WASPSYN: A Challenge for Domain Adaptive Synapse Detection in Microwasp Brain Connectomes.

The size of image volumes in connectomics studies now reaches terabyte and often petabyte scales with a great diversity of appearance due to different sample preparation procedures. However, manual annotation of neuronal structures (e.g., synapses) in these huge image volumes is time-consuming, leading to limited labeled training data often smaller than 0.001% of the large-scale image volumes in application. Methods that can utilize in-domain labeled data and generalize to out-of-domain unlabeled data are in urgent need. Although many domain adaptation approaches are proposed to address such issues in the natural image domain, few of them have been evaluated on connectomics data due to a lack of domain adaptation benchmarks. Therefore, to enable developments of domain adaptive synapse detection methods for large-scale connectomics applications, we annotated 14 image volumes from a biologically diverse set of Megaphragma viggianii brain regions originating from three different whole-brain datasets and organized the WASPSYN challenge at ISBI 2023. The annotations include coordinates of pre-synapses and post-synapses in the 3D space, together with their one-to-many connectivity information. This paper describes the dataset, the tasks, the proposed baseline, the evaluation method, and the results of the challenge. Limitations of the challenge and the impact on neuroscience research are also discussed. The challenge is and will continue to be available at https://codalab.lisn.upsaclay.fr/competitions/9169. Successful algorithms that emerge from our challenge may potentially revolutionize real-world connectomics research and further the cause that aims to unravel the complexity of brain structure and function.

Understanding of facial features in face perception: insights from deep convolutional neural networks.

Introduction: Face recognition has been a longstanding subject of interest in the fields of cognitive neuroscience and computer vision research. One key focus has been to understand the relative importance of different facial features in identifying individuals. Previous studies in humans have demonstrated the crucial role of eyebrows in face recognition, potentially even surpassing the importance of the eyes. However, eyebrows are not only vital for face recognition but also play a significant role in recognizing facial expressions and intentions, which might occur simultaneously and influence the face recognition process. Methods: To address these challenges, our current study aimed to leverage the power of deep convolutional neural networks (DCNNs), an artificial face recognition system, which can be specifically tailored for face recognition tasks. In this study, we investigated the relative importance of various facial features in face recognition by selectively blocking feature information from the input to the DCNN. Additionally, we conducted experiments in which we systematically blurred the information related to eyebrows to varying degrees. Results: Our findings aligned with previous human research, revealing that eyebrows are the most critical feature for face recognition, followed by eyes, mouth, and nose, in that order. The results demonstrated that the presence of eyebrows was more crucial than their specific high-frequency details, such as edges and textures, compared to other facial features, where the details also played a significant role. Furthermore, our results revealed that, unlike other facial features, the activation map indicated that the significance of eyebrows areas could not be readily adjusted to compensate for the absence of eyebrow information. This finding explains why masking eyebrows led to more significant deficits in face recognition performance. Additionally, we observed a synergistic relationship among facial features, providing evidence for holistic processing of faces within the DCNN. Discussion: Overall, our study sheds light on the underlying mechanisms of face recognition and underscores the potential of using DCNNs as valuable tools for further exploration in this field.

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method.

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

Cnidii Fructus: A traditional Chinese medicine herb and source of antiosteoporotic drugs.

BACKGROUND: Osteoporosis (OP) is a prevalent chronic metabolic bone disease for which limited countermeasures are available. Cnidii Fructus (CF), primarily derived from Cnidium monnieri (L.) Cusson., has been tested in clinical trials of traditional Chinese medicine for the management of OP. Accumulating preclinical studies indicate that CF may be used against OP. MATERIALS AND METHODS: Comprehensive documentation and analysis were conducted to retrieve CF studies related to its main phytochemical components as well as its pharmacokinetics, safety and pharmacological properties. We also retrieved information on the mode of action of CF and, in particular, preclinical and clinical studies related to bone remodeling. This search was performed from the inception of databases up to the end of 2022 and included PubMed, China National Knowledge Infrastructure, the National Science and Technology Library, the China Science and Technology Journal Database, Weipu, Wanfang, the Web of Science and the China National Patent Database. RESULTS: CF contains a wide range of natural active compounds, including osthole, bergapten, imperatorin and xanthotoxin, which may underlie its beneficial effects on improving bone metabolism and quality. CF action appears to be mediated via multiple processes, including the osteoprotegerin (OPG)/receptor activator of nuclear factor-κB ligand (RANKL)/receptor activator of nuclear factor-κB (RANK), Wnt/ß-catenin and bone morphogenetic protein (BMP)/Smad signaling pathways. CONCLUSION: CF and its ingredients may provide novel compounds for developing anti-OP drugs.

