Genetically encoded Runx3 and CD4+ intestinal epithelial lymphocyte deficiencies link SKG mouse and human predisposition to spondyloarthropathy.

Disturbances in immune regulation, intestinal dysbiosis and inflammation characterize ankylosing spondylitis (AS), which is associated with RUNX3 loss-of-function variants. ZAP70W163C mutant (SKG) mice have reduced ZAP70 signaling, spondyloarthritis and ileitis. In small intestine, Foxp3+ regulatory T cells (Treg) and CD4+CD8αα+TCRαß+ intraepithelial lymphocytes (CD4-IEL) control inflammation. TGF-ß and retinoic acid (RA)-producing dendritic cells and MHC-class II+ intestinal epithelial cells (IEC) are required for Treg and CD4-IEL differentiation from CD4+ conventional or Treg precursors, with upregulation of Runx3 and suppression of ThPOK. We show in SKG mouse ileum, that ZAP70W163C or ZAP70 inhibition prevented CD4-IEL but not Treg differentiation, dysregulating Runx3 and ThPOK. TGF-ß/RA-mediated CD4-IEL development, T-cell IFN-γ production, MHC class-II+ IEC, tissue-resident memory T-cell and Runx3-regulated genes were reduced. In AS intestine, CD4-IEL were decreased, while in AS blood CD4+CD8+ T cells were reduced and Treg increased. Thus, genetically-encoded TCR signaling dysfunction links intestinal T-cell immunodeficiency in mouse and human spondyloarthropathy.

A >50% Intraoperative Parathyroid Hormone Decrease Into Normal Reference Range Predicts Complete Excision of Malignancy in Patients With Parathyroid Carcinoma.

INTRODUCTION: The mainstay of successful treatment for parathyroid carcinoma remains complete surgical excision. Although intraoperative parathyroid hormone (ioPTH) monitoring is a useful adjunct during parathyroidectomy for benign primary hyperparathyroidism, its utility for parathyroid carcinoma remains unclear. METHODS: A retrospective review of 796 patients who underwent parathyroidectomy with ioPTH monitoring for primary hyperparathyroidism revealed 13 patients with parathyroid carcinoma on final pathology from two academic institutions. A systematic review yielded 5 additional parathyroid carcinoma patients. Complete excision of malignancy, or operative success (eucalcemia ≥6 mo. after parathyroidectomy); operative failure (persistent hypercalcemia <6 mo. after parathyroidectomy); and perioperative complications were evaluated. Comparison of the >50% ioPTH decrease alone to >50% ioPTH decrease into normal reference range was analyzed using Chi-squared, Kolmogorov-Smirnov, Kruskal-Wallis tests. RESULTS: All 18 parathyroid carcinoma patients achieved a >50% ioPTH decrease, and 14 patients also had a final ioPTH level decrease into normal reference range. 93% of patients who met normal parathyroid hormone reference range had operative success, whereas only two of the four (50%) patients with parathyroid carcinoma with a >50% ioPTH decrease alone demonstrated operative success. CONCLUSIONS: Parathyroidectomy guided by a >50% ioPTH decrease into normal reference range may better predict complete excision of malignant tissue in patients with parathyroid carcinoma compared to >50% ioPTH decrease alone. IoPTH monitoring should be used in conjunction with clinical judgment and complete en bloc resection for optimal treatment and success.

A Toolkit for Engineering Proteins in Living Cells: Peptide with a Tryptophan-Selective Ru-TAP Complex to Regioselectively Photolabel Specific Proteins.

