Diagnostic value of cervical spine ZOOM-DWI in cervical spondylotic myelopathy.

PURPOSE: To investigate the clinical application value of the non-shared incentive diffusion imaging technique (ZOOM-DWI) diagnoses of cervical spondylotic myelopathy (CSM). METHODS: 49 CSM patients who presented from January 2022 to December 2022 were selected as the patient group, and 50 healthy volunteers are recruited as the control group. All subjects underwent conventional MRI and ZOOM-DWI of the cervical spine and neurologic mJOA scores in patients with CSM. The spinal ADC values of segments C2-3, C4-5, C5-6, and C6-7 are measured and analyzed in all subjects, with C5-6 being the most severe level of spinal canal compression in the patient group. In addition, the study also analyzes and compares the relationship between the C5-6 ADC value and mJOA score in the patient group. RESULTS: The mean ADC shows no significantly different levels in the control group. Among the ADC values at each measurement level in the patient group, except for C4-5 and C6-7 segments are not statistically significant, the remaining pair-wise comparisons all show statistically significant differences (F = 24.368, p < 0.001). And these individuals have the highest ADC value at C5-6. The C5-6 ADC value in the patient group is significantly higher compared with the ADC value in the control group (t = 9.414, p < 0.001), with statistical significance. The ADC value at the patient stenosis shows a significant negative correlation with the mJOA score (r = -0.493, p < 0.001). CONCLUSION: Cervical ZOOM-DWI can be applied to diagnose CSM, and spinal ADC value can use as reliable imaging data for diagnosing cervical myelopathy.

Revealing the role of interfacial water and key intermediates at ruthenium surfaces in the alkaline hydrogen evolution reaction.

Ruthenium exhibits comparable or even better alkaline hydrogen evolution reaction activity than platinum, however, the mechanistic aspects are yet to be settled, which are elucidated by combining in situ Raman spectroscopy and theoretical calculations herein. We simultaneously capture dynamic spectral evidence of Ru surfaces, interfacial water, *H and *OH intermediates. Ru surfaces exist in different valence states in the reaction potential range, dissociating interfacial water differently and generating two distinct *H, resulting in different activities. The local cation tuning effect of hydrated Na+ ion water and the large work function of high-valence Ru(n+) surfaces promote interfacial water dissociation. Moreover, compared to low-valence Ru(0) surfaces, high-valence Ru(n+) surfaces have more moderate adsorption energies for interfacial water, *H, and *OH. They, therefore, facilitate the activity. Our findings demonstrate the regulation of valence state on interfacial water, intermediates, and finally the catalytic activity, which provide guidelines for the rational design of high-efficiency catalysts.

Establishment of protoplasts transient expression system in Pinellia ternata (Thunb.) Breit.

OBJECTIVE: In this study, we established an efficient and rapid transient expression system in the protoplasts of Pinellia ternata (Thunb.) Breit. (P. ternata). RESULTS: The protoplasts of P. ternata were prepared from plant leaves as the source material by digesting them with the combination of 20 g·l-1 cellulase and 15 g·l-1 macerozyme for 6 h. Based on the screening of PEG concentration, the conditions for PEG-mediated protoplast transformation were improved, and the highest transformation efficiency was found for 40% PEG 4000. Furthermore, we used the subcellular protein localization technique in P. ternata protoplasts to allow further validation of transient expression system. CONCLUSIONS: We present the method that can be applicable for studying both gene verification and expression in P. ternata protoplasts, thus allowing for engineering the improved varieties of P. ternata through molecular plant breeding techniques. This method can also be widely applicable for analyzing protein interactions, detecting promoter activity, for somatic cell fusion in plant breeding, as well as for other related studies.