Auto-Classification of Parkinson's Disease with Different Motor Subtypes Using Arterial Spin Labelling MRI Based on Machine Learning.

The purpose of this study was to automatically classify different motor subtypes of Parkinson's disease (PD) on arterial spin labelling magnetic resonance imaging (ASL-MRI) data using support vector machine (SVM). This study included 38 subjects: 21 PD patients and 17 normal controls (NCs). Based on the Unified Parkinson's Disease Rating Scale (UPDRS) subscores, patients were divided into the tremor-dominant (TD) subtype and the postural instability gait difficulty (PIGD) subtype. The subjects were in a resting state during the acquisition of ASL-MRI data. The automated anatomical atlas 3 (AAL3) template was registered to obtain an ASL image of the same size and shape. We obtained the voxel values of 170 brain regions by considering the location coordinates of these regions and then normalized the data. The length of the feature vector depended on the number of voxel values in each brain region. Three binary classification models were utilized for classifying subjects' data, and we applied SVM to classify voxels in the brain regions. The left subgenual anterior cingulate cortex (ACC_sub_L) was clearly distinguished in both NCs and PD patients using SVM, and we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21%, and AUCmax = 0.9585). For the right supramarginal gyrus (SupraMarginal_R), SVM distinguished the TD group from the other groups with satisfactory diagnostic rates (accuracy = 84.21%, sensitivity = 63.64%, specificity = 92.59%, and AUCmax = 0.9192). For the right intralaminar of thalamus (Thal_IL_R), SVM distinguished the PIGD group from the other groups with satisfactory diagnostic rates (accuracy = 89.47%, sensitivity = 70.00%, specificity = 6.43%, and AUCmax = 0.9464). These results are consistent with the changes in blood perfusion related to PD subtypes. In addition, the sensitive brain regions of the TD group and PIGD group involve the brain regions where the cerebellothalamocortical (CTC) and the striatal thalamocortical (STC) loops are located. Therefore, it is suggested that the blood perfusion patterns of the two loops may be different. These characteristic brain regions could become potential imaging markers of cerebral blood flow to distinguish TD from PIGD. Meanwhile, our findings provide an imaging basis for personalised treatment, thereby optimising clinical diagnostic and treatment approaches.

Effects of the toxic metal zinc on the growth, development, and reproduction of the wolf spider Pardosa laura through its food Drosophila melanogaster.

In recent years, toxic metal pollution has become a serious problem, and its influence on human society and the ecological environment has become a hot topic for people. Toxic metal pollution is ubiquitous in the environment, and it can affect the growth, development, and reproduction of organisms through food chain transmission. In this study, we used ZnSO4 and set three different Zn concentrations [0 mg/mL (CK), 0.25 mg/mL, and 0.50 mg/mL] to study the enrichment of toxic zinc metal in the wolf spider Pardosa laura through the food chain (medium-Drosophila melanogaster-P. laura) and the effects on the growth, development, and reproduction of D. melanogaster and P. laura. The results of the study showed that the Zn content in D. melanogaster and P. laura significantly increased with the increase of Zn concentration in the culture medium, reflecting the obvious food chain enrichment phenomenon. The inhibitory effect of zinc treatment on the growth, development, and reproduction of D. melanogaster was significant and more pronounced with increasing Zn concentration. The transfer of zinc through the food chain also had a significant inhibitory effect on the growth, development, and reproduction of P. laura. The developmental duration and preoviposition period were significantly prolonged. Moreover, the survival rate, body size, and egg laying amount were significantly reduced. This study will enrich and improve the research on the effects of toxic Zn metal pollution on spiders and provide a theoretical basis for monitoring and evaluating the environmental quality using farmland spiders.

Deep-Learning-Assisted Simultaneous Target Sensing and Super-Resolution Imaging.

