Sublethal cytochrome c release generates drug-tolerant persister cells.

Drug-tolerant persister cells (persisters) evade apoptosis upon targeted and conventional cancer therapies and represent a major non-genetic barrier to effective cancer treatment. Here, we show that cells that survive treatment with pro-apoptotic BH3 mimetics display a persister phenotype that includes colonization and metastasis in vivo and increased sensitivity toward ferroptosis by GPX4 inhibition. We found that sublethal mitochondrial outer membrane permeabilization (MOMP) and holocytochrome c release are key requirements for the generation of the persister phenotype. The generation of persisters is independent of apoptosome formation and caspase activation, but instead, cytosolic cytochrome c induces the activation of heme-regulated inhibitor (HRI) kinase and engagement of the integrated stress response (ISR) with the consequent synthesis of ATF4, all of which are required for the persister phenotype. Our results reveal that sublethal cytochrome c release couples sublethal MOMP to caspase-independent initiation of an ATF4-dependent, drug-tolerant persister phenotype.

A mitochondrial iron-responsive pathway regulated by DELE1.

The heme-regulated kinase HRI is activated under heme/iron deficient conditions; however, the underlying molecular mechanism is incompletely understood. Here, we show that iron-deficiency-induced HRI activation requires the mitochondrial protein DELE1. Notably, mitochondrial import of DELE1 and its subsequent protein stability are regulated by iron availability. Under steady-state conditions, DELE1 is degraded by the mitochondrial matrix-resident protease LONP1 soon after mitochondrial import. Upon iron chelation, DELE1 import is arrested, thereby stabilizing DELE1 on the mitochondrial surface to activate the HRI-mediated integrated stress response (ISR). Ablation of this DELE1-HRI-ISR pathway in an erythroid cell model enhances cell death under iron-limited conditions, suggesting a cell-protective role for this pathway in iron-demanding cell lineages. Our findings highlight mitochondrial import regulation of DELE1 as the core component of a previously unrecognized mitochondrial iron responsive pathway that elicits stress signaling following perturbation of iron homeostasis.

Neuron-specific translational control shift ensures proteostatic resilience during ER stress.

Proteostasis is essential for cellular survival and particularly important for highly specialised post-mitotic cells such as neurons. Transient reduction in protein synthesis by protein kinase R-like endoplasmic reticulum (ER) kinase (PERK)-mediated phosphorylation of eukaryotic translation initiation factor 2α (p-eIF2α) is a major proteostatic survival response during ER stress. Paradoxically, neurons are remarkably tolerant to PERK dysfunction, which suggests the existence of cell type-specific mechanisms that secure proteostatic stress resilience. Here, we demonstrate that PERK-deficient neurons, unlike other cell types, fully retain the capacity to control translation during ER stress. We observe rescaling of the ATF4 response, while the reduction in protein synthesis is fully retained. We identify two molecular pathways that jointly drive translational control in PERK-deficient neurons. Haem-regulated inhibitor (HRI) mediates p-eIF2α and the ATF4 response and is complemented by the tRNA cleaving RNase angiogenin (ANG) to reduce protein synthesis. Overall, our study elucidates an intricate back-up mechanism to ascertain translational control during ER stress in neurons that provides a mechanistic explanation for the thus far unresolved observation of neuronal resilience to proteostatic stress.

The integrated stress response in metabolic adaptation.

The integrated stress response (ISR) refers to signaling pathways initiated by stress-activated eIF2α kinases. Distinct eIF2α kinases respond to different stress signals, including amino acid deprivation and mitochondrial stress. Such stress-induced eIF2α phosphorylation attenuates general mRNA translation and, at the same time, stimulates the preferential translation of specific downstream factors to orchestrate an adaptive gene expression program. In recent years, there have been significant new advances in our understanding of ISR during metabolic stress adaptation. Here, I discuss those advances, reviewing among others the ISR activation mechanisms in response to amino acid deprivation and mitochondrial stress. In addition, I review how ISR regulates the amino acid metabolic pathways and how changes in the ISR impact the physiology and pathology of various disease models.

