Search Results (15)

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Background/Objectives: The Mood Disorder Questionnaire (MDQ) is a widely used tool for the early detection of Bipolar Disorder (BD), yet its diagnostic accuracy remains debated. In particular, the MDQ often yields false positives in individuals with anxiety, stress-related, or personality disorders, raising questions about its clinical utility. This study aimed primarily to evaluate the sensitivity, specificity, and predictive values of the MDQ in identifying BD within a large, community-based sample using structured clinical interviews. Additionally, we explored the construct of DYMERS (Dysregulation of Mood, Energy, and Social Rhythms Syndrome), a proposed condition characterized by mood instability, hyperactivation traits, and rhythm dysregulation among MDQ-positive individuals without a formal psychiatric diagnosis. Methods: A total of 4999 adults were surveyed across six Italian regions using a stratified random sampling method. Psychiatric diagnoses were established using DSM-IV-TR criteria via the Advanced Neuropsychiatric Tools and Assessment Schedule (ANTAS). The MDQ was administered face to face in its validated Italian version, with a positivity cut-off of ≥7. The MDQ exhibited low sensitivity and high specificity (0.962; 95% CI: 0.961–0.963). Results: Among 2337 analyzable cases, the MDQ showed high specificity (96.2%) but low sensitivity (42.9%) for BD, indicating limited effectiveness as a screening tool. In clinical terms, this implies that while MDQ-positive individuals are unlikely to be false positives, a substantial proportion of true BD cases are not identified. Notably, a significant subgroup of MDQ-positive individuals without psychiatric diagnoses displayed features consistent with DYMERS. Conclusions: Our findings confirm the limited screening value of the MDQ for BD in community samples. However, MDQ positivity may help identify a broader spectrum of mood and rhythm dysregulation not captured by current diagnostic systems. Future research should focus on validating DYMERS as a clinical entity and on developing targeted diagnostic instruments capable of capturing this emerging dimension of psychopathology. Full article

What is the impact of geopolitics on the geometry of global trade? What is the key structural role that led to the emergence of the US–China trade bipolarity? Here, we study the geometry of international trade, taking into account not only the direct but also the indirect trade relations. We consider the self-weight of each country as an indicator of its intrinsic robustness to exogenous shocks. We assess the vulnerability of a country to potential demand or supply shocks based on the entropy (diversification) of its trade flows. By considering the indirect trade relations, we found that the key structural role that led to the emergence of the US–China trade bipolarity is that of the intermediary hub that acts as a “bridge” between different trade clusters. The US and China occupied key network positions of high betweenness centrality as early as 2010. As international trade was increasingly dependent on only these two intermediary trade hubs, this fact led to geopolitical tensions such as the US–China trade war. Therefore, betweenness centrality could serve as a structural indicator, forewarning of possible upcoming geopolitical tensions. The US–China trade bipolarity is also strongly present in self-weights, where a race in terms of their intrinsic robustness to exogenous shocks is more than evident. It is also interesting that the US and China are not only the top shock spreaders but also the most susceptible to shocks. However, China can act more as a shock spreader than a shock receiver, while for the USA, the opposite is true. Regarding the impact of geopolitics, we found that the Russia–Ukraine conflict forced Ukraine to diversify both its exports and imports, aiming to lower its vulnerability to possible shocks. Finally, we found that international trade is becoming increasingly oligopolistic, even when indirect trade relationships are taken into account, thus indicating that a “Deep Oligopoly” has formed. Full article

Orally administered antipsychotic drugs are the first line of treatment in the management of psychotic disorders that affect millions of people globally and have a tremendous impact on patient and family lives, such as schizophrenia and bipolar disorder. Nevertheless, adverse drug reactions hinder clinical outcomes, resulting in patient non-compliance. The design and implementation of adequate formulation strategies for enhancing drug delivery and targeting to the brain has been a significant challenge, mainly due to the restrictive properties of the blood–brain barrier. However, recent pharmacokinetic and pharmacodynamic in vivo assays confirmed that there is evidence of the advantage of the intranasal route when compared to oral and intravenous administration, as it allows the possibility of direct nose-to-brain transport via neuronal olfactory and trigeminal pathways, reducing systemic side effects, and maximizing therapeutic outcomes. In addition, the formulation of polymeric and solid lipid nanoparticles, nanostructured lipid carriers, nanoemulsions, nanoemulgels, nanosuspensions, niosomes, Spanlastics and polymeric mixed micelles is a promising approach since they have a reduced particle size, ideal for nose-to-brain delivery, stability, high encapsulation efficiency, enhanced drug solubility, and drug protection from enzymatic degradation. Nevertheless, it is essential to continue research in this field, conducting more long-term studies with greater uniformity so that the true potential of these formulations can be assessed and a transposition into the pharmaceutical industry is someday possible. Full article