Metasurfaces have recently experienced revolutionary progress in sensing and super-resolution imaging fields, mainly due to their manipulation of electromagnetic waves on subwavelength scales. However, on the one hand, the addition of metasurfaces can multiply the complexity of retrieving target information from detected electromagnetic fields. On the other hand, many existing studies utilize deep learning methods to provide compelling tools for electromagnetic problems but mainly concentrate on resolving one single function, limiting their versatilities. In this work, a multifunctional deep learning network is demonstrated to reconstruct diverse target information in a metasurface-target interactive system. First, a preliminary experiment verifies that the metasurface-involved scenario can tolerate the system noises. Then, the captured electric field distributions are fed into the multifunctional network, which can not only accurately sense the quantity and relative permittivity of targets but also generate super-resolution images precisely. The deep learning network, thus, paves an alternative way to recover the targets' information in metasurface-target interactive systems, accelerating the progression of target sensing and superimaging areas. Besides, another new network that allows forward electromagnetic prediction is also proposed and demonstrated. To sum up, the deep learning methodology may hold promise for inverse reconstructions or forward predictions in many electromagnetic scenarios.

Correction: Liu et al. Apigenin Ameliorates Hyperuricemia and Renal Injury through Regulation of Uric Acid Metabolism and JAK2/STAT3 Signaling Pathway. Pharmaceuticals 2022, 15, 1442.

In the original publication [...].

Lycopene ameliorates islet function and down-regulates the TLR4/MyD88/NF-κB pathway in diabetic mice and Min6 cells.

The inflammation of the pancreatic islets triggers ß cell dysfunction and type 2 diabetes mellitus (T2DM) onset. While dietary lycopene consumption contributes to protection against T2DM in animal studies, the potential mechanism of this compound in the regulation of islet function in T2DM remains largely unclear. In this study, by using anti-diabetic metformin as a positive control, we demonstrated that lycopene treatment suppressed islet inflammation and apoptosis in both high-fat diet (HFD)/streptozotocin (STZ)-induced diabetic mice and in Min6 cells exposed to high glucose/palmitic acid (HG/PA)-RAW264.7 conditioned medium. Lycopene intervention resulted in M1/M2 macrophage polarization homeostasis, which is associated with increased insulin secretion, decreased fasting blood glucose levels, and improved lipid profiles in diabetic mice. Furthermore, the protective actions of lycopene were associated with the down-regulation of the TLR4/MyD88/NF-κB signaling pathway, which is positively related to inflammation in both diabetic mice and Min6 cells. Collectively, our findings indicated that lycopene ameliorates islet function and apoptosis and attenuates hyperglycemia and dyslipidemia by the regulation of the TLR4/MyD88/NF-κB signaling pathway. This study highlights dietary lycopene consumption as a novel strategy for the management of patients with diabetes.

A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping.

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.

Distinct lipid profile in haemolytic anaemia-related gallstones compared with the general gallstone.

BACKGROUND: Pigment gallstones are not uncommon among patients with chronic haemolytic anaemia. But their clinical characteristics have not been described in detail and not been directly compared with the general gallstone population. METHODS: Patients at Peking Union Medical College Hospital with haemolytic anaemia and subsequent gallstones from January 2012 to December 2022 were included. Cases were matched (1:2) based on age, sex and location of stones to randomly select non-anaemia patients with gallstones (controls). RESULTS: Screening 899 cases of gallstones, we finally included 76 cases and 152 controls. Total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) for cases were 3.02 ± 0.98 mmol/L, 0.89 ± 0.30 mmol/L and 1.58 ± 0.70 mmol/L, respectively, significantly lower than those in the control group (all p < 0.001). TC and HDL were both lower than the normal range, but triglyceride and LDL were within the normal range. Multiple stones were significantly more common for cases (n = 59, 78%) than for controls (n = 44, 29%, p < 0.001). The mean diameter of the maximal gallstone was 1.2 ± 0.6 cm and 1.5 ± 1.0 cm for cases and controls (p = 0.120), respectively. Stones in the elderly (p = 0.002 for univariate analysis, and 0.001 for multivariate analysis) and stones in the bile duct (p = 0.005 for univariate analysis, and 0.009 for multivariate analysis) were found to occur in a shorter period after anaemia. CONCLUSION: The lipid profile of haemolytic anaemia with gallstones was distinct, low TC, low HDL, and increased-to-normal LDL, compared with the general gallstone population. Patients with haemolytic anaemia were recommended an abdominal ultrasound if aged older than 50 years, with more frequent follow-up visits.KEY MESSAGESClinical characteristics of gallstones following chronic haemolytic anaemia were described and compared with the general gallstone population.The lipid profiles were distinctly different between the patients with gallstones following chronic haemolytic anaemia and the general gallstone population.Elder patients were complicated with gallstones in a shorter period after anaemia and thus were recommended an abdominal ultrasound if aged older than 50 years, with more frequent follow-up visits.

