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Background Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated. Methods The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set. Results The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set. Conclusions EasyScan GO’s expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

Sonic anemometers are used to study the outer scale in near ground level turbulence. Turbulence is expected to obey a Kolmogorov power spectrum within some inertial range, where the temperature or index of refraction fluctuations decrease as the inverse 11/3rds power of the spatial wavenumber. Below this inertial range (that is for sufficiently small spatial wavenumbers, or equivalently sufficiently large scale sizes) the form of the power spectrum isn’t predicted by theory, but it is expected to roll off. A levelling off of the power spectrum at low spatial frequencies corresponds to a levelling off of the structure function at large spatial separations, and this is the signal sought in the data. Near the ground there is some evidence the outer scale size may be as small as the height above ground. Sonic anemometer data was collected in the summer of 2019 in conjunction with optical turbulence experiments. These experiments showed good agreement between different ways of monitoring turbulence. In these experiments, the sonic anemometers were mostly mounted 2.64 meters above the ground. In this work, the anemometer data is being revisited to study the outer scale. Outer scale effects are quite subtle with optical techniques, which are arranged to be most sensitive to variations in index of refraction within the inertial range precisely in order to avoid inner and outer scale effects. Sonic anemometry usually achieves this by including only nearest neighbor measurements in turbulence estimation, but here we examine the variance of temperature differences across a wide range of baselines in order to study the structure function itself.

Knowledge of turbulence distribution along an experimental path can help in effective turbulence compensation and mitigation. Although scintillometers are traditionally used to measure the strength of turbulence, they provide a path-integrated measurement and have limited operational ranges. A technique to profile turbulence using time-lapse imagery of a distant target from spatially separated cameras is presented here. The method uses the turbulence induced differential motion between pairs of point features on a target, sensed at a single camera and between cameras to extract turbulence distribution along the path. The method is successfully demonstrated on a 511 m almost horizontal path going over half concrete and half grass. An array of Light-Emitting Diodes (LEDs) of non-uniform separation is imaged by a pair of cameras, and the extracted turbulence profiles are validated against measurements from 3D sonic anemometers placed along the path. A short-range experiment with a heat source to create local turbulence spike gives good results as well. Because the method is phase-based, it does not suffer from saturation issues and can potentially be applied over long ranges. Although in the present work, a cooperative target has been used, the technique can be used with non-cooperative targets. Application of the technique to images collected over slant paths with elevated targets can aid in understanding the altitude dependence of turbulence in the surface layer.

A dual-beacon Hartmann Turbulence Sensor (HTS) was used to estimate atmospheric turbulence profiles along a 1.8 km long slant path. This HTS uses a 16“ telescope and has 700 active subapertures. Its camera can take frames as fast as 8 kHz. The laser beacons were placed near the top of a 12 story high tower while the HTS telescope was located in a field 1848 meters away. While the field where the HTS was located was mostly flat for the first few hundred meters in the direction of the tower the ground then sloped up gradually gaining 60 meters by the time the path reached the tower. The terrain beneath the beam path was quite variegated with grassy fields, roads, forested areas and buildings giving the potential for changing turbulence along the path. The experiment was also conducted at different times of day and night and in different weather conditions to further vary the turbulence encountered. The techniques used to profile turbulence with this dual-beacon HTS will be reviewed and the results from this experimental campaign will be presented. Several other turbulence measurement systems were operating at locations near and along this path providing some other measurements for comparison.

