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A method and apparatus for detecting more than one fluorophore in a sample. A broad spectrum excitation light is passed through a shaping filter before illuminating the target sample with more than one fluorophore to be detected, where the optical density of the shaping filter changes based on the wavelength of the broad-spectrum excitation light passing through it.
A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion
Subunit vaccines are theoretically safe and easy to manufacture but require effective adjuvants and delivery systems to yield protective immunity, particularly at critical mucosal sites such as the lung. We investigated nanolipoprotein particles (NLPs) containing the Toll-like receptor 4 agonist monophosphoryl lipid A (MPLA) as a platform for intranasal vaccination against Bacillus anthracis. Modified lipids enabled attachment of disparate spore and toxin protein antigens. Intranasal vaccination of mice with B. anthracis antigen-MPLA-NLP constructs induced robust IgG and IgA responses in serum and in bronchoalveolar and nasal lavage. Typically, a single dose sufficed to induce sustained antibody titers over time. When multiple immunizations were required for sustained titers, specific antibodies were detected earlier in the boost schedule with MPLA-NLP-mediated delivery than with free MPLA. Administering combinations of constructs induced responses to multiple antigens, indicating potential for a multivalent vaccine preparation. No off-target responses to the NLP scaffold protein were detected. In summary, the NLP platform enhances humoral and mucosal responses to intranasal immunization, indicating promise for NLPs as a flexible, robust vaccine platform against B. anthracis and potentially other inhalational pathogens.
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distillation (KD), to enable the detection of anomalous signals in the data through efficient inference on resource-constrained hardware. The framework is targeted for deployment on computing platforms equipped with field-programmable gate arrays (FPGAs), GPUs, or CPUs, allowing low-latency selection of relevant activity directly from the raw detector data stream. We demonstrate that our approach is suitable for the detection and localization of anomalously "high-multiplicity" activity, and outline promising applications for LArTPC online data filtering and triggering.
Background. Bartonella spp. infect a variety of vertebrates throughout the world, with generally high prevalence. Several Bartonella spp. are known to cause diverse clinical manifestations in humans and have been recognized as emerging pathogens. These bacteria are mainly transmitted by blood-sucking arthropods, such as fleas and lice. The role of ticks in the transmission of Bartonella spp. is unclear. Methods. A recently developed quadruplex polymerase chain reaction (PCR) amplicon next-generation sequencing approach that targets Bartonella-specific fragments on gltA, ssrA, rpoB, and groEL was applied to test host-seeking Ixodes scapularis ticks (n=1641; consisting of 886 nymphs and 755 adults) collected in 23 states of the eastern half of the United States and Ixodes pacificus ticks (n=966; all nymphs) collected in California in the western United States for the presence of Bartonella DNA. These species were selected because they are common human biters and serve as vectors of pathogens causing the greatest number of vector-borne diseases in the United States. Results. No Bartonella DNA was detected in any of the ticks tested by any target. Conclusions. Owing to the lack of Bartonella detection in a large number of host-seeking Ixodes spp. ticks tested across a broad geographical region, our results strongly suggest that I. scapularis and I. pacificus are unlikely to contribute more than minimally, if at all, to the transmission of Bartonella spp.
The Oman Drilling Project established an “Active Alteration” multi-borehole observatory in peridotites undergoing low-temperature serpentinization in the Samail Ophiolite. The highly serpentinized rocks are in contact with strongly reducing fluids. Distinct hydrological regimes, governed by differences in rock porosity and fracture density, give rise to steep redox (Eh +200 to –750 mV) and pH (pH range 8.5–11.2) gradients within the 300–400 m deep boreholes. The serpentinites and fluids host an active subsurface ecosystem. Microbial cell abundances in serpentinite vary at least six orders of magnitude, from ≤3.5 × 10 1 to 2.9 × 10 7 cells/g. Low levels of biological sulfate reduction (2–1,000 fmol/cm 3 /day) can be detected in rock cores, particularly in rocks in contact with reduced groundwaters with pH < 10.5. Thermodesulfovibrio is the predominant sulfate reducer identified via metagenomic sequencing of adjacent groundwater communities. Here, we infer that transport and reaction of microbially generated sulfide with the serpentine and brucite assemblages gives rise to optical darkening and sulfide overprinting, including the formation of tochilinite-vallerite group minerals, potentially serving as an indicator that this system is inhabited by microbial life. Olivine mesh-cores replaced with ferroan brucite and minor awaruite, abundant veins containing hydroandradite garnet and polyhedral serpentine, and late-stage carbonate veins are suggested as targets for future spatially resolved life-detection investigations. The high-quality whole-round core samples that have been preserved can be further probed to define how life distributes itself and functions within a system where chemical disequilibria are sustained by low-temperature water/rock interaction, and how biosignatures of in situ microbial activity are generated.
