Proper network randomization is key to assessing social balance.

Social ties, either positive or negative, lead to signed network patterns, the subject of balance theory. For example, strong balance introduces cycles with even numbers of negative edges. The statistical significance of such patterns is routinely assessed by comparisons to null models. Yet, results in signed networks remain controversial. Here, we show that even if a network exhibits strong balance by construction, current null models can fail to identify it. Our results indicate that matching the signed degree preferences of the nodes is a critical step and so is the preservation of network topology in the null model. As a solution, we propose the STP null model, which integrates both constraints within a maximum entropy framework. STP randomization leads to qualitatively different results, with most social networks consistently demonstrating strong balance in three- and four-node patterns. On the basis our results, we present a potential wiring mechanism behind the observed signed patterns and outline further applications of STP randomization.

Utilizing the open-source programming language Python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology.

INTRODUCTION: Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the Python programming language to create a dashboard application for screening and diagnostic quality metrics. MATERIAL AND METHODS: Data from the 5-year period (2018-2022) were accessed. Versatile open-source Python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory "goal," "borderline," and "attention" zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed. RESULTS: We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://drkvcsstvn-dashboards.hf.space/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results. CONCLUSIONS: Python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard.

CT-Assessment of Epicardial Fat Identifies Increased Inflammation at the Level of the Left Coronary Circulation in Patients with Atrial Fibrillation.

Background: Atrial fibrillation (AF) can often be triggered by an inflammatory substrate. Perivascular inflammation may be assessed nowadays using coronary computed tomography angiography (CCTA) imaging. The new pericoronary fat attenuation index (FAI HU) and the FAI Score have prognostic value for predicting future cardiovascular events. Our purpose was to investigate the correlation between pericoronary fat inflammation and the presence of AF among patients with coronary artery disease. Patients and methods: Eighty-one patients (mean age 64.75 ± 7.84 years) who underwent 128-slice CCTA were included in this study and divided into two groups: group 1 comprised thirty-six patients with documented AF and group 2 comprised forty-five patients without a known history of AF. Results: There were no significant differences in the absolute value of fat attenuation between the study groups (p > 0.05). However, the mean FAI Score was significantly higher in patients with AF (15.53 ± 10.29 vs. 11.09 ± 6.70, p < 0.05). Regional analysis of coronary inflammation indicated a higher level of this process, especially at the level of the left anterior descending artery (13.17 ± 7.91 in group 1 vs. 8.80 ± 4.75 in group 2, p = 0.008). Conclusions: Patients with AF present a higher level of perivascular inflammation, especially in the region of the left coronary circulation, and this seems to be associated with a higher risk of AF development.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

BACKGROUND: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. METHODS: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. RESULTS: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. CONCLUSIONS: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Host space, not energy or symbiont size, constrains feather mite abundance across passerine bird species.

Comprehending symbiont abundance among host species is a major ecological endeavour, and the metabolic theory of ecology has been proposed to understand what constrains symbiont populations. We parameterized metabolic theory equations to investigate how bird species' body size and the body size of their feather mites relate to mite abundance according to four potential energy (uropygial gland size) and space constraints (wing area, total length of barbs and number of feather barbs). Predictions were compared with the empirical scaling of feather mite abundance across 106 passerine bird species (26,604 individual birds sampled), using phylogenetic modelling and quantile regression. Feather mite abundance was strongly constrained by host space (number of feather barbs) but not by energy. Moreover, feather mite species' body size was unrelated to the body size of their host species. We discuss the implications of our results for our understanding of the bird-feather mite system and for symbiont abundance in general.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.

Background: Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. Methods: Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. Results: scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Conclusion: We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Association of Atrial Fibrillation Recurrence with Right Coronary Atherosclerosis and Increased Left Arterial Epicardial Fat Following Catheter Ablation-Results of a Multimodality Study.