50 FPS Object-Level Saliency Detection via Maximally Stable Region.

The human visual system tends to consider saliency of an object as a whole. Some object-level saliency detection methods have been proposed by leveraging object proposals in bounding boxes, and regarding the entire bounding box as one candidate salient region. However, the bounding boxes can not provide exact object position and a lot of pixels in bounding boxes belong to the background. Consequently, background pixels in bounding box also show high saliency. Besides, acquiring object proposals needs high time cost. In order to compute object-level saliency, we consider region growing from some seed superpixels, to find one surrounding region which probably represents the whole object. The desired surrounding region has similar appearance inside and obvious difference with the outside, which is proposed as maximally stable region (MSR) in this paper. In addition, one effective seed superpixel selection strategy is presented to improve speed. MSR based saliency detection is more robust than pixel or superpixel level methods and object proposal based methods. The proposed method significantly outperforms the state-of-the-art unsupervised methods at 50 FPS. Compared with deep learning based methods, we show worse performance, but with about 1200-1600 times faster, which means better trade-off between performance and speed.

Understanding the strain effect of Au@Pd nanocatalysts by in situ surface-enhanced Raman spectroscopy.

A gap-mode configuration was developed for the in situ SERS study of the structure-activity relationship of Au@Pd core-shell nanocatalysts, which show much better performance in the selective oxidation of benzyl alcohol compared to Pd. The in situ SERS results reveal that the tensile strain in the Pd shell could promote the activation of oxygen, thus improving the activity. Such a tensile strain effect decreases with the increase of the Pd shell thickness, leading to a volcano correlation between the activity and the shell thickness.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Synthesis and characterization of Tamoxifen citrate modified reduced graphene oxide nano sheets for breast cancer therapy.

Theranostic agents are of immense consideration in the current generation nanomedicine. In this study, we have developed a facile approach for the fabrication of Tamoxifen citrate modified nanosized reduced graphene oxide (nano-rGO) with more stability and low cytotoxicity. The prepared nano-rGO sheets were characterized using HR-TEM and AFM imaging techniques. Further, the cytotoxicity was assessed using MTT assay on female BALB/c nude mice MCF-7 cell lines. In addition, by means of continuous-wave near-infrared laser, cancer cells in vivo were significantly ablated because of the photothermal effect stimulated by tamoxifen modified nano-rGO. These results indicated that the prepared tamoxifen modified nano-rGO has the ability to apply in the photothermal therapy of breast cancers. Consequently, further exploration of photothermal therapeutics is desirable for the synthesis of novel nano materials with additional functionalities.

300-FPS Salient Object Detection via Minimum Directional Contrast.

Global contrast considers the color difference between a target region or pixel and the rest of the image. It is frequently used to measure the saliency of the region or pixel. In previous global contrast-based methods, saliency is usually measured by the sum of contrast from the entire image. We find that the spatial distribution of contrast is one important cue of saliency that is neglected by previous works. Foreground pixel usually has high contrast from all directions, since it is surrounded by the background. Background pixel often shows low contrast in at least one direction, as it has to connect to the background. Motivated by this intuition, we first compute directional contrast from different directions for each pixel, and propose minimum directional contrast (MDC) as raw saliency metric. Then an O(1) computation of MDC using integral image is proposed. It takes only 1.5 ms for an input image of the QVGA resolution. In saliency post-processing, we use marker-based watershed algorithm to estimate each pixel as foreground or background, followed by one linear function to highlight or suppress its saliency. Performance evaluation is carried on four public data sets. The proposed method significantly outperforms other global contrast-based methods, and achieves comparable or better performance than the state-of-the-art methods. The proposed method runs at 300 FPS and shows six times improvement in runtime over the state-of-the-art methods.

Influence of Donor on the Sensing Performance of a Series of Through-Bond Energy Transfer-Based Two-photon Fluorescent Cu(2+) Probes.

