Current genome-editing systems generally rely on inducing DNA double-strand breaks (DSBs). This may limit their utility in clinical therapies, as unwanted mutations caused by DSBs can have deleterious effects. CRISPR/Cas9 system has recently been repurposed to enable target gene activation, allowing regulation of endogenous gene expression without creating DSBs. However, in vivo implementation of this gain-of-function system has proven difficult. Here, we report a robust system for in vivo activation of endogenous target genes through trans-epigenetic remodeling. The system relies on recruitment of Cas9 and transcriptional activation complexes to target loci by modified single guide RNAs. As proof-of-concept, we used this technology to treat mouse models of diabetes, muscular dystrophy, and acute kidney disease. Results demonstrate that CRISPR/Cas9-mediated target gene activation can be achieved in vivo, leading to measurable phenotypes and amelioration of disease symptoms. This establishes new avenues for developing targeted epigenetic therapies against human diseases.
The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to “debias” the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.
Ketosis, the metabolic response to energy crisis, is a mechanism to sustain life by altering oxidative fuel selection. Often overlooked for its metabolic potential, ketosis is poorly understood outside of starvation or diabetic crisis. Thus, we studied the biochemical advantages of ketosis in humans using a ketone ester-based form of nutrition without the unwanted milieu of endogenous ketone body production by caloric or carbohydrate restriction.
In five separate studies of 39 high-performance athletes, we show how this unique metabolic state improves physical endurance by altering fuel competition for oxidative respiration. Ketosis decreased muscle glycolysis and plasma lactate concentrations, while providing an alternative substrate for oxidative phosphorylation. Ketosis increased intramuscular triacylglycerol oxidation during exercise, even in the presence of normal muscle glycogen, co-ingested carbohydrate and elevated insulin. These findings may hold clues to greater human potential and a better understanding of fuel metabolism in health and disease.
The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it: the human brain. We suggest that the word “consciousness” conflates two different types of information-processing computations in the brain: the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense). We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.
Neuroprosthetics research in amputee patients aims at developing new prostheses that move and feel like real limbs. Targeted muscle and sensory reinnervation (TMSR) is such an approach and consists of rerouting motor and sensory nerves from the residual limb towards intact muscles and skin regions. Movement of the myoelectric prosthesis is enabled via decoded electromyography activity from reinnervated muscles and touch sensation on the missing limb is enabled by stimulation of the reinnervated skin areas. Here we ask whether and how motor control and redirected somatosensory stimulation provided via TMSR affected the maps of the upper limb in primary motor (M1) and primary somatosensory (S1) cortex, as well as their functional connections.
Functional connectivity in TMSR patients between upper limb maps in M1 and S1 was comparable with healthy controls, while being reduced in non-TMSR patients. However, connectivity was reduced between S1 and fronto-parietal regions, in both the TMSR and non-TMSR patients with respect to healthy controls. This was associated with the absence of a well-established multisensory effect (visual enhancement of touch) in TMSR patients. Collectively, these results show how M1 and S1 process signals related to movement and touch are enabled by targeted muscle and sensory reinnervation. Moreover, they suggest that TMSR may counteract maladaptive cortical plasticity typically found after limb loss, in M1, partially in S1, and in their mutual connectivity. The lack of multisensory interaction in the present data suggests that further engineering advances are necessary (e.g. the integration of somatosensory feedback into current prostheses) to enable prostheses that move and feel as real limbs.
This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 57.6k movie clips with actions localized in space and time, resulting in 210k action labels with multiple labels per human occurring frequently. The main differences with existing video datasets are: the definition of atomic visual actions, which avoids collecting data for each and every complex action; precise spatio-temporal annotations with possibly multiple annotations for each human; the use of diverse, realistic video material (movies). This departs from existing datasets for spatio-temporal action recognition, such as JHMDB and UCF datasets, which provide annotations for at most 24 composite actions, such as basketball dunk, captured in specific environments, i.e., basketball court.
We implement a state-of-the-art approach for action localization. Despite this, the performance on our dataset remains low and underscores the need for developing new approaches for video understanding. The AVA dataset is the first step in this direction, and enables the measurement of performance and progress in realistic scenarios.
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.
Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of continuous adaptation into the learning-to-learn framework. We develop a simple gradient-based meta-learning algorithm suitable for adaptation in dynamically changing and adversarial scenarios. Additionally, we design a new multi-agent competitive environment, RoboSumo, and define iterated adaptation games for testing various aspects of continuous adaptation strategies. We demonstrate that meta-learning enables significantly more efficient adaptation than reactive baselines in the few-shot regime. Our experiments with a population of agents that learn and compete suggest that meta-learners are the fittest.
Cancer incidence increase has multiple aetiologies. Mutant alleles accumulation in populations may be one of them due to strong heritability of many cancers. The opportunity for the operation of natural selection has decreased in the past ~150 years because of reduction in mortality and fertility. Mutation-selection balance may have been disturbed in this process and genes providing background for some cancers may have been accumulating in human gene pools. Worldwide, based on the WHO statistics for 173 countries the index of the opportunity for selection is strongly inversely correlated with cancer incidence in peoples aged 0–49 years and in people of all ages. This relationship remains significant when gross domestic product per capita (GDP), life expectancy of older people (e50), obesity, physical inactivity, smoking and urbanization are kept statistically constant for fifteen (15) of twenty-seven (27) individual cancers incidence rates. Twelve (12) cancers which are not correlated with relaxed natural selection after considering the six potential confounders are largely attributable to external causes like viruses and toxins. Ratios of the average cancer incidence rates of the 10 countries with lowest opportunities for selection to the average cancer incidence rates of the 10 countries with highest opportunities for selection are 2.3 (all cancers at all ages), 2.4 (all cancers in 0–49 years age group), 5.7 (average ratios of strongly genetically based cancers) and 2.1 (average ratios of cancers with less genetic background).
Awareness of bias in algorithms is growing among scholars and users of algorithmic systems. But what can we observe about how users discover and behave around such biases?
We used a cross-platform audit technique that analyzed online ratings of 803 hotels across three hotel rating platforms and found that one site’s algorithmic rating system biased ratings, particularly low-to-medium quality hotels, significantly higher than others (up to 37%).
Analyzing reviews of 162 users who independently discovered this bias, we seek to understand if, how, and in what ways users perceive and manage this bias. Users changed the typical ways they used a review on a hotel rating platform to instead discuss the rating system itself and raise other users’ awareness of the rating bias. This raising of awareness included practices like efforts to reverse engineer the rating algorithm, efforts to correct the bias, and demonstrations of broken trust.
We conclude with a discussion of how such behavior patterns might inform design approaches that anticipate unexpected bias and provide reliable means for meaningful bias discovery and response.
Biointegrated sensors can address various challenges in medicine by transmitting a wide variety of biological signals. A tempting possibility that has not been explored before is whether we can take advantage of genome editing technology to transform a small portion of endogenous tissue into an intrinsic and long-lasting sensor of physiological signals. The human skin and epidermal stem cells have several unique advantages, making them particularly suitable for genetic engineering and applications in vivo. In this report, we took advantage of a novel platform for manipulation and transplantation of epidermal stem cells, and presented the key evidence that genome-edited skin stem cells can be exploited for continuous monitoring of blood glucose level in vivo. Additionally, by advanced design of genome editing, we developed an autologous skin graft that can sense glucose level and deliver therapeutic proteins for diabetes treatment. Our results revealed the clinical potential for skin somatic gene therapy.
A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.
RNA has important and diverse roles in biology, but molecular tools to manipulate and measure it are limited. For example, RNA interference can efficiently knockdown RNAs, but it is prone to off-target effects, and visualizing RNAs typically relies on the introduction of exogenous tags. Here we demonstrate that the class 2 type VI RNA-guided RNA-targeting CRISPR–Cas effector Cas13a (previously known as C2c2) can be engineered for mammalian cell RNA knockdown and binding.
After initial screening of 15 orthologues, we identified Cas13a from Leptotrichia wadei (LwaCas13a) as the most effective in an interference assay in Escherichia coli. LwaCas13a can be heterologously expressed in mammalian and plant cells for targeted knockdown of either reporter or endogenous transcripts with comparable levels of knockdown as RNA interference and improved specificity. Catalytically inactive LwaCas13a maintains targeted RNA binding activity, which we leveraged for programmable tracking of transcripts in live cells.
