Distribution networks will experience more installations of distributed generation (DG) that is unpredictable and stochastic in nature. Greater distributed control and intelligence will allow challenges such as voltage control to be handled effectively. The partitioning of power networks into smaller clusters provides a method to split the control problem into manageable sub-problems. This paper presents a community detection-based partitioning technique for distribution networks considering local DGs, allowing them to be grouped and controlled in a distributed manner by using local signals and measurements. This method also allows each community to control the voltage using only neighboring DGs, and for each community to self-organize to reflect varying DG conditions and to maintain stable control. Simulations demonstrate that the partitioning of the large distribution network is effective, and each community is able to self-organize and to regulate the voltage independently using only its local DGs.
Tom Benhamou, James Cummings, Gabriel Goldberg
et al.
We introduce a new class of ultrafilters which generalizes the well-known class of simple $P$-point ultrafilters. We prove that for any well-founded $σ$-directed partial order $\mathbb{D}$ there is a mild forcing extension where there is an ultrafilter $U$ on $ω$ with a base $\mathcal{B}$ such that $(\mathcal{B},\supseteq^*)\cong \mathbb{D}$. On a measurable cardinal we prove a similar result: relative to a supercompact cardinal, it is consistent that $κ$ is supercompact, and for a $κ^+$-directed well-founded poset $\mathbb{D}$, there is a ${<}κ$-directed closed $κ^+$-cc forcing extension where there is a \emph{normal} ultrafilter $U$ on $κ$ with a base $\mathcal{B}$ such that $(\mathcal{B},\supseteq^*)\cong \mathbb{D}$. These are optimal results in the class of $P$-points and realize every potential structure of a $P$-point. We apply our constructions to obtain ultrafilters with controlled Tukey-type, in particular, an ultrafilter with non-convex Tukey and depth spectra is presented, answering questions from \cite{Benhamou_2024}. Our construction also provides new models where $\mathfrak{u}_κ<2^κ$, answering questions from \cite{Benhamou_Goldberg2025}.
This study investigates the efficiency of electrocoagulation (EC) in removing Tartrazine Yellow (TY) azo dye from synthetic wastewater using aluminium electrodes. The effects of current density, <i>i</i> (0.008–0.024 A cm<sup>−2</sup>), initial solution pH (3.0–7.0), and treatment time, <i>t</i> (10–50 min) on key process parameters, including pH, temperature (<i>T</i>), TY dye concentration (<i>c</i>) and removal efficiency (<i>R</i>), anode consumption, and sludge characterisation were studied. The experiments were conducted in a batch reactor according to the experimental plan developed in Design-Expert software, which was also used for the evaluation of the obtained results. As the EC process progresses, the removal efficiency of the TY dye increases, while the removal dynamics and the final value of <i>R</i> (ranging from about 28% to 99%) depend on the experimental conditions (<i>i</i>, initial pH, and <i>t</i>). A high <i>R</i>-value is reached faster with the application of higher current densities and lower initial pH. This is associated with a higher proportion of carbon and sulphur in the sludge (from the TY dye) after the EC process. Additionally, a mathematical model was developed to predict the experimental data. A numerical optimisation method using response surface methodology (RSM) was applied to determine the optimal operating conditions for TY dye removal. This resulted in the following conditions: pH = 3.37, <i>t</i> = 18.74 min, and <i>i</i> = 0.016 A cm<sup>−2</sup>, achieving a removal efficiency of ≈70%.
Animals and robots must self-right on the ground after overturning. Biology research described various strategies and motor patterns in many species. Robotics research devised many strategies. However, we do not well understand how the physical principles of how the need to generate mechanical energy to overcome the potential energy barrier governs behavioral strategies and 3-D body rotations given the morphology. Here I review progress on this which I led studying cockroaches self-righting on level, flat, solid, low-friction ground, by integrating biology experiments, robotic modeling, and physics modeling.
Joshua Pickard, Ram Prakash, Marc Andrew Choi
et al.
