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A significant limitation of numerous current genetic engineering therapy approaches is their limited control over the strength, timing, or cellular context of their therapeutic effect. Synthetic gene/genetic circuits are synthetic biology approaches that can control the generation, transformation, or depletion of a specific DNA, RNA, or protein and provide precise control over gene expression and cellular behavior. They can be designed to perform logical operations by carefully selecting promoters, repressors, and other genetic components. Patent search was performed in Espacenet, resulting in 38 selected patents with 15 most frequent international classifications. Patent embodiments were categorized into applications for the delivery of therapeutic molecules, treatment of infectious diseases, treatment of cancer, treatment of bleeding, and treatment of metabolic disorders. The logic gates of selected genetic circuits are described to comprehensively demonstrate their therapeutic applications. Synthetic gene circuits can be customized for precise control of therapeutic interventions, leading to personalized therapies that respond specifically to individual patient needs, enhancing treatment efficacy and minimizing side effects. They can be highly sensitive biosensors that provide real-time therapy by accurate monitoring various biomarkers or pathogens and appropriately synthesizing a therapeutic molecule. Synthetic gene circuits may also lead to the development of advanced regenerative therapies and to implantable biodevices that produce on-demand bioactive molecules. However, this technology faces challenges for commercial profitability. The genetic circuit designs need adjustments for specific applications, and may have disadvantages like toxicity from multiple regulators, homologous recombination, context dependency, resource overuse, and environmental variability.

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A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm-termed generalised Firing-to-Parameter (gFTP)-provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.

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Protists can endure challenging environments sustaining key ecosystem processes of the microbial food webs even under aridic or hypersaline conditions. We studied the diversity of protists at different latitudes of the Atacama Desert by massive sequencing of the hypervariable region V9 of the 18S rRNA gene from soils and microbial mats collected in the Andes. The main protist groups in soils detected in active stage through cDNA were cercozoans, ciliates, and kinetoplastids, while the diversity of protists was higher including diatoms and amoebae in the microbial mat detected solely through DNA. Co-occurrence networks from soils indicated similar assemblages dominated by amplicon sequence variants (ASVs) identified as Rhogostoma, Euplotes, and Neobodo. Microbial mat networks, on the other hand, were structured by ASVs classified as raphid-pennate diatoms and amoebae from the genera Hartmannella and Vannella, mostly negatively correlated to flagellates and microalgae. Additionally, our phylogenetic inferences of ASVs classified as Euplotes, Neobodo, and Rhogostoma were supported by sequence data of strains isolated during this study. Our results represent the first snapshot of the diversity patterns of culturable and unculturable protists and putative keystone taxa detected at remote habitats from the Atacama Desert.

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This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.

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Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

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Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and release of oocytes, especially in minimal ovarian stimulation treatments. Despite its significance for the ultimate success of IVF, determining the precise TT remains a complex challenge for physicians due to the involvement of multiple variables. This study aims to enhance TT by developing a machine learning multi-output model that predicts the expected number of retrieved oocytes, mature oocytes (MII), fertilized oocytes (2 PN), and useable blastocysts within a 48-h window after the trigger shot in minimal stimulation cycles. By utilizing this model, physicians can identify patients with possible early, late, or on-time trigger shots. The study found that approximately 27 % of treatments administered the trigger shot on a suboptimal day, but optimizing the TT using the developed Artificial Intelligence (AI) model can potentially increase useable blastocyst production by 46 %. These findings highlight the potential of predictive models as a supplementary tool for optimizing trigger shot timing and improving IVF outcomes, particularly in minimal ovarian stimulation. The experimental results underwent statistical validation, demonstrating the accuracy and performance of the model. Overall, this study emphasizes the value of AI prediction models in enhancing TT and making the IVF process safer and more efficient.

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Uncontrolled use of pesticides has caused a dramatic reduction in the number of pollinators, including bees. Studies on the effects of pesticides on bees have reported effects on both metabolic and neurological levels under chronic exposure. In this study, variations in the differential expression of head and thorax-abdomen proteins in Africanized A. mellifera bees treated acutely with sublethal doses of glyphosate and imidacloprid were studied using a proteomic approach. A total of 92 proteins were detected, 49 of which were differentially expressed compared to those in the control group (47 downregulated and 2 upregulated). Protein interaction networks with differential protein expression ratios suggested that acute exposure of A. mellifera to sublethal doses of glyphosate could cause head damage, which is mainly associated with behavior and metabolism. Simultaneously, imidacloprid can cause damage associated with metabolism as well as, neuronal damage, cellular stress, and impairment of the detoxification system. Regarding the thorax-abdomen fractions, glyphosate could lead to cytoskeleton reorganization and a reduction in defense mechanisms, whereas imidacloprid could affect the coordination and impairment of the oxidative stress response.

