95 research outputs found
One-shot ultraspectral imaging with reconfigurable metasurfaces
One-shot spectral imaging that can obtain spectral information from thousands
of different points in space at one time has always been difficult to achieve.
Its realization makes it possible to get spatial real-time dynamic spectral
information, which is extremely important for both fundamental scientific
research and various practical applications. In this study, a one-shot
ultraspectral imaging device fitting thousands of micro-spectrometers (6336
pixels) on a chip no larger than 0.5 cm, is proposed and demonstrated.
Exotic light modulation is achieved by using a unique reconfigurable
metasurface supercell with 158400 metasurface units, which enables 6336
micro-spectrometers with dynamic image-adaptive performances to simultaneously
guarantee the density of spectral pixels and the quality of spectral
reconstruction. Additionally, by constructing a new algorithm based on
compressive sensing, the snapshot device can reconstruct ultraspectral imaging
information (/~0.001) covering a broad (300-nm-wide)
visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm
standard deviation and spectral resolution of 0.8 nm. This scheme of
reconfigurable metasurfaces makes the device can be directly extended to almost
any commercial camera with different spectral bands to seamlessly switch the
information between image and spectral image, and will open up a new space for
the application of spectral analysis combining with image recognition and
intellisense
Microwave electrometry with Rydberg atoms in a vapor cell using microwave amplitude modulation
We have theoretically and experimentally studied the dispersive signal of the
Rydberg atomic electromagnetically induced transparency (EIT) - Autler-Townes
(AT) splitting spectra obtained using amplitude modulation of the microwave
(MW) field. In addition to the two zero-crossing points, the dispersion signal
has two positive maxima with an interval defined as the shoulder interval of
the dispersion signal . The relationship of MW field
strength and are studied at the MW
frequencies of 31.6 GHz, 22.1 GHz, and 9.2 GHz respectively. The results show
that can be used to character the much weaker
than the interval of two zero-crossing points and the traditional EIT-AT splitting interval , the minimum measured by
is about 30 times smaller than that by . As an example,
the minimum at 9.2 GHz that can be characterized by is 0.056 mV/cm, which is the minimum value characterized by
frequency interval using vapour cell without adding any auxiliary fields. The
proposed method can improve the weak limit and sensitivity of
measured by spectral frequency interval, which is important in the direct
measurement of weak
Predictability effects and parafoveal processing of compound words in natural Chinese reading
We report a boundary paradigm eye movement experiment to investigate whether the predictability of the second character of a two-character compound word affects how it is processed prior to direct fixation during reading. The boundary was
positioned immediately prior to the second character of the target word, which itself was either predictable or unpredictable. The preview was either a pseudocharacter (nonsense preview), or an identity preview. We obtained clear preview effects in all
conditions, but more importantly, skipping probability for the second character of the target word and the whole target word from pretarget was greater when it was predictable than when it was not predictable from the preceding context. Interactive
effects for later measures on the whole target word (gaze duration and go-past time) were also obtained. These results demonstrate that predictability information from preceding sentential context and information regarding the likely identity of upcoming characters are used concurrently to constrain the nature of lexical processing during natural Chinese reading
Recommended from our members
Egg intervention effect on linear growth no longer present after two years.
The Lulun Project, a randomized controlled trial conducted in 2015, found that one egg per day for 6 months during early complementary feeding reduced stunting by 47% and increased linear growth by 0.63 length-for-age Z (LAZ). This follow-up cohort study (Lulun Project II) aimed to test whether the growth effect remained in the egg intervention group compared with the control group after approximately 2 years. Mothers or caregivers from the Lulun Project were recontacted and recruited for this study. Enumerators collected data on socio-economic and demographic factors, 24-hr frequency of dietary intakes, morbidities, and anthropometric measures of height, weight, and head circumference using World Health Organization protocols. Statistical analyses followed the same analytical plan as Lulun Project, applying generalized linear models and regression modelling to test group differences in height-for-age z (HAZ) from LAZ at Lulun Project endline, and structural equation modelling for mediation. One hundred thirty-five mother-child dyads were included in Lulun II, with 11% losses to follow-up from endline Lulun Project. Growth faltering across all children was evident with HAZ -2.07 ± 0.91 and a stunting prevelance of 50%. Regression modelling showed no difference between egg and control groups for the HAZ outcome and other anthropometric outcomes, and significant declines in HAZ from endline Lulun Project in the egg intervention are compared with control groups. Current dietary egg intake, however, was associated with reduced growth faltering in HAZ from Lulun Project endline to Lulun Project II, independent of group assignment and through mediation, explaining 8.8% of the total effect. Findings suggest the need for a longer intervention period and ongoing nutrition support to young children during early childhood
Deciphering the contributions of cuproptosis in the development of hypertrophic scar using single-cell analysis and machine learning techniques
