Dopamine dysregulation in a mouse model of paroxysmal nonkinesigenic dyskinesia.

Paroxysmal nonkinesigenic dyskinesia (PNKD) is an autosomal dominant episodic movement disorder. Patients have episodes that last 1 to 4 hours and are precipitated by alcohol, coffee, and stress. Previous research has shown that mutations in an uncharacterized gene on chromosome 2q33-q35 (which is termed PNKD) are responsible for PNKD. Here, we report the generation of antibodies specific for the PNKD protein and show that it is widely expressed in the mouse brain, exclusively in neurons. One PNKD isoform is a membrane-associated protein. Transgenic mice carrying mutations in the mouse Pnkd locus equivalent to those found in patients with PNKD recapitulated the human PNKD phenotype. Staining for c-fos demonstrated that administration of alcohol or caffeine induced neuronal activity in the basal ganglia in these mice. They also showed nigrostriatal neurotransmission deficits that were manifested by reduced extracellular dopamine levels in the striatum and a proportional increase of dopamine release in response to caffeine and ethanol treatment. These findings support the hypothesis that the PNKD protein functions to modulate striatal neuro-transmitter release in response to stress and other precipitating factors

Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices

IntroductionAlgorithmic decision-making systems are widely used in various sectors, including criminal justice, employment, and education. While these systems are celebrated for their potential to enhance efficiency and objectivity, they also pose risks of perpetuating and amplifying societal biases and discrimination. This paper aims to provide an indepth analysis of the types of algorithmic discrimination, exploring both the challenges and potential solutions.MethodsThe methodology includes a systematic literature review, analysis of legal documents, and comparative case studies across different geographic regions and sectors. This multifaceted approach allows for a thorough exploration of the complexity of algorithmic bias and its regulation.ResultsWe identify five primary types of algorithmic bias: bias by algorithmic agents, discrimination based on feature selection, proxy discrimination, disparate impact, and targeted advertising. The analysis of the U.S. legal and regulatory framework reveals a landscape of principled regulations, preventive controls, consequential liability, self-regulation, and heteronomy regulation. A comparative perspective is also provided by examining the status of algorithmic fairness in the EU, Canada, Australia, and Asia.ConclusionReal-world impacts are demonstrated through case studies focusing on criminal risk assessments and hiring algorithms, illustrating the tangible effects of algorithmic discrimination. The paper concludes with recommendations for interdisciplinary research, proactive policy development, public awareness, and ongoing monitoring to promote fairness and accountability in algorithmic decision-making. As the use of AI and automated systems expands globally, this work highlights the importance of developing comprehensive, adaptive approaches to combat algorithmic discrimination and ensure the socially responsible deployment of these powerful technologies

Analyzing Dynamic Changes of Laboratory Indexes in Patients with Acute Heart Failure Based on Retrospective Study

Background. Changes of N-terminal probrain natriuretic peptide (NT-proBNP) have been studied whether in the long term or the short term in patients of acute heart failure (AHF); however, changes of NT-proBNP in the first five days and their association with other factors have not been investigated. Aims. To describe the dynamic changes of relevant laboratory indexes in the first five days between different outcomes of AHF patients and their associations. Methods and Results. 284 AHF with dynamic values recorded were analyzed. Changes of NT-proBNP, troponin T, and C-reactive protein were different between patients with different outcomes, with higher values in adverse group than in control group at the same time points ( < 0.05). Then, prognostic use and risk stratification of NT-proBNP were assessed by receiver-operating characteristic curve and logistic regression. NT-proBNP levels at day 3 showed the best prognostic power (area under the curve = 0.730, 95% confidence interval (CI): 0.657 to 0.794) and was an independent risk factor for adverse outcome (odds ratio, OR: 2.185, 95% CI: 1.584-3.015). Classified changes of NT-proBNP may be predictive for adverse outcomes in AHF patients. Conclusions. Sequential monitoring of laboratory indexes within the first 5 days may be helpful for management of AHF patients

Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost

Chlorophyll-a (Chl-a) concentration is one of the important indicators in water bodies for assessing the ecological health of water quality. In this paper, an OGolden-DBO-XGBoost Chl-a concentration inversion model is proposed using Wuliangsu Lake as the study area, and by combining the Sentinel-2 remote-sensing satellite images and measured Chl-a concentration data in Wuliangsu Lake, the XGBoost model is optimized using the hybrid-strategy-improved dung beetle optimization algorithm (OGolden-DBO), and an OGolden-DBO-XGBoost Chl-a concentration inversion model. The OGolden-DBO-XGBoost model’s coefficients of determination (R2s) were 0.8936 and 0.8850 on the training set and test set, according to the results. The root mean squared errors (RMSEs) were 3.1353 and 2.9659 μg/L, and the mean absolute errors (MAEs) were 1.8918 and 2.4282 μg/L. The model performed well and provided a strong support for the detection of Chl-a concentration in Wuliangsu Lake

Roman domination in regular graphs

AbstractA Roman domination function on a graph G=(V(G),E(G)) is a function f:V(G)→{0,1,2} satisfying the condition that every vertex u for which f(u)=0 is adjacent to at least one vertex v for which f(v)=2. The weight of a Roman dominating function is the value f(V(G))=∑u∈V(G)f(u). The minimum weight of a Roman dominating function on a graph G is called the Roman domination number of G. Cockayne et al. [E. J. Cockayne et al. Roman domination in graphs, Discrete Mathematics 278 (2004) 11–22] showed that γ(G)≤γR(G)≤2γ(G) and defined a graph G to be Roman if γR(G)=2γ(G). In this article, the authors gave several classes of Roman graphs: P3k,P3k+2,C3k,C3k+2 for k≥1, Km,n for min{m,n}≠2, and any graph G with γ(G)=1; In this paper, we research on regular Roman graphs and prove that: (1) the circulant graphs C(n;{1,3})(n≥7,n⁄≡4(mod5)) and C(n;{1,2,…,k})(k≤⌊n2⌋), n⁄≡1 (mod (2k+1)), (n≠2k) are Roman graphs, (2) the generalized Petersen graphs P(n,2k+1)(n≠4k+2,n≡0 (mod 4) and 0≤k≤⌊n2⌋), P(n,1) (n⁄≡2 (mod 4)), P(n,3) (n≥7,n⁄≡3 (mod 4)) and P(11,3) are Roman graphs, and (3) the Cartesian product graphs C5m□C5n(m≥1,n≥1) are Roman graphs

A K-means Algorithm Based On Feature Weighting

Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects. K-means clustering algorithm has been receiving much attention from scholars because of its excellent speed and good scalability. However, the traditional K-means algorithm does not consider the influence of each attribute on the final clustering result, which makes the accuracy of clustering have a certain impact. In response to the above problems, this paper proposes an improved feature weighting algorithm. The improved algorithm uses the information gain and ReliefF feature selection algorithm to weight the features and correct the distance function between clustering objects, so that the algorithm can achieve more accurate and efficient clustering effect. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm clustering results are stable, and the accuracy of clustering is significantly improved

Decoupling Control Strategy of Magnetic Levitation Planar Motor

There is tight electromagnetic and mechanical coupling among 6-degree of freedom (DOF) motions of magnetic levitation planar motor for photolithography. For decoupling control of the 6-DOF motions, this paper presents the current-controlled model of linear motors as the electromagnetic actuators, the decoupled kinetics model based on the modal forces, and the decoupling control strategy of the planar motor. The 6-DOF kinetics models are successfully decoupled by the modal forces and become simple 2nd order linear systems. The X position servo controller of the prototype is designed. The simulation results show that the controller proposed makes the decoupled X position control with the disired performaces and the control system has good immunity and robust stability