Global and single-nucleotide resolution detection of 7-methylguanosine in RNA.

RNA modifications, including N-7-methylguanosine (m7G), are pivotal in governing RNA stability and gene expression regulation. The accurate detection of internal m7G modifications is of paramount significance, given recent associations between altered m7G deposition and elevated expression of the methyltransferase METTL1 in various human cancers. The development of robust m7G detection techniques has posed a significant challenge in the field of epitranscriptomics. In this study, we introduce two methodologies for the global and accurate identification of m7G modifications in human RNA. We introduce borohydride reduction sequencing (Bo-Seq), which provides base resolution mapping of m7G modifications. Bo-Seq achieves exceptional performance through the optimization of RNA depurination and scission, involving the strategic use of high concentrations of NaBH4, neutral pH and the addition of 7-methylguanosine monophosphate (m7GMP) during the reducing reaction. Notably, compared to NaBH4-based methods, Bo-Seq enhances the m7G detection performance, and simplifies the detection process, eliminating the necessity for intricate chemical steps and reducing the protocol duration. In addition, we present an antibody-based approach, which enables the assessment of m7G relative levels across RNA molecules and biological samples, however it should be used with caution due to limitations associated with variations in antibody quality between batches. In summary, our novel approaches address the pressing need for reliable and accessible methods to detect RNA m7G methylation in human cells. These advancements hold the potential to catalyse future investigations in the critical field of epitranscriptomics, shedding light on the complex regulatory roles of m7G in gene expression and its implications in cancer biology.

Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser.

Excitable optoelectronic devices represent one of the key building blocks for implementation of artificial spiking neurons in neuromorphic (brain-inspired) photonic systems. This work introduces and experimentally investigates an opto-electro-optical (O/E/O) artificial neuron built with a resonant tunnelling diode (RTD) coupled to a photodetector as a receiver and a vertical cavity surface emitting laser as a transmitter. We demonstrate a well-defined excitability threshold, above which the neuron produces optical spiking responses with characteristic neural-like refractory period. We utilise its fan-in capability to perform in-device coincidence detection (logical AND) and exclusive logical OR (XOR) tasks. These results provide first experimental validation of deterministic triggering and tasks in an RTD-based spiking optoelectronic neuron with both input and output optical (I/O) terminals. Furthermore, we also investigate in simulation the prospects of the proposed system for nanophotonic implementation in a monolithic design combining a nanoscale RTD element and a nanolaser; therefore demonstrating the potential of integrated RTD-based excitable nodes for low footprint, high-speed optoelectronic spiking neurons in future neuromorphic photonic hardware.

Training deep neural networks with noisy clinical labels: toward accurate detection of prostate cancer in US data.

PURPOSE: Ultrasound is the standard-of-care to guide the systematic biopsy of the prostate. During the biopsy procedure, up to 12 biopsy cores are randomly sampled from six zones within the prostate, where the histopathology of those cores is used to determine the presence and grade of the cancer. Histopathology reports only provide statistical information on the presence of cancer and do not normally contain fine-grain information of cancer distribution within each core. This limitation hinders the development of machine learning models to detect the presence of cancer in ultrasound so that biopsy can be more targeted to highly suspicious prostate regions. METHODS: In this paper, we tackle this challenge in the form of training with noisy labels derived from histopathology. Noisy labels often result in the model overfitting to the training data, hence limiting its generalizability. To avoid overfitting, we focus on the generalization of the features of the model and present an iterative data label refinement algorithm to amend the labels gradually. We simultaneously train two classifiers, with the same structure, and automatically stop the training when we observe any sign of overfitting. Then, we use a confident learning approach to clean the data labels and continue with the training. This process is iteratively applied to the training data and labels until convergence. RESULTS: We illustrate the performance of the proposed method by classifying prostate cancer using a dataset of ultrasound images from 353 biopsy cores obtained from 90 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.73, 0.80, 0.63, and 0.69, respectively. CONCLUSION: Our approach is able to provide clinicians with a visualization of regions that likely contain cancerous tissue to obtain more accurate biopsy samples. The results demonstrate that our proposed method produces superior accuracy compared to the state-of-the-art methods.

Ultrafast neuromorphic photonic image processing with a VCSEL neuron.