Atmospheric turbulence profiles were estimated for a horizontal path based upon measurements made with a dual beacon Hartmann Turbulence Sensor (HTS) using simulation derived weighting functions. These results are compared to estimates made using a weighting functions computed from theory. These results are further compared to anemometer and scintillometer based turbulence estimates for the same path. The previously published theoretical weighting functions for this situation are based upon some presumptions of geometric optics and thus ignore both diffraction and scintillation effects. All of these weighting functions quantify how turbulence at different distances along the path contributes to the expected value of the differential tilt variances measured by the HTS. In the experiment, the HTS used a 16” Meade telescope with 700 subapertures along a 511 m path roughly 2 meters above the ground. Two HeNe lasers separated by 11 cm served as beacons, each was beam expanded to well overfill the telescope aperture. The same situation was simulated with wave optics. To create simulated weighting functions, a single (usually weak) random turbulence screen was inserted at a single plane perpendicularly to the propagation path. Light from one beacon was then numerically propagated to the telescope aperture where the tilts were computed over each subaperture and saved. This propagation was then carried out for the second beacon. This random phase screen was then inserted at a different propagation plane and this procedure was repeated. When all the desired positions along the beam path had been sampled a new random phase screen was generated and this whole procedure was repeated hundreds of times. The desired weighting functions were then generated by computing the differential tilt variance between the beacons and all pairs of horizontally separated subapertures for each path position. All equivalent subaperture separations within each range bin were then averaged together to produce weighting functions which depend on path position and subaperture separation distance. The weighting functions produced in this fashion showed some differences from the theoretical ones. They were a little weaker far from the telescope, and they showed a somewhat broadened notch where the beacons overlapped compared to the theoretical ones. The effect of these differences on the resulting turbulence profile estimates will be discussed.

Turbulence distribution along a path is estimated from differential motion of pairs of LEDs on a target board, sensed by a single camera and between two cameras. The method is compared to point measurements from sonic anemometers.

A time-lapse imaging method for turbulence profiling using targets of opportunity is demonstrated. The approach uses differential tilt variances between target features sensed by two cameras. These measurements can help understand altitude dependence of turbulence.

Using two public soil spectral libraries, we examined potential for low-cost nearinfrared devices for in-field testing. Results showed strong promise with conventional neural networks, comparable to previously published results with deep learning.

Distribution of substandard and falsified (SF) medicines is on the rise, and its impact on public health, particularly in low-resource countries, is becoming increasingly significant. Portable, nondestructive screening devices can support regulatory authorities in their defense against the spread of SF medicines. Vibrational spectroscopy is an ideal candidate due to its sampling ease and speed. In this work, five portable, among which four are considered low-cost, spectroscopic devices based on near-infrared (NIR), Raman, and mid-infrared (MIR) were evaluated to quantify active pharmaceutical ingredients (APIs) and formulation accuracy within simulated authentic, falsified, and substandard medicines. Binary sample mixtures containing a typical API in antimalarial, antiretroviral, or anti-tuberculosis medicines were assessed. In both univariate and multivariate analyses, the API quantification performance of the digital light processing (DLP) NIR spectrometer and a handheld Raman device consistently matched or exceeded that of the other NIR spectrometers and a scientific grade MIR spectrometer. In the formulation accuracy tests, data from all devices, other than the silicon photodiode array NIR spectrometer, were able to create regression models with less than 6% error. From this exploratory study, we conclude that certain portable NIR devices hold significant promise as cost-effective screening tools for falsified and potentially substandard medicines, and they warrant further investigation and development.

Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared slides vary widely in quality and presentation, and artifacts often heavily outnumber relatively rare parasites. In this work, we describe a complete, fully-automated framework for thin film malaria analysis that applies ML methods, including convolutional neural nets (CNNs), trained on a large and diverse dataset of field-prepared thin blood films. Quantitation and species identification results are close to sufficiently accurate for the concrete needs of drug resistance monitoring and clinical use-cases on field-prepared samples. We focus our methods and our performance metrics on the field use-case requirements. We discuss key issues and important metrics for the application of ML methods to malaria microscopy.

Smallholder farmers and extension workers in sub-Saharan Africa have limited access to the soil information needed to improve crop yields, and therefore boost incomes and improve food security. A promising approach to provide this information at farm level is a low-cost optical spectroscopy device and an accompanying decision support application. This paper describes our efforts to explore spectral libraries, both publicly accessible and acquired through our own efforts, to define the instrument specifications and data handling procedures for such an approach. Spectral ranges throughout the mid-infrared (MIR) and near-infrared (NIR) regions and spectral resolution pertinent to available hardware technologies were evaluated. Given the complex physical-chemical properties of soil samples, numerous preprocessing strategies were applied prior to regression with partial least squares, support vector machine, and multilayer perceptron neural network (MLPNN) models for predicting relevant soil properties. The experiments with existing spectral datasets demonstrated that the MLPNN approach outperformed other approaches most consistently for soil property prediction, and that the MIR region offers better performance than NIR, even with truncated spectral ranges and degraded spectral resolutions. At this time, however, there are hardware limitations for MIR systems that prevent a low cost device from collecting acceptable signals, thus making an NIR solution more appealing overall.

