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Beyond HAART: unveiling the reality of antiretroviral therapy knowledge among pregnant women living with HIV in Kaduna State, Nigeria
Big data show idiosyncratic patterns and rates of geomorphic river mobility
Abstract Big data present unprecedented opportunities to test long-standing theories regarding patterns and rates of geomorphic river adjustments. Here, we use locational probabilities derived from Landsat imagery (1988-2019) to quantify the dynamics of 600 km2 of riverbed in 10 Philippine catchments. Analysis of lateral adjustments reveals spatially non-uniform variability in along-valley patterns of geomorphic river mobility, with zones of relative stability interspersed with zones of relative instability. Hotspots of mobility vary in magnitude, size and location between catchments. We could not identify monotonic relationships between local factors (active channel width, valley floor width and confinement ratio) and mobility. No relation between the channel pattern type and rates of adjustment was evident. We contend that satellite-derived locational probabilities provide a spatially continuous dynamic metric that can help unravel and contextualise forms and rates of geomorphic river adjustment, thereby helping to derive insights into idiosyncrasies of river behaviour in dynamic landscapes.
Comparative analysis of stereotactic aspiration via supraorbital keyhole versus Kocher’s point for basal ganglia intracerebral hematoma: computational simulation and propensity score-matched study
A comparison of income inequality in the Roman and Chinese Han empires
Time-warping analysis for biological signals: methodology and application
Abstract Any set of biological signals has variability, both in the temporal and spatial domains. To extract characteristic features of the ensemble, these spatiotemporal profiles are typically summarized by their mean and variance, often requiring prior padding or resampling of the data to equalize signal length. Such compression can conceal essential information in the signal. This work presents the method of time-warping, reformulated as elastic functional data analysis (EFDA), in an accessible way. This powerful approach rescales the temporal evolution of signals, aligns them accurately, decouples their spatial and temporal variability, and faithfully extracts their characteristics. This technique was compared to conventional methods of normalizing or padding data followed by averaging, using synthetized signals with controlled variability and real human data from a complex manipulation task. Comparative analysis demonstrates that EFDA successfully reveals otherwise concealed features and teases apart temporal and spatial variability. Critical advances to the more common method of dynamic time-warping (DTW) are discussed. Application of EFDA and potential new insights are illustrated in the context of human motor neuroscience. Annotated code to facilitate the use of this technique is provided.
Disorder-driven sintering-free garnet-type solid electrolytes
Explainable artificial intelligence to diagnose early Parkinson’s disease via voice analysis
A differential requirement for ciliary transition zone proteins in human and mouse neural progenitor fate specification
Abstract Studying ciliary genes in the context of the human central nervous system is crucial for understanding the underlying causes of neurodevelopmental ciliopathies. Here, we use pluripotent stem cell-derived spinal organoids to reveal distinct functions of the ciliopathy gene RPGRIP1L in humans and mice, and uncover an unexplored role for cilia in human axial patterning. Previous research has emphasized Rpgrip1l critical functions in mouse brain and spinal cord development through the regulation of SHH/GLI pathway. Here, we show that RPGRIP1L is not required for SHH activation or motoneuron lineage commitment in human spinal progenitors and that this feature is shared by another ciliopathy gene, TMEM67 . Furthermore, human RPGRIP1L -mutant motoneurons adopt hindbrain and cervical identities instead of caudal brachial identity. Temporal transcriptome analysis reveals that this antero-posterior patterning defect originates in early axial progenitors and correlates with cilia loss. These findings provide important insights into the role of cilia in human neural development.
Genetic structure and haplotype analysis of predominant genetic group of Bemisia tabaci Asia II 1 from Asia and India
Spatiotemporal molecular tracing of ultralow-volume biofluids via a soft skin-adaptive optical monolithic patch sensor
Multimodal MRI analysis of COVID-19 effects on pediatric brain
Molecular basis for the interaction between Saccharomyces cerevisiae Rtt103 and the Rat1-Rai1 complex
Low-profile conformal single-sided miniaturized frequency selective surface for wideband shielding
Optimization on multifractal loss landscapes explains a diverse range of geometrical and dynamical properties of deep learning
Abstract Gradient descent and its variants are foundational in solving optimization problems across many disciplines. In deep learning, these optimizers demonstrate a remarkable ability to dynamically navigate complex loss landscapes, ultimately converging to solutions that generalize well. To elucidate the mechanism underlying this ability, we introduce a theoretical framework that models the complexities of loss landscapes as multifractal. Our model unifies and explains a broad range of realistic geometrical signatures of loss landscapes, including clustered degenerate minima, multiscale structure, and rich optimization dynamics in deep neural networks, such as the edge of stability, non-stationary anomalous diffusion, and the extended edge of chaos without requiring fine-tuning parameters. We further develop a fractional diffusion theory to illustrate how these optimization dynamics, coupled with multifractal structure, effectively guide optimizers toward smooth solution spaces housing flatter minima, thus enhancing generalization. Our findings suggest that the complexities of loss landscapes do not hinder optimization; rather, they facilitate the process. This perspective not only has important implications for understanding deep learning but also extends potential applicability to other disciplines where optimization unfolds on complex landscapes.