Our results establish CRISPR–Cas13a as a flexible platform for studying RNA in mammalian cells and therapeutic development.
Microsatellite repeat expansions in DNA produce pathogenic RNA species that cause dominantly inherited diseases such as myotonic dystrophy type 1 and 2 (DM1/2), Huntington’s disease, and C9orf72-linked amyotrophic lateral sclerosis (C9-ALS). Means to target these repetitive RNAs are required for diagnostic and therapeutic purposes. Here, we describe the development of a programmable CRISPR system capable of specifically visualizing and eliminating these toxic RNAs. We observe specific targeting and efficient elimination of microsatellite repeat expansion RNAs both when exogenously expressed and in patient cells. Importantly, RNA-targeting Cas9 (RCas9) reverses hallmark features of disease including elimination of RNA foci among all conditions studied (DM1, DM2, C9-ALS, polyglutamine diseases), reduction of polyglutamine protein products, relocalization of repeat-bound proteins to resemble healthy controls, and efficient reversal of DM1-associated splicing abnormalities in patient myotubes. Finally, we report a truncated RCas9 system compatible with adeno-associated viral packaging. This effort highlights the potential of RCas9 for human therapeutics.
Despite their fundamental biological and clinical importance, the molecular mechanisms that regulate the first cell fate decisions in the human embryo are not well understood. Here we use CRISPR–Cas9-mediated genome editing to investigate the function of the pluripotency transcription factor OCT4 during human embryogenesis. We identified an efficient OCT4-targeting guide RNA using an inducible human embryonic stem cell-based system and microinjection of mouse zygotes. Using these refined methods, we efficiently and specifically targeted the gene encoding OCT4 (POU5F1) in diploid human zygotes and found that blastocyst development was compromised. Transcriptomics analysis revealed that, in POU5F1-null cells, gene expression was downregulated not only for extra-embryonic trophectoderm genes, such as CDX2, but also for regulators of the pluripotent epiblast, including NANOG. By contrast, Pou5f1-null mouse embryos maintained the expression of orthologous genes, and blastocyst development was established, but maintenance was compromised. We conclude that CRISPR–Cas9-mediated genome editing is a powerful method for investigating gene function in the context of human development.
Chronic wounds do not heal in an orderly fashion in part due to the lack of timely release of biological factors essential for healing. Topical administration of various therapeutic factors at different stages is shown to enhance the healing rate of chronic wounds. Developing a wound dressing that can deliver biomolecules with a predetermined spatial and temporal pattern would be beneficial for effective treatment of chronic wounds. Here, an actively controlled wound dressing is fabricated using composite fibers with a core electrical heater covered by a layer of hydrogel containing thermoresponsive drug carriers. The fibers are loaded with different drugs and biological factors and are then assembled using textile processes to create a flexible and wearable wound dressing. These fibers can be individually addressed to enable on-demand release of different drugs with a controlled temporal profile. Here, the effectiveness of the engineered dressing for on-demand release of antibiotics and vascular endothelial growth factor (VEGF) is demonstrated for eliminating bacterial infection and inducing angiogenesis in vitro. The effectiveness of the VEGF release on improving healing rate is also demonstrated in a murine model of diabetic wounds.
Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects. Excitatory lateral connections across columns allow the network to more rapidly infer objects based on the partial knowledge of adjacent columns. Because columns integrate input over time and space, the network learns models of complex objects that extend well beyond the receptive field of individual cells. Our network model introduces a new feature to cortical columns. We propose that a representation of location relative to the object being sensed is calculated within the sub-granular layers of each column. The location signal is provided as an input to the network, where it is combined with sensory data. Our model contains two layers and one or more columns. Simulations show that using Hebbian-like learning rules small single-column networks can learn to recognize hundreds of objects, with each object containing tens of features. Multi-column networks recognize objects with significantly fewer movements of the sensory receptors. Given the ubiquity of columnar and laminar connectivity patterns throughout the neocortex, we propose that columns and regions have more powerful recognition and modeling capabilities than previously assumed.
Not on artificial intelligence per se, but Jeff Hawkins was the first to suggest a unifying theory of how the brain works in 2005 with his book On Intelligence. It’s interesting to see how the theory has been refined in last 12 years and how it might influence today’s development of AI algorithms.