Recent advancements in Large Language Models (LLMs) are transforming biology, computer science, engineering, and every day life. However, integrating the wide array of computational tools, databases, and scientific literature continues to pose a challenge to biological research. LLMs are well-suited for unstructured integration, efficient information retrieval, and automating standard workflows and actions from these diverse resources. To harness these capabilities in bioinformatics, we present a prototype Bioinformatics Retrieval Augmented Digital assistant (BRAD). BRAD is a chatbot and agentic system that integrates a variety of bioinformatics tools. The Python package implements an AI \texttt{Agent} that is powered by LLMs and connects to a local file system, online databases, and a user's software. The \texttt{Agent} is highly configurable, enabling tasks such as Retrieval-Augmented Generation, searches across bioinformatics databases, and the execution of software pipelines. BRAD's coordinated integration of bioinformatics tools delivers a context-aware and semi-autonomous system that extends beyond the capabilities of conventional LLM-based chatbots. A graphical user interface (GUI) provides an intuitive interface to the system.
Chiara Maria Lavinia Loeffler, Omar S. M. El Nahhas, Hannah Sophie Muti
et al.
Abstract Background Homologous recombination deficiency (HRD) is recognized as a pan-cancer predictive biomarker that potentially indicates who could benefit from treatment with PARP inhibitors (PARPi). Despite its clinical significance, HRD testing is highly complex. Here, we investigated in a proof-of-concept study whether Deep Learning (DL) can predict HRD status solely based on routine hematoxylin & eosin (H&E) histology images across nine different cancer types. Methods We developed a deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. As part of our approach, we calculated a genomic scar HRD score by combining loss of heterozygosity (LOH), telomeric allelic imbalance (TAI), and large-scale state transitions (LST) from whole genome sequencing (WGS) data of n = 5209 patients across two independent cohorts. The model’s effectiveness was evaluated using the area under the receiver operating characteristic curve (AUROC), focusing on its accuracy in predicting genomic HRD against a clinically recognized cutoff value. Results Our study demonstrated the predictability of genomic HRD status in endometrial, pancreatic, and lung cancers reaching cross-validated AUROCs of 0.79, 0.58, and 0.66, respectively. These predictions generalized well to an external cohort, with AUROCs of 0.93, 0.81, and 0.73. Moreover, a breast cancer-trained image-based HRD classifier yielded an AUROC of 0.78 in the internal validation cohort and was able to predict HRD in endometrial, prostate, and pancreatic cancer with AUROCs of 0.87, 0.84, and 0.67, indicating that a shared HRD-like phenotype occurs across these tumor entities. Conclusions This study establishes that HRD can be directly predicted from H&E slides using attMIL, demonstrating its applicability across nine different tumor types.
Neurological illnesses such as Alzheimer's disease (AD), Parkinson's disease (PD), and epilepsy (EP) have a significant impact on worldwide health. This study uses network pharmacology and genomic analysis to find shared genes and pathways linked to various illnesses.The STRING database was used to identify shared genes between AD, PD, and EP. Associated proteins of common genes were obtained and imported into Cytoscape to design and analyze networks. Gene enrichment analysis was performed using ShinyGO V0.77. AD, PD, and EP share three genes: KIF5A, NDUFB9, and MT-ND1. Network analysis showed relationships between these genes and their associated proteins. Pathway enrichment study revealed major pathways, including Alzheimer's, Parkinson's disease, Neurodegeneration, and oxidative phosphorylation pathways. The current study revealed genetic interconnectivity of AD, PD, and EP, underlining the role of mitochondrial failure, oxidative stress, and synaptic dysfunction in their development. KIF5A, NDUFB9, and MT-ND1 play critical roles in these pathways, making them attractive therapeutic targets. Indirect interactions between these genes via common proteins such as SNCA and MAPT indicate complicated regulatory networks. Identifying common genes and pathways sheds light on shared mechanisms underlying AD, PD, and EP. Drug repurposing opportunities targeting key proteins like SNCA and MAPT may offer novel therapeutic avenues.