BACKGROUND: Identification of predictors for atrial fibrillation (AF) recurrence after pulmonary vein isolation (PVI) can lead to better long-term results. Our aim was to investigate the association between novel CT imaging markers reflecting the severity of coronary atherosclerosis and the risk of recurrence following PVI. METHODS: This study included 80 patients with paroxysmal/persistent AF who underwent PVI. The patients were divided into two groups: Group 1-23 patients with recurrence and Group 2-57 patients without recurrence. RESULTS: Patients with recurrence presented with a more enlarged left atrial diameter and reduced left ventricle EF, as assessed by echocardiography. Elevated calcium scores and right coronary artery (RCA) stenosis were correlated with a higher risk of AF recurrence (25.38 ± 4.1% vs. 9.76 ± 2.32%, p = 0.001). Patients with AF recurrence presented a higher left atrial volume index (LAVI) (61.38 ± 11.12 mm3/m2 vs. 46.34 ± 12.27 mm3/m2, p < 0.0001). The bi-atrial volume index (BAVI) was similarly higher in the AF recurrence group (98.23 ± 14.44 mm3/m2 vs. 76.48 ± 17.61 mm3/m2, p < 0.0001). Increased EAT volumes located around the LA (EAT-LA) were correlated with recurrence (25.55 ± 6.37 vs. 15.54 ± 8.44, p < 0.0001). CONCLUSIONS: RCA stenosis, together with atrial volumes and EAT-AS evaluated by CCTA, is associated with the risk of AF recurrence following PVI.

Neuroscience Needs Network Science.

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

A positive statistical benchmark to assess network agreement.

Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation.

Neuroscience needs Network Science.

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

Elevated FAI Index of Pericoronary Inflammation on Coronary CT Identifies Increased Risk of Coronary Plaque Vulnerability after COVID-19 Infection.

Inflammation is a key factor in the development of atherosclerosis, a disease characterized by the buildup of plaque in the arteries. COVID-19 infection is known to cause systemic inflammation, but its impact on local plaque vulnerability is unclear. Our study aimed to investigate the impact of COVID-19 infection on coronary artery disease (CAD) in patients who underwent computed tomography angiography (CCTA) for chest pain in the early stages after infection, using an AI-powered solution called CaRi-Heart®. The study included 158 patients (mean age was 61.63 ± 10.14 years) with angina and low to intermediate clinical likelihood of CAD, with 75 having a previous COVID-19 infection and 83 without infection. The results showed that patients who had a previous COVID-19 infection had higher levels of pericoronary inflammation than those who did not have a COVID-19 infection, suggesting that COVID-19 may increase the risk of coronary plaque destabilization. This study highlights the potential long-term impact of COVID-19 on cardiovascular health, and the importance of monitoring and managing cardiovascular risk factors in patients recovering from COVID-19 infection. The AI-powered CaRi-Heart® technology may offer a non-invasive way to detect coronary artery inflammation and plaque instability in patients with COVID-19.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster.

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Different Conical Angle Connection of Implant and Abutment Behavior: A Static and Dynamic Load Test and Finite Element Analysis Study.

Dental implants are artificial dental roots anchoring prosthetic restorations to replace natural teeth. Dental implant systems may have different tapered conical connections. Our research focused on the mechanical examination of implant-superstructure connections. Thirty-five samples with 5 different cone angles (24°, 35°, 55°, 75°, and 90°) were tested for static and dynamic loads, carried out by a mechanical fatigue testing machine. Fixing screws were fixed with a torque of 35 Ncm before measurements. For static loading, samples were loaded with a force of 500 N in 20 s. For dynamic loading, the samples were loaded for 15,000 cycles with a force of 250 ± 150 N. In both cases, the compression resulting from load and reverse torque was examined. At the highest compression load of the static tests, a significant difference (p = 0.021) was found for each cone angle group. Following dynamic loading, significant differences (p < 0.001) for the reverse torques of the fixing screw were also shown. Static and dynamic results showed a similar trend: under the same loading conditions, changing the cone angle-which determines the relationship between the implant and the abutment-had led to significant differences in the loosening of the fixing screw. In conclusion, the greater the angle of the implant-superstructure connection, the smaller the screw loosening due to loading, which may have considerable effects on the long-term, safe operation of the dental prosthesis.

