CRISPR–Cas9 gene editing holds transformative promise for genetic therapies, but is hindered by off-target effects that undermine its precision and safety. To address this, we developed CRISMER, a hybrid deep-learning architecture that uses multi-branch convolutional neural networks to extract k-mer features and transformer blocks to capture long-range dependencies. This hybrid approach enhances the prediction and optimization of single-guide RNA (sgRNA) designs. CRISMER was trained on Change-seq and Site-seq datasets, using a 20 × 16 sparse one-hot encoding scheme, and evaluated on independent datasets including Circle-seq, Guide-seq, Surro-seq, and TTISS. CRISMER outperformed existing tools, achieving an F1 score of 0.7092 and a PR-AUC of 0.8006 on the CRISPR-DIPOff dataset. It also excelled in measuring sgRNA specificity and optimizing designs for genes, such as PCSK9 and BCL11A, yielding sgRNAs with reduced off-target activity. For example, a G-to-C mutation at position 12 in the sgRNA for PCSK9 and at position 11 for BCL11A led to significant improvements in off-target profiles. Interpretability analysis via integrated gradients confirmed the model’s focus on critical PAM-proximal regions and mismatch patterns. These results demonstrate that CRISMER significantly improves the accuracy and safety of CRISPR-Cas9, advancing its reliability for therapeutic applications.

Multiple sequence alignment (MSA) is a preliminary task for estimating phylogenies. It is used for homology inference among the sequences of a set of species. Generally, the MSA task is handled as a single-objective optimization process. The alignments computed under one criterion may be different from the alignments generated by other criteria, inferring discordant homologies and thus leading to different hypothesized evolutionary histories relating the sequences. The multiobjective (MO) formulation of MSA has recently been advocated by several researchers, to address this issue. An MO approach independently optimizes multiple (often conflicting) objective functions at the same time and outputs a set of competitive alignments. However, no conceptual or experimental rational from a real-world application perspective has been reported so far for any MO formulation of MSA. This article work investigates the impact of MO formulation in the context of an important scientific problem, namely, phylogeny estimation. Employing popular evolutionary MO algorithms, we show that: 1) trees inferred based on alignments produced by the existing MSA methods used in practice are substantially worse in quality than the trees inferred based on the alignment's output by an MO algorithm and 2) even high-quality alignments (according to popular measures available in the literature) may fail to achieve acceptable accuracy in generating phylogenetic trees. Thus, we essentially ask the following natural question: ``can a phylogeny-aware (i.e., application-aware) metric guide in selecting appropriate MO formulations to ensure better phylogeny estimation?'' Here, we report a carefully designed extensive experimental study that positively answers this question.

Automated mobile microscopy in Internet of Things (IoT) networks is essential for scaling malaria screening in resource-constrained environments. Deploying standard convolutional architectures here introduces severe adversarial vulnerabilities. Post-Training Quantization (PTQ) mitigates hardware constraints by converting floating-point models to 8-bit integers (INT8); however, its impact on clinical safety and security remains unexplored. This study presents an adversarial audit of quantized Vision Transformers for medical edge deployment. We evaluated a Swin-Tiny transformer against ViT-Tiny and MobileNetV3 baselines using a 27,558-image malaria dataset and an out-of-distribution (OOD) White Blood Cell dataset. Our findings redefine the “Quantization Shield” hypothesis. PTQ compresses the Swin model by 3.9× (to 27.89 MB) with a negligible 0.11% accuracy drop, maintaining statistical reliability on OOD tests. However, the hypothesized architectural resilience shatters under white-box Projected Gradient Descent (PGD) attacks. Despite robustness against single-step attacks, both MobileNetV3 and the INT8 Swin-Tiny collapse to 0.00% accuracy under iterative PGD. Conversely, the quantized Swin-Tiny resists black-box transfer attacks from a surrogate, maintaining 81.00% accuracy. We conclude that while quantized Vision Transformers meet mobile sensor constraints, integer quantization provides zero innate defense against targeted iterative perturbations, exposing a critical vulnerability in diagnostic IoT networks.

Even though traffic signal scheduling is one of the fundamental problems addressed in Intelligent Transportation Systems, most of the related existing studies solved this problem in the contexts of lane-based and homogeneous traffic. These studies ignored various distinctive contexts prevalent in developing countries, such as non-lane-based (i.e., designated lanes are not followed) and heterogeneous (i.e., motorized and non-motorized) traffic. Therefore, in this study, we propose a new methodology incorporating chronological implementations of multi-objective optimization and appropriate simulations to develop an efficient traffic signal scheduling system appropriate for non-lane-based and heterogeneous traffic. First, we explore NSGA-II: Non-dominated Sorting Genetic Algorithm with binary and real encoding-based populations.We propose two novel objective functions considering the non-lane-based heterogeneous nature of traffic. Among the two encoding-based implementations, we find that real-encoded NSGA-II performs the best in providing an optimal traffic signal schedule on a numerical simulator. Further, we integrate the real-encoded NSGA-II in DhakaSim, which is a microscopic traffic simulator that realizes distinctive contexts of developing countries. Afterward, we conduct extensive simulations over three road networks from Dhaka, Miami, and Riyadh. Our simulation results show that the proposed method can lead to a 163.1% increase in average speed and a 56.1% reduction in average waiting time across Dhaka city’s road network during peak traffic hours, compared to traditional fixed-time signal scheduling currently used in some parts of the city. Besides, our proposed methodology confirms improved average speed and average waiting time over the road networks of Miami and Riyadh, underscoring the approach’s generalizability. These results highlight the effectiveness of the proposed multi-objective signal optimization framework for non-lane-based heterogeneous traffic and its potential to improve urban traffic flow in diverse, congested settings significantly.