The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100 ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.

Coarse label refinement for improving prostate cancer detection in ultrasound imaging.

PURPOSE: Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels. However, this introduces significant amount of label noise to training and degrades the classification performance. Previously, we suggested that histopathology-reported cancer involvement can be a reasonable approximation for the label noise. METHODS: Here, we propose an involvement-based label refinement (iLR) method to correct corrupted labels and improve cancer classification. The difference between predicted and true cancer involvements is used to guide the label refinement process. We further incorporate iLR into state-of-the-art methods for learning with noisy labels and predicting cancer involvement. RESULTS: We use 258 biopsy cores from 70 patients and demonstrate that our proposed label refinement method improves the performance of multiple noise-tolerant approaches and achieves a balanced accuracy, correlation coefficient, and mean absolute error of 76.7%, 0.68, and 12.4, respectively. CONCLUSIONS: Our key contribution is to leverage a data-centric method to deal with noisy labels using histopathology reports, and improve the performance of prostate cancer diagnosis through a hierarchical training process with label refinement.

Exposure risk assessment to organic compounds based on their concentrations in return water from shale gas developments.

Because of shale gas operations, significant amounts of return water from hydraulic fracturing are stored in tanks and/or ponds on the surface. These waters contain varying concentrations of toxic organic compounds; hence, there is reasonable concern about the occurrence of hypothetical leakages, which would cause adverse environmental effects and pose a risk to human health. In this study, the chronic and acute carcinogenic and non-carcinogenic risks from exposure to these pollutants by inhalation, ingestion and dermal contact have been assessed for an affected area. The first part of this study focused on estimating the concentrations of organic compounds in the water-soil-atmosphere system. These models are of a general nature and can be applied to any site. In this study, they are applied to the Marcellus shale formation. The analyses developed in this work show that the risks - both carcinogenic and non-carcinogenic - regarding the inhalation of volatile organic compounds (VOCs) increase rapidly and exceed the acceptable thresholds by several orders of magnitude in all scenarios, irrespective of the different recharge rates considered. Given that the hypothetical leakage under consideration occurs at a depth of 50 cm, in the buried part of a semi-buried tank-type reservoir, the direct contamination via wastewater of the most superficial parts of the soil is less likely, and soil particles are generally widely dispersed in air before inhaling. Moreover, the sensitivity analysis indicated that the variable contributing the most to the determined risk levels was the pollutant concentration, followed by the exposure time. Therefore, using appropriate technology to reduce pollutant concentrations in storage ponds is the best strategy to minimise the associated risk to human health.

Towards targeted ultrasound-guided prostate biopsy by incorporating model and label uncertainty in cancer detection.

PURPOSE: Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable. However, the models proposed to-date do not consider the uncertainty present in their output or the data to benefit clinical decision making for targeting biopsy. METHODS: We propose a deep network for improved detection of prostate cancer in systematic biopsy considering both the label and model uncertainty. The architecture of our model is based on U-Net, trained with temporal enhanced ultrasound (TeUS) data. We estimate cancer detection uncertainty using test-time augmentation and test-time dropout. We then use uncertainty metrics to report the cancer probability for regions with high confidence to help the clinical decision making during the biopsy procedure. RESULTS: Experiments for prostate cancer classification includes data from 183 prostate biopsy cores of 41 patients. We achieve an area under the curve, sensitivity, specificity and balanced accuracy of 0.79, 0.78, 0.71 and 0.75, respectively. CONCLUSION: Our key contribution is to automatically estimate model and label uncertainty towards enabling targeted ultrasound-guided prostate biopsy. We anticipate that such information about uncertainty can decrease the number of unnecessary biopsy with a higher rate of cancer yield.

Image edge detection with a photonic spiking VCSEL-neuron.

We report both experimentally and in theory on the detection of edge features in digital images with an artificial optical spiking neuron based on a vertical-cavity surface-emitting laser (VCSEL). The latter delivers fast (< 100 ps) neuron-like optical spikes in response to optical inputs pre-processed using convolution techniques; hence representing image feature information with a spiking data output directly in the optical domain. The proposed technique is able to detect target edges of different directionalities in digital images by applying individual kernel operators and can achieve complete image edge detection using gradient magnitude. Importantly, the neuromorphic (brain-like) spiking edge detection of this work uses commercially sourced VCSELs exhibiting responses at sub-nanosecond rates (many orders of magnitude faster than biological neurons) and operating at the important telecom wavelength of 1300 nm; hence making our approach compatible with optical communication and data-centre technologies.