Assessment of community efforts to advance network-based prediction of protein-protein interactions.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.

Surface Free Energy and Composition Changes and Ob Cellular Response to CHX-, PVPI-, and ClO2-Treated Titanium Implant Materials.

The study evaluated the interaction of a titanium dental implant surface with three different antibacterial solutions: chlorhexidine, povidone-iodine, and chlorine dioxide. Implant surface decontamination is greatly challenging modern implant dentistry. Alongside mechanical cleaning, different antibacterial agents are widely used, though these could alter implant surface properties. Commercially pure (CP) grade 4 titanium (Ti) discs were treated with three different chemical agents (chlorhexidine 0.2% (CHX), povidone-iodine 10% (PVPI), chlorine dioxide 0.12% (ClO2)) for 5 min. Contact angle measurements, X-ray photoelectron spectroscopy (XPS) analysis, and cell culture studies were performed. Attachment and proliferation of primary human osteoblast cells were investigated via MTT (dimethylthiazol-diphenyl tetrazolium bromide), alamarBlue, LDH (lactate dehydrogenase), and fluorescent assays. Contact angle measurements showed that PVPI-treated samples (Θ = 24.9 ± 4.1) gave no difference compared with controls (Θ = 24.6 ± 5.4), while CHX (Θ = 47.2 ± 4.1) and ClO2 (Θ = 39.2 ± 9.8) treatments presented significantly higher Θ values. All samples remained in the hydrophilic region. XPS analysis revealed typical surface elements of CP grade 4 titanium (Ti, O, and C). Both MTT and alamarBlue cell viability assays showed similarity between treated and untreated control groups. The LDH test revealed no significant difference, and fluorescent staining confirmed these results. Although there was a difference in surface wettability, a high proliferation rate was observed in all treated groups. The in vitro study proved that CHX, PVPI, and ClO2 are proper candidates as dental implant decontamination agents.

Bioreactor as the root cause of the "manganese effect" during Aspergillus niger citric acid fermentations.

High-yield citric acid production by the filamentous Ascomycete fungus Aspergillus niger requires a combination of extreme nutritional conditions, of which maintaining a low manganese (II) ion concentration (<5 µg L-1) is a key feature. Technical-scale production of citric acid predominantly uses stainless-steel tank fermenters, but glass bioreactors used for strain improvement and manufacturing process development also contain stainless steel components, in which manganese is an essential alloying element. We show here that during citric acid fermentations manganese (II) ions were leaching from the bioreactor into the growth media, resulting in altered fungal physiology and morphology, and significant reduction of citric acid yields. The leaching of manganese (II) ions was dependent on the fermentation time, the acidity of the culture broth and the sterilization protocol applied. Manganese (II) ion leaching was partially mitigated by electrochemical polishing of stainless steel components of the bioreactor. High concentrations of manganese (II) ions during early cultivation led to a reduction in citric acid yield. However, the effect of manganese (II) ions on the reduction of citric acid yield diminished towards the second half of the fermentation. Since maintaining low concentrations of manganese (II) ions is costly, the results of this study can potentially be used to modify protocols to reduce the cost of citric acid production.

Uncommon Surgical Management by AVF between the Great Saphenous Vein and Anterior Tibial Artery for Old Radiocephalic AVF Failure.

INTRODUCTION: Autologous native arteriovenous fistula (AVF) created in the non-dominant arm is the gold standard vascular access for dialysis in end-stage renal disease, but the post-surgical vascular access dysfunction causes a reduction in the patient's quality of life. Creating a functional upper extremity permanent arteriovenous access is limited by the upper limb's vascular resources, so good management of a complicated arteriovenous fistula may improve patient outcomes. This article highlights the importance of new surgical options in treating complicated AVFs. CASE REPORT: We present the case of a patient with a 17-year-old complex radio-cephalic arterio-venous fistula and a series of surgical interventions performed for life salvage in the first place and functional vascular access in the second place. Furthermore, we describe a successfully created uncommon type of fistula in the lower extremity between the great saphenous vein and the anterior tibial artery as the last possible access for hemodialysis in this patient. RESULTS: The patient underwent the first successful dialysis using the newly created lower limb fistula 1 month after the surgery. CONCLUSION: Applying new surgical techniques to manage AVFs gives a unique chance to improve the quality of life and reduce morbidity and mortality in these patients.