Using a chemical approach to crosslink functionally versatile bioeffectors (such as peptides) to native proteins of interest (POI) directly inside a living cell is a useful toolbox for chemical biologists. However, this goal has not been reached due to unsatisfactory chemoselectivity, regioselectivity, and protein selectivity in protein labeling within living cells. Herein, we report the proof of concept of a cytocompatible and highly selective photolabeling strategy using a tryptophan-specific Ru-TAP complex as a photocrosslinker. Aside from the high selectivity, the photolabeling is blue light-driven by a photoinduced electron transfer (PeT) and allows the bioeffector to bear an additional UV-responsive unit. The two different photosensitivities are demonstrated by blue light-photocrosslinking a UV-sensitive peptide to POI. Our visible light photolabeling can generate photocaged proteins for subsequent activity manipulation by UV light. Cytoskeletal dynamics regulation is demonstrated in living cells via the unprecedented POI photomanipulation and proves that our methodology opens a new avenue to endogenous protein modification.

Heterogeneous recurrence analysis of spatial data.

Nonlinear dynamical systems often generate significant amounts of observational data such as time series, as well as high-dimensional spatial data. To delineate recurrence dynamics in the spatial data, prior efforts either extended the recurrence plot, which is a widely used tool for time series, to a four-dimensional hyperspace or utilized the network approach for recurrence analysis. However, very little has been done to differentiate heterogeneous types of recurrences in the spatial data (e.g., recurrence variations of state transitions in the spatial domain). Therefore, we propose a novel heterogeneous recurrence approach for spatial data analysis. First, spatial data are traversed with the Hilbert Space-Filling Curve to transform the variations of recurrence patterns from the spatial domain to the state-space domain. Second, we design an Iterated Function System to derive the fractal representation for the state-space trajectory of spatial data. Such a fractal representation effectively captures self-similar behaviors of recurrence variations and multi-state transitions in the spatial data. Third, we develop the Heterogeneous Recurrence Quantification Analysis of spatial data. Experimental results in both simulation and real-world case studies show that the proposed approach yields superior performance in the extraction of salient features to characterize and quantify heterogeneous recurrence dynamics in spatial data.

Video Smoke Detection Method Based on Change-Cumulative Image and Fusion Deep Network.

Smoke detection technology based on computer vision is a popular research direction in fire detection. This technology is widely used in outdoor fire detection fields (e.g., forest fire detection). Smoke detection is often based on features such as color, shape, texture, and motion to distinguish between smoke and non-smoke objects. However, the salience and robustness of these features are insufficiently strong, resulting in low smoke detection performance under complex environment. Deep learning technology has improved smoke detection performance to a certain degree, but extracting smoke detail features is difficult when the number of network layers is small. With no effective use of smoke motion characteristics, indicators such as false alarm rate are high in video smoke detection. To enhance the detection performance of smoke objects in videos, this paper proposes a concept of change-cumulative image by converting the YUV color space of multi-frame video images into a change-cumulative image, which can represent the motion and color-change characteristics of smoke. Then, a fusion deep network is designed, which increases the depth of the VGG16 network by arranging two convolutional layers after each of its convolutional layer. The VGG16 and Resnet50 (Deep residual network) network models are also arranged using the fusion deep network to improve feature expression ability while increasing the depth of the whole network. Doing so can help extract additional discriminating characteristics of smoke. Experimental results show that by using the change-cumulative image as the input image of the deep network model, smoke detection performance is superior to the classic RGB input image; the smoke detection performance of the fusion deep network model is better than that of the single VGG16 and Resnet50 network models; the smoke detection accuracy, false positive rate, and false alarm rate of this method are better than those of the current popular methods of video smoke detection.

Recurrence network modeling and analysis of spatial data.

Nonlinear dynamical systems exhibit complex recurrence behaviors. Recurrence plot is widely used to graphically represent the patterns of recurrence dynamics and further facilitates the quantification of recurrence patterns, namely, recurrence quantification analysis. However, traditional recurrence methods tend to be limited in their ability to handle spatial data due to high dimensionality and geometric characteristics. Prior efforts have been made to generalize the recurrence plot to a four-dimensional space for spatial data analysis, but this framework can only provide graphical visualization of recurrence patterns in the projected reduced-dimension space (i.e., two- or three- dimensions). In this paper, we propose a new weighted recurrence network approach for spatial data analysis. A weighted network model is introduced to represent the recurrence patterns in spatial data, which account for both pixel intensities and spatial distance simultaneously. Note that each network node represents a location in the high-dimensional spatial data. Network edges and weights preserve complex spatial structures and recurrence patterns. Network representation is shown to be an effective means to provide a complete picture of recurrence patterns in the spatial data. Furthermore, we leverage network statistics to characterize and quantify recurrence properties and features in the spatial data. Experimental results in both simulation and real-world case studies show that the generalized recurrence network approach yields superior performance in the visualization of recurrence patterns in spatial data and in the extraction of salient features to characterize recurrence dynamics in spatial systems.