The true bipolar modular multilevel converter-based multi-terminal direct current (MMC-MTDC) DC transmission line is prone to single-pole grounding fault, which may cause overload and overcurrent of the non-fault DC line with fault poles, resulting in system protection misoperation and system collapse. Therefore, the power transfer control strategy should be adopted to improve system stability. In addition, considering that the commutator stations of true bipolar MMC-MTDC system may have unipolar faults, it is necessary to adopt the control strategy of inter-pole power transfer or inter-station power transfer to improve the transmission capacity of the system under fault conditions. In this paper, a power transfer control strategy is proposed, which is widely applicable to MMC-MTDC system. In the case of line fault, the power transfer takes into account the line power margin and the power margin of converter station. The inter-pole power transfer is better than the inter-station power transfer under the converter station fault condition, and the inter-station power transfer takes into account the priority of the power margin of the converter station. At the same time, based on the Zhangbei four-terminal flexible direct current transmission project, the Zhangbei four-terminal flexible direct current transmission system is built by using PSCAD/EMTDC, and the flexibility and effectiveness of the proposed strategy are verified by simulation. Full article

Background: Individuals with psychiatric disorders (PD) have a high prevalence of tobacco use. Therefore, we assessed the hazard of receiving a tobacco-related (TR) cancer diagnosis among individuals with PD. Methods: Several population-based provincial databases were used to identify individuals in BC diagnosed with depression, schizophrenia, bipolar disorder, anxiety disorders, or multiple PD between 1990 and 2013. A primary population proxy comparison group (appendicitis) was also identified and matched to the psychiatric cohort based on age at cohort entry, gender, year of cohort entry, and postal code. We linked individuals in the cohort and comparison groups with the BC Cancer Registry. Using a competing risks approach, we estimated the effect of having a PD on the risk of receiving a TR cancer diagnosis, in light of the competing risk of mortality. Results: In total, 165,289 patients were included. Individuals with depression (HR = 0.81; p < 0.01; 95% CI: 0.73–0.91), anxiety disorders (HR = 0.84; p = 0.02; 95% CI: 0.73–0.97), or multiple PD (HR = 0.74; p < 0.01; 95% CI: 0.66–0.83) had a statistically significant lower risk of a TR cancer diagnosis compared to the comparison group. Individuals with schizophrenia (HR = 0.86; p = 0.40; 95% CI: 0.62–1.21) or bipolar disorder (HR = 0.58; p = 0.12; 95% CI: 0.29–1.14), however, showed no evidence of a statistically significant difference from the comparison group. Interpretation: We found that individuals with depression, anxiety disorders, or multiple PD diagnoses had a significantly reduced risk of receiving a tobacco-related cancer diagnosis. These results were unexpected and could be explained by individuals with a PD having barriers to a cancer diagnosis rather than a true decreased incidence. Full article

The formation and maintenance of the mitotic spindle during cell division requires several microtubule-interacting motor proteins. Members of the kinesin-5 family play an essential role in the bipolar organization of the spindle. These highly conserved, homotetrameric proteins cross-link anti-parallel microtubules and slide them apart to elongate the spindle during the equal separation of chromosomes. Whereas vertebrate kinesin-5 proteins are well studied, knowledge about the biochemical properties and the function of plant kinesin-5 proteins is still limited. Here, we characterized the properties of AtKRP125b, one of four kinesin-5 proteins in Arabidopsis thaliana. In in vitro motility assays, AtKRP125b displayed the archetypal characteristics of a kinesin-5 protein, a low velocity of about 20 nm·s⁻¹, and a plus end-directed, processive movement. Moreover, AtKRP125b was able to cross-link microtubules and to slide them apart, as required for developing and maintaining the mitotic spindle. In line with such a function, GFP-AtKRP125b fusion proteins were predominantly detected in the nucleus when expressed in Arabidopsis thaliana leaf protoplasts or Nicotiana benthamiana epidermis cells and analyzed by confocal microscopy. However, we also detected GFP signals in the cytoplasm, suggesting additional functions. By generating and analyzing AtKRP125b promoter-reporter lines, we showed that the AtKRP125b promoter was active in the vascular tissue of roots, lateral roots, cotyledons, and true leaves. Remarkably, we could not detect promoter activity in meristematic tissues. Taken together, our biochemical data support a role of AtKRP125b in mitosis, but it may also have additional functions outside the nucleus and during interphase. Full article