Computational Inference of Synaptic Polarities in Neuronal Networks.

Synaptic polarity, that is, whether synapses are inhibitory (-) or excitatory (+), is challenging to map, despite being a key to understand brain function. Here, synaptic polarity is inferred computationally considering three experimental scenarios, depending on the nature of available input data, using the Caenorhabditis elegans connectome as an example. First, the inputs consist of detailed neurotransmitter (NT) and receptor (R) gene expression, integrated through the connectome model (CM). The CM formulates the problem through a wiring rule network that summarizes how NT-R pairs govern synaptic polarity, and resolves 356 synaptic polarities in addition to the 1752 known polarities. Second, known synaptic polarities are considered as an input, in addition to the NT and R gene expression data, but without wiring rules. These data train the spatial connectome model, which infers the polarity of 81% of the CM-resolved connections at >95$>95$ % precision, while also inferring 147 of the remaining unknown polarities. Last, without known expression or wiring rules, polarities are inferred through a network sign prediction problem. As an illustration of high performance in this case, the generalized CM is introduced. These results address imminent challenges in unveiling large-scale synaptic polarities, an essential step toward more realistic brain models.

Limited reciprocal surrogacy of bird and habitat diversity and inconsistencies in their representation in Romanian protected areas.

Because it is impossible to comprehensively characterize biodiversity at all levels of organization, conservation prioritization efforts need to rely on surrogates. As species distribution maps of relished groups as well as high-resolution remotely sensed data increasingly become available, both types of surrogates are commonly used. A good surrogate should represent as much of biodiversity as possible, but it often remains unclear to what extent this is the case. Here, we aimed to address this question by assessing how well bird species and habitat diversity represent one another. We conducted our study in Romania, a species-rich country with high landscape heterogeneity where bird species distribution data have only recently started to become available. First, we prioritized areas for conservation based on either 137 breeding bird species or 36 habitat classes, and then evaluated their reciprocal surrogacy performance. Second, we examined how well these features are represented in already existing protected areas. Finally, we identified target regions of high conservation value for the potential expansion of the current network of reserves (as planned under the new EU Biodiversity Strategy for 2030). We found a limited reciprocal surrogacy performance, with bird species performing slightly better as a conservation surrogate for habitat diversity than vice versa. We could also show that areas with a high conservation value based on habitat diversity were represented better in already existing protected areas than areas based on bird species, which varied considerably between species. Our results highlight that taxonomic and environmental (i.e., habitat types) data may perform rather poorly as reciprocal surrogates, and multiple sources of data are required for a full evaluation of protected areas expansion.

Geometry of rare regions behind Griffiths singularities in random quantum magnets.

In many-body systems with quenched disorder, dynamical observables can be singular not only at the critical point, but in an extended region of the paramagnetic phase as well. These Griffiths singularities are due to rare regions, which are locally in the ordered phase and contribute to a large susceptibility. Here, we study the geometrical properties of rare regions in the transverse Ising model with dilution or with random couplings and transverse fields. In diluted models, the rare regions are percolation clusters, while in random models the ground state consists of a set of spin clusters, which are calculated by the strong disorder renormalization method. We consider the so called energy cluster, which has the smallest excitation energy and calculate its mass and linear extension in one-, two- and three-dimensions. Both average quantities are found to grow logarithmically with the linear size of the sample. Consequently, the energy clusters are not compact: for the diluted model they are isotropic and tree-like, while for the random model they are quasi-one-dimensional.