Simiao San alleviates hyperuricemia and kidney inflammation by inhibiting NLRP3 inflammasome and JAK2/STAT3 signaling in hyperuricemia mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Simiao San (SmS), a famous traditional Chinese formula, is clinically used to treat patients with hyperuricemia (HUA). However, its mechanism of action on lowering uric acid (UA) and inhibiting inflammation still deserves further investigation. AIM OF THE STUDY: To examine the effect and its possible underlying mechanism of SmS on UA metabolism and kidney injury in HUA mouse. MATERIALS AND METHODS: The HUA mouse model was constructed with the combined administration of both potassium oxalate and hypoxanthine. The effects of SmS on UA, xanthine oxidase (XOD), creatinine (CRE), blood urea nitrogen (BUN), interleukin-10 (IL-10), interleukin-1ß (IL-1ß), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) were determined by ELISA or biochemical assays. Hematoxylin and eosin (H&E) was used to observe pathological alterations in the kidneys of HUA mice. The expression levels of organic anion transporter 1 (OAT1), recombinant urate transporter 1 (URAT1), glucose transporter 9 (GLUT9), nucleotide binding domain and leucine rich repeat pyrin domain containing 3 (NLRP3), Cleaved-Caspase 1, apoptosis-associated speck like protein (ASC), nuclear factor kappa-B (NF-κB), IL-6, janus kinase 2 (JAK2), phosphor (P)-JAK2, signal transducers and activators of transcription 3 (STAT3), P-STAT3, suppressor of cytokine signaling 3 (SOCS3) were examined by Western blot and/or immunohistochemical (IHC) staining. The major ingredients in SmS were identified by a HPLC-MS assay. RESULTS: HUA mouse exhibited an elevation in serum levels of UA, BUN, CRE, XOD, and the ratio of urinary albumin to creatinine (UACR), and a decline in urine levels of UA and CRE. In addition, HUA induces pro-inflammatory microenvironment in mouse, including an increase in serum levels of IL-1ß, IL-6, and TNF-α, and renal expressions of URAT1, GULT9, NLRP3, ASC, Cleaved-Caspase1, P-JAK2/JAK2, P-STAT3/STAT3, and SOCS3, and a decrease in serum IL-10 level and renal OAT1 expression as well as a disorganization of kidney pathological microstructure. In contrast, SmS intervention reversed these alterations in HUA mouse. CONCLUSION: SmS could alleviate hyperuricemia and renal inflammation in HUA mouse. The action mechanisms behind these alterations may be associated with a limitation of the NLRP3 inflammasome and JAK2/STAT3 signaling pathways.

Spin-decoupled excitation and wavefront shaping of structured surface waves via on-chip terahertz metasurfaces.

Surface waves (SWs) are of great importance in terahertz (THz) photonics applications due to their subwavelength properties. Hence, it is crucial to develop surface wavefront shaping techniques, which is urgent in modern information technologies. In this paper, a new scheme is proposed to realize SW excitation and spin-decoupled wavefront shaping with an ultracompact planar meta-device working in the THz range. The meta-device is composed of two parts: meta-atoms (in the center) and plasmonic metals (on the left and right sides). By carefully setting the geometry size and rotation angle of each meta-atom, the encoded spin-decoupled phase distributions for both left circularly polarized (LCP) and right circularly polarized (RCP) incident THz waves are determined. In this way, circularly polarized (CP) incident THz waves can be converted to SWs propagating along plasmonic metals with unique wavefront profiles, i.e., Bessel and focusing profiles. Full-wave simulations and THz near-field scanning experiments were performed to verify the functionalities of the meta-device, both of which are in great agreement with theoretical predictions. Our findings may provide more solutions to design THz integrated photonic devices and systems.

Hydroxychloroquine significantly decreases the risk of preeclampsia in pregnant women with autoimmune disorders: a systematic review and meta-analysis.