Although cellular therapies represent a promising strategy for a number of conditions, current approaches face major translational hurdles, including limited cell sources and the need for cumbersome pre-processing steps (for example, isolation, induced pluripotency). In vivo cell reprogramming has the potential to enable more-effective cell-based therapies by using readily available cell sources (for example, fibroblasts) and circumventing the need for ex vivo pre-processing.
Existing reprogramming methodologies, however, are fraught with caveats, including a heavy reliance on viral transfection. Moreover, capsid size constraints and/or the stochastic nature of status quo approaches (viral and non-viral) pose additional limitations, thus highlighting the need for safer and more deterministic in vivo reprogramming methods.
Here, we report a novel yet simple-to-implement non-viral approach to topically reprogram tissues through a nanochannelled device validated with well-established and newly developed reprogramming models of induced neurons and endothelium, respectively. We demonstrate the simplicity and utility of this approach by rescuing necrotizing tissues and whole limbs using two murine models of injury-induced ischaemia.
Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis).
More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action.
Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells.
We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.
The emergence of CRISPR-Cas9 gene editing has given new urgency to calls from social scientists, bench scientists, and scientific associations for broad public dialogue about human genome editing and its applications. Most recently, these calls were formalized in a consensus report on the science, ethics, and governance of human genome editing released by the U.S. National Academy of Sciences (NAS) and the National Academy of Medicine (NAM) that argued for public engagement to be incorporated into the policy-making process for human genome editing (1). So, where does the public stand on the issue of human genome editing? And how do those attitudes translate into the desire for more public input on human genome editing as new applications emerge in the policy arena?
The development of wearable and implantable electrical devices has been in great demand recently. However, most existing energy storage systems are based on strong corrosive or toxic electrolytes, posing a huge safety hazard as a result of solution leakage.
Here, we have developed a family of safe and flexible belt- and fiber-shaped aqueous sodium-ion batteries (SIBs) by using various Na+-containing aqueous electrolytes, including Na2SO4 solution, normal saline, and cell-culture medium. The resulting SIBs exhibit high flexibility and excellent electrochemical performance and can be safely applied in wearable electronics. Flexible SIBs with normal saline or cell-culture medium as the electrolyte showed excellent performance, indicating potential application in implantable electronic devices.
In addition, the fiber-shaped electrode in normal saline or cell-culture medium electrolyte can consume O2 and change the pH, implying promising application in biological and medical investigations.
For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting.
PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.
Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function.
We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A.
Many procedures in modern clinical medicine rely on the use of electronic implants in treating conditions that range from acute coronary events to traumatic injury. However, standard permanent electronic hardware acts as a nidus for infection: bacteria form biofilms along percutaneous wires, or seed haematogenously, with the potential to migrate within the body and to provoke immune-mediated pathological tissue reactions. The associated surgical retrieval procedures, meanwhile, subject patients to the distress associated with re-operation and expose them to additional complications.
Here, we report materials, device architectures, integration strategies, and in vivo demonstrations in rats of implantable, multifunctional silicon sensors for the brain, for which all of the constituent materials naturally resorb via hydrolysis and/or metabolic action, eliminating the need for extraction. Continuous monitoring of intracranial pressure and temperature illustrates functionality essential to the treatment of traumatic brain injury; the measurement performance of our resorbable devices compares favourably with that of non-resorbable clinical standards.
In our experiments, insulated percutaneous wires connect to an externally mounted, miniaturized wireless potentiostat for data transmission. In a separate set-up, we connect a sensor to an implanted (but only partially resorbable) data-communication system, proving the principle that there is no need for any percutaneous wiring. The devices can be adapted to sense fluid flow, motion, pH or thermal characteristics, in formats that are compatible with the body’s abdomen and extremities, as well as the deep brain, suggesting that the sensors might meet many needs in clinical medicine.
Genome editing has potential for the targeted correction of germline mutations. Here we describe the correction of the heterozygous MYBPC3 mutation in human preimplantation embryos with precise CRISPR–Cas9-based targeting accuracy and high homology-directed repair efficiency by activating an endogenous, germline-specific DNA repair response.