Long QT syndrome type 1 (LQT1) is a rare heart disorder caused by a loss-of-function mutation in the KCNQ1 gene that causes loss of Kv7.1 channel function, which can lead to Palpitations, Syncope, and Sudden cardiac arrest. We derived induced pluripotent stem cells from PBMC of LQT1 patients carrying a pathogenic variant (c.734G>A; p.Gly245Glu). The non-integrative Sendai virus-mediated iPSC reprogramming method was used for iPSC line generation. These iPSC cell lines exhibit stem cell pluripotency, differentiation capability, and cell morphology, resulting in a reliable cell source to study the effects of KCNQ1 mutation in disease-specific cell types.
Oana Mesaros, Madalina Onciul, Emilia Matei
et al.
Acute myeloid leukemia (AML) is a heterogenous malignant hemopathy, and although new drugs have emerged recently, current treatment options still show limited efficacy. Therapy resistance remains a major concern due to its contribution to treatment failure, disease relapse, and increased mortality among patients. The underlying mechanisms of resistance to therapy are not fully understood, and it is crucial to address this challenge to improve therapy. Macrophages are immune cells found within the bone marrow microenvironment (BMME), of critical importance for leukemia development and progression. One defining feature of macrophages is their plasticity, which allows them to adapt to the variations in the microenvironment. While this adaptability is advantageous during wound healing, it can also be exploited in cancer scenarios. Thus, clinical and preclinical investigations that target macrophages as a therapeutic strategy appear promising. Existing research indicates that targeting macrophages could enhance the effectiveness of current AML treatments. This review addresses the importance of macrophages as therapeutic targets including relevant drugs investigated in clinical trials such as pexidartinib, magrolimab or bexmarilimab, but also provides new insights into lesser-known therapies, like macrophage receptor with a collagenous structure (MACRO) inhibitors and Toll-like receptor (TLR) agonists.
The long-term resilient property of ecosystems has been quantified as ecological robustness (RECO) in terms of the energy transfer over food webs. The RECO of resilient ecosystems favors a balance of food webs' network efficiency and redundancy. By integrating RECO with power system constraints, the authors are able to optimize power systems' inherent resilience as ecosystems through network design and system operation. A previous model used on real power flows and aggregated redundant components for a rigorous mapping between ecosystems and power systems. However, the reactive power flows also determine power systems resilience; and the power components' redundancy is part of the global network redundancy. These characteristics should be considered for RECO-oriented evaluation and optimization for power systems. Thus, this paper extends the model for quantifying RECO in power systems using real, reactive, and apparent power flows with the consideration of redundant placement of generators. Recalling the performance of RECO-oriented optimal power flows under N-x contingencies, the analyses suggest reactive power flows and redundant components should be included for RECO to capture power systems' inherent resilience.
Tahany Saleh Aldayel, Mohamed M. Badran, Abdullah H. Alomrani
et al.
Aloe perryi (AP) has gained considerable interest as a medicinal herb in various biological applications due to its rich phytochemical composition. However, the therapeutic benefits of AP could be potentiated by utilizing nanotechnology. Moreover, cationic solid lipid nanoparticles (CSLNs) possess remarkable characteristics that can greatly enrich a variety of biological uses. An optimization approach was used to achieve high-quality CSLNs to maximize the therapeutic efficacy of AP. Therefore, a factorial design was used to investigate the influence of various variables on the attributes of CSLNs quality. In this study, the factors under investigation were compritol 888 ATO (C-888, X1), poloxamer 188 (PL188, X2), and chitosan (CS, X3), which served as independent variables. The parameters measured as dependent variables included particle size (Y1), zeta potential (Y2), and encapsulation efficiency EE (Y3). The relationship among these variables was determined by Analysis of Variance (ANOVA) and response surface plots. The results revealed that PL188 played a significant role in reducing the particle size of CSLNS (ranging from 207 to 261 nm with 1 % PL188 to 167–229 nm with 3 % PL188). Conversely, an increase in the concentration of CS led to a rise in the particle size. The magnitude of positive zeta potential values was dependent on the increased concentration of CS. Moreover, the higher amounts of C-888 and PL188 improved the EE% of the CSLNs from 42 % to 86 %. Furthermore, a concentration-dependent antioxidant effect of the optimized AP-CSLNs was observed. The antioxidant activity of the optimized AP-CSLNs at 100 μg/mL was 75 % compared to 62 % and 60 % for AP-SLNs and AP solution, respectively. A similar pattern of improvement was also observed with antimicrobial, and anticancer activities of the optimized AP-CSLNs. These findings demonstrated the potential of AP-CSLNs as a carrier system, enhancing the biological activities of AP, opening new possibilities in herbal medicines.
