A luminescent organic cocrystal for detecting 2,4-dinitroaniline.

2,4-dinitroaniline (2,4DNBA), a significant hazardous chemical, is extensively used in industry and agriculture. The chemical accumulates in the environment for a long time, causing irreversible damage to the ecosystem. Currently, it is quite challenging to identify it by common analysis and detection techniques. Herein, a luminescent organic cocrystal (TCNB-8HQ) was prepared using 1,2,4,5-tetracyanobenzene (TCNB) as the electron acceptor and 8-hydroxyquinoline (8HQ) as the electron donor. The prepared TCNB-8HQ was used as a fluorescent probe with a fast and specific response to 2,4DNBA. This detection method possessed a linear range of 0.5-200 µmol/L with a detection limit as low as 0.085 µmol/L to detect 2,4DNBA in real samples with satisfactory spiking recovery. As revealed by fluorescence spectrum and UV-vis absorption spectrum, the detection mechanism involved competitive absorption between cocrystal material and 2,4DNBA. Moreover, the feasibility of the system was explored by preparing portable indicator strips for 2,4DNBA from organic cocrystal (TCNB-8HQ). This study not only provided an environmentally friendly gram-level preparation strategy to synthesize the fluorescent material but also investigated their application in chemical detection.

Application of the strip clear-cutting system in a running bamboo (Phyllostachys glauca McClure) forest: feasibility and sustainability assessments.

Introduction: As a renewable forest resource, bamboo plays a role in sustainable forest development. However, traditional cutting systems, selection cutting (SeC) and clear-cutting (ClC), result in an unsustainable production of bamboo forests due to labor-consuming or bamboo degradation. Recently, a strip clear-cutting (StC) was theoretically proposed to promote the sustainability of bamboo production, while little is known about its application consequence. Methods: Based on a 6-year experiment, we applied the strip clear-cutting system in a typical running bamboo (Phyllostachys glauca McClure) forest to assess its feasibility and sustainability. Using SeC and ClC as controls, we set three treatments with different strip widths (5 m, 10 m, and 20 m) for strip clear-cutting, simplified as StC-5, StC-10, and StC-20, respectively. Then, we investigated leaf physiological traits, bamboo size and productivity, population features, and economic benefits for all treatments. Results: The stands managed by StC had high eco-physiological activities, such as net photosynthetic rate (P n), photosynthetic nitrogen use efficiency (PNUE), and photosynthetic phosphorus use efficiency (PPUE), and thus grew well, achieved a large diameter at breast height (DBH), and were tall. The stand biomass of StC (8.78 t hm-2 year-1) was 1.19-fold and 1.49-fold greater than that of SeC and ClC, respectively, and StC-10 and StC-20 were significantly higher than SeC or ClC (p< 0.05). The income and profit increased with the increase in stand density and biomass, and StC-20 and StC-10 were significantly higher than SeC or ClC (p< 0.05). Using principal components analysis and subordinate function analysis, we constructed a composite index to indicate the sustainability of bamboo forests. For the sustainability assessment, StC-10 had the highest productive sustainability (0.59 ± 0.06) and the second highest economic sustainability (0.59 ± 0.11) in all cutting treatments. StC-10 had the maximum overall sustainability, with a value of 0.53 ± 0.02, which was significantly higher than that of ClC (p< 0.05). Conclusion: The results verified that StC for Phyllostachys glauca forests is feasible and sustainable as its sustainability index outweighs those of traditional cutting systems (SeC and ClC), and 10 m is the optimum distance for the strip width of StC. Our findings provide a new cutting system for managing other running bamboo forests sustainably.

Unifying Fourteen Post-hoc Attribution Methods with Taylor Interactions.

Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the final output. However, existing attribution methods are often built upon different heuristics. There remains a lack of a unified theoretical understanding of why these methods are effective and how they are related. Furthermore, there is still no universally accepted criterion to compare whether one attribution method is preferable over another. In this paper, we resort to Taylor interactions and for the first time, we discover that fourteen existing attribution methods, which define attributions based on fully different heuristics, actually share the same core mechanism. Specifically, we prove that attribution scores of input variables estimated by the fourteen attribution methods can all be mathematically reformulated as a weighted allocation of two typical types of effects, i.e., independent effects of each input variable and interaction effects between input variables. The essential difference among these attribution methods lies in the weights of allocating different effects. Inspired by these insights, we propose three principles for fairly allocating the effects, which serve as new criteria to evaluate the faithfulness of attribution methods. In summary, this study can be considered as a new unified perspective to revisit fourteen attribution methods, which theoretically clarifies essential similarities and differences among these methods. Besides, the proposed new principles enable people to make a direct and fair comparison among different methods under the unified perspective.

Neoadjuvant therapy for HER2-positive acantholytic squamous cell breast carcinoma: a case report.

HER2-positive acantholytic squamous cell carcinoma (ASCC) of the breast is exceptionally rare, and its clinicopathologic features are poorly understood. The impact of neoadjuvant therapy on HER2-positive breast ASCC is unclear. Here we report on a 58-year-old woman who was diagnosed with HER2-positive ASCC of the right breast, who underwent neoadjuvant treatment with albumin-paclitaxel, carboplatin, and trastuzumab, and surgery. Neoadjuvant therapy was effective, with no recurrence or metastasis after 1.5 years of postoperative follow-up.

Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling.

The emphasis on fairness in predictive healthcare modeling has increased in popularity as an approach for overcoming biases in automated decision-making systems. The aim is to guarantee that sensitive characteristics like gender, race, and ethnicity do not influence prediction outputs. Numerous algorithmic strategies have been proposed to reduce bias in prediction results, mitigate prejudice toward minority groups and promote prediction fairness. The goal of these strategies is to ensure that model prediction performance does not exhibit significant disparity among sensitive groups. In this study, we propose a novel fairness-achieving scheme based on multitask learning, which fundamentally differs from conventional fairness-achieving techniques, including altering data distributions and constraint optimization through regularizing fairness metrics or tampering with prediction outcomes. By dividing predictions on different sub-populations into separate tasks, we view the fairness problem as a task-balancing problem. To ensure fairness during the model-training process, we suggest a novel dynamic re-weighting approach. Fairness is achieved by dynamically modifying the gradients of various prediction tasks during neural network back-propagation, and this novel technique applies to a wide range of fairness criteria. We conduct tests on a real-world use case to predict sepsis patients' mortality risk. Our approach satisfies that it can reduce the disparity between subgroups by 98% while only losing less than 4% of prediction accuracy.

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint.

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased.

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant.

Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure. Analyzing the post-transplant cause of death (CoD) after organ transplant provides a powerful tool for clinical decision making, including personalized treatment and organ allocation. However, traditional methods like Model for End-stage Liver Disease (MELD) score and conventional machine learning (ML) methods are limited in CoD analysis due to two major data and model-related challenges. To address this, we propose a novel framework called CoD-MTL leveraging multi-task learning to model the semantic relationships between various CoD prediction tasks jointly. Specifically, we develop a novel tree distillation strategy for multi-task learning, which combines the strength of both the tree model and multi-task learning. Experimental results are presented to show the precise and reliable CoD predictions of our framework. A case study is conducted to demonstrate the clinical importance of our method in the liver transplant.

miR-30a targets STOX2 to increase cell proliferation and metastasis in hydatidiform moles via ERK, AKT, and P38 signaling pathways.

