Maize smart-canopy architecture enhances yield at high densities.

Increasing planting density is a key strategy for enhancing maize yields1-3. An ideotype for dense planting requires a 'smart canopy' with leaf angles at different canopy layers differentially optimized to maximize light interception and photosynthesis4-6, among other features. Here we identified leaf angle architecture of smart canopy 1 (lac1), a natural mutant with upright upper leaves, less erect middle leaves and relatively flat lower leaves. lac1 has improved photosynthetic capacity and attenuated responses to shade under dense planting. lac1 encodes a brassinosteroid C-22 hydroxylase that predominantly regulates upper leaf angle. Phytochrome A photoreceptors accumulate in shade and interact with the transcription factor RAVL1 to promote its degradation via the 26S proteasome, thereby inhibiting activation of lac1 by RAVL1 and decreasing brassinosteroid levels. This ultimately decreases upper leaf angle in dense fields. Large-scale field trials demonstrate that lac1 boosts maize yields under high planting densities. To quickly introduce lac1 into breeding germplasm, we transformed a haploid inducer and recovered homozygous lac1 edits from 20 diverse inbred lines. The tested doubled haploids uniformly acquired smart-canopy-like plant architecture. We provide an important target and an accelerated strategy for developing high-density-tolerant cultivars, with lac1 serving as a genetic chassis for further engineering of a smart canopy in maize.

Rapid and Accurate Quantification of Viable Lactobacillus Cells in Infant Formula by Flow Cytometry Combined with Propidium Monoazide and Signal-Enhanced Fluorescence In Situ Hybridization.

Lactobacillus is an important member of the probiotic bacterial family for regulating human intestinal microflora and preserving its normalcy, and it has been widely used in infant formula. An appropriate and feasible method to quantify viable Lactobacilli cells is urgently required to evaluate the quality of probiotic-fortified infant formula. This study presents a rapid and accurate method to count viable Lactobacilli cells in infant formula using flow cytometry (FCM). First, Lactobacillus cells were specifically and rapidly stained by oligonucleotide probes based on a signal-enhanced fluorescence in situ hybridization (SEFISH) technique. A DNA-binding fluorescent probe, propidium monoazide (PMA), was then used to accurately recognize viable Lactobacillus cells. The entire process of this newly developed PMA-SEFISH-FCM method was accomplished within 2.5 h, which included pretreatment, dual staining, and FCM analysis; thus, this method showed considerably shorter time-to-results than other rapid methods. This method also demonstrated a good linear correlation (R2 = 0.9994) with the traditional plate-based method with a bacterial recovery rate of 91.24%. To the best of our knowledge, the present study is the first report of FCM combined with PMA and FISH for the specific detection of viable bacterial cells.

SPOCK2 modulates neuropathic pain by interacting with MT1-MMP to regulate astrocytic MMP-2 activation in rats with chronic constriction injury.

BACKGROUND: Neuropathic pain (NP) is a kind of intractable pain. The pathogenesis of NP remains a complicated issue for pain management practitioners. SPARC/osteonectin, CWCV, and Kazal-like domains proteoglycan 2 (SPOCK2) are members of the SPOCK family that play a significant role in the development of the central nervous system. In this study, we investigated the role of SPOCK2 in the development of NP in a rat model of chronic constriction injury (CCI). METHODS: Sprague-Dawley rats were randomly grouped to establish CCI models. We examined the effects of SPOCK2 on pain hpersensitivity and spinal astrocyte activation after CCI-induced NP. Paw withdrawal threshold (PWT) and paw withdrawal latency (PWL) were used to reflects the pain behavioral degree. Molecular mechanisms involved in SPOCK2-mediated NP in vivo were examined by western blot analysis, immunofluorescence, immunohistochemistry, and co-immunoprecipitation. In addition, we examined the SPOCK2-mediated potential protein-protein interaction (PPI) in vitro coimmunoprecipitation (Co-IP) experiments. RESULTS: We founded the expression level of SPOCK2 in rat spinal cord was markedly increased after CCI-induced NP, while SPOCK2 downregulation could partially relieve pain caused by CCI. Our research showed that SPOCK2 expressed significantly increase in spinal astrocytes when CCI-induced NP. In addition, SPOCK2 could act as an upstream signaling molecule to regulate the activation of matrix metalloproteinase-2 (MMP-2), thus affecting astrocytic ERK1/2 activation and interleukin (IL)-1ß production in the development of NP. Moreover, in vitro coimmunoprecipitation (Co-IP) experiments showed that SPOCK2 could interact with membrane-type 1 matrix metalloproteinase (MT1-MMP/MMP14) to regulate MMP-2 activation by the SPARC extracellular (SPARC_EC) domain. CONCLUSIONS: Research shows that SPOCK2 can interact with MT1-MMP to regulate MMP-2 activation, thus affecting astrocytic ERK1/2 activation and IL-1ß production to achieve positive promotion of NP.