Extreme Heat and Occupational Health Risks.

Climate change poses a significant occupational health hazard. Rising temperatures and more frequent heat waves are expected to cause increasing heat-related morbidity and mortality for workers across the globe. Agricultural, construction, military, firefighting, mining, and manufacturing workers are at particularly high risk for heat-related illness (HRI). Various factors, including ambient temperatures, personal protective equipment, work arrangements, physical exertion, and work with heavy equipment may put workers at higher risk for HRI. While extreme heat will impact workers across the world, workers in low- and middle-income countries will be disproportionately affected. Tracking occupational HRI will be critical to informing prevention and mitigation strategies. Renewed investment in these strategies, including workplace heat prevention programs and regulatory standards for indoor and outdoor workers, will be needed. Additional research is needed to evaluate the effectiveness of interventions in order to successfully reduce the risk of HRI in the workplace.

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control.

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

Enhancing Human-Robot Collaboration through a Multi-Module Interaction Framework with Sensor Fusion: Object Recognition, Verbal Communication, User of Interest Detection, Gesture and Gaze Recognition.

With the increasing presence of robots in our daily lives, it is crucial to design interaction interfaces that are natural, easy to use and meaningful for robotic tasks. This is important not only to enhance the user experience but also to increase the task reliability by providing supplementary information. Motivated by this, we propose a multi-modal framework consisting of multiple independent modules. These modules take advantage of multiple sensors (e.g., image, sound, depth) and can be used separately or in combination for effective human-robot collaborative interaction. We identified and implemented four key components of an effective human robot collaborative setting, which included determining object location and pose, extracting intricate information from verbal instructions, resolving user(s) of interest (UOI), and gesture recognition and gaze estimation to facilitate the natural and intuitive interactions. The system uses a feature-detector-descriptor approach for object recognition and a homography-based technique for planar pose estimation and a deep multi-task learning model to extract intricate task parameters from verbal communication. The user of interest (UOI) is detected by estimating the facing state and active speakers. The framework also includes gesture detection and gaze estimation modules, which are combined with a verbal instruction component to form structured commands for robotic entities. Experiments were conducted to assess the performance of these interaction interfaces, and the results demonstrated the effectiveness of the approach.

Perspectives in Wearable Systems in the Human-Robot Interaction (HRI) Field.

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human-machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.

A Unified Multimodal Interface for the RELAX High-Payload Collaborative Robot.

This manuscript introduces a mobile cobot equipped with a custom-designed high payload arm called RELAX combined with a novel unified multimodal interface that facilitates Human-Robot Collaboration (HRC) tasks requiring high-level interaction forces on a real-world scale. The proposed multimodal framework is capable of combining physical interaction, Ultra Wide-Band (UWB) radio sensing, a Graphical User Interface (GUI), verbal control, and gesture interfaces, combining the benefits of all these different modalities and allowing humans to accurately and efficiently command the RELAX mobile cobot and collaborate with it. The effectiveness of the multimodal interface is evaluated in scenarios where the operator guides RELAX to reach designated locations in the environment while avoiding obstacles and performing high-payload transportation tasks, again in a collaborative fashion. The results demonstrate that a human co-worker can productively complete complex missions and command the RELAX mobile cobot using the proposed multimodal interaction framework.

The Prohibitin-Binding Compound Fluorizoline Activates the Integrated Stress Response through the eIF2α Kinase HRI.

Fluorizoline is a synthetic molecule that induces apoptosis, by selectively targeting prohibitins (PHBs), through induction of the BH3-only protein NOXA. This induction is transcriptionally regulated by the integrated stress response (ISR)-related transcription factors ATF3 and ATF4. Here, we evaluate the role of the four eIF2α kinases, to decipher which is responsible for the mechanism of ISR activation triggered by fluorizoline in HeLa and HAP1 cells. First, we demonstrated the involvement of the eIF2α kinases using ISR inhibitor (ISRIB) and by simultaneous downregulation of all four eIF2α kinases, as both approaches were able to increase cell resistance to fluorizoline-induced apoptosis. Furthermore, we confirmed that fluorizoline treatment results in endoplasmic reticulum (ER) stress, as evidenced by PERK activation. Despite PERK activation, this kinase was not directly involved in the ISR activation by fluorizoline. In this regard, we found that the eIF2α kinases are capable of compensating for each other's loss of function. Importantly, we demonstrated that the mitochondrial-stress-related eIF2α kinase HRI mediates ISR activation after fluorizoline treatment.