BACKGROUND: A hydatidiform mole is a condition caused by abnormal proliferation of trophoblastic cells. MicroRNA miR-30a acts as a tumor suppressor gene in most tumors and participates in the development of various cancers. However, its role in hydatidiform moles is not clear. METHODS: Quantitative real-time reverse transcription PCR was used to verify the expression level of miR-30a and STOX2 (encoding storkhead box 2). Flow cytometry assays were performed to detect the cell cycle in cell with different expression levels of miR-30a and STOX2. Cell Cycle Kit-8, 5-ethynyl-2'-deoxyuridine, and colony formation assays were used to detect cell proliferation and viability. Transwell assays was used to test cell invasion and migration. Dual-luciferase reporter assays and western blotting were used to investigate the potential mechanisms involved. RESULT: Low miR-30a expression promoted the proliferation, migration, and invasion of trophoblastic cells (JAR and HTR-8). Dual luciferase assays confirmed that STOX2 is a target of miR-30a and resisted the effect of upregulated miR-30a in trophoblastic cells. In addition, downregulation of STOX2 by miR-30a could activate ERK, AKT, and P38 signaling pathways. These results revealed a new mechanism by which ERK, AKT, and P38 activation by miR-30a/STOX2 results in excessive proliferation of trophoblast cells in the hydatidiform mole. CONCLUSIONS: In this study, we found that miR-30a plays an important role in the development of the hydatidiform mole. Our findings indicate that miR-30a might promote the malignant transformation of human trophoblastic cells by regulating STOX2, which strengthens our understanding of the role of miR-30a in regulating trophoblastic cell transformation.

Defense Against Explanation Manipulation.

Explainable machine learning attracts increasing attention as it improves the transparency of models, which is helpful for machine learning to be trusted in real applications. However, explanation methods have recently been demonstrated to be vulnerable to manipulation, where we can easily change a model's explanation while keeping its prediction constant. To tackle this problem, some efforts have been paid to use more stable explanation methods or to change model configurations. In this work, we tackle the problem from the training perspective, and propose a new training scheme called Adversarial Training on EXplanations (ATEX) to improve the internal explanation stability of a model regardless of the specific explanation method being applied. Instead of directly specifying explanation values over data instances, ATEX only puts constraints on model predictions which avoids involving second-order derivatives in optimization. As a further discussion, we also find that explanation stability is closely related to another property of the model, i.e., the risk of being exposed to adversarial attack. Through experiments, besides showing that ATEX improves model robustness against manipulation targeting explanation, it also brings additional benefits including smoothing explanations and improving the efficacy of adversarial training if applied to the model.

Lipocalin-2 silencing suppresses inflammation and oxidative stress of acute respiratory distress syndrome by ferroptosis via inhibition of MAPK/ERK pathway in neonatal mice.

Neonatal acute respiratory distress syndrome (ARDS) has high morbidity and mortality rates worldwide, but there is a lack of pharmacologic treatment and clinical targeted therapies. In this study, we aimed to explore the effects of Lipocalin-2 (LCN2) on ferroptosis-mediated inflammation and oxidative stress in neonatal ARDS and the potential mechanism. In this study, we established an in vivo ARDS mouse model and an in vitro ARDS cell model by LPS (Lipopolysaccharide) stimulation. Lung tissue injury was evaluated by wet/dry ratios and histopathological examination. LCN2 expression was detected by qRT-PCR and Western blot. Inflammatory factors, oxidative stress and apoptosis were also detected. Ferroptosis was identified by detection of Fe2+ level and ferroptosis-associated protein expressions. Mitogen-activated protein kinases (MAPK)/extracellular signal-regulated kinase (ERK) pathway signaling was examined by Western blot analysis. The data revealed that LCN2 expression was significantly upregulated in neonatal mice with ARDS. Interference with LCN2 protected LPS-induced lung in neonatal mouse by reducing the radio of wet/dry and alleviating pathological damages. In addition, LCN2 silencing repressed LPS-induced inflammation, oxidative stress in vivo and in vitro, as well as apoptosis. Meanwhile, decreased level of Fe2+ and transferrin while increased levels of ferritin heavy chain 1 (FTH1) and glutathione peroxidase 4 (GPX4) were observed. The expression MAPK/ERK pathway was inhibited by depletion of LCN2. The present results suggest that LCN2 knockdown protected LPS-induced ARDS model via inhibition of ferroptosis-related inflammation and oxidative stress by inhibiting the MAPK/ERK pathway, thereby presenting a novel target for the treatment of ARDS.

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning.