Isotopic analysis on nanogram quantities of carbon from dissolved insect cuticle: a method for paleoenvironmental inferences.

RATIONALE: Carbon isotope (δ13 C ) data from arthropod cuticles provide invaluable information on past and present biogeochemical processes. However, such analyses typically require large sample sizes that may mask important variation in δ13 C values within or among species. METHODS: We have evaluated a spooling-wire microcombustion (SWiM) device and isotope ratio mass spectrometry (IRMS) to measure the δ13 C values of carbon dissolved from the cuticle of chitinous aquatic zooplankton. The effects of temperature, pH, and reaction time on the δ13 C values of acid-dissolved bulk cuticle and purified chitin fractions obtained from a single species of chironomid from four commercial suppliers were assessed. These results were compared with baseline δ13 C values obtained on solid cuticle using conventional EA (elemental analyzer)/IRMS. RESULTS: The results indicate differential, time-dependent dissolution of chitin, lipid and protein fractions of cuticle concomitant with slow depolymerization and deacetylation of chitin. Isotopic offsets between dissolved bulk head capsules and a purified chitin fraction suggest the contributions of other isotopically lighter components of the bulk head capsules to bulk chitin extracts. The SWiM/IRMS δ13 C results obtained on dissolved cuticle using a treatment of 4 N HCl at 25 °C for 24 h produced generally stable δ13 C values, large sample/blank CO2 yields and a positive correlation with conventional EA/IRMS results on unprocessed cuticle. CONCLUSIONS: The SWiM/IRMS system offers a reliable method to determine δ13 C values on nanogram quantities of carbon from dissolved insect cuticle, thus reducing sample size requirements and providing new opportunities to use δ13 C variation among/within species for reconstructing paleo-biogeochemical processes.

Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor.

Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields.

Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths.

BACKGROUND: Transitional care interventions can be utilized to reduce post-hospital discharge adverse events (AEs). However, no methodology exists to effectively identify high-risk patients of any disease across multiple hospital sites and patient populations for short-term postdischarge AEs. OBJECTIVES: To develop and validate a 3-day (72 h) AEs prediction model using electronic health records data available at the time of an indexed discharge. RESEARCH DESIGN: Retrospective cohort study of admissions between June 2012 and June 2014. SUBJECTS: All adult inpatient admissions (excluding in-hospital deaths) from a large multicenter hospital system. MEASURES: All-cause 3-day unplanned readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model was developed using clinical, administrative, and socioeconomic data, with data preprocessing steps and stacked classification. Patients were divided randomly into training (66.7%), and testing (33.3%) cohorts to avoid overfitting. RESULTS: The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The c-statistic (also known as area under the receiver operating characteristic curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 for the derivation and validation cohort, respectively. CONCLUSIONS: The 3-day REDD model predicts high-risk patients with fair discriminative power. The discriminative power of the 30-day REDD model is also better than the previously reported models under similar settings. The 3-day REDD model has been implemented and is being used to identify patients at risk for AEs.

Assessing the role of surface layer and molecular probe size in diffusion within meniscus tissue.