Risk Assessment of Pesticide Residues by GC-MSMS and UPLC-MSMS in Edible Vegetables.

In recent years, there has been a significant increase related to pesticide residues in foods, which may increase the risks to the consumer of these foods with the different quality and concentrations of pesticide residues. Pesticides are used for controlling pests that reduce yields. On the other hand, it has become a major public health concern due to its toxic properties. Thus, the objective of the current study employed the application of Quick Easy Cheap Effective Rugged Safe (QuEChERS) method, in combination with gas and liquid chromatography-tandem mass spectrometric detection (GCMSMS, LCMSMS) in order to determine 137 pesticide residues (63 insecticides, 41 acaricides, 40 herbicide, 55 fungicide, nematicide, growth regulator, Chitin synthesis inhibitors, and Juvenile hormone mimics), in 801 vegetables such as 139 tomatoes, 185 peppers, 217 squash, 94 eggplants, and 166 cucumbers from different locations in Hail and Riyadh cities. The results showed that the majority of pesticide residues were detected for each of the following pesticides: acetaimpride, metalaxyl, imidaclopride, bifenthrin, pyridaben, difenoconazole, and azoxystrobien, which were repeated in the samples studied 39, 21, 11, 10, 8, 7, and 5, respectively. In addition, results observed that the tomato was the most contaminated with pesticide residues; it was contaminated with 19 compounds and was followed by pepper, cucumber, and squash, and the last commodity in the contaminated ranking was eggplant. The highest calculated estimated daily intakes (EDIs) were recorded for tomatoes which were estimated between 0.013 to 0.516 mg/kg of body weight per day (bw/day) while the lowest EDIs value was between 0.000002 to 0.0005 mg/kg of bw/day for cucumber. Results indicated that the EDIs values were lower than the acceptable daily intake (ADI) values. Results observed that the most of pesticide residues exposure in food consumption in Saudi Arabia were lower than ADIs. In addition, the highest value for health risk index (HRI) was recorded with Ethion residue in tomato, but in sweet pepper, the highest value for HRI was 127.5 in the form of fipronil residue. On the other hand, results found that the highest values of HRI were 1.54, 1.61, and 0.047 for difenoconazole, bifenthrin, and pyridaben residues in squash, eggplant, and cucumber.

The eIF2α kinase HRI triggers the autophagic clearance of cytosolic protein aggregates.

Large cytosolic protein aggregates are removed by two main cellular processes, autophagy and the ubiquitin-proteasome system, and defective clearance of these protein aggregates results in proteotoxicity and cell death. Recently, we found that the eIF2α kinase heme-regulated inhibitory (HRI) induced a cytosolic unfolded protein response to prevent aggregation of innate immune signalosomes, but whether HRI acts as a general sensor of proteotoxicity in the cytosol remains unclear. Here we show that HRI controls autophagy to clear cytosolic protein aggregates when the ubiquitin-proteasome system is inhibited. We further report that silencing the expression of HRI resulted in decreased levels of BAG3 and HSPB8, two proteins involved in chaperone-assisted selective autophagy, suggesting that HRI may control proteostasis in the cytosol at least in part through chaperone-assisted selective autophagy. Moreover, knocking down the expression of HRI resulted in cytotoxic accumulation of overexpressed α-synuclein, a protein known to aggregate in Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. In agreement with these data, protein aggregate accumulation and microglia activation were observed in the spinal cord white matter of 7-month-old Hri-/- mice as compared with Hri+/+ littermates. Moreover, aged Hri-/- mice showed accumulation of misfolded α-synuclein in the lateral collateral pathway, a region of the sacral spinal cord horn that receives visceral sensory afferents from the bladder and distal colon, a pathological feature common to α-synucleinopathies in humans. Together, these results suggest that HRI contributes to a general cytosolic unfolded protein response that could be leveraged to bolster the clearance of cytotoxic protein aggregates.