Machine Learning-Guided Prediction of Desalination Capacity and Rate of Porous Carbons for Capacitive Deionization.

Nowadays, capacitive deionization (CDI) has emerged as a prominent technology in the desalination field, typically utilizing porous carbons as electrodes. However, the precise significance of electrode properties and operational conditions in shaping desalination performance remains blurry, necessitating numerous time-consuming and resource-intensive CDI experiments. Machine learning (ML) presents an emerging solution, offering the prospect of predicting CDI performance with minimal investment in electrode material synthesis and testing. Herein, four ML models are used for predicting the CDI performance of porous carbons. Among them, the gradient boosting model delivers the best performance on test set with low root mean square error values of 2.13 mg g-1 and 0.073 mg g-1 min-1 for predicting desalination capacity and rate, respectively. Furthermore, SHapley Additive exPlanations is introduced to analyze the significance of electrode properties and operational conditions. It highlights that electrolyte concentration and specific surface area exert a substantially more influential role in determining desalination performance compared to other features. Ultimately, experimental validation employing metal-organic frameworks-derived porous carbons and biomass-derived porous carbons as CDI electrodes is conducted to affirm the prediction accuracy of ML models. This study pioneers ML techniques for predicting CDI performance, offering a compelling strategy for advancing CDI technology.

Electrically tunable virtual image Luneburg lens using graphene.

Virtual image lenses play essential roles in various optical devices and applications, including vision correction, photography, and scientific instruments. Here, we introduce an approach for creating virtual image Luneburg lenses (LL) on graphene. Remarkably, the graphene plasmonic lens (GPL) exhibits electrically tunable virtual focusing capabilities. The design principle of the tunability is based on the nonlinear relationship between surface plasmon polariton (SPP) wave mode index and chemical potential of graphene. By controlling the gate voltage of graphene, we can achieve continuous tuning of virtual focus. A ray-tracing technique is employed to determine the required gate voltages for various virtual focal lengths. The proposed GPL facilitates adjustable virtual focusing, promising advancements in highly adaptive and transformative nanophotonic devices.

Off-plane quartz-enhanced photoacoustic spectroscopy.

In this work, we developed off-plane quartz-enhanced photoacoustic spectroscopy (OP-QEPAS). In the OP-QEPAS the light beam went neither through the prong spacing of the quartz tuning fork (QTF) nor in the QTF plane. The light beam is in parallel with the QTF with an optimal distance, resulting in low background noise. A radial-cavity (RC) resonator was coupled with the QTF to enhance the photoacoustic signal by the radial resonance mode. By offsetting both the QTF and the laser position from the central axis, we enhance the effect of the acoustic radial resonance and prevent the noise generated by direct laser irradiation of the QTF. Compared to IP-QEPAS based on a bare QTF, the developed OP-QEPAS with a RC resonator showed a >10× signal-to-noise ratio (SNR) enhancement. The OP-QEPAS system has great advantages in the use of light emitting devices (LEDs), long-wavelength laser sources such as mid-infrared quantum cascade lasers, and terahertz sources. When employing a LED as the excitation source, the noise level was suppressed by ∼2 orders of magnitude. Furthermore, the radial and longitudinal resonance modes can be combined to further improve the sensor performance.

A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally found along the utero-placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue. PURPOSE: To develop a DL tool for antenatal diagnosis of PAS using T2-weighted MR images. STUDY TYPE: Retrospective. SUBJECTS: Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets. FIELD STRENGTH/SEQUENCE: Sagittal T2-weighted fast spin echo sequence at 1.5 T and 3 T. ASSESSMENT: An nnU-Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero-placental boundary region. The UPB image was then fed into DenseNet-PAS for PAS diagnosis. DenseNet-PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard. STATISTICAL TESTS: Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann-Whitney U-test or the chi-squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs. RESULTS: Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737-0.770). DATA CONCLUSION: By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE: Prospective. POPULATION/SUBJECTS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION: Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.