Optical properties of a series of molecular two-photon fluorescent Cu(2+) probes containing the same acceptor (rhodamine group) are analyzed using time-dependent density functional theory in combination with analytical response theory. Special emphasis is placed on evolution of the probes' optical properties in the presence of Cu(2+) . In this study, the compound with naphthalene as the donor is shown to be excellent ratiometric fluorescent chemosensor, whereas the compound with quinoline derivative as the donor shows off/on-typed colorimetric fluorescent response. For the compound with naphthalimide derivative as the donor, changing the connection between the donor and acceptor can efficiently prevent the fluorescent quenching of the probe both in the absence and presence of Cu(2+) . The donor moiety and the connection between donor and acceptor are thus found to play dominant roles on sensing performance of these probes. Moreover, distributions of molecular orbitals involved in the excitation and emission of the probes are analyzed to explore responsive mechanism of the probes. The through-bond energy transfer process is theoretically demonstrated. Our results are used to elucidate the available experimental measurements. This work is helpful to understand the relationships of structure with optical properties for the studied probes.

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

[Establishment of a cell model of insulin-resistant 3T3-L1 adipocytes].

OBJECTIVE: To investigate the optimal conditions for establishing insulin-resistant 3T3-L1 adipocytes. METHOS: Dexamethason (DEX), 3-isobutyl-methylxanthine (IBMX) and different concentrations of insulin (10(-8), 10(-7), and 10(-6) mol·L(-1)) were used to induce 3T3-L1 preadipocytes into mature adipocytes identified by oil red O staining. We established insulin- resistant 3T3-L1 adipocytes cell model (IR-3T3-L1) by exposing the cells to 1µmol·L(-1) DEX, and the changes of glucose concen- tration in the cell culture were determined by glucose oxidase-peroxidase (GOD-POD) assay. RESULTS: Treatment of 3T3-L1 cells with DEX, IBMX and 10(-6) mol·L(-1)) insulin for 9 days resulted in the differentiation of >90% of the cells into mature adipocytes. IR-3T3-L1 cells cultured for 96 h in the culture media containing 1 µmol·L(-1) DEX showed significantly increased glucose consumption (P=0.0003) as compared with the control group at 36 h (P<0.001). CONCLUSION: 3T3-L1 cells can be induced into mature adipocytes by exposure to 1 µmol·L(-1) DEX, 0.5 mmol·L(-1) IBMX and 10(-6) mol·L(-1)) insulin. A 96 h exposure to 1 µmol·L(-1) DEX can induce 3T3-L1 adipocytes to acquire insulin resistance that can be maintained for 36 h.

[Effects of rutaecarpine on inflammatory cytokines in insulin resistant primary skeletal muscle cells].

It is now well established that inflammation plays an important role in the development of numerous chronic metabolic diseases including insulin resistance (IR) and type 2 diabetes (T2DM). Skeletal muscle is responsible for 75% of total insulin-dependent glucose uptake; consequently, skeletal muscle IR is considered to be the primary defect of systemic IR development. Our pre- vious study has shown that rutaecarpine (Rut) can benefit blood lipid profile, mitigate inflammation, and improve kidney, liver, pan- creas pathology status of T2DM rats. However, the effects of Rut on inflammatory cytokines in the development of IR-skeletal muscle cells have not been studied. Thus, our objective was to investigate effects of Rut on inflammatory cytokines interleukiri (IL)-1, IL-6 and tumor necrosis factor (TNF)-α in insulin resistant primary skeletal muscle cells (IR-PSMC). Primary cultures of skeletal muscle cells were prepared from 5 neonate SD rats, and the primary rat skeletal muscle cells were identified by cell morphology, effect of ru- taecarpine on cell proliferation by MTT assay. IR-PSMC cells were induced by palmitic acid (PA), the glucose concentration was measured by glucose oxidase and peroxidase (GOD-POD) method. The effects of Rut on inflammatory cytokines IL-1, IL-6 and TNF-α in IR-PSMC cells were tested by enzyme-linked immunosorbent assay (ELISA) kit. The results show that the primary skeletal muscle cells from neonatal rat cultured for 2-4 days, parallel alignment regularly, and cultured for 7 days, cells fused and myotube formed. It was shown that Rut in concentration 0-180. 0 µmol x L(-1) possessed no cytotoxic effect towards cultured primary skeletal muscle cells. However, after 24 h exposure to 0.6 mmol x L(-1) PA, primary skeletal muscle cells were able to induce a state of insulin resistance. The results obtained indicated significant decrease (P < 0.05 to P < 0.001) IL-1, IL-6 and TNF-α production by cultured IR-PSMC cells when incubating 24 hours with Rut, beginning from 20 to 180.0 µmol x L(-1). IL-1, IL-6 and TNF-α in the Rut treated groups were dose-dependently decreased compared with that in the IR-PSMC control group. Our results demonstrated that the Rut promoted glucose consumption and improved insulin resistance possibly through suppression of inflammatory cytokines in the IR-PSMC cells.