Diffusion within extracellular matrix is essential to deliver nutrients and larger metabolites to the avascular region of the meniscus. It is well known that both structure and composition of the meniscus vary across its regions; therefore, it is crucial to fully understand how the heterogenous meniscal architecture affects its diffusive properties. The objective of this study was to investigate the effect of meniscal region (core tissue, femoral, and tibial surface layers) and molecular weight on the diffusivity of several molecules in porcine meniscus. Tissue samples were harvested from the central area of porcine lateral menisci. Diffusivity of fluorescein (MW 332 Da) and three fluorescence-labeled dextrans (MW 3k, 40k, and 150k Da) was measured via fluorescence recovery after photobleaching. Diffusivity was affected by molecular size, decreasing as the Stokes' radius of the solute increased. There was no significant effect of meniscal region on diffusivity for fluorescein, 3k and 40k dextrans (p>0.05). However, region did significantly affect the diffusivity of 150k Dextran, with that in the tibial surface layer being larger than in the core region (p = 0.001). Our findings contribute novel knowledge concerning the transport properties of the meniscus fibrocartilage. This data can be used to advance the understanding of tissue pathophysiology and explore effective approaches for tissue restoration.

Asymmetry in kinematics of dominant/nondominant lower limbs in central and lateral positioned college and sub-elite soccer players.

Change of direction, stops, and pivots are among the most common non-contact movements associated with anterior cruciate ligament (ACL) injuries in soccer. By observing these dynamic movements, clinicians recognize abnormal kinematic patterns that contribute to ACL tears such as increased knee valgus or reduced knee flexion. Different motions and physical demands are observed across playing positions, which may result in varied lower limb kinematic patterns. In the present study, 28 college and sub-elite soccer players performed four dynamic motions (change of direction with and without ball, header, and instep kick) with the goal of examining the effect of on-field positioning, leg dominance, and gender in lower body kinematics. Motion capture software monitored joint angles in the knee, hip, and ankle. A three-way ANOVA showed significant differences in each category. Remarkably, centrally positioned players displayed significantly greater knee adduction (5° difference, p = 0.013), hip flexion (9° difference, p = 0.034), hip adduction (7° difference, p = 0.016), and dorsiflexion (12° difference, p = 0.022) when performing the instep kick in comparison to their laterally positioned counterparts. These findings suggest that central players tend to exhibit a greater range of motion when performing an instep kicking task compared to laterally positioned players. At a competitive level, this discrepancy could potentially lead to differences in lower limb muscle development among on-field positions. Accordingly, it is suggested to implement position-specific prevention programs to address these asymmetries in lower limb kinematics, which can help mitigate dangerous kinematic patterns and consequently reduce the risk of ACL injury in soccer players.

A Facile NMR Method for Pre-MRI Evaluation of Trigger-Responsive T1 Contrast Enhancement.

There is a growing interest in developing paramagnetic nanoparticles as responsive magnetic resonance imaging (MRI) contrast agents, which feature switchable T1 image contrast of water protons upon biochemical cues for better discerning diseases. However, performing an MRI is pragmatically limited by its cost and availability. Hence, a facile, routine method for measuring the T1 contrast is highly desired in early-stage development. This work presents a single-point inversion recovery (IR) nuclear magnetic resonance (NMR) method that can rapidly evaluate T1 contrast change by employing a single, optimized IR pulse sequence that minimizes water signal for "off-state" nanoparticles and allows for sensitively measuring the signal change with "switch-on" T1 contrast. Using peptide-induced liposomal gadopentetic acid (Gd3+ -DTPA) release and redox-sensitive manganese oxide (MnO2 ) nanoparticles as a demonstration of generality, this method successfully evaluates the T1 shortening of water protons caused by liposomal Gd3+ -DTPA release and Mn2+ formation from MnO2 reduction. Furthermore, the NMR measurement is highly correlated to T1 -weighted MRI scans, suggesting its feasibility to predict the MRI results at the same field strength. This NMR method can be a low-cost, time-saving alternative for pre-MRI evaluation for a diversity of responsive T1 contrast systems.

Recurrence Network Analysis of Histopathological Images for the Detection of Invasive Ductal Carcinoma in Breast Cancer.

