Biology subjects

Ali, Y. H.

Publications and source records attributed to Ali, Y. H..

4 recordsLinked to original sources

bioRxiv2026

Molecular epidemiology of the globally spreading genetic lineage IV of peste des petits ruminants virus

Peste des petits ruminants (PPR) is a highly contagious viral disease of small ruminants caused by the peste des petits ruminants virus (PPRV), which is classified into four distinct genetic lineages (I-IV). A critical concern in the recent epidemiological history of PPRV is the rapid and widespread expansion of lineage IV (LIV) across West Africa over the past decade. This dominance suggests a potential adaptive advantage of circulating LIV strains in the regions current epidemiological context. In this study, we obtain the genome sequence of 26 new PPRV samples, including historical (pre-2000) and many recent African LIV isolates, offering the first opportunity to investigate the evolutionary history of LIV in Africa and identify genetic events potentially associated with its recent spread. Phylogenomic analyses implemented on a dataset of 167 curated PPRV genome sequences reveal that the most ancestral LIV group comprises strains circulating in Sub-Saharan Africa (designated clade LIVssa), providing robust evidence for an African origin of lineage IV. Our results further indicate that PPRV strains linked to the recent West African expansion of LIV belong to a specific LIVssa subgroup, termed NigB. We identified multiple signatures of selection pressure within the LIVssa sublineage, particularly in the NigB cluster. Several amino acid substitutions unique to LIVssa or NigB were detected, some of which may impact protein function and warrant prioritised investigation. Additional genomic data are required to confirm the association between the NigB group and the ongoing spread of LIV in West Africa. The evolutionary adaptations observed in LIVssa - potentially enhancing transmission efficiency, host range or pathogenicity - could undermine current disease control strategies in regions where PPR poses significant threats to food security and local economies. Author SummaryPeste des petits ruminants virus (PPRV) infects sheep and goats across Africa, Middle East, Asia and Europe, causing disease with major impact on global economy and food security. One genetic lineage of PPRV, called lineage IV (LIV), is at the origin of most recent expansion of the distribution of the disease, including replacement of other lineages in areas of African where PPRV is historically present. Here, we generated genome sequences from PPRV LIV isolates from different dates and places to study the evolution of this genetic lineage and explore whether its recent spread can be associated with the appearance of new mutations in the virus genome. Our results provide evidence that the PPRV LIV originated in Sub-Saharan Africa and identify mutations present only virus isolates currently spready in new regions of Africa. Further research should investigate the impact of these mutations on protein functions and capacity of transmission of PPRV.

evolutionary biology↗

bioRxiv2025

Preparatory encoding of diverse features of intended movement in the human motor cortex

Over the course of a voluntary movement, motor cortical activity exhibits a transition from preparation to execution, with markedly different activity across these phases. Preparatory activity in particular might be used to improve brain-computer interfaces (BCIs) that harness brain activity to control external assistive devices, for example by anticipating a users intended movement trajectory for quick and fluid performance. However, to leverage preparatory activity for clinical BCIs, we must first understand which features of upcoming movements are encoded by preparatory activity in humans. In this work, we collected intracortical recordings from 3 research participants in the BrainGate2 clinical trial to investigate whether diverse features of movement, such as direction, curvature, and distance, are encoded by preparatory activity in the human motor cortex. We first show that preparatory activity is tuned to the direction of upcoming movements, and this tuning is largely preserved across movements with different effectors. Further investigation demonstrated this preparatory activity is also informative of initial and endpoint directions of curved movement trajectories, and encodes movement distance and speed independently. Finally, we present an online control paradigm that leverages preparatory activity to predict movements towards intended directions in advance, yielding rapid, self-paced control of a computer cursor by human participants. Altogether, these results demonstrate that preparatory activity in the human motor cortex encodes rich features of upcoming movement, highlighting its potential use for high performance brain-computer interface applications.

neuroscience↗

bioRxiv2023

BRAND: A platform for closed-loop experiments with deep network models

Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes, which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.

neuroscience↗

bioRxiv2022

Stabilizing brain-computer interfaces through alignment of latent dynamics

Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. In current iBCIs, instabilities at the neural interface result in a degradation of decoding performance, which necessitates frequent supervised recalibration using new labeled data. One potential solution is to use the latent manifold structure that underlies neural population activity to facilitate a stable mapping between brain activity and behavior. Recent efforts using unsupervised approaches have improved iBCI stability using this principle; however, existing methods treat each time step as an independent sample and do not account for latent dynamics. Dynamics have been used to enable high performance prediction of movement intention, and may also help improve stabilization. Here, we present a platform for Nonlinear Manifold Alignment with Dynamics (NoMAD), which stabilizes iBCI decoding using recurrent neural network models of dynamics. NoMAD uses unsupervised distribution alignment to update the mapping of nonstationary neural data to a consistent set of neural dynamics, thereby providing stable input to the iBCI decoder. In applications to data from monkey motor cortex collected during motor tasks, NoMAD enables accurate behavioral decoding with unparalleled stability over weeks-to months-long timescales without any supervised recalibration.

neuroscience↗