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Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Theranostic nanomaterials have emerged in the past decade that combine therapeutic delivery and diagnostic imaging into one package. Such materials offer the opportunity to aid diagnosis, track therapeutic biodistribution, and monitor drug release. We have developed a series of nucleic acid delivery polymers containing oligoethylene amines that are able to be protonated at physiological pH (for binding/compacting pDNA) and a lanthanide-chelating domain, which imparts diagnostic functionality. Diamine monomers (containing between 3 and 6 Boc-protected ethyleneamines) were prepared via a multistep procedure involving the selective protection and deprotection of primary and secondary amines. The polymer structures were then synthesized by step-growth polymerization of the oligoethylene diamines with a bisanhydride of diethylenetriamine pentaacetic acid (DTPA-BA), yielding degrees of polymerization between 18 and 24. Chelation of the polymers with gadolinium and terbium was performed to offer MRI contrast agent and luminescence properties, respectively. All of the polymer chelates were found to house approximately one water coordination site, as calculated by the Horrock's equation and possess longitudinal relaxivities (r1, on a per Gd basis) at least twice that of Magnevist, a clinical contrast agent. All the structures formed polyplexes with pDNA with highly positive zeta potentials and hydrodynamic diameters around 50-80 nm. Lanthanide resonance energy transfer (LRET) was used to monitor polyplex association and dissociation. Polyplexes were formed using the donor-acceptor pair comprising of terbium-chelated polymer with five ethyleneamines within the repeat unit (6c-Tb) and tetramethyl rhodamine (TMR)-labeled pDNA. Association/dissociation in the presence of heparin and NaCl was monitored. The effect of amine number along the polymer backbone on transfection efficiency and cytotoxicity was also investigated. None of the polymers revealed cytotoxic effects with cultured cells; however, the polymer with six ethyleneamines clearly offered the highest transfection efficiency. This preliminary study offers insight into the development of materials with the ability to monitor polyplex unpackaging over time within the cellular environment.

Wireless control and power harvesting systems that operate injectable, cellular-scale optoelectronic components provide important demonstrated capabilities in neuromodulatory techniques such as optogenetics. Here, we report a radio frequency (RF) control/harvesting device that offers dramatically reduced size, decreased weight and improved efficiency compared to previously reported technologies. Combined use of this platform with ultrathin, multijunction, high efficiency solar cells allows for hundred-fold reduction of transmitted RF power, which greatly enhances the wireless coverage.Fabrication involves separate construction of the harvester and the injectable µ-ILEDs. To test whether the presence of the implantable device alters behavior, we implanted one group of wild type mice and compared sociability behavior to unaltered controls. Social interaction experiments followed protocols defined by Silverman et al. with minor modifications.The results presented here demonstrate that miniaturized RF harvesters, and RF control strategies with photovoltaic harvesters can, when combined with injectable µ-ILEDs, offer versatile capabilities in optogenetics. Experimental and modeling studies establish a range of effective operating conditions for these two approaches. Optogenetics studies with social groups of mice demonstrate the utility of these systems.The addition of miniaturized, high performance photovoltaic cells significantly expands the operating range and reduces the required RF power. The platform can offer capabilities to modulate signaling path in the brain region of freely-behaving animals. These suggest its potential for widespread use in neuroscience.

Organisms evolving toward greater complexity were selected across aeons to use energy and resources efficiently. Efficiency depended on prediction at every stage: first a clock to predict the planet’s statistical regularities; then a brain to predict bodily needs and compute commands that dynamically adjust the flows of energy and nutrients. Predictive regulation (allostasis) frugally matches resources to needs and thus forms a core principle of our design. Humans, reaching a pinnacle of cognitive complexity, eventually produced a device (the steam engine) that converted thermal energy to work and were suddenly awash in resources. Today boundless consumption in many nations challenges all our regulatory mechanisms, causing obesity, diabetes, drug addiction and their sequelae. So far we have sought technical solutions, such as drugs, to treat complex circuits for metabolism, appetites and mood. Here I argue for a different approach which starts by asking: why does our regulatory system, which evolution tuned for small satisfactions, now constantly demand 'more'?

Consumption of renewable energy is on the rise because new technologies have made it cheaper and easier to meet the needs of a long-term energy source. In the present study, the idea of optimal usage of sustainable energy is discussed, taking into consideration the environmental and economic conditions that exist in Pakistan’s textile manufacturing industry. By taking into account the regional potential for the application of renewable energy resources, solar energy generators are taken into consideration, and a fully intuitionistic fuzzy (FIF) textile energy model is constructed. Using the FIF model to determine the optimal distribution of solar energy units resulted in a tolerable number of unused energy units. These units may be returned to the central power supply station, which would save both money and energy.

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste‐collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution‐free cities.

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.