Liver transplant is an essential therapy performed for severe liver diseases. The fact of scarce liver resources makes the organ assigning crucial. Model for End-stage Liver Disease (MELD) score is a widely adopted criterion when making organ distribution decisions. However, it ignores post-transplant outcomes and organ/donor features. These limitations motivate the emergence of machine learning (ML) models. Unfortunately, ML models could be unfair and trigger bias against certain groups of people. To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant. Specifically, knowledge distillation is employed to handle dense and sparse features by combining the advantages of tree models and neural networks. A two-step debiasing method is tailored for this framework to enhance fairness. Experiments are conducted to analyze unfairness issues in existing models and demonstrate the superiority of our method in both prediction and fairness performance.

Coordination of nitrogen uptake and assimilation favours the growth and competitiveness of moso bamboo over native tree species in high-NH4+ environments.

Phenotypic plasticity and competitive strength are major mechanisms determining the success of invasive species and are influenced by abiotic factors. A rise in the ratio of ammonium (NH4+) to nitrate (NO3-) in soils is frequently associated with the invasion of bamboo into broad-leaved evergreen forests. However, the influence of soil nitrogen (N) chemistry on plant growth and interspecific competition in the context of invasion remains insufficiently studied. In the present work, differences in plasticity and interspecific competition between native tree species in broad-leaved evergreen forests and invasive bamboo in response to different N forms were investigated using seedlings grown in a controlled environment. We show that moso bamboo responded positively and strongly to increased soil NH4+/NO3- ratios, while the native tree species Sapium sebiferum, Camellia oleifera, and Machilus pauhoi responded negatively and exhibited limited plasticity. Native tree species growth was significantly inhibited in the presence of moso bamboo under high-NH4+ conditions, whereas native tree species were less affected by interspecific competition when NO3- was supplied as the sole N source. By contrast, moso bamboo growth was significantly inhibited, followed by seedling death, in both monoculture and in mixed culture with prolonged NO3- treatment. All species tested exhibited significantly higher rates of 15NH4+ than 15NO3- uptake, but the Michaelis constant (Km) for 15NH4+ uptake was lower in moso bamboo, indicating higher substrate affinity. Nitrate reductase (NR) and nitrite reductase (NiR) activities showed no inducible effects in moso bamboo compared to the induction response seen in the native tree species in response to NO3-. Activities of glutamine synthetase (GS), glutamate synthase (GOGAT), and glutamate dehydrogenase (GDH) significantly increased with NH4+ provision in roots of moso bamboo, contrasted by a less plastic response in the native tree species. Enhanced ammonification and reduced nitrification in soils is typically observed during bamboo invasion and appears to create a positive soil-plant feedback loop that, due to highly flexible and opportunistic NH4+-acquisition pathways, favours bamboo fitness and invasion into native forests when NH4+ is the dominant N form.

Nitrogen form plays an important role in the growth of moso bamboo (Phyllostachys edulis) seedlings.

BACKGROUND: This study aimed to gain an understanding of the growth response of Phyllostachys edulis (moso bamboo) seedlings to nitrogen (N) and potassium (K) to benefit nutrient management practices and the design of proper fertilizer in nursery cultivation. METHODS: An orthogonal array L8(4×24) was used to study the effects of N forms (NH4 +, NO3 -), N concentrations (8, 32 mmol/L), and K+concentrations (0, 0.5, 1.5, 3 mmol/L) on seedling height, leaf number, chlorophyll content (SPAD value), biomass, root systems, and N content of P. edulis seedlings. Plants were grown in vermiculite under controlled greenhouse conditions. RESULTS: Our study showed that N form played a significant role in the overall performance of P. edulis seedlings, followed by the effect of N and K+ concentrations. Among the N forms, NH4 + significantly improved the growth of P. edulis seedlings compared with NO3 -. Seedling height, leaf number, chlorophyll SPAD value, biomass, and root system architecture (root length, root surface area, root volume, and root tips) were greater with 8 mmol/L of NH4 + treatments than with 32 mmol/L of NH4 +treatments, whereas root diameter and N content of P. edulis seedlings were higher with 32 mmol/L of NH4 + than with 8 mmol/L of NH4 +. K displayed inconsistent effects on the growth of P. edulis seedlings. Specifically, seedling height, leaf number, biomass and root volume increased when the K+ concentration was increased from 0 to 0.5 mmol/L, followed by a decrease when the K+ concentration was further increased from 0.5 to 3 mmol/L. Root average diameter of the seedlings was the highest with a K+ concentration of 1.5 mmol/L, and K had some inhibitory effects on the chlorophyll SPAD value of the seedlings. P. edulis seedlings performed well with 8 mmol/L NH4 +and further tolerated a higher concentration of both NH4 + and NO3 -, although excessive N could inhibit seedling growth. A lower concertation of K (≤ 0.5 mmol/L) promoted seedling growth and increasing K+ concentration in the nutrient solution did not alleviate the inhibitory effect of high N on the growth of P. edulis seedlings. Therefore, NH4 +nitrogen as the main form of N fertilizer, together with a low concertation of K+, should be supplied in the cultivation and nutrient management practices of moso bamboo.

