Network Strength Research Articles

Differential effects of specific emotions on spatial decision-making: evidence from cross-frequency functionally independent brain networks.

Emotions significantly shape the way humans make decisions. However, the underlying neural mechanisms of this influence remain elusive. In this study, we designed an experiment to investigate how emotions (specifically happiness, fear, and sadness) impact spatial decision-making, utilizing EEG data. To address the inherent limitations of sensor-level investigations previously conducted, we employed standard low-resolution brain electromagnetic tomography and functional independent component analysis to analyze the EEG data at the cortical source level. Our findings showed that across various spectral-spatial networks, positive emotion activated the decision-making network in the left middle temporal gyrus and inferior temporal gyrus, in contrast to negative emotions. We also identified the common spectral-spatial networks and observed significant differences in network strength across emotions. These insights further revealed the important role of the gamma-band prefrontal network. Our research provides a basis for deciphering the roles of brain networks in the impact of emotions on decision-making.

Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia.

To assess the interplay among psychopathological symptoms and real-life functioning, and to further detect their influence with violent behavior in patient with schizophrenia. A sample of 1,664 patients with post-violence assessments and their propensity score-matched controls without violence from a disease registration report system of community mental health service in Guangdong, China, were studied by network analysis. Ising-Model was used to estimate networks of psychopathological symptoms and real-life functioning. Then, we tested whether network properties indicated the patterns of interaction were different between cases and controls, and calculated centrality indices of each node to identify the central nodes. Sensitivity analysis was conducted to examine the difference of interaction patterns between pre-violence and post-violence assessments in violence cases. Some nodes in the same domain were highly positive interrelations, while psychopathological symptoms were negatively related to real-life functioning in all networks. Many symptom-symptom connections and symptom-functioning connections were disconnected after the violence. The network density decreased from 23.53% to 12.42% without statistical significance (p = 0.338). The network structure, the global network strength, and the global clustering coefficient decreased significantly after the violence (p < 0.001, p = 0.019, and p = 0.045, respectively). Real-life functioning had a higher node strength. The strength of sleeping, lack of spontaneity and flow of conversation, and preoccupation were decreased in post-violence network of patients. The decreasing connectivity may indicate an increased risk of violence and early warning for detecting violence. Interventions and improving health state based on nodes with high strength might prevent violence in schizophrenia patients.

Detection of violence incitation expressions in Urdu tweets using convolutional neural network

The popularity and widespread use of social media are constantly generating unmonitored data, spreading unwanted content such as hate speech and expressions that incite violence. Automatic detection of violence incitation is a challenging task and to the best of our knowledge, Urdu language has been completely neglected. Therefore, a robust framework is proposed for identifying expressions exhibiting violence incitation in Urdu tweets. The potentials of the semantic, word embeddings, and language models are explored to learn contextualized representations of the violence incitation in Urdu tweets. In addition, the strength of the 1-Dimensional Convolutional Neural Network (1D-CNN) is exploited by tunning its parameters on the newly proposed annotated Urdu corpus. The annotated dataset consists of 4808 tweets manually collected from Pakistani Twitter accounts. The performance of 1D-CNN with word uni-gram, Urdu Bidirectional Encoder Representations from Transformer (Urdu-BERT), and Urdu- Robustly Optimized BERT Approach (Urdu-RoBERTa) models is compared to fine-tuned Urdu-RoBERTa, Bidirectional Long short-term memory (BiLSTM), Convolutional BiLSTM (CBi-LSTM), and six state-of-the-art Machine Learning (ML) models. The results reveal that the 1D-CNN with word uni-gram model shows benchmark performance by demonstrating 89.84% accuracy and 89.80% macro f1-score. Furthermore, it outperforms all comparable models and achieves 89.76% f1-score for the violence class, and 89.84% f1-score for not-violence class identification. The uniqueness of the proposed model is evaluated using MARS shine-through and MARS occlusion metrics and the CNN model outperformed the others. The MARS metrics facilitate evaluation and visualization of the classifier performance in terms of capturing unique true positive samples that are not predicted by other models. The findings of the proposed framework are very supportive for further investigation in this domain.

Network Structure of Childhood Trauma, Bodily Disturbances, and Schizotypy in Schizophrenia and Nonclinical Controls.