The histopathological image analysis is one of the most crucial diagnostic procedures to identify Invasive ductal carcinoma (IDC) in breast cancers. However, this diagnosis process is currently time-consuming and heavily dependent on human expertise. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for identifying IDC. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. We first utilize wavelet decomposition to delineate the subtle information in the images. Then, we model the patches with a weighted recurrence network approach to characterize the recurrence patterns of the histopathological images. Finally, we develop automated IDC detection models leveraging machine learning methods with spatial recurrence features extracted. The developed recurrence analysis models successfully characterize the complex microstructures of histopathological images and achieve the IDC detection performances of at least AUC = 0.96. This research developed a spatial recurrence analysis methodology to effectively identify IDC regions in histopathological images for BC. It shows a high potential to assist physicians in the decision-making process. The proposed methodology can further be applicable to image processing for other medical or biological applications.

Metabonomics and 16S rRNA gene sequencing to study the therapeutic mechanism of Danggui Sini decoction on collagen-induced rheumatoid arthritis rats with Cold Bi syndrome.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by persistent joint inflammation. The development of rheumatoid arthritis is directly correlated with the disturbance of gut microbiome and its metabolites. RA can be effectively treated with the Danggui Sini decoction (DSD), a Traditional Chinese medicine (TCM) prescription from the Treatise on Febrile Diseases. Further research is needed to clarify the precise mechanism of DSD in the treatment of RA. In this study, 1H NMR metabonomics and 16 S rRNA gene sequencing techniques were used to clarify the intervention of DSD on CIA-induced RA. The results of 1H NMR metabolomics of feces revealed that five metabolites (alanine, glucose, taurine, betaine, and xylose) were disturbed, which could be regarded as potential biomarkers of RA. The intestinal microbiome of RA rats had changed, according to the results of 16 S rRNA gene sequencing; eight microbes (g_norank_f_Eubacterium_coprostanoligenes_group, g_Ruminococcus_torques_group, g_Dubosiell, g_Lactobacillus, g_norank_f_Desulfovibrionaceae, g_Bacteroides, g_Oscillibacter, and g_Romboutsia) occurred significantly at the genus level, and DSD significantly impacted six of them (g_Dubosiell, g_Lactobacillus, g_norank_f_Eubacterium_coprostanoligenes_group, g_Ruminococcus_torques_grou, g_Bacteroides, and g_Romboutsia). Three of them (g_norank_f_Eubacterium_ coprostanoligenes_group, g_Romboutsia, and g_Lactobacillus) were regarded as key microbiomes for DSD to treat RA, and three common metabolic pathways (taurine and hypotaurine metabolism; alanine, aspartate, and glutamate metabolism; primary bile acid biosynthesis) were discovered based on the 1H NMR metabonomics and PICRUST2 prediction of 16 S rRNA gene sequencing. Six SCFAs in feces (acetic acid, butyric acid, propionic acid, caproic acid, isobutyric acid, and valeric acid) increased significantly in RA, according to the outcomes of targeting SCFAs, while five SCFAs (acetic acid, butyric acid, propionic acid, caproic acid, and valeric acid) had decreased significantly due to DSD treatment. In conclusion, our study indicated that DSD could regulate RA's metabolic disorder by affecting intestinal microbiome and its metabolites. It also establishes a framework for future research into exploiting gut microbes therapeutic to treat RA.

Urine-derived exosomal PSMA is a promising diagnostic biomarker for the detection of prostate cancer on initial biopsy.