[Probe excavations about using gene chip to identify original plants in Chinese pharmacopoeia based on NCBI sequence database].

OBJECTIVE: Based on the DNA fragments of medicinal plants of NCBJ database, the DNA Probe,which can be used to identify original plants in the Chinese Pharmacopoeia (2010 edition), was got. METHODS: First of all, get the Latin name of the original plants by collating the Chinese Pharmacopoeia. Next,download the medicinal plants' DNA fragments from the NCBI database, including ITS, matK, rbcL, psbK-psbI and trnH-psbA, then design probe by using Array Designer 4. 2. Finally, analyze each probe's versatility in the same kind of original plant and conservatism in different kinds of original plants by using Matlab, then determine the specificity of the probe. RESULTS: Regarding the Latin name of 586 original plants in the Chinese Pharmacopoeia (2010 edition) and the above five gene fragments as retrieval condition, 7 613 sequences were downloaded from NCBI, then 315 436 probes were got in total by analyzing. What's more, after analyzing versatility and conservatism of the probes,13 814 specific probes were got. Furthermore,in theory, 376 kinds of original plants could be detected. Because there existed the lack of related gene fragments in the NCBI database,or the sequences were short of specificity,210 species of original plants which were involved in the Chinese Pharmacopoeia didn't receive the corresponding probe. CONCLUSION: The results of the study can provide the further development of medicinal plants' identification chip with vital information support,and the excavation methods of probe can be widely used. Furthermore,the results of the study indicate the original plants which need sequencing importantly in the future.

Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: a simultaneous recording study.

The feasibility of functional near-infrared spectroscopy (fNIRS) to assess resting-state functional connectivity (RSFC) has already been demonstrated. However the validity of fNIRS-based RSFC has rarely been studied. In the present study, fNIRS and fMRI data were simultaneously acquired from 21 subjects during the resting state. After the spatial correspondence was established between the two imaging modalities by transforming the fMRI data into fNIRS measurements space, the index of Between-Modality-Similarity (BMS) of RSFC was evaluated across multiple spatial scales. First, the RSFC between the bilateral primary motor ROI was quite similar between fNIRS and fMRI for all the subjects (BMS(ROI) = 0.95 ± 0.04 for HbO and BMS(ROI) = 0.86 ± 0.13 for HbR). Second, group-level sensorimotor RSFC maps (0.79 for HbO and 0.74 for HbR) showed higher between-modality similarity than individual-level RSFC maps (0.48 ± 0.16 for HbO and 0.41 ± 0.15 for HbR). Finally, for the first time, we combined fNIRS and graph theory to investigate topological properties of resting-state brain networks. The clustering coefficient (C(p)) and characteristic path length (L(p)) which are the most important network topological parameters, both showed high between-modality similarities (BMS(Cp) = 0.90 ± 0.03 for HbO and 0.90 ± 0.06 for HbR; BMS(Lp) = 0.92 ± 0.04 for HbO and 0.91 ± 0.05 for HbR). In summary, the converged results across all the spatial scales demonstrated that fNIRS is capable of providing comparable RSFC measures to fMRI, and thus provide direct evidence for the validity of the optical brain connectivity and the optical brain network approaches to functional brain integration during resting state.