This meta-analysis aimed to investigate whether hydroxychloroquine (HCQ) intervention could decrease the incidence of preeclampsia and other maternal and fetal outcomes among pregnant women with autoimmune disorders. PubMed, EMBASE, Web of Science, and the Cochrane databases were searched from inception until January 2022. Data on maternal or fetal outcomes of the control and hydroxychloroquine treatment groups were gathered and analyzed. Pooled odds ratio (OR) with 95% confidence intervals (CIs) were determined. Cochran's Q test, I2 statistics, leave-one-out analysis, Baujat plot analysis, GOSH plot analysis, and multivariable meta-regression were applied to assess between-study heterogeneity. The meta-analysis was performed using the Stata V.16.1 software. Baujat plot analysis and GOSH plot analysis were performed using the R V.4.0.0 software. Our study included 21 cohort studies and one case-control study with a total of 3948 pregnancies with immune disorders. HCQ treatment significantly reduced the incidence of preeclampsia (OR 0.45, 95% CI 0.33-0.63, p = 0.000, I2 3.68%). After outlier omission, HCQ treatment significantly reduced the incidence of premature delivery (OR 0.84, 95% CI 0.73-0.96, p = 0.01, I2 44.81%) in pregnant women with autoimmune disorders. In sub-group analysis, HCQ also significantly reduced the incidence of gestational hypertension (OR 0.42, 95% CI 0.26-0.68, p = 0.001, I2 49.33%) and preterm birth (OR 0.63, 95% CI 0.48-0.82, p = 0.001, I2 27.63%) in pregnant women with lupus. The heterogeneity of the findings mentioned above was low to moderate. There were no significant differences in the risk of other outcomes, including gestational diabetes, HELLP syndrome, thrombosis, spontaneous abortion, fetal loss, small for gestational age infant (SGA), low birth weight, stillbirth, APGAR score < 7, and congenital malformation. This meta-analysis indicated that HCQ treatment could significantly decrease the incidence of preeclampsia and premature delivery in pregnant women with autoimmune disorders. In addition, HCQ could reduce the risk of gestational hypertension in pregnant lupus patients.

Fast 3D reconstruction via event-based structured light with spatio-temporal coding.

Event-based structured light (SL) systems leverage bio-inspired event cameras, which are renowned for their low latency and high dynamics, to drive progress in high-speed structured light systems. However, existing event-based structured light methods concentrate on the independent construction of either time-domain or space-domain features for stereo matching, ignoring the spatio-temporal consistency towards depth. In this work, we build an event-based SL system that consists of a laser point projector and an event camera, and we devise a spatial-temporal coding strategy that realizes depth encoding in dual domains through a single shot. To exploit the spatio-temporal synergy, we further present STEM, a novel Spatio-Temporal Enhanced Matching approach for 3D reconstruction. STEM is comprised of two parts, the spatio-temporal enhancing (STE) algorithm and the spatio-temporal matching (STM) algorithm. Specifically, STE integrates the dual-domain information to increase the saliency of the temporal coding, providing a more robust basis for matching. STM is a stereo matching algorithm explicitly tailored to the unique characteristics of event data modality, which computes the disparity via a meticulously designed hybrid cost function. Experimental results demonstrate the superior performance of our proposed method, achieving a reconstruction rate of 16 fps and a low root mean square error of 0.56 mm at a distance of 0.72 m.

Apigenin Ameliorates Hyperuricemia and Renal Injury through Regulation of Uric Acid Metabolism and JAK2/STAT3 Signaling Pathway.

Hyperuricemia (HUA) is a kind of metabolic disease with high incidence that still needs new countermeasures. Apigenin has uric-lowering and kidney-protective activities, but how apigenin attenuates HUA and renal injury remains largely unexploited. To this end, an acute HUA mouse model was established by intraperitoneal injection of potassium oxazinate and oral administration with hypoxanthine for 7 consecutive days. Apigenin intervention decreased serum uric acid (UA), creatinine (CRE), blood urea nitrogen (BUN), interleukin-1ß (IL-1ß), interleukin-6 (IL-6), tumor necrosis factor (TNF-α), interleukin-18 (IL-18), liver xanthine oxidase (XOD), and urine protein levels, and increased serum interleukin-10 (IL-10) and urine UA and CRE levels in HUA mice. Moreover, administration of apigenin to HUA mice prevented renal injury, decreased renal glucose transporter 9 (GLUT9) and urate anion transporter 1 (URAT1) levels, and increased renal organic anion transporter 1 (OAT1). These alterations were associated with an inhibition of IL-6, phospho-janus kinase 2 (P-JAK2), phospho-signal transducer, and activator of transcription 3 (P-STAT3), and suppression of cytokine signaling 3 (SOCS3) expression in the kidneys. Additionally, the molecular docking results showed that apigenin had strong binding capacity with UA transporters and JAK2 proteins. In summary, apigenin could improve UA metabolism and attenuate renal injury through inhibiting UA production, promoting excretion, and suppressing the JAK2/STAT3 signaling pathway in HUA mice. The results suggest that apigenin may be a suitable drug candidate for management of HUA and its associated renal injury.