Christine Njiru, Wenxin Xue, Sander De Rouck
et al.
Abstract Background Generalist herbivores such as the two-spotted spider mite Tetranychus urticae thrive on a wide variety of plants and can rapidly adapt to novel hosts. What traits enable polyphagous herbivores to cope with the diversity of secondary metabolites in their variable plant diet is unclear. Genome sequencing of T. urticae revealed the presence of 17 genes that code for secreted proteins with strong homology to “intradiol ring cleavage dioxygenases (DOGs)” from bacteria and fungi, and phylogenetic analyses show that they have been acquired by horizontal gene transfer from fungi. In bacteria and fungi, DOGs have been well characterized and cleave aromatic rings in catecholic compounds between adjacent hydroxyl groups. Such compounds are found in high amounts in solanaceous plants like tomato, where they protect against herbivory. To better understand the role of this gene family in spider mites, we used a multi-disciplinary approach to functionally characterize the various T. urticae DOG genes. Results We confirmed that DOG genes were present in the T. urticae genome and performed a phylogenetic reconstruction using transcriptomic and genomic data to advance our understanding of the evolutionary history of spider mite DOG genes. We found that DOG expression differed between mites from different plant hosts and was induced in response to jasmonic acid defense signaling. In consonance with a presumed role in detoxification, expression was localized in the mite’s gut region. Silencing selected DOGs expression by dsRNA injection reduced the mites’ survival rate on tomato, further supporting a role in mitigating the plant defense response. Recombinant purified DOGs displayed a broad substrate promiscuity, cleaving a surprisingly wide array of aromatic plant metabolites, greatly exceeding the metabolic capacity of previously characterized microbial DOGs. Conclusion Our findings suggest that the laterally acquired spider mite DOGs function as detoxification enzymes in the gut, disarming plant metabolites before they reach toxic levels. We provide experimental evidence to support the hypothesis that this proliferated gene family in T. urticae is causally linked to its ability to feed on an extremely wide range of host plants.
Laila Loudiki, Mustapha Kchikech, El Hassan Essaky
Due to their broad application to different fields of theory and practice, generalized Petersen graphs $GPG(n,s)$ have been extensively investigated. Despite the regularity of generalized Petersen graphs, determining an exact formula for the diameter is still a difficult problem. In their paper, Beenker and Van Lint have proved that if the circulant graph $C_n(1,s)$ has diameter $d$, then $GPG(n,s)$ has diameter at least $d+1$ and at most $d+2$. In this paper, we provide necessary and sufficient conditions so that the diameter of $GPG(n,s)$ is equal to $d+1,$ and sufficient conditions so that the diameter of $GPG(n,s)$ is equal to $d+2.$ Afterwards, we give exact values for the diameter of $GPG(n,s)$ for almost all cases of $n$ and $s.$ Furthermore, we show that there exists an algorithm computing the diameter of generalized Petersen graphs with running time $O$(log$n$).
High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.
Alicia Arnott, Jenny Draper, Rebecca J. Rockett
et al.
Abstract Objective To adapt ‘fishplots’ to describe real-time evolution of SARS-CoV-2 genomic clusters. Results This novel analysis adapted the fishplot to depict the size and duration of circulating genomic clusters over time in New South Wales, Australia. It illuminated the effectiveness of interventions on the emergence, spread and eventual elimination of clusters and distilled genomic data into clear information to inform public health action.