Determination of dominant frequency of resting-state brain interaction within one functional system.

Accumulating evidence has revealed that the resting-state functional connectivity (RSFC) is frequency specific and functional system dependent. Determination of dominant frequency of RSFC (RSFC(df)) within a functional system, therefore, is of importance for further understanding the brain interaction and accurately assessing the RSFC within the system. Given the unique advantages over other imaging techniques, functional near-infrared spectroscopy (fNIRS) holds distinct merits for RSFC(df) determination. However, an obstacle that hinders fNIRS from potential RSFC(df) investigation is the interference of various global noises in fNIRS data which could bring spurious connectivity at the frequencies unrelated to spontaneous neural activity. In this study, we first quantitatively evaluated the interferences of multiple systemic physiological noises and the motion artifact by using simulated data. We then proposed a functional system dependent and frequency specific analysis method to solve the problem by introducing anatomical priori information on the functional system of interest. Both the simulated and real resting-state fNIRS experiments showed that the proposed method outperforms the traditional one by effectively eliminating the negative effects of the global noises and significantly improving the accuracy of the RSFC(df) estimation. The present study thus provides an effective approach to RSFC(df) determination for its further potential applications in basic and clinical neurosciences.

Is resting-state functional connectivity revealed by functional near-infrared spectroscopy test-retest reliable?

Recently, resting-state functional near-infrared spectroscopy (rs-fNIRS) research has experienced tremendous progress. Resting-state functional connectivity (RSFC) has been adopted as a pivotal biomarker in rs-fNIRS studies. However, it is yet to be clear if the RSFC derived from rs-fNIRS is reliable. This concern impedes extensive utilization of rs-fNIRS. We systematically address the issue of reliability. Sixteen subjects participate in two rs-fNIRS sessions held one week apart. RSFC in sensorimotor system is calculated using the seed-correlation approach. Then, test-retest reliability is evaluated at three different scales (map-, cluster-, and channelwise) for individual- and group-level RSFC derived from different types of fNIRS signals [oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT)]. The results show that, for HbO signals, individual-level RSFC generally has good-to-excellent map-/clusterwise reliability, while group-level RSFC has excellent reliability. For HbT signals, the results are similar. For HbR signals, the clusterwise reliability is comparable to that for HbO while the mapwise reliability is slightly lower (fair to good). Focusing on RSFC at a single channel, we report poor channelwise reliability for all three types of signals. We hereby propose that fNIRS-derived RSFC is a reliable biomarker if interpreted in map- and clusterwise manners. However, channelwise interpretation of individual RSFC should proceed with caution.

Dynamic Cascades with Bidirectional Bootstrapping for Action Unit Detection in Spontaneous Facial Behavior.

Automatic facial action unit detection from video is a long-standing problem in facial expression analysis. Research has focused on registration, choice of features, and classifiers. A relatively neglected problem is the choice of training images. Nearly all previous work uses one or the other of two standard approaches. One approach assigns peak frames to the positive class and frames associated with other actions to the negative class. This approach maximizes differences between positive and negative classes, but results in a large imbalance between them, especially for infrequent AUs. The other approach reduces imbalance in class membership by including all target frames from onsets to offsets in the positive class. However, because frames near onsets and offsets often differ little from those that precede them, this approach can dramatically increase false positives. We propose a novel alternative, dynamic cascades with bidirectional bootstrapping (DCBB), to select training samples. Using an iterative approach, DCBB optimally selects positive and negative samples in the training data. Using Cascade Adaboost as basic classifier, DCBB exploits the advantages of feature selection, efficiency, and robustness of Cascade Adaboost. To provide a real-world test, we used the RU-FACS (a.k.a. M3) database of nonposed behavior recorded during interviews. For most tested action units, DCBB improved AU detection relative to alternative approaches.

Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy.

Recent studies of resting-state functional near-infrared spectroscopy (fNIRS) have emerged as a hot topic and revealed that resting-state functional connectivity (RSFC) is an inherent characteristic of the resting brain. However, it is currently unclear if fNIRS-based RSFC is test-retest reliable. In this study, we utilized independent component analysis (ICA) as an effective RSFC detection tool to address the reliability question. Sixteen subjects participated in two resting-state fNIRS recording sessions held 1week (6.88±1.09 days) apart. Then, RSFC in the sensorimotor regions was extracted using ICA. Test-retest reliability was assessed for intra- and inter-sessions, at both individual and group levels, and for different hemoglobin concentration signals. Our results clearly demonstrated that map-wise reliability was excellent at the group level (with Pearson's r coefficients up to 0.88) and generally fair at the individual level. Cluster-wise reliability was better at the group level (having reproducibility indices of up to 0.97 for the size and up to 0.80 for the location of the detected RSFC) and was weaker but still fair at the individual level (0.56 and 0.46 for intra- and inter-session reliabilities, respectively). Cluster-wise intra-class correlation coefficients (ICCs) also exhibited fair-to-good reliability (with single-measure ICC up to 0.56), while channel-wise single-measure ICCs indicated lower reliability. We conclude that fNIRS-based, ICA-derived RSFC is an essential and reliable biomarker at the individual and group levels if interpreted in map- and cluster-wise manners. Our results also suggested that channel-wise individual-level RSFC results should be interpreted with caution if no optode co-registration procedure had been conducted and indicated that "cluster" should be treated as a minimal analytical unit in further RSFC studies using fNIRS.

Detecting resting-state functional connectivity in the language system using functional near-infrared spectroscopy.

Functional connectivity has become one of the important approaches to understanding the functional organization of the human brain. Recently, functional near-infrared spectroscopy (fNIRS) was demonstrated as a feasible method to study resting-state functional connectivity (RSFC) in the sensory and motor systems. However, whether such fNIRS-based RSFC can be revealed in high-level and complex functional systems remains unknown. In the present study, the feasibility of such an approach is tested on the language system, of which the neural substrates have been well documented in the literature. After determination of a seed channel by a language localizer task, the correlation strength between the low frequency fluctuations of the fNIRS signal at the seed channel and those at all other channels is used to evaluate the language system RSFC. Our results show a significant RSFC between the left inferior frontal cortex and superior temporal cortex, components both associated with dominant language regions. Moreover, the RSFC map demonstrates left lateralization of the language system. In conclusion, the present study successfully utilized fNIRS-based RSFC to study a complex and high-level neural system, and provides further evidence for the validity of the fNIRS-based RSFC approach.

Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements.

As a promising non-invasive imaging technique, functional near infrared spectroscopy (fNIRS) has recently earned increasing attention in resting-state functional connectivity (RSFC) studies. Preliminary fNIRS-based RSFC studies adopted a seed correlation approach and yielded interesting results. However, the seed correlation approach has several inherent problems, such as neglecting of interactions among multiple regions and a dependence on seed region selection. Moreover, ineffectively reduced noise and artifacts in fNIRS measurements also negatively affect RSFC results. In this study, independent component analysis (ICA) was introduced to meet these challenges in RSFC detection based on resting-state fNIRS measurements. The results of ICA on data from the sensorimotor and the visual systems both showed functional system-specific RSFC maps. Results from comparison between ICA and the conventional seed correlation approach demonstrated, both qualitatively and quantitatively, the superior performance of ICA with higher sensitivity and specificity, especially in the case of higher noise level. The capability of ICA to separate noise and artifacts from resting-state fNIRS data was also demonstrated, and the extracted noise and artifacts were illustrated. Finally, some practical issues on performing ICA on resting-state fNIRS data were discussed.