Optical spectroscopic devices have historically been too expensive or not portable enough to take full advantage of their abilities to offer real-time, on-site, objective results, especially in the developing world. Recent advancements toward smaller and cheaper hardware, especially in the visible and near infrared (NIR) ranges, could enable widespread use in low resource settings, down to a rural health clinic or at the individual farm level. We recently designed and tested a spectroscopic device with these goals in mind. It is based on an initial commercial version of a low cost MEMS spectral detection chip operating in the NIR, or more properly short wave infrared (SWIR) region. Custom optics, electronics, and mechanical designs were created to produce a complete handheld system capable of operation in the lab or in the field. Initial lab testing indicated excellent reproducibility both within and between five different devices. We have verified desired performance (e.g. acceptable signal to noise for target integration times, spectral features equivalent to lab-grade devices, etc.) for applications including pharmaceutical analysis and for analyzing multiple agricultural materials, including soils, plants, fertilizers, and manures. We have also developed a custom mobile app to accompany the devices in upcoming field testing, which will validate their performance in realistic settings in sub-Saharan Africa.

Poor-quality medicines are a major problem for health-care systems in resource-poor settings as identifying falsified medicines requires a complex laboratory infrastructure such as a Medicines Quality Control Laboratory. We report here an evaluation of a low-cost, handheld near-infrared spectrometer (NIRS) device by analyzing a library of artemisinin-based combination therapy (ACT) medicines to determine its usefulness as a drug-screening tool. The "SCiO" research prototype device was used to collect NIR spectra of a library of ACT and artesunate monotherapy medicine samples previously collected in Bioko Island and Equatorial Guinea and Kintampo, Ghana. The quality of these samples had been categorized as falsified, substandard, and quality assured based on the amount of stated active pharmaceutical ingredients detected using high-performance liquid chromatography photodiode array. Numerical analyses were performed on the NIR spectra to assess the usefulness of NIR to identify falsified and substandard medicines. The NIRS device was successful at detecting falsified medicines in all cases where the library contained both quality assured and falsified medicines of the same stated brand of medicines. The NIRS device was successful at identifying substandard amounts of artesunate but not amodiaquine in the ACT samples (N = 15) of artesunate-amodiaquine. This work reveals that this low-cost, portable NIRS device is promising for screening ACTs for falsified samples and could enable widespread drug screening at all points of the health system.

Background Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid application is practical but not reproducible or accurate. The objective of this study was to develop a “deep learning”-based visual evaluation algorithm that automatically recognizes cervical precancer/cancer. Methods A population-based longitudinal cohort of 9406 women ages 18–94 years in Guanacaste, Costa Rica was followed for 7 years (1993–2000), incorporating multiple cervical screening methods and histopathologic confirmation of precancers. Tumor registry linkage identified cancers up to 18 years. Archived, digitized cervical images from screening, taken with a fixed-focus camera (“cervicography”), were used for training/validation of the deep learning-based algorithm. The resultant image prediction score (0–1) could be categorized to balance sensitivity and specificity for detection of precancer/cancer. All statistical tests were two-sided. Results Automated visual evaluation of enrollment cervigrams identified cumulative precancer/cancer cases with greater accuracy (area under the curve [AUC] = 0.91, 95% confidence interval [CI] = 0.89 to 0.93) than original cervigram interpretation (AUC = 0.69, 95% CI = 0.63 to 0.74; P Conclusions The results support consideration of automated visual evaluation of cervical images from contemporary digital cameras. If achieved, this might permit dissemination of effective point-of-care cervical screening.

Adulteration of milk for economic gains is a widespread issue throughout the developing world that can have far-reaching health and nutritional impacts. Milk analysis technologies, such as infrared spectroscopy, can screen for adulteration, but the cost of these technologies has prohibited their use in low resource settings. Recent developments in infrared and Raman spectroscopy hardware have led to commercially available low-cost devices. In this work, we evaluated the performance of two such spectrometers in detecting and quantifying the presence of milk adulterants. Five common adulterants – ammonium sulfate, melamine, sodium bicarbonate, sucrose, and urea, were spiked into five different raw cow and goat milk samples at different concentrations. Collected MIR and Raman spectra were analyzed using partial least squares regression. The limit of detection (LOD) for each adulterant was determined to be in the range of 0.04 to 0.28% (400 to 2800 ppm) using MIR spectroscopy. Raman spectroscopy showed similar LOD’s for some of the adulterants, notably those with strong amine group signals, and slightly higher LOD’s (up to 1.0%) for other molecules. Overall, the LODs were comparable to other spectroscopic milk analyzers on the market, and they were within the economically relevant concentration range of 100 to 4000 ppm. These lower cost spectroscopic devices therefore appear to hold promise for use in low resource settings.