Quality changes and shelf-life prediction model of postharvest apples using partial least squares and artificial neural network analysis.

The quality of postharvest apples is greatly affected by storage temperatures. In this paper, the sensory qualities, such as flavor, texture, color, and taste change of apples during storage at 4 °C and 20 °C were investigated. After correlation analysis, the partial least squares (PLS) and artificial neural network (ANN) techniques were used to build a shelf-life prediction model. The results showed that lower temperature storage can better maintain the color, flesh hardness, and release of volatile compounds of apples. The acidity of apples stored at 20 °C decreased much faster than that at 4 °C. The PLS models were successful in predicting the apple shelf life. When modeling using PLS with a single type index, the order of accuracy of the prediction model was texture, color, and flavor. As a nonlinear algorithm, the ANN model was also an effective predictive tool of apple shelf life at both temperatures.

Semi-Supervised Neuron Segmentation via Reinforced Consistency Learning.

Emerging deep learning-based methods have enabled great progress in automatic neuron segmentation from Electron Microscopy (EM) volumes. However, the success of existing methods is heavily reliant upon a large number of annotations that are often expensive and time-consuming to collect due to dense distributions and complex structures of neurons. If the required quantity of manual annotations for learning cannot be reached, these methods turn out to be fragile. To address this issue, in this article, we propose a two-stage, semi-supervised learning method for neuron segmentation to fully extract useful information from unlabeled data. First, we devise a proxy task to enable network pre-training by reconstructing original volumes from their perturbed counterparts. This pre-training strategy implicitly extracts meaningful information on neuron structures from unlabeled data to facilitate the next stage of learning. Second, we regularize the supervised learning process with the pixel-level prediction consistencies between unlabeled samples and their perturbed counterparts. This improves the generalizability of the learned model to adapt diverse data distributions in EM volumes, especially when the number of labels is limited. Extensive experiments on representative EM datasets demonstrate the superior performance of our reinforced consistency learning compared to supervised learning, i.e., up to 400% gain on the VOI metric with only a few available labels. This is on par with a model trained on ten times the amount of labeled data in a supervised manner. Code is available at https://github.com/weih527/SSNS-Net.

Ultrasonic and other sterilization methods on nutrition and flavor of cloudy apple juice.

Sterilization plays an important role in extending the shelf-life of apple juice; however, it also affects the nutritional and flavor profile of the juice. This study was initiated to evaluate the effects of several common sterilization methods (conventional pasteurization, microwave sterilization, ultrasonic sterilization, and ultra-high-pressure sterilization) on some important quality parameters of apple juice. The results showed that the content of soluble pectin and soluble protein in juice decreased significantly after ultra-high-pressure sterilization. Sonication was found to be effective in increasing the level of ascorbic acid in apple juice. The sugar:acid ratio increased significantly after pasteurization, microwave sterilization, and ultra-high-pressure sterilization, which changed the taste of juice. Microwave sterilization caused the highest volatile compound loss, while ultra-high-pressure sterilization led to a higher retention rate of volatile compounds in juice. This study could be helpful in seeking suitable sterilization methods retaining the quality of cloudy apple juice.

miR-139-3p/Kinesin family member 18B axis suppresses malignant progression of gastric cancer.

miR-139-3p exerts tumor-suppressing functions in various cancers. We analyzed and identified that miR-139-3p expression was notably low in gastric cancer (GC) via edgeR differential analysis based on The Cancer Genome Atlas database and quantitative real-time polymerase chain reaction (qRT-PCR) assay. The binding relationship between Kinesin Family Member 18B (KIF18B) and miR-139-3p was predicted by bioinformatics databases, and verified through dual-luciferase assay. Western blot and qRT-PCR results also indicated that miR-139-3p restrained KIF18 expression at mRNA and protein levels. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, wound healing, transwell, flow cytometry assays were introduced to evaluate cell proliferation, migration, invasion, and cell cycle, respectively, where the results indicated that upregulating miR-139-3p inhibited proliferative, migratory, and invasive abilities of GC cells, while caused cell-cycle arrest. Moreover, the results of rescue experiments illustrated that miR-139-3p hampered the progression of GC cells by targeting and suppressing KIF18B. To sum up, we concluded that miR-139-3p suppressed GC progression by targeting KIF18B.