policy paradigm shifts will be necessary to achieve that specific emphasis on type 2 diabetes: conceptually integrating primary and secondary prevention along a clinical continuum; recognizing the central importance of early detection of prediabetes and undiagnosed diabetes in implementing cost-effective prevention and control; integrating community and clinical expertise, and resources, within organized and affordable service delivery systems; and sharing and adopting evidence-based policies at the global level.

Passive motion paradigm: an alternative to optimal control.

IN THE LAST YEARS, OPTIMAL CONTROL THEORY (OCT) HAS EMERGED AS THE LEADING APPROACH FOR INVESTIGATING NEURAL CONTROL OF MOVEMENT AND MOTOR COGNITION FOR TWO COMPLEMENTARY RESEARCH LINES: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the "degrees of freedom (DoFs) problem," the common core of production, observation, reasoning, and learning of "actions." OCT, directly derived from engineering design techniques of control systems quantifies task goals as "cost functions" and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative "softer" approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that "animates" the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints "at runtime," hence solving the "DoFs problem" without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of "potential actions." In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures.

Decreased prevalence of lymphatic filariasis among subjects with type-1 diabetes.

Several animal studies have shown a protective effect of helminth infections against type-1 diabetes mellitus (T1DM). However, epidemiologic studies demonstrating this protective relationship with T1DM are largely lacking, although an inverse correlation between the prevalence of lymphatic filariasis (LF) and prevalence of allergies and autoimmunity has been shown. A cross-sectional study was undertaken in southern India to assess the baseline prevalence of seropositivity of LF among persons with T1DM (n = 200) and normal glucose tolerant (NGT) persons (n = 562). The prevalence of LF was 0% among persons with T1DM and 2.6% among NGT persons (P = 0.026). The percentage of persons who were positive for filarial antigen-specific IgG4 (but not antigen-specific IgG) was also significantly lower in persons with T1DM (2%) compared with NGT persons (28%) (P < 0.001). Thus, there appears to be a striking inverse relationship between the prevalence of LF and T1DM in southern India.

Association of cystatin-C with metabolic syndrome in normal glucose-tolerant subjects (CURES-97).

BACKGROUND: This study looked at the association of cystatin-C (cys-C) with severity of metabolic syndrome in Asian Indians. METHODS: Five sets of normal glucose tolerance subjects were recruited from the Chennai Urban Rural Epidemiology Study (CURES), a population-based study in southern India: 43 subjects with no metabolic risk factors, 44 subjects with one metabolic risk factor, 37 subjects with two risk factors, 40 subjects with three risk factors, and 40 subjects with four or five metabolic risk factors. Metabolic syndrome was defined using National Cholesterol Education Program criteria for adults modified for waist measured using the World Health Organization Asia Pacific guidelines. Serum cys-C was estimated by a high-sensitivity particle-enhancing nephelometry assay. RESULTS: Subjects with four or five metabolic abnormalities had the highest cys-C levels, and with decreasing number of metabolic abnormalities, the cys-C levels decreased linearly (P for trend < 0.001). Regression analysis showed a linear increase in cys-C levels with increasing number of metabolic abnormalities. CONCLUSION: Cys-C levels are highly correlated with the number of metabolic abnormalities in Asian Indians.

Risk factors for microvascular complications of diabetes among South Indian subjects with type 2 diabetes--the Chennai Urban Rural Epidemiology Study (CURES) Eye Study-5.

BACKGROUND: This study assessed the relationship between and risk factors for microvascular complications of diabetes in an urban South Indian type 2 diabetes population. METHODS: Subjects with diabetes (n = 1,736) were selected from the population-based Chennai Urban Rural Epidemiology Study (CURES) Eye Study conducted on a representative population of Chennai city in south India. Four-field stereo retinal color photography was done, and diabetic retinopathy (DR) was classified according to the Early Treatment DR Study grading system. Neuropathy was diagnosed if the vibratory perception threshold of the big toe using biothesiometry was ≥ 20V. Overt nephropathy was diagnosed if the subjects had persistent macroalbuminuria (urinary albumin excretion ≥ 300 µg/mg of creatinine) and microalbuminuria if it was between 30 and 299 µg/mg of creatinine. Among the 1,715 subjects with gradable fundus photographs, 1,608 individuals who had information on all test parameters were included. RESULTS: Overall, DR was present in 282 (17.5%), neuropathy in 414 (25.7%), overt nephropathy in 82 (5.1%), and microalbuminuria in 426 (26.5%) subjects. Eighteen subjects had all three microvascular complications of diabetes. The risk of nephropathy (odds ratio [OR] = 5.3, P<0.0001) and neuropathy (OR = 2.9, P<0.0001) was significantly higher among the subjects with sight-threatening DR compared to those without DR. Common risk factors identified for all the three microvascular complications of diabetes were age, glycated hemoglobin, duration of diabetes, and serum triglycerides. DR was associated with nephropathy after adjusting for age, gender, hemoglobin A1c, systolic blood pressure, serum triglycerides, and duration of diabetes (OR = 2.140, 95% confidence interval = 1.261-3.632, P = 0.005). CONCLUSIONS: This is the first population-based study from India to report on all microvascular complications of diabetes and reveals that the association between DR and nephropathy is stronger than that with neuropathy.

A neural mechanism of synergy formation for whole body reaching.

The present study proposes a computational model for the formation of whole body reaching synergy, i.e., coordinated movements of lower and upper limbs, characterized by a focal component (the hand must reach a target) and a postural component (the center of mass must remain inside the support base). The model is based on an extension of the equilibrium point hypothesis that has been called Passive Motion Paradigm (PMP), modified in order to achieve terminal attractor features and allow the integration of multiple constraints. The model is a network with terminal attractor dynamics. By simulating it in various conditions it was possible to show that it exhibits many of the spatio-temporal features found in experimental data. In particular, the motion of the center of mass appears to be synchronized with the motion of the hand and with proportional amplitude. Moreover, the joint rotation patterns can be accounted for by a single functional degree of freedom, as shown by principal component analysis. It is also suggested that recent findings in motor imagery support the idea that the PMP network may represent the motor cognitive part of synergy formation, uncontaminated by the effect of execution.