Cervical cancer is the fourth most common cancer among women worldwide and is especially prevalent in low resource settings due to lack of screening and treatment options. Visual inspection with acetic acid (VIA) is a widespread and cost-effective screening method for cervical pre-cancer lesions, but accuracy depends on the experience level of the health worker. Digital cervicography, capturing images of the cervix, enables review by an off-site expert or potentially a machine learning algorithm. These reviews require images of sufficient quality. However, image quality varies greatly across users. A novel algorithm was developed to evaluate the sharpness of images captured with the MobileODT’s digital cervicography device (EVA System), in order to, eventually provide feedback to the health worker. The key challenges are that the algorithm evaluates only a single image of each cervix, it needs to be robust to the variability in cervix images and fast enough to run in real time on a mobile device, and the machine learning model needs to be small enough to fit on a mobile device’s memory, train on a small imbalanced dataset and run in real-time. In this paper, the focus scores of a preprocessed image and a Gaussian-blurred version of the image are calculated using established methods and used as features. A feature selection metric is proposed to select the top features which were then used in a random forest classifier to produce the final focus score. The resulting model, based on nine calculated focus scores, achieved significantly better accuracy than any single focus measure when tested on a holdout set of images. The area under the receiver operating characteristics curve was 0.9459.

We propose and demonstrate a purely optical approach to trap and align particles using the interaction of polarized light with periodic nanostructures to generate enhanced trapping force. With a weakly focused laser beam, we observed efficient trapping and transportation of polystyrene beads with sizes ranging from 10 mum down to 190 nm as well as cancer cell nuclei. In addition, alignment of non-spherical dielectric particles to a 1-D periodic nanostructure was achieved with low laser intensity without attachment to birefringent crystals. Bacterial cells were trapped and aligned with incident optical intensity as low as 17 microW/microm(2).

We report long-range trapping of vanadium dioxide (VO2) and vanadium oxyhydroxide (H2V3O8) nanowires at a distance as large as 50 mum outside the laser spot using plasmonic tweezers and controlled rotation of the nanowires by combining trapping with microfluidic drag force. The plasmonic tweezers are built upon a self-assembled gold nanoparticle array platform. In addition to the long-range trapping and rotation capability, the required optical intensity for the plasmonic tweezers to initiate trapping is much lower (8 muW/mum2) than that required by conventional optical tweezers for similar nanowires. We also investigate possible mechanisms for the unique long-range trapping of nanowires through performing control experiments.

We propose a compact and low-cost design for a variable wave plate using tunable form-birefringent grating structures in polydimethylsiloxane (PDMS). The described device operates through PDMS deformation via electrostatically actuated transparent electrodes. The optical and mechanical properties of tunable form-birefringent structures are modeled using birefringence, rigorous coupled-wave analysis, and simple structural mechanics theory. The device is fabricated by a form of soft nanoimprint lithography. The optical properties are characterized for different structure geometries. Operating principles were verified through testing of the completed device. The experimental results and theoretical study show that an irregular grating structure performs better than a periodic subwavelength structure. [2007-0303].

Malaria remains a major global health burden, and new methods for low-cost, high-sensitivity, diagnosis are essential, particularly in remote areas with low-resource around the world. In this paper, a cost effective, optical cell-phone based transmission polarized light microscope system is presented for imaging the malaria pigment known as hemozoin. It can be difficult to determine the presence of the pigment from background and other artifacts, even for skilled microscopy technicians. The pigment is much easier to observe using polarized light microscopy. However, implementation of polarized light microscopy lacks widespread adoption because the existing commercial devices have complicated designs, require sophisticated maintenance, tend to be bulky, can be expensive, and would require re-training for existing microscopy technicians. To this end, a high fidelity and high optical resolution cell-phone based polarized light microscopy system is presented which is comparable to larger bench-top polarized microscopy systems but at much lower cost and complexity. The detection of malaria in fixed and stained blood smears is presented using both, a conventional polarized microscope and our cell-phone based system. The cell-phone based polarimetric microscopy design shows the potential to have both the resolution and specificity to detect malaria in a low-cost, easy-to-use, modular platform.