This paper investigates the feasibility of using a linear current sensing (LCS) technique integrated on an unmanned aerial system (UAS) for detecting and mapping underground infrastructure rapidly and cost-effectively. The LCS technique is based on data from a wide band of electromagnetic induction frequencies (50 kHz to 2 MHz) using a vector magnetic field gradiometer. This technique takes advantage of a slowly decaying secondary magnetic field in order to achieve greater standoff detection distance ($\frac{1}{R^2}$ vs. $\frac{1}{R^6}$ for compact metallic targets during EMI sensing, where R is the distance from a target to the sensor). These secondary magnetic fields are produced by the excite current on long conductors, allowing detection at a distance of 10 meters or more. The system operates between tens of kHz to a few MHz and uses either an active EMI source or existing EM fields to excite this linear current on a long metallic subsurface target. Once excited, these linear currents produce a secondary magnetic field that is detected with an above ground triaxial magnetic field gradiometer. By moving and tracking its geolocation, the system outputs rich datasets sufficient to support high-fidelity forward and inverse EMI models for estimating the depth and orientation of deep underground long linear metallic infrastructure. The system’s hardware and its integration to a UAS system are outlined, along with the formulation of LCS theory, and numerical and experimental data are presented. The results illustrate that the LCS technique offers large standoff detection, is adaptable to UAS, and could be used effectively for detecting deep underground infrastructure such as wires and pipes.
We outline the unique opportunities and challenges in the search for "ultraheavy" dark matter candidates with masses between roughly 10 TeV and the Planck scale $m_{\rm pl} ≈ 10^{16}$ TeV. This mass range presents a wide and relatively unexplored dark matter parameter space, with a rich space of possible models and cosmic histories. We emphasize that both current detectors and new, targeted search techniques, via both direct and indirect detection, are poised to contribute to searches for ultraheavy particle dark matter in the coming decade. We highlight the need for new developments in this space, including new analyses of current and imminent direct and indirect experiments targeting ultraheavy dark matter and development of new, ultra-sensitive detector technologies like next-generation liquid noble detectors, neutrino experiments, and specialized quantum sensing techniques.
Heating, ventilation, and air-conditioning (HVAC) systems can develop faults due to poor installation practices or gradual wear and tear, leading to decreased HVAC system’s efficiency, compromised thermal comfort, and shortened equipment lifespan (EERE, 2018). Automated fault detection and diagnosis (AFDD) technologies offer a solution by identifying energy-wasting HVAC faults, such as inadequate indoor airflow and incorrect refrigerant charge, and guiding technicians to enhance system efficiency. In the realm of residential HVAC, AFDD can be implemented through various fault detection and diagnosis capabilities, sensor configurations, and target applications. These technologies typically fall into three categories: smart diagnostic tools, original equipment manufacturer (OEM)-embedded tools, and add-on tools. Smart diagnostic tools employ temporarily installed sensors to directly measure HVAC system characteristics, while OEM-embedded tools utilize factory-installed sensors to identify faults or assess system performance. However, both these types of AFDD technologies are often only accessible for high-end HVAC equipment or require additional sensor installation by qualified technicians, resulting in high investment costs and limited applicability for low-income residential buildings. On the other hand, add-on tools rely solely on data from smart thermostats and meters to detect faults by continuously analyzing equipment runtime or energy usage. As smart thermostat and meter costs decrease and their prevalence increases, these tools can be readily deployed in low-income residential buildings. However, they possess limited capabilities as they rely solely on basic trend analysis. Enhancing such tools with advanced machine learning algorithms can significantly improve their effectiveness.
A method for producing a solution including a positron emitting zinc cation of 63Zn, and the method comprises bombarding a solution target including 63 Cu with high energy protons to produce a solution including a positron emitting zinc cation. A method for detecting or ruling out Alzheimer's disease in a patient comprises administering to a patient a detectable amount of a compound including a positron emitting zinc cation, wherein the zinc cation is targeted to β-amyloid in the patient, and acquiring an image to detect the presence or absence of β-amyloid in the patient. A method for detecting or ruling out cancer in a patient comprises administering to a patient a detectable amount of a compound including a positron emitting zinc cation, wherein the zinc cation is targeted to tissue in the patient; and acquiring an image using a medical imaging technique to detect zinc distribution in tissue in the patient.