Induced double-strand breaks (DSBs) at the mutant paternal allele were predominantly repaired using the homologous wild-type maternal gene instead of a synthetic DNA template. By modulating the cell cycle stage at which the DSB was induced, we were able to avoid mosaicism in cleaving embryos and achieve a high yield of homozygous embryos carrying the wild-type MYBPC3 gene without evidence of off-target mutations.
Thin-film electronic devices can be integrated with skin for health monitoring and/or for interfacing with machines. Minimal invasiveness is highly desirable when applying wearable electronics directly onto human skin. However, manufacturing such on-skin electronics on planar substrates results in limited gas permeability. Therefore, it is necessary to systematically investigate their long-term physiological and psychological effects.
As a demonstration of substrate-free electronics, here we show the successful fabrication of inflammation-free, highly gas-permeable, ultrathin, lightweight and stretchable sensors that can be directly laminated onto human skin for long periods of time, realized with a conductive nanomesh structure. A one-week skin patch test revealed that the risk of inflammation caused by on-skin sensors can be significantly suppressed by using the nanomesh sensors.
Furthermore, a wireless system that can detect touch, temperature and pressure is successfully demonstrated using a nanomesh with excellent mechanical durability. In addition, electromyogram recordings were successfully taken with minimal discomfort to the user.
Augmentation of brain function is no longer just a theme of science fiction. Due to advances in neural sciences, it has become a matter of reality that a person may consider at some point in life, for example as a treatment of a neurodegenerative disease. Currently, several approaches offer enhancements for sensory, motor and cognitive brain functions, as well as for mood and emotions. Such enhancements may be achieved pharmacologically, using brain implants for recordings, stimulation and drug delivery, by employing brain-machine interfaces, or even by ablation of certain brain areas.
I plan to review all of them.
Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.
Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.
Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.
Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.
This article applies tools from argumentation theory to slippery slope arguments used in current ethical debates on genetic engineering. Among the tools used are argumentation schemes, value-based argumentation, critical questions, and burden of proof. It is argued that so-called drivers such as social acceptance and rapid technological development are also important factors that need to be taken into account alongside the argumentation scheme. It is shown that the slippery slope argument is basically a reasonable (but defeasible) form of argument, but is often flawed when used in ethical debates because of failures to meet the requirements of its scheme.
This paper describes some likely semiotic consequences of genetic engineering on what Gregory Bateson has called “the mental ecology” (1979) of future humans, consequences that are less often raised in discussions surrounding the safety of GMOs (genetically modified organisms). The effects are as follows: an increased 1) habituation to the presence of GMOs in the environment, 2) normalization of empirically false assumptions grounding genetic reductionism, 3) acceptance that humans are capable and entitled to decide what constitutes an evolutionary improvement for a species, 4) perception that the main source of creativity and problem solving in the biosphere is anthropogenic. Though there are some tensions between them, these effects tend to produce self-validating webs of ideas, actions, and environments, which may reinforce destructive habits of thought. Humans are unlikely to safely develop genetic technologies without confronting these escalating processes directly. Intervening in this mental ecology presents distinct challenges for educators, as will be discussed.
The field of cardiac tissue engineering aims at replacing the scar tissue created after a patient has suffered from a myocardial infarction. Various technologies have been developed toward fabricating a functional engineered tissue that closely resembles that of the native heart. While the field continues to grow and techniques for better tissue fabrication continue to emerge, several hurdles still remain to be overcome. In this review we will focus on several key advances and recent technologies developed in the field, including biomimicking the natural extracellular matrix structure and enhancing the transfer of the electrical signal. We will also discuss recent developments in the engineering of bionic cardiac tissues which integrate the fields of tissue engineering and electronics to monitor and control tissue performance.
Electronic skin (E-skin), a popular research topic at present, has achieved significant progress in a variety of sophisticated applications. However, the poor sensitivity and stability severely limit the development of its application. Here, we present a facile, cost-effective, and scalable method for manufacturing E-skin devices with bionic hierarchical microstructure and microcracks. Our devices exhibit high sensitivity (10 kPa–1) and excellent durability (10 000 cycles). The synergistic enhancement mechanism of the hierarchical microstructure and the microcracks on the conductive layers was discovered. Moreover, we carried out a series of studies on the airflow detection and the noncontact speech recognition.