Standardization of MRI Screening and Reporting in Individuals With Elevated Risk of Pancreatic Ductal Adenocarcinoma: Consensus Statement of the PRECEDE Consortium.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies, with a dismal survival rate. Screening the general population for early detection of PDAC is not recommended, but because early detection improves survival, high-risk individuals, defined as those meeting criteria based on a family history of PDAC and/or the presence of known pathogenic germline variant genes with PDAC risk, are recommended to undergo screening with MRI and/or endoscopic ultrasound at regular intervals. The Pancreatic Cancer Early Detection (PRECEDE) Consortium was formed in 2018 and is composed of gastroenterologists, geneticists, pancreatic surgeons, radiologists, statisticians, and researchers from 40 sites in North America, Europe, and Asia. The overarching goal of the PRECEDE Consortium is to facilitate earlier diagnosis of PDAC for high-risk individuals to increase survival of the disease. A standardized MRI protocol and reporting template are needed to enhance the quality of screening examinations, improve consistency of clinical management, and facilitate multiinstitutional research. We present a consensus statement to standardize MRI screening and reporting for individuals with elevated risk of pancreatic cancer.

Understanding Emotions in Children with Developmental Disabilities during Robot Therapy Using EDA.

Recent technological advancements have led to the emergence of supportive robotics to help children with developmental disabilities become independent. In conventional research, in robot therapy, experiments are often conducted by operating the robot out of the subject's sight. In this paper, robot therapy using a system that can autonomously recognize the emotions of a child with developmental disabilities and provide feedback was developed. The aim was to quantitatively infer emotional changes in children using skin conductance (EDA) during robot therapy. It was demonstrated that the robot could recognize emotions autonomously and provide feedback to the subjects. Additionally, a quantitative evaluation was conducted using EDA. By analyzing the symptoms related to developmental disorders, it may be possible to improve the recognition rate and tailor therapy based on symptoms.

Cobot Motion Planning Algorithm for Ensuring Human Safety Based on Behavioral Dynamics.

Recently, the safety of workers has gained increasing attention due to the applications of collaborative robots (cobot). However, there is no quantitative research on the impact of cobot behavior on humans' psychological reactions, and these results are not applied to the cobot motion planning algorithms. Based on the concept of the gravity field, this paper proposes a model of the psychological safety field (PSF), designs a comprehensive experiment on different speeds and minimum distances when approaching the head, chest, and abdomen, and obtains the ordinary surface equation of psychological stress about speed and minimum distance by using data fitting. By combining social rules and PSF models, we improve the robot motion planning algorithm based on behavioral dynamics. The validation experiment results show that our proposed improved robot motion planning algorithm can effectively reduce psychological stress. Eighty-seven point one percent (87.1%) of the experimental participants think that robot motion planned by improved robot motion planning algorithms is more "friendly", can effectively reduce psychological stress, and is more suitable for human-robot interaction scenarios.

Gender Identification in a Two-Level Hierarchical Speech Emotion Recognition System for an Italian Social Robot.

The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.

Human-robot interaction: how worker influence in task allocation improves autonomy.

Task allocation research is often efficiency-focussed, but procedural and work-psychological perspectives are required to enable human-centred human-robot interaction (HRI). Hence, the motivational and cognitive outcomes of the degree of worker influence over task allocation are relevant to research objects for allocation process design. In a laboratory experiment, 87 participants manufactured goods in collaboration with a robot under three conditions: (1) a support system decided the allocation, (2) a support-system allocation could be revised, (3) the participant determined the allocation. Conditions affected mental effort, process control and autonomy, resulting in higher values when participants allocated tasks themselves. Satisfaction with the process appears lower with no worker influence. Trust in the support-system moderates the condition effect, with higher satisfaction depending on trust when a system is involved in allocation. An allocation made by the workers and adaptability is preferred. Results show the importance of worker influence over task allocation in HRI. Practitioner Summary: Our experiment on allocation processes seeks to satisfy the gap in human-centred psychological research on task allocation in human-robot interaction (HRI). For successful, ergonomic HRI, it is found that workers should be provided with influence over task allocation.