The central objective of the proposed work in this research is to introduce the innovative concept of an m-polar fuzzy set (m-PFS) in semigroups, that is, the expansion of bipolar fuzzy set (BFS). Our main focus in this study is the generalization of some important results of BFSs to the results of m-PFSs. This paper provides some important results related to m-polar fuzzy subsemigroups (m-PFSSs), m-polar fuzzy ideals (m-PFIs), m-polar fuzzy generalized bi-ideals (m-PFGBIs), m-polar fuzzy bi-ideals (m-PFBIs), m-polar fuzzy quasi-ideals (m-PFQIs) and m-polar fuzzy interior ideals (m-PFIIs) in semigroups. This research paper shows that every m-PFBI of semigroups is the m-PFGBI of semigroups, but the converse may not be true. Furthermore this paper deals with several important properties of m-PFIs and characterizes regular and intra-regular semigroups by the properties of m-PFIs and m-PFBIs. Full article

Response scales in auditory perception assessment are critical for capturing the true responses of listeners. Despite its impact on data, response scales have received the least attention in auditory perception assessment. In this study, the usability of visual analogue scales for auditory perception assessment was investigated. Five response scales (a unipolar visual analogue scale–negated to regular, a unipolar visual analogue scale—regular to negated, a bipolar visual analogue scale–positive to negative, a bipolar visual analogue scale—negative to positive, and a unipolar 11-point scale (ISO/TS 15666:2021)) for auditory perception assessment are presented. Music and traffic noise were presented to 60 university students at two different levels, i.e., 45 and 65 dBA, respectively. A web-based experimental design was implemented, and tablet pads were provided to the respondents to record their responses. The unipolar 11-point scale required the longest response time, followed by the two unipolar visual analogue scales and two bipolar visual analogue scales with statistical significance. All response scales used in this study achieved statistical reliability and sensitivity for the auditory perception assessment. Among the five response scales, the bipolar visual analogue scale (negative to positive) ranked first in reliability over repeated measures, exhibited sensitivity in differentiating sound sources, and was preferred by the respondents under the conditions of the present study. None of the respondents preferred the unipolar 11-point scale. The visual analogue scale was favoured over the traditional unipolar 11-point scale by young educated adults in a mobile-based testing environment. Moreover, the bipolar visual analogue scale demonstrated the highest reliability and sensitivity, and it was preferred the most by the respondents. The semantic labelling direction from negated to regular, or from negative to positive, is preferred over its opposite counterpart. Further research is necessary to investigate the use of response scales for the general public including children and the elderly, as well as that of semantic adjectives and their counterparts for auditory perception assessment. Full article

Attention deficit hyperactivity disorder (ADHD) is a condition that usually has its onset in childhood. Although the disorder persists into adulthood in half of cases, adult ADHD is often not recognized due to different psychopathological characteristics, quite often overlapping with other diagnoses such as mood, anxiety and personality disorders. This is especially true for bipolar disorder (BD), which shares several symptoms with adult ADHD. Moreover, besides an overlapping clinical presentation, BD is often co-occurring in adults with ADHD, with comorbidity figures as high as 20%. This review will focus on the comorbidity between ADHD and BD by exploring the magnitude of the phenomenon and evaluating the clinical and functional characteristics associated with ADHD–BD comorbidity in adults. Finally, the review will address the implications of pharmacologically treating the ADHD–BD comorbidity, providing suggestions in how to treat these complex patients and addressing the issue of treatment-induced manic switch with the use of stimulants and other medications for ADHD. Full article

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images. Full article

Around 800,000 people worldwide die from suicide every year and it’s the 10th leading cause of death in the US. It is of great value to build a mathematic model that can accurately predict suicide especially in high-risk populations. Several different ML-based models were trained and evaluated using features obtained from electronic medical records (EMRs). The contribution of each feature was calculated to determine how it impacted the model predictions. The best-performing model was selected for analysis and decomposition. Random forest showed the best performance with true positive rates (TPR) and positive predictive values (PPV) of greater than 80%. The use of Sertraline, Fentanyl, Aripiprazole, Lamotrigine, and Tramadol were strong indicators for no SREs within one year. The use of Haloperidol, Trazodone and Citalopram, a diagnosis of autistic disorder, schizophrenic disorder, or substance use disorder at the time of a diagnosis of both PTSD and bipolar disorder, predicted the onset of SREs within one year. Additional features with potential protective or hazardous effects for SREs were identified by the model. We constructed an ML-based model that was successful in identifying patients in a subpopulation at high-risk for SREs within a year of diagnosis of both PTSD and bipolar disorder. The model also provides feature decompositions to guide mechanism studies. The validation of this model with additional EMR datasets will be of great value in resource allocation and clinical decision making. Full article