Abstract Plants and other photosynthetic organisms have been suggested as potential pervasive biosensors for nuclear nonproliferation monitoring. We demonstrate that ultrafast laser filament-induced fluorescence of chlorophyll in the green alga Chlamydomonas reinhardtii is a promising method for remote, in-field detection of stress from exposure to nuclear materials. This method holds an advantage over broad-area surveillance, such as solar-induced fluorescence monitoring, when targeting excitation of a specific plant would improve the detectability, for example when local biota density is low. After exposing C. reinhardtii to uranium, we find that the concentration of chlorophyll a, chlorophyll fluorescence lifetime, and carotenoid content increase. The increased fluorescence lifetime signifies a decrease in non-photochemical quenching. The simultaneous increase in carotenoid content implies oxidative stress, further confirmed by the production of radical oxygen species evidence in the steady-state absorption spectrum. This is potentially a unique signature of uranium, as previous work finds that heavy metal stress generally increases non-photochemical quenching. We identify the temporal profile of the chlorophyll fluorescence to be a distinguishing feature between uranium-exposed and unexposed algae. Discrimination of uranium-exposed samples is possible at a distance of $$\sim $$ ∼ 35 m with a single laser shot and a modest collection system, as determined through a combination of experiment and simulation of distance-scaled uncertainty in discriminating the temporal profiles. Illustrating the potential for remote detection, detection over 125 m would require 100 laser shots, commensurate with the detection time on the order of 1 s.
The Cryogenic Dark Matter Search low ionization threshold experiment (CDMSlite) achieved effcient detection of very small recoil energies in its germanium target, resulting in sensitivity to Lightly Ionizing Particles (LIPs) in a previously unexplored region of charge, mass, and velocity parameter space. We report first direct-detection limits on the vertical intensity of cosmogenically-produced LIPs with an electric charge smaller than e/(3x10^5), as well as the strongest limits for charge <= e/160, with a minimum vertical intensity of 1.36 x 10^-7 cm^-2s^-1sr^-1 at charge e/160. These results apply over a wide range of LIP masses (5 MeV/c^2 to 100 TeV/c^2) and cover a wide range of beta-gamma values (0.1--10^6), thus excluding non-relativistic LIPs with beta-gamma as small as 0.1 for the first time.
The described BPE-enabled device includes two separated chambers which perform detection and reporting independently. Analytical reaction of a target molecule in the analytical cell is coupled to and monitored by an electrochromic reaction in the reporting cell. The color change in the reporting cell can be determined spectrophotometrically by RGB analysis of a CCD image acquired via smartphone. This detection method provides a linear response and a low limit of detection due to the redox cycling behavior in both chambers. The BPE based electrochromic detector can be modified for sensing of multiple analytes by integrating three or more sets of detection chemistries into one single device. Multiple analytes with different concentrations can be detected within this device simultaneously. The BPE based electrochromic device can be used for metabolite detection, wherein a redox mediator can be combined with specific oxidases to form an electrochemical mediator-electrocatalyst pair that completes redox cycling reactions.
Absorption of fermionic dark matter leads to a range of distinct and novel signatures at dark matter direct detection and neutrino experiments. We study the possible signals from fermionic absorption by nuclear targets, which we divide into two classes of four Fermi operators: neutral and charged current. In the neutral current signal, dark matter is absorbed by a target nucleus and a neutrino is emitted. This results in a characteristically different nuclear recoil energy spectrum from that of elastic scattering. The charged current channel leads to induced $\beta$ decays in isotopes which are stable in vacuum as well as shifts of the kinematic endpoint of $ \beta$ spectra in unstable isotopes. To confirm the possibility of observing these signals in light of other constraints, we introduce UV completions of example higher dimensional operators that lead to fermionic absorption signals and study their phenomenology. Most prominently, dark matter which exhibits fermionic absorption signals is necessarily unstable leading to stringent bounds from indirect detection searches. Nevertheless, we find a large viable parameter space in which dark matter is sufficiently long lived and detectable in current and future experiments.