The optical microscope is one of the most widely used tools for diagnosing infectious diseases in the developing world. Due to its reliance on trained microscopists, field microscopy often suffers from poor sensitivity, specificity, and reproducibility. The goal of this work, called the Autoscope, is a low-cost automated digital microscope coupled with a set of computer vision and classification algorithms, which can accurately diagnose of a variety of infectious diseases, targeting use-cases in the developing world. Our initial target is malaria, because of the high difficulty of the task and because manual microscopy is currently a central but highly imperfect tool for malaria work in the field. In addition to diagnosis, the algorithm performs species identification and quantitation of parasite load, parameters which are critical in many field applications but which are not effectively determined by rapid diagnostic tests (RDTs). We have built a hardware prototype which can scan approximately 0.1 μL of blood volume in a standard Giemsa-stained thick smear blood slide in approximately 20 minutes. We have also developed a comprehensive machine learning framework, leveraging computer vision and machine learning techniques including support vector machines (SVMs) and convolutional neural networks (CNNs). The Autoscope has undergone successful initial field testing for malaria diagnosis in Thailand.

This paper reports the phase behavior of a multi-component borosilicate glass melt with 0–3 mass% Cr 2 O 3 at 800–1500 °C in equilibrium with air. Both upper and lower liquidus temperatures were observed. When the temperature was between the upper and lower liquidus temperatures, eskolaite (Cr 2 O 3 ) formed in melts with >2 mass% Cr 2 O 3 . Below the lower liquidus temperature, a dispersed chromate phase appeared in the melt that eventually became macroscopically segregated. The chemical durability of the glasses was virtually unaffected by chromium concentration. The particular glass studied was prototypic for vitrifying high-Cr high-level radioactive wastes stored in underground tanks at the Hanford site. The results suggest a significant potential cost benefit for Hanford tank waste cleanup through increased waste solubility in glass.

The equilibrium concentration (Co) of spinel was measured in 16 high-level waste (HLW) glasses as a function of temperature (T). Glasses were formulated by increasing or decreasing concentrations of Al2O3, Cr2O3, Fe2O3, Li2O, MgO, Na2O, or NiO, one-at-a-time, from a baseline composition. Data were fitted using the quasi-ideal-solution relationship between Co and T. The coefficients of this relationship were expressed as functions of glass composition using first-order approximation. All glass components had an effect on liquidus temperature (TL), but only NiO and Fe2O3 had a significant impact on spinel concentration below TL. The temperature at which Co had a given value was also expressed as a function of glass composition. These results can be used to optimize a HLW glass formulation to meet a constraint of either no spinel or a limited spinel fraction in the melter. In addition, the measurement of the size of spinel crystals and subsequent calculation of crystal number density (n) showed that Cr2O4 and Al2O3 increase n.

Background: The haemozoin crystal continues to be investigated extensively for its potential as a biomarker for malaria diagnostics. In order for haemozoin to be a valuable biomarker, it must be present in detectable quantities in the peripheral blood and distinguishable from false positives. Here, dark-field microscopy coupled with sophisticated image processing algorithms is used to characterize the abundance of detectable haemozoin within infected erythrocytes from field samples in order to determine the window of detection in peripheral blood. Methods: Thin smears from Plasmodium falciparum-infected and uninfected patients were imaged in both dark field (DF) unstained and bright field (BF) Giemsa-stained modes. The images were co-registered such that each parasite had thumbnails in both BF and DF modes, providing an accurate map between parasites and DF objects. This map was used to find the abundance of haemozoin as a function of parasite stage through careful parasite staging and correlation with DF objects. An automated image-processing and classification algorithm classified the bright spots in the DF images as either haemozoin or non-haemozoin objects. Results: The algorithm distinguishes haemozoin from non-haemozoin objects in DF images with an object-level sensitivity of 95% and specificity of 97%. Ring stages older than about 6 hours begin to show detectable haemozoin, and rings between 10–16 hours reliably contain detectable haemozoin. However, DF microscopy coupled with the image-processing algorithm detect no haemozoin in rings younger than six hours. Discussion: Although this method demonstrates the most sensitive detection of haemozoin in field samples reported to date, it does not detect haemozoin in ring-stage parasites younger than six hours. Thus, haemozoin is a poor biomarker for field samples primarily composed of young ring-stage parasites because the crystal is not present in detectable quantities by the methods described here. Based on these results, the implications for patient-level diagnosis and recommendations for future work are discussed.