Superior growth, N uptake and NH4+ tolerance in the giant bamboo Phyllostachys edulis over the broad-leaved tree Castanopsis fargesii at elevated NH4+ may underlie community succession and favor the expansion of bamboo.

The unbridled expansion of bamboo has imposed serious threats on ecosystem processes and functions. Considerable evidence indicates that bamboo invasions can alter plant-available soil nitrogen (N) pools and rates of N cycling, but the consequences of altered N availability for plant growth and community structure have thus far been poorly characterized. The primary soil-accessible N forms for most plants are ammonium (NH4+) and nitrate (NO3-), but plants differ in their ability to use the different N forms, and these differences can be related to their ecological characteristics and drive community structure. In this context, we evaluated the growth response, N uptake and interspecific competition in two subtropical species, Phyllostachys edulis (Carrière) J. Houzeau (Synonym Phyllostachys heterocycla Carrière) and Castanopsis fargesii Franch., dominant species of bamboo and secondary evergreen broad-leaved forests, respectively, under changing N availability in seedlings supplied with different N concentrations and NH4+/NO3- proportions, in vermiculite culture, in a controlled environment. The results show that (i) both species display an NH4+ preference at elevated N concentrations. The growth of P. edulis seedlings was strongly enhanced at increased ratios of NH4+ to NO3- especially at higher N concentrations, but to a much lesser extent in C. fargesii. (ii) NH4+ preference at the level of N uptake in both species was confirmed by the Non-invasive Micro-test Technology and by examining 15N signatures. Phyllostachys edulis had higher NH4+ net fluxes and N concentration under NH4+ treatments than C. fargesii. (iii) NH4+ at higher concentrations caused toxicity in both species as it inhibited root growth and even caused seedling death, but P. edulis had a higher NH4+-tolerance threshold (24 mM) than C. fargesii (16 mM). (iv) When mixed-species cultures were examined in an NH4+-rich medium, the growth of C. fargesii, but not P. edulis, was significantly inhibited compared with growth in monoculture. Therefore, P. edulis exhibited stronger plasticity and adaptation to changing N availability, whereas C. fargesii had low responsiveness and capacity to acclimate to soil N changes. Phyllostachys edulis displayed a significant competitive growth advantage compared with C. fargesii on NH4+-dominated substrates.

miR-196b inhibits cell migration and invasion through targeting MAP3K1 in hydatidiform mole.

MicroRNAs (miRNAs) are a class of small non-coding RNAs that are closely associated with carcinogenesis. Accumulating data indicate that miR-196b participates in the development of various types of cancers. However, the role of miR-196b in the formation of hydatidiform mole (HM) is still unclear. Our previous studies have demonstrated that miR-196b levels were decreased in JAR and BeWo cells and in HM tissue samples, as demonstrated by RT-PCR analysis. Furthermore, we discovered that overexpression of miR-196b in JAR and BeWo cells inhibited cellular proliferation, migration and invasion, as shown by Cell counting kit-8 (CCK-8) and transwell assays, respectively. Subsequently, we explored the interaction of miR-196b with its target gene in human choriocarcinoma cell lines. MAP3K1 is a target gene predicted by bioinformatic analysis that was previously shown to exhibit reduced expression levels following treatment with miR-196b in JAR and BeWo cells. We demonstrated that MAP3K1 was a direct target of miR-196b using the dual-luciferase reporter assay in Hela cells. In summary, the present study demonstrated that miR-196b suppressed proliferation, migration and invasion of human choriocarcinoma cells by inhibiting its transcriptional target MAP3K1. miR-196b and MAP3K1 may be considered potential targets for the clinical treatment of HM.