Integrated use of inverse and biotic ligand modelling for lake water quality resilience estimation: A case of Ramsar wetland, (Deepor Beel), Assam, India.

The present study evaluates the vulnerability of the lake system (Deepor Beel) due to the combined exposure of toxic metals, major ions and mineral dissociation. The hydro-chemistry of the Deepor Beel lake reveals the dominance of carbonate weathering with strong evidences of ion-exchange reaction occurring throughout the monsoon season of 2014 and 2015. Through an integrated application of multivariate analysis, the occurrence of albite weathering was confirmed, although as an isolated incidence only, along-with substantial evidence of waste water intrusion from fertilizer industries. The moderate cation exchange capacity (CEC) of the soil, indicates the presence of illite, chlorite and kaolinite which provides a strong buffering action in terms of phosphorous and nutrient retention. However, occurrences of chemical waste in the form of bleaching powder (Ca(OCl)2) are causing acidification of lake soil, which will trigger the release of phosphorous and may enhance the eutrophication level in near future. Through the simulations of Biotic Ligand (BL) model on ceriodaphnia dubia, it is being revealed, that the free availability of Ca2+ in the lake water, provides a higher adsorptive competition for labile metal species especially for Cu and Zn. Additionally, the risk among children from drinking lake water, has increased by three to seven times in a duration of just one year. The present study is a pioneering work, which has evaluated the vulnerability of Deepor Beel lake by adopting a sequential assessment strategy of lake internal as well as external ecology. Our proposed methodology can be a used as a scientific basis for future assessment of the lake health.

Integration of 3D Printed Flexible Pressure Sensors into Physical Interfaces for Wearable Robots.

Sensing pressure at the physical interface between the robot and the human has important implications for wearable robots. On the one hand, monitoring pressure distribution can give valuable benefits on the aspects of comfortability and safety of such devices. Additionally, on the other hand, they can be used as a rich sensory input to high level interaction controllers. However, a problem is that the commercial availability of this technology is mostly limited to either low-cost solutions with poor performance or expensive options, limiting the possibilities for iterative designs. As an alternative, in this manuscript we present a three-dimensional (3D) printed flexible capacitive pressure sensor that allows seamless integration for wearable robotic applications. The sensors are manufactured using additive manufacturing techniques, which provides benefits in terms of versatility of design and implementation. In this study, a characterization of the 3D printed sensors in a test-bench is presented after which the sensors are integrated in an upper arm interface. A human-in-the-loop calibration of the sensors is then shown, allowing to estimate the external force and pressure distribution that is acting on the upper arm of seven human subjects while performing a dynamic task. The validation of the method is achieved by means of a collaborative robot for precise force interaction measurements. The results indicate that the proposed sensors are a potential solution for further implementation in human-robot interfaces.

First Step toward Gestural Recognition in Harsh Environments.

We are witnessing a rise in the use of ground and aerial robots in first response missions. These robots provide novel opportunities to support first responders and lower the risk to people's lives. As these robots become increasingly autonomous, researchers are seeking ways to enable natural communication strategies between robots and first responders, such as using gestural interaction. First response work often takes place in harsh environments, which hold unique challenges for gesture sensing and recognition, including in low-visibility environments, making the gestural interaction non-trivial. As such, an adequate choice of sensors and algorithms needs to be made to support gestural recognition in harsh environments. In this work, we compare the performances of three common types of remote sensors, namely RGB, depth, and thermal cameras, using various algorithms, in simulated harsh environments. Our results show 90 to 96% recognition accuracy (respectively with or without smoke) with the use of protective equipment. This work provides future researchers with clear data points to support them in their choice of sensors and algorithms for gestural interaction with robots in harsh environments.