Distributed systematic grid-connected inverter practice needs to improve insulated gate bipolar transistor (IGBT) stability to ensure the safe operation. This study is to ensure the safety and reliability operation of the IGBT module in symmetry to meet the reliable and stable distributed systematic grid-connected inverter practice and the junction temperature is a parameter to assess its operating state. It is difficult to accurately acquire the IGBT junction temperature to be solved by a single method of combining the test and the modeling. The saturation voltage drop or collector current and module junction temperature data under different power cycles are measured by the power cycle test and the single pulse test. The improved chicken swarm optimization increases the chickens diversity and self-learning ability. The prediction model of the improved chicken swarm optimization-support vector machine is proposed to forecast the module junction temperature. The result showed to compare with the particle swarm optimization-support vector machine model and chicken swarm optimization-support vector machine model and showed the coincidence degree between the proposed model prediction value and the true value is higher. The mean absolute error ratio indicates the proposed model has a smaller error and a better prediction performance. The proposed model has a positive impact on improving the distributed systematic grid-connected inverter industrial development and promotes the new energy usage. Full article

Operations in the Delta-Sigma ( $\Delta \mathrm{\Sigma }$ ) domain are a broad field of research. In this article the main, focus is on applications in control systems, nevertheless the results are generally applicable for $\Delta \mathrm{\Sigma }$ -signal processing ( $\Delta \mathrm{\Sigma }$ SP) in other fields. As the bit-stream does not have an instantaneous value, algebraic operations cannot be executed directly. The first approaches were made in the 1980s based on small-scale integration logic chips by Kouvaras and by Lagoyannis. Further algebraic operations and other implementations were introduced by Zrilic, by Ng, by Bradshaw and by Homann. Other publications utilize complex networks and operations to achieve the desired algebraic operations. These presented operations can be divided into different operation classes by the based implementation idea. In this paper, the known algebraic operation classes are further developed and new operation classes are presented. All implementations are compared and evaluated. For linear operations in control applications, the introduced Bipolar Interpretation is best rated. It compensates for the signal offset of bipolar bit-streams and results in the best signal quality by mapping the logic values true and false of bit-stream to plus and minus one before the algebraic operation. The output of the algebraic operation is a multibit value, to achieve a bit-stream as output value a third step is taken. The result is modulated by a digital $\Delta \mathrm{\Sigma }$ -modulator ( $\Delta \mathrm{\Sigma }$ -M). For nonlinear operations the most universal implementation is also based on three steps. In the first stage, the bit-streams are processed with short sinc ${}^3$ filters, resulting in multibit values. This signal is processed by digital signal processing (DSP). The output stage is a $\Delta \mathrm{\Sigma }$ -M. For some nonlinear algebraic operations there can be better solutions than DSP, like shown for limiting. In short, this paper gives a detailed overview about different $\Delta \mathrm{\Sigma }$ SP classes for linear and nonlinear operations. Newly presented are the scaling with Bit-Stream Modification, the Bipolar Interpretation class, the nonlinear operation class based on digital signal processing (DSP), the modified multiplication based on Delta Adder and benchmarks of all presented operations. Full article

The psychiatric and neurological aspects of health may present methodological challenges in the diagnosis and treatment of disease. This is especially true for patients whose symptoms indicate the coexistence of multiple sclerosis (MS) and schizophrenia (SCZ). These cases raise critical questions regarding the relationship between the mind and the brain. Studies have noted that patients with MS have an increased risk of developing SCZ or bipolar disorder (BD). It is suggested here that MS and a subgroup of SCZ have similar etiologies. Factors such as gender, ethnicity, geography and season also have an influence on the occurrence of MS and SCZ. This paper aims to examine the differences and similarities between SCZ and MS. For this purpose, scientific papers examining various factors associated with these disorders were reviewed, and similarities and differences in genetic, immunological, seasonal, geographical, and gender-related risk factors and limited similarities in ethnic factors between the two diseases were identified. The findings suggest that subgroups of these two diseases may belong to the same class of disorders. Full article