Automated Nanoscale Absolute Accuracy Alignment System for Transfer Printing.

The heterogeneous integration of micro- and nanoscale devices with on-chip circuits and waveguide platforms is a key enabling technology, with wide-ranging applications in areas including telecommunications, quantum information processing, and sensing. Pick and place integration with absolute positional accuracy at the nanoscale has been previously demonstrated for single proof-of-principle devices. However, to enable scaling of this technology for realization of multielement systems or high throughput manufacturing, the integration process must be compatible with automation while retaining nanoscale accuracy. In this work, an automated transfer printing process is realized by using a simple optical microscope, computer vision, and high accuracy translational stage system. Automatic alignment using a cross-correlation image processing method demonstrates absolute positional accuracy of transfer with an average offset of <40 nm (3σ < 390 nm) for serial device integration of both thin film silicon membranes and single nanowire devices. Parallel transfer of devices across a 2 × 2 mm2 area is demonstrated with an average offset of <30 nm (3σ < 705 nm). Rotational accuracy better than 45 mrad is achieved for all device variants. Devices can be selected and placed with high accuracy on a target substrate, both from lithographically defined positions on their native substrate or from a randomly distributed population. These demonstrations pave the way for future scalable manufacturing of heterogeneously integrated chip systems.

High-Throughput Electrical Characterization of Nanomaterials from Room to Cryogenic Temperatures.

We present multiplexer methodology and hardware for nanoelectronic device characterization. This high-throughput and scalable approach to testing large arrays of nanodevices operates from room temperature to milli-Kelvin temperatures and is universally compatible with different materials and integration techniques. We demonstrate the applicability of our approach on two archetypal nanomaterials-graphene and semiconductor nanowires-integrated with a GaAs-based multiplexer using wet or dry transfer methods. A graphene film grown by chemical vapor deposition is transferred and patterned into an array of individual devices, achieving 94% yield. Device performance is evaluated using data fitting methods to obtain electrical transport metrics, showing mobilities comparable to nonmultiplexed devices fabricated on oxide substrates using wet transfer techniques. Separate arrays of indium-arsenide nanowires and micromechanically exfoliated monolayer graphene flakes are transferred using pick-and-place techniques. For the nanowire array mean values for mobility µFE = 880/3180 cm2 V-1 s-1 (lower/upper bound), subthreshold swing 430 mV dec-1, and on/off ratio 3.1 decades are extracted, similar to nonmultiplexed devices. In another array, eight mechanically exfoliated graphene flakes are transferred using techniques compatible with fabrication of two-dimensional superlattices, with 75% yield. Our results are a proof-of-concept demonstration of a versatile platform for scalable fabrication and cryogenic characterization of nanomaterial device arrays, which is compatible with a broad range of nanomaterials, transfer techniques, and device integration strategies from the forefront of quantum technology research.

Three-dimensional cross-nanowire networks recover full terahertz state.

Terahertz radiation encompasses a wide band of the electromagnetic spectrum, spanning from microwaves to infrared light, and is a particularly powerful tool for both fundamental scientific research and applications such as security screening, communications, quality control, and medical imaging. Considerable information can be conveyed by the full polarization state of terahertz light, yet to date, most time-domain terahertz detectors are sensitive to just one polarization component. Here we demonstrate a nanotechnology-based semiconductor detector using cross-nanowire networks that records the full polarization state of terahertz pulses. The monolithic device allows simultaneous measurements of the orthogonal components of the terahertz electric field vector without cross-talk. Furthermore, we demonstrate the capabilities of the detector for the study of metamaterials.

Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study.