OBJECTIVES: To investigate the MRI radiomics signatures in predicting pathologic response among patients with locally advanced esophageal squamous cell carcinoma (ESCC), who received neoadjuvant chemotherapy (NACT). METHODS: Patients who underwent NACT from March 2015 to October 2019 were prospectively included. Each patient underwent esophageal MR scanning within one week before NACT and within 2-3 weeks after completion of NACT, prior to surgery. Radiomics features extracted from T2-TSE-BLADE were randomly split into the training and validation sets at a ratio of 7:3. According to the progressive tumor regression grade (TRG), patients were stratified into two groups: good responders (GR, TRG 0 + 1) and poor responders (non-GR, TRG 2 + 3). We constructed the Pre/Post-NACT model (Pre/Post-model) and the Delta-NACT model (Delta-model). Kruskal-Wallis was used to select features, logistic regression was used to develop the final model. RESULTS: A total of 108 ESCC patients were included, and 3/2/4 out of 107 radiomics features were selected for constructing the Pre/Post/Delta-model, respectively. The selected radiomics features were statistically different between GR and non-GR groups. The highest area under the curve (AUC) was for the Delta-model, which reached 0.851 in the training set and 0.831 in the validation set. Among the three models, Pre-model showed the poorest performance in the training and validation sets (AUC, 0.466 and 0.596), and the Post-model showed better performance than the Pre-model in the training and validation sets (AUC, 0.753 and 0.781). CONCLUSIONS: MRI-based radiomics models can predict the pathological response after NACT in ESCC patients, with the Delta-model exhibiting optimal predictive efficacy. CLINICAL RELEVANCE STATEMENT: MRI radiomics features could be used as a useful tool for predicting the efficacy of neoadjuvant chemotherapy in esophageal carcinoma patients, especially in selecting responders among those patients who may be candidates to benefit from neoadjuvant chemotherapy. KEY POINTS: • The MRI radiomics features based on T2WI-TSE-BLADE could potentially predict the pathologic response to NACT among ESCC patients. • The Delta-model exhibited the best predictive ability for pathologic response, followed by the Post-model, which similarly had better predictive ability, while the Pre-model performed less well in predicting TRG.

Nickel-catalyzed acylation of vinylpyridine with alkylzincs under 1 atm CO.

A nickel-catalyzed acylation of vinylpyridines with CO at atmospheric pressure is reported, allowing for an expedient approach to synthesize ß-acyl pyridine derivatives with high regio- and chemoselectivity. The electron-withdrawing property of pyridine plays pivotal roles in activating the alkenyl group, thereby facilitating this carbonylative process. In addition to vinylpyridines, other alkenylheterocycles such as thiazole and quinoline were also suitable for this method.

Effect of omega-3 fatty acids supplementation on the prognosis of coronary artery disease: A meta-analysis of randomized controlled trials.

AIMS: To evaluate whether omega-3 fatty acids (É·-3 FAs) supplementation can improve cardiovascular outcomes in patients with established coronary artery disease (CAD). DATA SYNTHESIS: Five electronic databases were searched for randomized controlled trials that evaluated the effect of É·-3 FAs on cardiovascular outcomes in patients with CAD. The language was restricted to English. The risk ratio was pooled. Subgroup analyses were conducted to evaluate whether study-level variables might act as effect modifiers. A total of 12 studies involving 29913 patients were included. É·-3 FAs had no effects on major adverse cardiovascular events (MACEs) (RR, 0.93; 95 % CI: 0.85 to 1.01, P = 0.09). While É·-3 FAs reduced the incidences of all-cause death (RR, 0.90; 95 % CI: 0.83 to 0.97, P = 0.005), cardiovascular death (RR, 0.82; 95 % CI: 0.75 to 0.90, P < 0.0001), myocardial infarction (RR, 0.77; 95 % CI: 0.68 to 0.86, P < 0.0001), revascularization (RR, 0.80; 95 % CI: 0.69 to 0.93, P = 0.003), sudden cardiac death (RR, 0.67; 95 % CI: 0.52 to 0.86, P = 0.002) and hospitalization for heart failure or unstable angina pectoris (RR, 0.75; 95 % CI: 0.58 to 0.97, P = 0.03) in CAD. It did not statistically reduce the risk of stroke (RR, 0.96; 95 % CI: 0.77 to 1.21, P = 0.76). The favorable effects of É·-3 FAs on MACEs were significant in subgroups of intervention with EPA and baseline triglyceride ≥1.7 mmol/L. CONCLUSION: É·-3 FAs supplementation, especially EPA, appears to be an effective adjunct therapy for improving the prognosis of CAD. REGISTRATION NUMBER: PROSPERO CRD42022311237.

Can irisin be developed as the molecular evolutionary clock based on the origin and functions?