We demonstrate a dark-field imaging technique capable of automated identification of individual bacteria on a dry slide with greater than 95% accuracy without staining or tagging.

Avalanche concentration, a rapid, long-range accumulation of particles around a laser spot in a liquid sample, is demonstrated and characterized for various nanoparticles (NPs). The effect is driven by a convective flow in the sample, caused by efficient heating of NPs with high absorption efficiencies. Several types of concentration behavior were observed and characterized. Control of optical power and initial particle density was found to be effective in determining the assembly process. VO(2) nanowires, carbon nanotube (CNT), and quantum dot (QD) electrode gap bridges were assembled with a variety of sizes and geometries to show the utility of the method for nano-assembly. Bridges were assembled from as many as thousands to as few as one NP and were found to form solid electrical contact between the electrodes, as verified by measuring the current--voltage (I-V) characteristic.

Plasmonics, a rapidly emerging subdiscipline of nanophotonics, is aimed at exploiting surface plasmons for important applications, including sensing, waveguiding, and imaging. Parallel to these research efforts, technology yielding enhanced scattering and absorption of localized surface plasmons (LSPs) provides promising routes for trapping and manipulation of micro and nano scale particles, as well as biomolecules with low laser intensity due to high energy conversion efficiency under resonant excitation. In this paper, we show that the LSP-induced scattering field from a self-assembled gold nanoparticle array can be used to sustain trapping of single micron-sized particles with low laser intensity. Moreover, we demonstrate for the first time efficient localized concentration of sub-micron sized particles and DNAs of various sizes through photothermal effect of plasmonics.

We demonstrate that microfluidic convective flow created by the enhanced light absorption through localized surface plasmons can be utilized to realize the efficient concentration of DNA.

We propose and demonstrate a dark-field imaging technique capable of automated identification of individual bacteria. An 87-channel multispectral system capable of angular and spectral resolution was used to measure the scattering spectrum of various bacteria in culture smears. Spectra were compared between various species and between various preparations of the same species. A 15-channel system was then used to prove the viability of bacterial identification with a relatively simple microscope system. A simple classifier was able to identify four of six bacterial species with greater than 90% accuracy in bacteria-by-bacteria testing.

The scattering characteristics of the malaria byproduct hemozoin, including its scattering distribution and depolarization, are modeled using Discrete Dipole Approximation (DDA) and compared to those of healthy red blood cells. Scattering (or dark-field) spectroscopy and imaging are used to identify hemozoin in fresh rodent blood samples. A new detection method is proposed and demonstrated using dark-field in conjunction with cross-polarization imaging and spectroscopy. SNRs greater than 50:1 are achieved for hemozoin in fresh blood without the addition of stains or reagents. The potential of such a detection system is discussed.

We present an optical microfluidic mixing approach via thermally induced convective flow sustained by localized surface plasmon (LSP) energy. The phonon energy associated with the nonradiative damping of LSP from a Au nanoparticle (NP) array under optical excitation creates a thermal gradient which initiates a convective fluidic flow. Experimental evidence and modeling results both show that LSP from the Au NPs is crucial in establishing a temperature gradient with sufficient magnitude to induce the convective flow with low input optical intensity.

This meeting report presents the key findings and discussion points of a 1-day meeting entitled 'Fake anti-malarials: start with the facts' held on 28th May 2015, in Geneva, Switzerland, to disseminate the findings of the artemisinin combination therapy consortium's drug quality programme. The teams purchased over 10,000 samples, using representative sampling approaches, from six malaria endemic countries: Equatorial Guinea (Bioko Island), Cambodia, Ghana, Nigeria, Rwanda and Tanzania. Laboratory analyses of these samples showed that falsified anti-malarials (