PURPOSE: Ultrasound imaging is routinely used in prostate biopsy, which involves obtaining prostate tissue samples using a systematic, yet, non-targeted approach. This approach is blinded to individual patient intraprostatic pathology, and unfortunately, has a high rate of false negatives. METHODS: In this paper, we propose a deep network for improved detection of prostate cancer in systematic biopsy. We address several challenges associated with training such network: (1) Statistical labels: Since biopsy core's pathology report only represents a statistical distribution of cancer within the core, we use multiple instance learning (MIL) networks to enable learning from ultrasound image regions associated with those data; (2) Limited labels: The number of biopsy cores are limited to at most 12 per patient. As a result, the number of samples available for training a deep network is limited. We alleviate this issue by effectively combining Independent Conditional Variational Auto Encoders (ICVAE) with MIL. We train ICVAE to learn label-invariant features of RF data, which is subsequently used to generate synthetic data for improved training of the MIL network. RESULTS: Our in vivo study includes data from 339 prostate biopsy cores of 70 patients. We achieve an area under the curve, sensitivity, specificity, and balanced accuracy of 0.68, 0.77, 0.55 and 0.66, respectively. CONCLUSION: The proposed approach is generic and can be applied to several other scenarios where unlabeled data and noisy labels in training samples are present.

Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons.

In today's data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems.

Characterization, Selection, and Microassembly of Nanowire Laser Systems.

Semiconductor nanowire (NW) lasers are a promising technology for the realization of coherent optical sources with ultrasmall footprint. To fully realize their potential in on-chip photonic systems, scalable methods are required for dealing with large populations of inhomogeneous devices that are typically randomly distributed on host substrates. In this work two complementary, high-throughput techniques are combined: the characterization of nanowire laser populations using automated optical microscopy, and a high-accuracy transfer-printing process with automatic device spatial registration and transfer. Here, a population of NW lasers is characterized, binned by threshold energy density, and subsequently printed in arrays onto a secondary substrate. Statistical analysis of the transferred and control devices shows that the transfer process does not incur measurable laser damage, and the threshold binning can be maintained. Analysis on the threshold and mode spectra of the device populations proves the potential for using NW lasers for integrated systems fabrication.

Forecasting concentrations of organic chemicals in the vadose zone caused by spills of hydraulic fracturing wastewater.

The return water from hydraulic fracturing operations is characterised by high concentrations of salts and toxic organic compounds. This water is stored on the surface in storage tanks and/or ponds. Wastewater spills caused by inappropriate storage can lead to the contamination of various environmental compartments, thus posing a risk to human health. Such risk can be determined by estimating the concentrations of the substances in the storage system and the behaviour of the same in function of the characteristics of the environment in which they are released. To this end, here we addressed the evolution of the concentrations of pollutants in a tank used to store wastewater from hydraulic fracturing operations. To do this, we estimated both the volume of flowback and the concentrations of the pollutants found in these waters. We then examined the dynamic behaviour of spill-derived compounds in the various environmental compartments in function of the conditions of the medium (humid, semi-arid, and arid). This approach allowed us to rank the hazard posed by the chemical compounds in question, as well as to determine those parameters associated with both the compounds and external natural conditions that contribute to environmental risk. Our results shed greater light on the mechanism by which external environmental variables (especially recharge rate) influence the migration of organic compounds in the vadose zone, and contribute to the prediction of their concentrations. Also, by estimating the time that chemicals remain in contaminated areas, we identify the phases of contamination that pose the greatest risk to human health. In summary, the approach used herein allows the ranking of compounds on the basis of risk to human health and can thus facilitate the design of pollutant management strategies. Of note, our ranked list highlights the relevance of benzene.

Aerobic and anaerobic microorganisms and antibiotic sensitivity of odontogenic maxillofacial infections.

This study aimed to identify the aerobic and anaerobic causal microorganisms of odontogenic infections and their antibiotic sensitivity. Purulent exudates were taken from patients with odontogenic infections by transdermal puncture, and aerobic and anaerobic microorganisms were identified using biochemical tests. Susceptibility to antibiotics was tested using the Kirby-Bauer method; the inhibition halos were measured according to NCCLS, and based on the results, the microorganisms were classified as susceptible, intermediate or resistant to each antibiotic. Frequencies of species and percentages of resistance were calculated. The microorganisms associated with odontogenic infections were principally anaerobic (65.3% anaerobic vs. 35.7% aerobic), and the susceptibility to antibiotics was higher in anaerobic than in aerobic microorganisms. The majority of isolated microorganisms (82%) showed susceptibility to amoxicillin/clavulanic acid. The causal agents of odontogenic infections were anaerobic microorganisms, which exhibited high resistance to antibiotics.