Exposure to childhood trauma has been linked to the development of psychosis and bodily self-disturbances, 2 hallmarks of schizophrenia (SZ). Prior work demonstrated that bodily disturbances serve as a bridge between childhood trauma and SZ symptomatology, but the diagnostic specificity of these connections remains unknown. This study uses network analysis to bridge this gap by comparing the interplays between childhood trauma, bodily self-disturbances, and schizotypy in clinical and general populations. Networks were constructed to examine the relationships between schizotypy (Schizotypal Personality Questionnaire; SPQ), bodily self-disturbances (Perceptual Aberration Scale; PAS), and childhood trauma (Childhood Trauma Questionnaire, CTQ) in 152 people with SZ and 162 healthy comparison participants (HC). The Fused Graphical Lasso was used to jointly estimate the networks in the 2 groups and the structure and strength of the networks were compared. Node centrality and shortest paths between CTQ, PAS, and schizotypy were examined. When comparing SZ and HC, the network of bodily self-disturbances, childhood trauma, and schizotypy were similarly structured, but the network was significantly stronger in SZ than HC. In both groups, bodily self-disturbances were on one of the shortest paths between childhood trauma to schizotypal experiences. Our findings revealed reliable associations between childhood trauma, bodily self-disturbance, and schizotypy, with bodily disturbances acting as a bridge from childhood trauma to schizotypy. The elevated strength of the SZ network indicates a more highly interconnected, and therefore reactive network in which exposure to childhood trauma can more easily activate bodily disturbances and schizotypy.

Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

Network analytical investigation of relationships between symptoms of common mental disorders among refugees and asylum seekers in Türkiye.

Forcibly displaced people, such as refugees and asylum-seekers (RAS), are at higher risk of mental disorders, mainly post-traumatic stress disorder (PTSD), depression and anxiety. Little is known about the complex relationships between these mental disorders among culturally and linguistically diverse RAS. To investigate this, the present study applied a novel network analytical approach to examine and compare the central and bridge symptoms within and between PTSD, depression and anxiety among Afghan and Syrian RAS in Türkiye. A large-scale online survey study with 785 Afghan and 798 Syrian RAS in Türkiye was conducted in 2021. Symptoms of PTSD (the short form of Post-Traumatic Stress Disorders Checklist [PCL-5]), depression and anxiety (Hopkins Symptoms Checklist-25) [HSCL-25]) were measured via self-administrated validated instruments. We conducted network analysis to identify symptoms that are most strongly connected with other symptoms (central symptoms) and those that connect the symptoms of different disorders (bridge symptoms) in R Studio using the qgraph package. Overall, Afghans and Syrians differed in terms of network structure, but not in network strength. Results showed that feeling blue, feeling restless and spells of terror or panic were the most central symptoms maintaining the overall symptom structure of common mental disorders among Afghan participants. For Syrian participants, worrying too much, feeling blue and feeling tense were identified as the central symptoms. For both samples, anger and irritability and feeling low in energy acted as a bridge connecting the symptoms of PTSD, depression and anxiety. The current findings provide insights into the interconnectedness within and between the symptoms of common mental disorders and highlight the key symptoms that can be potential targets for psychological interventions for RAS. Addressing these symptoms may aid in tailoring existing evidence-based interventions and enhance their effectiveness. This contributes to reducing the overall mental health burden and improving well-being in this population.

Water-air interface revisited by means of path-integral ab initio molecular dynamics.

Although nuclear quantum effects (NQEs) have been considered on bulk liquid water, the impact of these latter on the air-water interface has not yet been reported. Herein, by performing and comparing ab initio molecular dynamics (AIMD) and path integral AIMD (PI-AIMD) simulations, we reveal the impact of NQEs on structural, dynamical and electronic properties as well as IR spectra of the air-water interface at room temperature. NQEs, being able to describe a more accurate proton delocalization in H-bonded system than AIMD, reveal a different structural arrangement and dynamical behaviour of both bulk and interfacial water molecules in comparison to AIMD results. A more de-structured and de-bound water arrangement and coordination are identified when the quantum nature of nuclei are considered for both bulk and interfacial water molecules. Structural properties, such as inter-/intra-molecular bond lengths, coordination numbers and H-bonding angles of bulk and interfacial water molecules here calculated, are affected by NQEs mitigating the overstructured description given by AIMD. Further evidences of an AIMD overstructured description of bulk water are in the computed IR spectra, where an increased absorption peak intensity and an increased strength of the hydrogen-bond network are alleviated by NQEs. In addition, NQEs show a valuable impact on the electronic structure of the air-water interface, reducing the total valence bandwidth and the electronic energy band-gap when passing from bulk to interfacial water. This work proves how NQEs significantly affect properties and features of the air-water interface, that are essential to accurately describe H-bonded interfacial systems.

