2,023 research outputs found
Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects.
We demonstrate the use of a compressive sampling algorithm for on-chip fluorescent imaging of sparse objects over an ultra-large field-of-view (>8 cm(2)) without the need for any lenses or mechanical scanning. In this lensfree imaging technique, fluorescent samples placed on a chip are excited through a prism interface, where the pump light is filtered out by total internal reflection after exciting the entire sample volume. The emitted fluorescent light from the specimen is collected through an on-chip fiber-optic faceplate and is delivered to a wide field-of-view opto-electronic sensor array for lensless recording of fluorescent spots corresponding to the samples. A compressive sampling based optimization algorithm is then used to rapidly reconstruct the sparse distribution of fluorescent sources to achieve approximately 10 microm spatial resolution over the entire active region of the sensor-array, i.e., over an imaging field-of-view of >8 cm(2). Such a wide-field lensless fluorescent imaging platform could especially be significant for high-throughput imaging cytometry, rare cell analysis, as well as for micro-array research
Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution.
We demonstrate lensfree holographic microscopy on a chip to achieve approximately 0.6 microm spatial resolution corresponding to a numerical aperture of approximately 0.5 over a large field-of-view of approximately 24 mm2. By using partially coherent illumination from a large aperture (approximately 50 microm), we acquire lower resolution lensfree in-line holograms of the objects with unit fringe magnification. For each lensfree hologram, the pixel size at the sensor chip limits the spatial resolution of the reconstructed image. To circumvent this limitation, we implement a sub-pixel shifting based super-resolution algorithm to effectively recover much higher resolution digital holograms of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area, which is also equivalent to the imaging field-of-view (24 mm2) due to unit magnification. We demonstrate the success of this pixel super-resolution approach by imaging patterned transparent substrates, blood smear samples, as well as Caenoharbditis Elegans
Learning Multi-Level Information for Dialogue Response Selection by Highway Recurrent Transformer
With the increasing research interest in dialogue response generation, there
is an emerging branch formulating this task as selecting next sentences, where
given the partial dialogue contexts, the goal is to determine the most probable
next sentence. Following the recent success of the Transformer model, this
paper proposes (1) a new variant of attention mechanism based on multi-head
attention, called highway attention, and (2) a recurrent model based on
transformer and the proposed highway attention, so-called Highway Recurrent
Transformer. Experiments on the response selection task in the seventh Dialog
System Technology Challenge (DSTC7) show the capability of the proposed model
of modeling both utterance-level and dialogue-level information; the
effectiveness of each module is further analyzed as well
Influence of culture age on exopolymeric substances from common laboratory bacterial strains: a study on yield, profile and Cu(II) biosorption
Extracellular polymeric substances (EPS) produced by laboratory strains Bacillus cereus and Pseudomonas aeruginosa were extracted from cultures incubated at various incubation periods (24, 48, 72, 96 and 120 h). At each sampling time, the EPS were analysed for yield, quality, functional groups present, and their efficacies in copper (Cu(II)) biosorption (using 30 and 50 ppm EPS). Results revealed that EPS yield was influenced by incubation period, with 48-h culture of B. cereus and 96-h culture of P. aeruginosa producing the highest yield of EPS at 8.30 mg and 6.95 mg, respectively. The EPS produced at various incubation periods have similar characteristics in solubility, quality and major functional groups (C-O, CH3, C=C, O-H) present. Efficacy of Cu(II) biosorption was influenced by the amount of EPS used and the EPS-metal incubation time. Although Cu(II) removal was higher for EPS from 24-h B. cereus (18.96%) and 48-h P. aeruginosa (19.19%) when 30 ppm was used, application of 50 ppm EPS demonstrated no distinct differences in amount of Cu(II) removed. This suggested that higher biomass of EPS used and longer EPS-metal incubation period, superseded the efficacy of EPS from various incubation periods
Sperm trajectories form chiral ribbons.
We report the discovery of an entirely new three-dimensional (3D) swimming pattern observed in human and horse sperms. This motion is in the form of 'chiral ribbons', where the planar swing of the sperm head occurs on an osculating plane creating in some cases a helical ribbon and in some others a twisted ribbon. The latter, i.e., the twisted ribbon trajectory, also defines a minimal surface, exhibiting zero mean curvature for all the points on its surface. These chiral ribbon swimming patterns cannot be represented or understood by already known patterns of sperms or other micro-swimmers. The discovery of these unique patterns is enabled by holographic on-chip imaging of >33,700 sperm trajectories at >90-140 frames/sec, which revealed that only ~1.7% of human sperms exhibit chiral ribbons, whereas it increases to ~27.3% for horse sperms. These results might shed more light onto the statistics and biophysics of various micro-swimmers' 3D motion
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
Tracking dialogue states is an essential topic in task-oriented dialogue
systems, which involve filling in the necessary information in pre-defined
slots corresponding to a schema. While general pre-trained language models have
been shown effective in slot-filling, their performance is limited when applied
to specific domains. We propose a graph-based framework that learns
domain-specific prompts by incorporating the dialogue schema. Specifically, we
embed domain-specific schema encoded by a graph neural network into the
pre-trained language model, which allows for relations in the schema to guide
the model for better adaptation to the specific domain. Our experiments
demonstrate that the proposed graph-based method outperforms other multi-domain
DST approaches while using similar or fewer trainable parameters. We also
conduct a comprehensive study of schema graph architectures, parameter usage,
and module ablation that demonstrate the effectiveness of our model on
multi-domain dialogue state tracking
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
As an essential component in task-oriented dialogue systems, dialogue state
tracking (DST) aims to track human-machine interactions and generate state
representations for managing the dialogue. Representations of dialogue states
are dependent on the domain ontology and the user's goals. In several
task-oriented dialogues with a limited scope of objectives, dialogue states can
be represented as a set of slot-value pairs. As the capabilities of dialogue
systems expand to support increasing naturalness in communication,
incorporating dialogue act processing into dialogue model design becomes
essential. The lack of such consideration limits the scalability of dialogue
state tracking models for dialogues having specific objectives and ontology. To
address this issue, we formulate and incorporate dialogue acts, and leverage
recent advances in machine reading comprehension to predict both categorical
and non-categorical types of slots for multi-domain dialogue state tracking.
Experimental results show that our models can improve the overall accuracy of
dialogue state tracking on the MultiWOZ 2.1 dataset, and demonstrate that
incorporating dialogue acts can guide dialogue state design for future
task-oriented dialogue systems.Comment: Published in Spoken Dialogue Systems I, Interspeech 2021. Code is now
publicly available on Github: https://github.com/youlandasu/ACT-AWARE-DS
Automated single-cell motility analysis on a chip using lensfree microscopy.
Quantitative cell motility studies are necessary for understanding biophysical processes, developing models for cell locomotion and for drug discovery. Such studies are typically performed by controlling environmental conditions around a lens-based microscope, requiring costly instruments while still remaining limited in field-of-view. Here we present a compact cell monitoring platform utilizing a wide-field (24 mm(2)) lensless holographic microscope that enables automated single-cell tracking of large populations that is compatible with a standard laboratory incubator. We used this platform to track NIH 3T3 cells on polyacrylamide gels over 20 hrs. We report that, over an order of magnitude of stiffness values, collagen IV surfaces lead to enhanced motility compared to fibronectin, in agreement with biological uses of these structural proteins. The increased throughput associated with lensfree on-chip imaging enables higher statistical significance in observed cell behavior and may facilitate rapid screening of drugs and genes that affect cell motility
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