Externally-Triggered Activation and Inhibition of Optical Pulsating Regimes in Quantum-Dot Mode-locked Lasers.

Controlled generation and inhibition of externally-triggered picosecond optical pulsating regimes are demonstrated experimentally in a quantum dot mode locked laser (QDMLL) subject to external injection of an amplitude modulated optical signal. This approach also allows full control and repeatability of the time windows of generated picosecond optical pulses; hence permitting to define precisely their temporal duration (from <1 ns spans) and repetition frequency (from sub-Hz to at least hundreds of MHz). The use of a monolithic QDMLL, operating at 1300 nm, provides a system with a very small footprint that is fully compatible with optical telecommunication networks. This offers excellent prospects for use in applications requiring the delivery of ultrashort optical pulses at precise time instants and at tunable rates, such as optical imaging, time-of-flight diagnostics and optical communication systems.

A model for predicting organic compounds concentration change in water associated with horizontal hydraulic fracturing.

Horizontal drilling and hydraulic fracturing are technologies designed to increase natural gas flow and to improve productivity in low permeability formations. During this drilling operation, tons of flowback and produced water, which contain several organic compounds, return to the surface with a potential risk of influencing the surrounding environment and human health. In order to conduct predictive risk assessments a mathematical model is needed to evaluate organic compound behaviour along the water transportation process as well as concentration changes over time throughout the operational life cycle. A comprehensive model, which fits the experimental data, combining an Organic Matter Transport Dynamic Model with a Two-Compartment First-order Rate Constant (TFRC) Model has been established to quantify the organic compounds concentrations. This algorithm model incorporates two transportation rates, fast and slow. The results show that the higher the value of the organic carbon partition coefficient (koc) in chemicals, the later the maximum concentration in water will be reached. The maximum concentration percentage would reach up to 90% of the available concentration of each compound in shale formation (whose origin may be associated to drilling fluid, connate water and/or rock matrix) over a sufficiently long period of time. This model could serve as a contribution to enhance monitoring strategy, increase benefits out of optimizing health risk assessment for local residents and provide initial baseline data to further operations.

Vertically Emitting Indium Phosphide Nanowire Lasers.

Semiconductor nanowire (NW) lasers have attracted considerable research effort given their excellent promise for nanoscale photonic sources. However, NW lasers currently exhibit poor directionality and high threshold gain, issues critically limiting their prospects for on-chip light sources with extremely reduced footprint and efficient power consumption. Here, we propose a new design and experimentally demonstrate a vertically emitting indium phosphide (InP) NW laser structure showing high emission directionality and reduced energy requirements for operation. The structure of the laser combines an InP NW integrated in a cat's eye (CE) antenna. Thanks to the antenna guidance with broken asymmetry, strong focusing ability, and high Q-factor, the designed InP CE-NW lasers exhibit a higher degree of polarization, narrower emission angle, enhanced internal quantum efficiency, and reduced lasing threshold. Hence, this NW laser-antenna system provides a very promising approach toward the achievement of high-performance nanoscale lasers, with excellent prospects for use as highly localized light sources in present and future integrated nanophotonics systems for applications in advanced sensing, high-resolution imaging, and quantum communications.

Integration of Semiconductor Nanowire Lasers with Polymeric Waveguide Devices on a Mechanically Flexible Substrate.

Nanowire lasers are integrated with planar waveguide devices using a high positional accuracy microtransfer printing technique. Direct nanowire to waveguide coupling is demonstrated, with coupling losses as low as -17 dB, dominated by mode mismatch between the structures. Coupling is achieved using both end-fire coupling into a waveguide facet, and from nanowire lasers printed directly onto the top surface of the waveguide. In-waveguide peak powers up to 11.8 µW are demonstrated. Basic photonic integrated circuit functions such as power splitting and wavelength multiplexing are presented. Finally, devices are fabricated on a mechanically flexible substrate to demonstrate robust coupling between the on-chip laser source and waveguides under significant deformation of the system.