Causal Brain Network in Clinically-Annotated Epileptogenic Zone Predicts Surgical Outcomes of Drug-Resistant Epilepsy.

Patients with drug-resistant epilepsy (DRE) are commonly treated using neurosurgery, while its success rate is limited with approximately 50%. Predicting surgical outcomes is currently a prominent topic. The DRE is recognized as a network disorder involving a seizure triggering mechanism within epileptogenic zone (EZ); however, a systematic exploration of the EZ causal network remains lacking. This paper will advance DRE study by: (1) developing a novel causal coupling algorithm, "full convergent cross mapping (FCCM)" to improve the quantization performance; (2) characterizing the DRE's multi-frequency epileptogenic network by FCCM calculation of ictal iEEG; (3) predicting surgical outcomes using network features and machine learning. Numerical validations demonstrate the FCCM's superior quantization in terms of nonlinearity, accuracy, and stability. A multicenter cohort containing 22 DRE patients with 81 seizures is included. Based on the Mann-Whitney-U-test, coupling strength of the epileptogenic network in successful surgeries is significantly higher than that of the failed group, with the most significant difference observed in α -iEEG network (p = 1.52e - 07 ) Other clinical covariates are also considered and all th α -iEEG networks demonstrate consistent differences comparing successful and failed groups, with p = 0.014 and 9.23e - 06 for lesional and non-lesional DRE, p = 2.32e - 05, 0.0074 and 0.0030 for three clinical centers CHFU, JHU and NIH. Using FCCM features and 10-fold cross validation, the SVM achieves the highest accuracy of 87.65% in predicting surgical outcomes. The epileptogenic causal network is a reliable biomarker for estimating DRE's surgical outcomes. The proposed approach is promising to facilitate DRE precision medicine.

Dispersion, ionic bonding and vibrational shifts in phospho-aluminosilicate glasses.

Understanding the relationships between structure and properties of aluminosilicate glasses is of interest in magmatic studies as well as for glass applications as mechanical or optical components. Glass properties may be tailored by the incorporation of additional elements, and here we studied the effect of phosphate incorporation on refractive index and the degree of ionic bonding in aluminosilicate glasses. The studied glasses in the system SiO2-Al2O3-Na2O-P2O5 had a metaluminous composition (Al:Na = 1) with the content of SiO2 ranging from 50 to 70 mol% and of P2O5 from 0 to 7.5 mol%. Refractive index was measured at four wavelengths from visible to near-infrared and found to decrease both with increasing P2O5 content (at the expense of NaAlO2) and with increasing SiO2 content (by substitution of SiO4 for AlO4 groups). This trend correlated with a decrease in density while, additionally, the formation of Al-O-P bonds with an SiO2-like structure may account for this change. The degree of ionic bonding, assessed via optical basicity and oxygen polarisability, decreased with increasing P2O5 and SiO2 content. Despite the complexity of the studied glasses, oxygen polarisability and optical basicity were found to follow Duffy's empirical equation for simple oxide glasses. In the high frequency infrared and Raman spectra, band shifts were observed with increasing P2O5 and SiO2 content. They indicated changing average bond strength of the glass network and showed a linear correlation with optical basicity.

Online Preventive Control for Transmission Overload Relief Using Safe Reinforcement Learning With Enhanced Spatial-Temporal Awareness

The risk of transmission overload (TO) in power grids is increasing with the large-scale integration of intermittent renewable energy sources. An effective online preventive control schemes proves to be vital in safeguarding the security of power systems. In this paper, we formulate the online preventive control problem for TO alleviation as a constrained Markov decision process (CMDP), targeted to reduce the load rate of overloaded lines by implementing generation re-dispatch, transmission and busbar switching actions. The CMDP is solved with a state-of-the-art safe deep reinforcement learning method, based on the computationally efficient interior-point policy optimization (IPO), which facilitates desirable learning behavior towards constraint satisfaction and policy improvement simultaneously. The performance of IPO method is further improved with an enhanced perception of spatial-temporal correlations in power gird nodal and edge features, combining the strength of edge conditioned convolutional network and long short-term memory network, fostering more effective and robust preventive control policies to be devised. Case studies on a real-world system and a large-scale system validate the superior performance of the proposed method in TO alleviation, constraint handling, uncertainty adaptability and stability preservation, as well as its favorable computational performance, through benchmarking against both model-based and reinforcement learning-based baseline methods.