PURPOSE: It is well-established that the lack of accurate diagnostic modalities for prostate cancer (PCa) leads to overdiagnosis and overtreatments. Accordingly, this study aimed to assess the value of urine-derived exosomal prostate-specific membrane antigen (PSMA) as a biomarker for the diagnosis of PCa and clinically significant prostate cancer (csPCa). METHODS: A total of 284 urine samples were collected from patients after the digital rectal examination (DRE). Urinary exosomes were extracted using commercial kits, and urine-derived exosomal PSMA was determined via enzyme-linked immunosorbent assay (ELISA). Evaluation of diagnostic accuracy of PSMA was performed via receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and waterfall plots. RESULTS: We found that urine-derived exosomal PSMA was significantly higher in PCa and csPCa than in benign prostatic hyperplasia (BPH) and BPH + non-aggressive prostate cancer (naPCa) groups (P < 0.001). Furthermore, the urine-derived exosome PSMA yielded area under the ROC curve (AUC) values of 0.876 and 0.826 for detecting PCa and csPCa, respectively, suggesting better performance than traditional clinical biomarkers. Besides, when the cutoff value used corresponded to a sensitivity of 95%, urine-derived exosomal PSMA could avoid unnecessary biopsies in 41.2% of cases and missed only 0.7% of csPCa cases. CONCLUSIONS: Urine-derived exosomal PSMA exhibits a good diagnostic yield for detecting PCa and csPCa. Findings of the present study provide the foothold for future studies on cancer management and research in this patient population.

Assessment of lake area in response to climate change at varying elevations: A case study of Mt. Tianshan, Central Asia.

Changes in lake area (water surface area) are often considered accurate and sensitive representations of climate change. However, the role that elevation plays in this dynamic is somewhat unclear; studies remain inconclusive as to whether lake responses are consistent across elevation gradients. Here, we used Landsat and keyhole satellite images to quantify lake area changes from the 1960s to 2020 at different elevations in Central Asia's Tianshan Mountains and relate them to both climatic and anthropogenic factors. The results revealed that all low-elevation lakes showed a decreasing trend, and the total area of all monitored low-elevation lakes was reduced by 18.50 %. The total area of the mid-elevation lakes decreased by 0.16 %, while the total area of the high-elevation glacial lakes increased by 4.35 %. Lakes are recharged by a variety of influxes including glacial meltwater and precipitation. Notably, human activities (urban and agricultural water consumption) were the dominant factors in the shrinkage of low-elevation lakes. Climatic factors were the main driving factors of mid-elevation lake changes, and these lakes appeared to be more sensitive to temperature changes than lakes at other elevations. In addition, significant warming dominated area changes in high-elevation proglacial and unconnected glacial lakes. Overall, those results emphasized that when using lakes to reconstruct paleoclimates or predict lake evolution, it is necessary to consider how elevation gradients and recharge types may affect lake sensitivity to variations in climatic and anthropogenic activity.

Evaluation of aristolochic acid Ι nephrotoxicity in mice via 1H NMR quantitative metabolomics and network pharmacology approaches.

Background: Although many studies have shown that herbs containing aristolochic acids can treat various human diseases, AAΙ in particular has been implicated as a nephrotoxic agent. Methods and results: Here, we detail the nephrotoxic effect of AAΙ via an approach that integrated 1H NMR-based metabonomics and network pharmacology. Our findings revealed renal injury in mice after the administration of AAΙ. Metabolomic data confirmed significant differences among the renal metabolic profiles of control and model groups, with significant reductions in 12 differential metabolites relevant to 23 metabolic pathways. Among them, there were seven important metabolic pathways: arginine and proline metabolism; glycine, serine, and threonine metabolism; taurine and hypotaurine metabolism; ascorbate and aldehyde glycolate metabolism; pentose and glucosinolate interconversion; alanine, aspartate, and glutamate metabolism; and glyoxylate and dicarboxylic acid metabolism. Relevant genes, namely, nitric oxide synthase 1 (NOS1), pyrroline-5-carboxylate reductase 1 (PYCR1), nitric oxide synthase 3 (NOS3) and glutamic oxaloacetic transaminase 2 (GOT2), were highlighted via network pharmacology and molecular docking techniques. Quantitative real-time PCR findings revealed that AAI administration significantly downregulated GOT2 and NOS3 and significantly upregulated NOS1 and PYCR1 expression and thus influenced the metabolism of arginine and proline. Conclusion: This work provides a meaningful insight for the mechanism of AAΙ renal injury.