Irisin, a myokine identified in 2012, has garnered research interest for its capacity to induce browning of adipocytes and improve metabolic parameters. As such, the potential therapeutic applications of this exercise-induced peptide continue to be explored. Though present across diverse animal species, sequence analysis has revealed subtle variation in the irisin protein. In this review, we consider the effects of irisin on disease states in light of its molecular evolution. We summarize current evidence for irisin's influence on pathologies and discuss how sequence changes may inform development of irisin-based therapies. Furthermore, we propose that the phylogenetic variations in irisin could potentially be leveraged as a molecular clock to elucidate evolutionary relationships.

Rapid and accurate quantification of viable Bifidobacterium cells in milk powder with a propidium monoazide - antibiotic fluorescence in situ hybridization - flow cytometry method.

Due to its beneficial effects on human health, Bifidobacterium is commonly added to milk powder. Accurate quantification of viable Bifidobacterium is essential for assessing the therapeutic efficacy of milk powder. In this study, we introduced a novel propidium monoazide (PMA) - antibiotic fluorescence in situ hybridization (AFISH) - flow cytometry (FCM) method to rapidly and accurately quantify viable Bifidobacterium cells in milk powder. Briefly, Bifidobacterium cells were treated with chloramphenicol (CM) to increase their rRNA content, followed by staining with RNA-binding oligonucleotide probes, based on the AFISH technique. Then, the DNA-binding dye PMA was used to differentiate between viable and non-viable cells. The PMA-AFISH-FCM method, including sample pretreatment, CM treatment, dual staining, and FCM analysis, required around 2 h and was found to be better than the current methods. This is the first study to implement FCM combined with PMA and oligonucleotide probe for detecting Bifidobacterium.

Comparison of different criteria for estimating major pathological response in resectable non-small cell lung cancer treated with neoadjuvant chemoimmunotherapy.

BACKGROUND: Major pathological response (MPR) is proposed as a surrogate endpoint for survival in non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy. However, the criteria for estimating MPR differ between the recommendations of the International Association for the Study of Lung Cancer (IASLC) and the immune-related pathologic response criterion (irPRC). IASLC's criteria focus solely on evaluating the primary tumor, while irPRC's criteria encompass both the primary tumor and lymph node metastasis. Our objective is to compare the prognostic value of different criteria for estimating MPR. METHODS: We conducted a retrospective study on a cohort of 235 patients with NSCLC after neoadjuvant chemoimmunotherapy. The survival endpoint was event-free survival (EFS). The MPR status of each patient was evaluated using both IASLC's criteria and irPRC's criteria. The prognostic value was compared using the Area Under Curve (AUC). RESULTS: The MPR rates were 63.4 % (149/235) and 57.4 % (135/235) according to IASLC's and irPRC's criteria, respectively. Inconsistent cases, characterized by MPR status according to IASLC's criteria but non-MPR status according to irPRC's criteria, constituted 6.0 % (14/235) of the overall cohort and 15.2 % (14/92) of patients with pretreatment N positive disease. Interestingly, all inconsistent patients showed no recurrence during the study period. Although both MPR statuses according to IASLC (p = 0.00039) and irPRC (p = 0.0094) were associated with improved EFS, IASLC's criteria (AUC = 0.65) were superior to irPRC's criteria (AUC = 0.62) with a higher AUC value. CONCLUSION: IASLC's criteria for estimating MPR were superior to irPRC's criteria in predicting EFS for NSCLC after neoadjuvant chemoimmunotherapy.

Bearing Fault Diagnosis via Stepwise Sparse Regularization with an Adaptive Sparse Dictionary.

Vibration monitoring is one of the most effective approaches for bearing fault diagnosis. Within this category of techniques, sparsity constraint-based regularization has received considerable attention for its capability to accurately extract repetitive transients from noisy vibration signals. The optimal solution of a sparse regularization problem is determined by the regularization term and the data fitting term in the cost function according to their weights, so a tradeoff between sparsity and data fidelity has to be made inevitably, which restricts conventional regularization methods from maintaining strong sparsity-promoting capability and high fitting accuracy at the same time. To address the limitation, a stepwise sparse regularization (SSR) method with an adaptive sparse dictionary is proposed. In this method, the bearing fault diagnosis is modeled as a multi-parameter optimization problem, including time indexes of the sparse dictionary and sparse coefficients. Firstly, sparsity-enhanced optimization is conducted by amplifying the regularization parameter, making the time indexes and the number of atoms adaptively converge to the moments when impulses occur and the number of impulses, respectively. Then, fidelity-enhanced optimization is carried out by removing the regularization term, thereby obtaining the high-precision reconstruction amplitudes. Simulations and experiments verify that the reconstruction accuracy of the SSR method outperforms other sparse regularization methods under most noise conditions, and thus the proposed method can provide more accurate results for bearing fault diagnosis.