Intrusion Detection in IoT Networks Using Machine Learning Techniques

The advent of the IoT has undeniably transformed the manner in which we connect with the world innovative solutions across a multitude of sectors. However, the proliferation of IoT applications has brought to the fore a complex and multifaceted problem that warrants immediate attention: the security and energy efficiency challenges within IoT networks. As the number of IoT devices continues to surge and their applications diversify, the demand for ubiquitous and secure data access has never been greater. These applications, ranging from healthcare monitoring to industrial automation, require not only real-time data processing but also the assurance of data security and privacy. So, the research explores the machine learning techniques application for intrusion detection in IoT networks, aiming to enhance security in the rapidly evolving landscape of interconnected devices. The research emphasizes the significance of proactive measures in safeguarding IoT ecosystems and highlights the potential of machine learning to detect and mitigate intrusions effectively. In this research, a model was developed for detecting anomalies in Internet of Things networks using artificial neural networks. The research aims to develop and evaluate an effective intrusion detection model for IoT networks by leveraging machine learning techniques. The purpose is to enhance the security posture of interconnected devices, addressing the unique challenges posed by the dynamic and heterogeneous nature of IoT environments. The study tries to contribute important experiences and functional answers to reinforce the strength of IoT networks against developing digital dangers.

Impact of Lallemantia royleana seed mucilage on the physicochemical, textural, rheological, and in-mouth breakdown behavior properties of cold-set whey protein isolate emulsion-filled gel

This work focused on the Lallemantia royleana seed mucilage (LRSM) effect on the rheological, physicochemical, and microstructural attributes, and the in vivo oral breakdown of cold-set emulsion-filled gel (EFG) based on the whey protein isolate (WPI). Emulsions containing 0.1 and 0.5% LRSM displayed the largest and smallest droplet sizes (3.38 vs. 1.26 μm) and polydispersity index (0.60 vs. 0.23), respectively. Also, the 0.1–0.5% LRSM-WPI EFGs compared to the control had greater elastic modulus in the linear viscoelastic (LVE) region (7006.1–7167.8 vs. 4438.3 Pa), flow-point stress (484.6–540.1 vs. 327.8 Pa), network strength (7227.8–7451.2 vs. 5380.1 Pa.s1z), network extension (10.91–11.44 vs. 10.05), lower LVE limit (1.74–2.26 vs. 2.53 %), same loss tangent (0.17), similar frequency dependence of elastic modulus (0.087–0.091 vs. 0.095), and inhomogeneous microstructures. Hardness, springiness, and chewiness at 0.1% and adhesiveness and cohesiveness at 0.5% LRSM-WPI EFGs were the highest. Raising the LRSM from 0% to 0.5% improved the water-holding capacity (75.49%–87.68%), swelling ratio (0.20%–2.59%), and decreased syneresis (11.08%–9.13%). The 0.5% LRSM-WPI EFG compared to the control needed a higher chew duration (16.22 vs. 11.80 s) and chew cycle (24.76 vs. 18.31), and its bolus had a smaller particle size (0.94 vs. 1.65 mm).

Relationships between medical student wellness, self-efficacy, and academic performance during the "post-COVID" period.

This study was a part of a longitudinal study investigating the relationships between medical student wellness, self-efficacy, and performance. Eighty-two eligible students were asked to complete online surveys during their second (M2) and third (M3) years. Performance outcomes included scores on various summative assessments during the M.D. program. Wellness survey results indicated that the sample of 38 M2 and 28 M3 students were overall well and self-efficacious, and they broadly maintained similar wellness characteristics across their medical education despite COVID-19 disruptions. Twenty-three students completed both surveys, and a paired analysis for this subgroup showed modest increases in stress and burnout in the M3 year. Notable correlations were observed between self-efficacy for academic work and a whole range of wellness variables for M2 students. M2 academic performance was modestly correlated to self-efficacy (rs = 0.38, P = 0.02, n = 38) and student burnout (rs = -0.34, P = 0.04, n = 38). In contrast, for the M3 students there was little correlation between wellness, clinical self-efficacy, and clinical performance, with the only significant relationships observed to be between overall clinical self-efficacy and the strength of social networks (rs = 0.41, P = 0.03, n = 28) and between scores for postencounter notes during Objective Structure Clinical Examinations (OSCEs) and self-efficacy in evidence-based medicine (rs = 0.44, P = 0.02, n = 28). In conclusion, 1) students remained generally well throughout the post-COVID period, and 2) self-efficacy for academic work is a good predictor of student wellness and performance during the preclerkship period but not during clinical training.NEW & NOTEWORTHY This study followed a group of medical students through the "post-COVID" period to assess their wellness as they transitioned from basic sciences to clinical training. We found that their wellness and belief in their ability to succeed (self-efficacy) remained strong, showing their resiliency. We observed correlations between self-efficacy and their level of wellness and academic performance during basic science classes but not during clinical training.