Hypertrophic scar (HS) is a chronic inflammatory skin disease characterized by excessive deposition of extracellular matrix, but the exact mechanisms related to its formation remain unclear, making it difficult to treat. This study aimed to investigate the potential role of cuproptosis in the information of HS. To this end, we used single-cell sequencing and bulk transcriptome data, and screened for cuproptosis-related genes (CRGs) using differential gene analysis and machine learning algorithms (random forest and support vector machine). Through this process, we identified a group of genes, including ATP7A, ULK1, and MTF1, as novel therapeutic targets for HS. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to confirm the mRNA expression of ATP7A, ULK1, and MTF1 in both HS and normal skin (NS) tissues. We also constructed a diagnostic model for HS and analyzed the immune infiltration characteristics. Additionally, we used the expression profiles of CRGs to perform subgroup analysis of HS. We focused mainly on fibroblasts in the transcriptional profile at single-cell resolution. By calculating the cuproptosis activity of each fibroblast, we found that cuproptosis activity of normal skin fibroblasts increased, providing further insights into the pathogenesis of HS. We also analyzed the cell communication network and transcription factor regulatory network activity, and found the existence of a fibroblast-centered communication regulation network in HS, where cuproptosis activity in fibroblasts affects intercellular communication. Using transcription factor regulatory activity network analysis, we obtained highly active transcription factors, and correlation analysis with CRGs suggested that CRGs may serve as potential target genes for transcription factors. Overall, our study provides new insights into the pathophysiological mechanisms of HS, which may inspire new ideas for the diagnosis and treatment
Revealing the roles of glycosphingolipid metabolism pathway in the development of keloid: a conjoint analysis of single-cell and machine learning
Keloid is a pathological scar formed by abnormal wound healing, characterized by the persistence of local inflammation and excessive collagen deposition, where the intensity of inflammation is positively correlated with the size of the scar formation. The pathophysiological mechanisms underlying keloid formation are unclear, and keloid remains a therapeutic challenge in clinical practice. This study is the first to investigate the role of glycosphingolipid (GSL) metabolism pathway in the development of keloid. Single cell sequencing and microarray data were applied to systematically analyze and screen the glycosphingolipid metabolism related genes using differential gene analysis and machine learning algorithms (random forest and support vector machine), and a set of genes, including ARSA,GBA2,SUMF2,GLTP,GALC and HEXB, were finally identified, for which keloid diagnostic model was constructed and immune infiltration profiles were analyzed, demonstrating that this set of genes could serve as a new therapeutic target for keloid. Further unsupervised clustering was performed by using expression profiles of glycosphingolipid metabolism genes to discover keloid subgroups, immune cells, inflammatory factor differences and the main pathways of enrichment between different subgroups were calculated. The single-cell resolution transcriptome landscape concentrated on fibroblasts. By calculating the activity of the GSL metabolism pathway for each fibroblast, we investigated the activity changes of GSL metabolism pathway in fibroblasts using pseudotime trajectory analysis and found that the increased activity of the GSL metabolism pathway was associated with fibroblast differentiation. Subsequent analysis of the cellular communication network revealed the existence of a fibroblast-centered communication regulatory network in keloids and that the activity of the GSL metabolism pathway in fibroblasts has an impact on cellular communication. This contributes to the further understanding of the pathogenesis of keloids. Overall, we provide new insights into the pathophysiological mechanisms of keloids, and our results may provide new ideas for the diagnosis and treatment of keloids
Conditionally Immortalized Mouse Embryonic Fibroblasts Retain Proliferative Activity without Compromising Multipotent Differentiation Potential
Mesenchymal stem cells (MSCs) are multipotent cells which reside in many tissues and can give rise to multiple lineages including bone, cartilage and adipose. Although MSCs have attracted significant attention for basic and translational research, primary MSCs have limited life span in culture which hampers MSCs' broader applications. Here, we investigate if mouse mesenchymal progenitors can be conditionally immortalized with SV40 large T antigen and maintain long-term cell proliferation without compromising their multipotency. Using the system which expresses SV40 large T antigen flanked with Cre/loxP sites, we demonstrate that mouse embryonic fibroblasts (MEFs) can be efficiently immortalized by SV40 large T antigen. The conditionally immortalized MEFs (iMEFs) exhibit an enhanced proliferative activity and maintain long-term cell proliferation, which can be reversed by Cre recombinase. The iMEFs express most MSC markers and retain multipotency as they can differentiate into osteogenic, chondrogenic and adipogenic lineages under appropriate differentiation conditions in vitro and in vivo. The removal of SV40 large T reduces the differentiation potential of iMEFs possibly due to the decreased progenitor expansion. Furthermore, the iMEFs are apparently not tumorigenic when they are subcutaneously injected into athymic nude mice. Thus, the conditionally immortalized iMEFs not only maintain long-term cell proliferation but also retain the ability to differentiate into multiple lineages. Our results suggest that the reversible immortalization strategy using SV40 large T antigen may be an efficient and safe approach to establishing long-term cell culture of primary mesenchymal progenitors for basic and translational research, as well as for potential clinical applications
Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering
Disease heterogeneity has been a critical challenge for precision diagnosis
and treatment, especially in neurologic and neuropsychiatric diseases. Many
diseases can display multiple distinct brain phenotypes across individuals,
potentially reflecting disease subtypes that can be captured using MRI and
machine learning methods. However, biological interpretability and treatment
relevance are limited if the derived subtypes are not associated with genetic
drivers or susceptibility factors. Herein, we describe Gene-SGAN - a
multi-view, weakly-supervised deep clustering method - which dissects disease
heterogeneity by jointly considering phenotypic and genetic data, thereby
conferring genetic correlations to the disease subtypes and associated
endophenotypic signatures. We first validate the generalizability,
interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We
then demonstrate its application to real multi-site datasets from 28,858
individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes
associated with hypertension, from MRI and SNP data. Derived brain phenotypes
displayed significant differences in neuroanatomical patterns, genetic
determinants, biological and clinical biomarkers, indicating potentially
distinct underlying neuropathologic processes, genetic drivers, and
susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease
subtyping and endophenotype discovery, and is herein tested on disease-related,
genetically-driven neuroimaging phenotypes
- …