A Data Adaptive Biological Sequence Representation for Supervised Learning.

Proper expression of the genes plays a vital role in the function of an organism. Recent advancements in DNA microarray technology allow for monitoring the expression level of thousands of genes. One of the important tasks in this context is to understand the underlying mechanisms of gene regulation. Recently, researchers have focused on identifying local DNA elements, or motifs to infer the relation between the expression and the nucleotide sequence of the gene. This study proposes a novel data adaptive representation approach for supervised learning to predict the response associated with the biological sequences. Biological sequences such as DNA and protein are a class of categorical sequences. In machine learning, categorical sequences are generally mapped to a lower dimensional representation for learning tasks to avoid problems with high dimensionality. The proposed method, namely SW-RF (sliding window-random forest), is a feature-based approach requiring two main steps to learn a representation for categorical sequences. In the first step, each sequence is represented by overlapping subsequences of constant length. Then a tree-based learner on this representation is trained to obtain a bag-of-words like representation which is the frequency of subsequences on the terminal nodes of the tree for each sequence. After representation learning, any classifier can be trained on the learned representation. A lasso logistic regression is trained on the learned representation to facilitate the identification of important patterns for the classification task. Our experiments show that proposed approach provides significantly better results in terms of accuracy on both synthetic data and DNA promoter sequence data. Moreover, a common problem for microarray datasets, namely missing values, is handled efficiently by the tree learners in SW-RF. Although the focus of this paper is on biological sequences, SW-RF is flexible in handling any categorical sequence data from different applications.

Metabolic connectivity for differential diagnosis of dementing disorders.

Presently, visual and quantitative approaches for image-supported diagnosis of dementing disorders rely on regional intensity rather than on connectivity measurements. Here, we test metabolic connectivity for differentiation between Alzheimer's disease and frontotemporal lobar degeneration. Positron emission tomography with 18F-fluorodeoxyglucose was conducted in 47 patients with mild Alzheimer's disease, 52 patients with mild frontotemporal lobar degeneration, and 45 healthy elderly subjects. Sparse inverse covariance estimation and selection were used to identify patterns of metabolic, inter-subject covariance on the basis of 60 regional values. Relative to healthy subjects, significantly more pathological within-lobe connections were found in the parietal lobe of patients with Alzheimer's disease, and in the frontal and temporal lobes of subjects with frontotemporal lobar degeneration. Relative to the frontotemporal lobar degeneration group, more pathological connections between the parietal and temporal lobe were found in the Alzheimer's disease group. The obtained connectivity patterns differentiated between two patients groups with an overall accuracy of 83%. Linear discriminant analysis and univariate methods provided an accuracy of 74% and 69%, respectively. There are characteristic patterns of abnormal metabolic connectivity in mild Alzheimer's disease and frontotemporal lobar degeneration. Such patterns can be utilized for single-subject analyses and might be more accurate in the differential diagnosis of dementing disorders than traditional intensity-based analyses.

Metabolic connectivity as index of verbal working memory.

Positron emission tomography (PET) data are commonly analyzed in terms of regional intensity, while covariant information is not taken into account. Here, we searched for network correlates of healthy cognitive function in resting state PET data. PET with [(18)F]-fluorodeoxyglucose and a test of verbal working memory (WM) were administered to 35 young healthy adults. Metabolic connectivity was modeled at a group level using sparse inverse covariance estimation. Among 13 WM-relevant Brodmann areas (BAs), 6 appeared to be robustly connected. Connectivity within this network was significantly stronger in subjects with above-median WM performance. In respect to regional intensity, i.e., metabolism, no difference between groups was found. The results encourage examination of covariant patterns in FDG-PET data from non-neurodegenerative populations.