Effect of MnO addition on chemical durability, glass-crystal transition, thermal, structural, optical, EPR, and color properties of Na2O-CaO-P2O5 ternary glass system

This paper investigates the effect of CaO substitution for MnO on the physicochemical properties in 25Na2O-(30-x)CaO-xMnO-45P2O5 (0 ≤ x ≤ 30 mol%) glass system. These materials were elaborated by the conventional melt-quenching technique at 1050 °C. Free-manganese glass is colorless, while the glasses containing manganese are purple. X-ray diffraction (XRD) confirmed the amorphous state of the studied glasses and showed that the annealing of these glasses leads to a mixture of crystalline phases. Replacing Ca-O bonds with more covalent Mn-O ones in the vitreous matrix increased density (ρ) and glass transition temperature (Tg), indicating the strength of the glass network. Infrared and Raman spectroscopies revealed the existence of PO3, PO2 and PO4 units with P-O-P linkages in the glassy network. EPR spectra exhibited absorptions centered at g ≈ 2, characteristic of isolated Mn2+ ions in distorted octahedral coordination and the optical absorption spectra disclosed bands due to Mn2+ and Mn3+ ions. Decreasing the band gap energy values (Eg) with increasing MnO content agrees with strengthening the vitreous network. Chemical durability results showed the existence of ionic exchanges between distilled water and the studied glasses, and good water resistance is detected for glasses with low Mn content (x = 5 and 10 mol%).

The scalp time-varying network of auditory spatial attention in “cocktail-party” situations

Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.

AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems.

Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models can generate hypotheses about the neurocomputations underlying cortical processing that are grounded in macro- and mesoscopic anatomical properties of the network's biological counterpart. Whereas, goal-driven modeling is already becoming prevalent in the neuroscience of perception, its application to the sensorimotor domain is currently hampered by the complexity of the methods required to train models comprising the closed sensation-action loop. This paper describes AngoraPy, a Python library that mitigates this obstacle by providing researchers with the tools necessary to train complex recurrent convolutional neural networks that model the human sensorimotor system. To make the technical details of this toolkit more approachable, an illustrative example that trains a recurrent toy model on in-hand object manipulation accompanies the theoretical remarks. An extensive benchmark on various classical, 3D robotic, and anthropomorphic control tasks demonstrates AngoraPy's general applicability to a wide range of tasks. Together with its ability to adaptively handle custom architectures, the flexibility of this toolkit demonstrates its power for goal-driven sensorimotor modeling.

Effect of intracranial electrical stimulation on dynamic functional connectivity in medically refractory epilepsy.

The objective of this study was to explore the distributed network effects of intracranial electrical stimulation in patients with medically refractory epilepsy using dynamic functional connectivity (dFC) and graph indicators. The time-varying connectivity patterns of dFC (state-based metrics) as well as topological properties of static functional connectivity (sFC) and dFC (graph indicators) were assessed before and after the intracranial electrical stimulation. The sliding window method and k-means clustering were used for the analysis of dFC states, which were characterized by connectivity strength, occupancy rate, dwell time, and transition. Graph indicators for sFC and dFC were obtained using group statistical tests. DFCs were clustered into two connectivity configurations: a strongly connected state (state 1) and a sparsely connected state (state 2). After electrical stimulation, the dwell time and occupancy rate of state 1 decreased, while that of state 2 increased. Connectivity strengths of both state 1 and state 2 decreased. For graph indicators, the clustering coefficient, k-core, global efficiency, and local efficiency of patients showed a significant decrease, but the brain networks of patients exhibited higher modularity after electrical stimulation. Especially, for state 1, there was a significant decrease in functional connectivity strength after stimulation within and between the frontal lobe and temporary lobe, both of which are associated with the seizure onset. Our findings demonstrated that intracranial electrical stimulation significantly changed the time-varying connectivity patterns and graph indicators of the brain in patients with medically refractory epilepsy. Specifically, the electrical stimulation decreased functional connectivity strength in both local-level and global-level networks. This might provide a mechanism of understanding for the distributed network effects of intracranial electrical stimulation and extend the knowledge of the pathophysiological network of medically refractory epilepsy.