Here, a straightforward in situ detection method for dengue infection was demonstrated through the molecular imprinting of a dengue nonstructural protein 1 (NS1) epitope into an electropolymerized molecularly imprinted polyterthiophene (E-MIP) film sensor. The key enabling step in the sensor fabrication is based on an epitope imprinting strategy, in which short peptide sequences derived from the original target molecules were employed as the main template for detection and analysis. The formation of the E-MIP sensor films was facilitated using cyclic voltammetry (CV) and monitored in situ by electrochemical quartz crystal microbalance (EC-QCM). Surface properties were analyzed using different techniques including atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and polarization modulation-infrared reflection-adsorption (PM-IRRAS). The standard calibration curve (R = 0.9830) was generated for the detection of the epitope, Ac-VHTWTEQYKFQ-NH 2 , with a linear range of 0.2 to 30 μg/mL and detection limit of 0.073 μg/mL. A separate calibration curve (R = 0.9786) was obtained using spiked buffered solutions of dengue NS1 protein, which resulted in a linear range of 0.2 to 10 μg/mL and a detection limit of 0.056 μg/mL. The fabricated E-MIP sensor exhibited long-term stability, high sensitivity, and good selectivity towards the targeted molecules. These results indicated that the formation of the exact and stable cavity imprints in terms of size, shape, and functionalities was successful. In our future work, we aim to use our E-MIP sensors for NS1 detection in real-life samples such as serum and blood.
We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.
We report the results of a ∼4 yr direct imaging survey of 104 stars to resolve and characterize circumstellar debris disks in scattered light as part of the Gemini Planet Imager (GPI) Exoplanet Survey. We targeted nearby (≲150 pc), young (≲500 Myr) stars with high infrared (IR) excesses (L {sub IR}/L {sub ⋆} > 10{sup −5}), including 38 with previously resolved disks. Observations were made using the GPI high-contrast integral field spectrograph in H-band (1.6 μm) coronagraphic polarimetry mode to measure both polarized and total intensities. We resolved 26 debris disks and 3 protoplanetary/transitional disks. Seven debris disks were resolved in scattered light for the first time, including newly presented HD 117214 and HD 156623, and we quantified basic morphologies of five of them using radiative transfer models. All of our detected debris disks except HD 156623 have dust-poor inner holes, and their scattered-light radii are generally larger than corresponding radii measured from resolved thermal emission and those inferred from spectral energy distributions. To assess sensitivity, we report contrasts and consider causes of nondetections. Detections were strongly correlated with high IR excess and high inclination, although polarimetry outperformed total intensity angular differential imaging for detecting low-inclination disks (≲70°). Based on postsurvey statistics, we improved upon our presurvey target prioritization metric predicting polarimetric disk detectability. We also examined scattered-light disks in the contexts of gas, far-IR, and millimeter detections. Comparing H-band and ALMA fluxes for two disks revealed tentative evidence for differing grain properties. Finally, we found no preference for debris disks to be detected in scattered light if wide-separation substellar companions were present.
In nature, the bioaccumulative potent neurotoxin methylmercury (MeHg) is produced from inorganic mercury (Hg) predominantly by anaerobic microorganisms. Hg-contaminated soils are a potential source of MeHg due to microbial activity. We examine streambank soils collected from the contaminated East Fork Poplar Creek (EFPC) in Tennessee, USA, where seasonal variations in MeHg levels have been observed throughout the year, suggesting active microbial Hg methylation. In this study, we characterized the microbial community in contaminated bank soil samples collected from two locations over a period of one year and compared the results to soil samples from an uncontaminated reference site with similar geochemistry (n = 12). Microbial community composition and diversity were assessed by 16S rRNA gene amplicon sequencing. Furthermore, to isolate potential methylators from soils, enrichment cultures were prepared using selective media. A set of three clade-specific primers targeting the gene hgcA were used to detect Hg methylators among the δ-Proteobacteria in EFPC bank soils across all seasons. Two families among the δ-Proteobacteria that have been previously associated with Hg methylation, Geobacteraceae and Syntrophobacteraceae, were found to be predominant with relative abundances of 0.13% and 4.0%, respectively. However, in soil enrichment cultures, Firmicutes were predominant among families associated with Hg methylation. Specifically, Clostridiaceae and Peptococcaceae and their genera Clostridium and Desulfosporosinus were among the ten most abundant genera with relative abundances of 2.6% and 1.7%, respectively. These results offer insights into the role of microbial communities on Hg transformation processes in contaminated bank soils in EFPC. Identifying the biogeochemical drivers of MeHg production is critical for future remediation efforts.