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Despite a growing interest in the gut microbiome of non-industrialized countries, data linking deeply sequenced microbiomes from such settings to diverse host phenotypes and situational factors remain uncommon. Using metagenomic data from a community-based cohort of 1,871 people from 19 isolated villages in the Mesoamerican highlands of western Honduras, we report associations between bacterial species and human phenotypes and factors. Among them, socioeconomic factors account for 51.44% of the total associations. Meta-analysis of species-level profiles across several datasets identified several species associated with body mass index, consistent with previous findings. Furthermore, the inclusion of strain-phylogenetic information modifies the overall relationship between the gut microbiome and the phenotypes, especially for some factors like household wealth (e.g., wealthier individuals harbor different strains of Eubacterium rectale). Our analysis suggests a role that gut microbiome surveillance can play in understanding broad features of individual and public health.

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All-dielectric magnetophotonic nanostructures are promising for integrated nanophotonic devices with high resolution and sensitivity, but their design requires computationally demanding electromagnetic simulations evaluated through trial and error. In this paper, we propose a machine-learning approach to accelerate the design of these nanostructures. Using a data set of 12 170 samples containing four geometric parameters of the nanostructure and the incidence wavelength, trained neural network and polynomial regression algorithms were capable of predicting the amplitude of the transverse magneto-optical Kerr effect (TMOKE) within a time frame of 10-3 s and mean square error below 4.2%. With this approach, one can readily identify nanostructures suitable for sensing at ultralow analyte concentrations in aqueous solutions. As a proof of principle, we used the machine-learning models to determine the sensitivity (S = |Δθres/Δna|) of a nanophotonic grating, which is competitive with state-of-the-art systems and exhibits a figure of merit of 672 RIU-1. Furthermore, researchers can use the predictions of TMOKE peaks generated by the algorithms to assess the suitability for experimental setups, adding a layer of utility to the machine-learning methodology.

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Background: Anthropogenic activities significantly impact natural ecosystems, leading to alterations in plant and pollinator diversity and abundance. These changes often result in shifts within interacting communities, potentially reshaping the structure of plant-pollinator interaction networks. Given the escalating human footprint on habitats, evaluating the response of these networks to anthropization is critical for devising effective conservation and management strategies. Methods: We conducted a comprehensive review of the plant-pollinator network literature to assess the impact of anthropization on network structure. We assessed network metrics such as nestedness measure based on overlap and decreasing fills (NODF), network specialization (H2'), connectance (C), and modularity (Q) to understand structural changes. Employing a meta-analytical approach, we examined how anthropization activities, such as deforestation, urbanization, habitat fragmentation, agriculture, intentional fires and livestock farming, affect both plant and pollinator richness. Results: We generated a dataset for various metrics of network structure and 36 effect sizes for the meta-analysis, from 38 articles published between 2010 and 2023. Studies assessing the impact of agriculture and fragmentation were well-represented, comprising 68.4% of all studies, with networks involving interacting insects being the most studied taxa. Agriculture and fragmentation reduce nestedness and increase specialization in plant-pollinator networks, while modularity and connectance are mostly not affected. Although our meta-analysis suggests that anthropization decreases richness for both plants and pollinators, there was substantial heterogeneity in this regard among the evaluated studies. The meta-regression analyses helped us determine that the habitat fragment size where the studies were conducted was the primary variable contributing to such heterogeneity. Conclusions: The analysis of human impacts on plant-pollinator networks showed varied effects worldwide. Responses differed among network metrics, signaling nuanced impacts on structure. Activities like agriculture and fragmentation significantly changed ecosystems, reducing species richness in both pollinators and plants, highlighting network vulnerability. Regional differences stressed the need for tailored conservation. Despite insights, more research is crucial for a complete understanding of these ecological relationships.

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