Underused Marine Resources: Sudden Properties of Cod Skin Gelatin Gel.

The main object of this work was to characterize the structure and properties of laboratory-made fish gelatin from cod skin in comparison with known commercial gelatins of fish and mammalian origin. This is one way we can contribute to the World Food Program and characterize foodstuff resources from alternative natural sources. Our research was based on the combination of an expanded set of complementary physical-chemical methods to study the similarities and distinctions of hydrogels from traditional and novel gelatin sources from underused marine resources. In this work, we have compared the morphology, supramolecular structure and colloid properties of two commercial (mammalian and fish) gelatins with gelatin we extracted from cold-water cod skin in laboratory conditions. The obtained results are novel, showing that our laboratory-produced fish gelatin is much closer to the mammalian one in terms of such parameters as thermal stability and strength of structural network under temperature alterations. Especially interesting are our experimental observations comparing both fish gelatins: it was shown that the laboratory-extracted cod gelatin is essentially more thermally stable compared to its commercial analogue, being even closer in its rheological properties to the mammalian one.

Analisis Daya Saing Industri Koran di Kota Medan (Pendekatan SWOT)

This research aims to understand the problems faced by the newspaper industry in facing changes in consumer behavior and competition from digital media. Although newspapers remain a print medium, challenges arise from the transition of readers to digital platforms. The method used is descriptive qualitative research by collecting data from field observations and interviews with informants related to the newspaper industry. SWOT analysis identifies factors that influence the newspaper industry, from the strength of national networks to technological limitations. This literature review describes the opportunities and threats for the newspaper industry, while emphasizing the importance of adaptation to digital platforms and strong collaboration to compete in the modern media era. The research results identify internal and external factors that influence the newspaper industry in Medan, including strengths, weaknesses, opportunities, and threats.

Behavioral functional networks underlying the effect of Alzheimer’s pathology on cognition

AbstractBackgroundTau pathology is associated with impaired cognitive function in both aging and Alzheimer’s disease (AD), but the mechanisms by which tau and cognition are linked remain unclear. We hypothesized that the integrity of behaviorally‐defined functional brain networks would help explain the relationship between tau and cognitive performance.MethodResting state fMRI, tau PET, and Ab PET were collected in two independent cohorts: 120 cognitively normal older adults from the Berkeley Aging Cohort Study (BACS; 77.6 ± 6.4 yrs), and 174 cognitively normal and MCI individuals from ADNI3 (72.8 ± 8.3 yrs). For each cohort, functional connectivity (FC) for each ROI‐ROI pair of a 246‐region atlas was computed, and LASSO+BIC regression of these values against episodic memory (EM) and executive function (EF) composite measures was used to define a sparse subset of connections positively associated with each cognitive measure. Overall network strength was computed as the sum of FC values for all selected ROI‐ROI pairs.ResultEM and EF networks were identified within each cohort, with Dice similarity coefficients indicating greater overlap of network regions within cohorts (BACS networks: 0.43; ADNI: 0.33) than within cognitive measures between cohorts (EM networks: 0.25; EF: 0.23; Figure 1). Adjusting for age and sex, lower EM network strength was associated with greater meta‐temporal ROI tau in both BACS (b = ‐4.05, p = .014) and ADNI (b = ‐2.20, p &lt;.001), whereas lower EF network strength was related to meta‐ROI tau in ADNI only (b = ‐1.80, p &lt;.001; Figure 2). In addition, causal mediation analysis revealed that the effect of meta‐ROI tau on EM performance was mediated by EM network strength in both BACS (p = .017) and ADNI (p = .002), but EF network strength was a significant mediator in ADNI only (p &lt;.001; Figure 3).ConclusionGreater AD pathology is associated with reduced integrity of EM‐related networks in older adults, a relationship that may also extend to EF‐related networks as individuals progress from cognitively normal to MCI. Given that network strength was observed to mediate the relationship between tau pathology and cognition, behavioral brain networks may be a key mechanism in the effect of neuropathology on cognitive function in aging and AD.