Light physical activity predicts long-term mortality in individuals with a different cardiovascular health status: a cohort study.

Studies have showed that LIPA seems to be favorably associated with mortality in the general population and illness individuals, but the association between different cardiovascular health status and mortality is not clear. After adjustment , the HRs of LIPA in individuals with CVRF and CVD from quartiles 2-4 were less than 1, which were 0.78 (95%CI, 0.61 ~ 0.99; P = 0.042), 0.63 (95%CI, 0.47 ~ 0.83; P = 0.001), 0.55（95%CI, 0.40 ~ 0.76; P < 0.001）, and 0.52 (95%CI, 0.37 ~ 0.74; P < 0.001)，0.39 (95%CI, 0.27 ~ 0.58; P < 0.001), 0.33 (95%CI, 0.22 ~ 0.51; P < 0.001) LIPA is beneficial for reducing mortality, but the shape of the association depends on cardiovascular health status.

MiR146a modulates chondrogenesis of bone marrow mesenchymal stem cells by modulating Lsm11 expression.

MicroRNAs play a critical role in bone marrow mesenchymal stem cell (MSC) chondrogenesis and regulate the progression of joint regeneration in osteoarthritis. Our previous research confirmed that miR146a relieves osteoarthritis by modulating cartilage homeostasis. However, few studies have revealed the relationship between miR146a and the chondrogenesis of MSCs, and the exact mechanisms remain unclear. This study aimed to determine the function of miR146a in the chondrogenic differentiation of MSCs and the potential mechanisms involved. MiR146a expression increased during chondrogenesis. MiR146a knockout (KO) led to the increased chondrogenesis of MSCs compared to that in wild-type (WT) MSCs, whereas the overexpression of miR146a by mimics resulted in the decreased chondrogenesis of MSCs, as determined by the mRNA expression of collagen, type II, alpha 1 (COL2A1), aggrecan, cartilage oligomeric matrix protein (COMP), and matrix metallopeptidase 13 (MMP13). Furthermore, cartilage defects could be treated better when injected with spheres induced from miR146aKO MSCs than from WT MSCs, indicating that miR146a inhibits chondrogenesis in vivo. In addition, based on miRNA-mRNA prediction analysis and a dual-luciferase reporter assay, we observed that the deletion of miR146a led to the increased expression of Lsm11 during chondrogenesis and demonstrated that miR146a targeted Lsm11 by binding to its 3'-untranslated region (UTR) and inhibited its translation. The inhibition of Lsm11 by silencing RNA (siRNA) reversed the increased ability of chondrogenesis by knocking out miR146a both in vivo and in vitro, suggesting that miR146a inhibits chondrogenesis by directly inhibiting Lsm11 in MSCs, which may be a novel target for treating osteoarthritis.

Quartz-Enhanced Photoacoustic Spectroscopy-Conductance Spectroscopy for Gas Mixture Analysis.

A novel spectroscopic method, named quartz-enhanced photoacoustic spectroscopy-conductance spectroscopy (QEPAS-CS), was first developed for gas mixture analysis. In QEPAS-CS, the advantage of photoacoustic detection and conductance analysis was realized by a quartz tuning fork (QTF). Two-component gas analysis was done by photoacoustic detection and conductance detection. For an explicit application, natural spider silk was used as a water vapor transducer to modify the QTF, making a conductance sensing channel. A 2004 nm laser diode was used as an excitation source for a photoacoustic sensing channel. Such a QEPAS-CS sensor was used for H2O/CO2 gas mixture analysis in a cell incubator. This provides a solution to calibrate an infrared photoacoustic spectroscopy gas sensor. This example effectively confirms the capacity of multigas analysis by the QEPAS-CS sensor.

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Electrically tunable graphene plasmonic lens: from Maxwell Fisheye Lens to Luneburg Lens.

A graphene plasmonic lens with an electrically tunable focal length is proposed and numerically investigated. The design philosophy of the proposed tunable lens is based on the nonlinear relationship of surface plasmon polariton (SPP) wave index with respect to chemical potential of graphene. By controlling the gate voltage of graphene, the proposed lens can be continuously tuned from a Maxwell Fisheye lens to a Luneburg lens. A ray-tracing method is employed to find out the corresponding gate voltages for various focal lengths. Full-wave EM simulations using COMSOL show that excellent focusing performances can be achieved. This work offers a new way in exploiting active transformational plasmonic elements in the mid-infrared region.