The Test Based on Meta-Analysis on "Does Workaholism Prefer Task Performance or Contextual Performance?"

The relationship between workaholism and work performance is explored by meta-analysis in this article. After searching relevant references, we had gained 94 individual effect sizes (n = 57,352), 45 individual samples, and 37 references. Through the heterogeneity test, it was shown that the random effect model is more suitable. The main effect analysis showed that there is a significant positive correlation between workaholism, working excessively, working compulsively, and work performance, and further analysis showed that workaholism emphasizes the improvement of contextual performance. The subgroup test showed that the relationship between workaholism, working excessively, working compulsively, and work performance is influenced by the measurement tools of workaholism, but not influenced by the cultural background differences and time-lag research. The above results show that workaholism and its dimensions have different influences on different aspects of work performance. Besides, it is worthy to consider the moderating function of the measurement tools of workaholism in the relationship between workaholism and work performance.

Liposomal PHD2 Inhibitors and the Enhanced Efficacy in Stabilizing HIF-1α.

Prolyl hydroxylase domain-containing protein 2 (PHD2) inhibition, which stabilizes hypoxia-inducible factor (HIF)-1α and thus triggers adaptation responses to hypoxia in cells, has become an important therapeutic target. Despite the proven high potency, small-molecule PHD2 inhibitors such as IOX2 may require a nanoformulation for favorable biodistribution to reduce off-target toxicity. A liposome formulation for improving the pharmacokinetics of an encapsulated drug while allowing a targeted delivery is a viable option. This study aimed to develop an efficient loading method that can encapsulate IOX2 and other PHD2 inhibitors with similar pharmacophore features in nanosized liposomes. Driven by a transmembrane calcium acetate gradient, a nearly 100% remote loading efficiency of IOX2 into liposomes was achieved with an optimized extraliposomal solution. The electron microscopy imaging revealed that IOX2 formed nanoprecipitates inside the liposome's interior compartments after loading. For drug efficacy, liposomal IOX2 outperformed the free drug in inducing the HIF-1α levels in cell experiments, especially when using a targeting ligand. This method also enabled two clinically used inhibitors-vadadustat and roxadustat-to be loaded into liposomes with a high encapsulation efficiency, indicating its generality to load other heterocyclic glycinamide PHD2 inhibitors. We believe that the liposome formulation of PHD2 inhibitors, particularly in conjunction with active targeting, would have therapeutic potential for treating more specifically localized disease lesions.

Response of altitudinal vegetation belts of the Tianshan Mountains in northwestern China to climate change during 1989-2015.

Within the mountain altitudinal vegetation belts, the shift of forest tree lines and subalpine steppe belts to high altitudes constitutes an obvious response to global climate change. However, whether or not similar changes occur in steppe belts (low altitude) and nival belts in different areas within mountain systems remain undetermined. It is also unknown if these, responses to climate change are consistent. Here, using Landsat remote sensing images from 1989 to 2015, we obtained the spatial distribution of altitudinal vegetation belts in different periods of the Tianshan Mountains in Northwestern China. We suggest that the responses from different altitudinal vegetation belts to global climate change are different. The changes in the vegetation belts at low altitudes are spatially different. In high-altitude regions (higher than the forest belts), however, the trend of different altitudinal belts is consistent. Specifically, we focused on analyses of the impact of changes in temperature and precipitation on the nival belts, desert steppe belts, and montane steppe belts. The results demonstrated that the temperature in the study area exhibited an increasing trend, and is the main factor of altitudinal vegetation belts change in the Tianshan Mountains. In the context of a significant increase in temperature, the upper limit of the montane steppe in the eastern and central parts will shift to lower altitudes, which may limit the development of local animal husbandry. The montane steppe in the west, however, exhibits the opposite trend, which may augment the carrying capacity of pastures and promote the development of local animal husbandry. The lower limit of the nival belt will further increase in all studied areas, which may lead to an increase in surface runoff in the central and western regions.