Overcoming myelosuppression due to synthetic lethal toxicity for FLT3-targeted acute myeloid leukemia therapy.

Activating mutations in FLT3 confer poor prognosis for individuals with acute myeloid leukemia (AML). Clinically active investigational FLT3 inhibitors can achieve complete remissions but their utility has been hampered by acquired resistance and myelosuppression attributed to a 'synthetic lethal toxicity' arising from simultaneous inhibition of FLT3 and KIT. We report a novel chemical strategy for selective FLT3 inhibition while avoiding KIT inhibition with the staurosporine analog, Star 27. Star 27 maintains potency against FLT3 in proliferation assays of FLT3-transformed cells compared with KIT-transformed cells, shows no toxicity towards normal human hematopoiesis at concentrations that inhibit primary FLT3-mutant AML blast growth, and is active against mutations that confer resistance to clinical inhibitors. As a more complete understanding of kinase networks emerges, it may be possible to define anti-targets such as KIT in the case of AML to allow improved kinase inhibitor design of clinical agents with enhanced efficacy and reduced toxicity

Performance-Based Plastic Design Method for Steel Concentrically Braced Frames Using Target Drift and Yield Mechanism

Under severe earthquakes, steel concentrically braced frames (SCBFs) will experience large  inelastic deformations in an uncontrolled manner. According to the energy-work balance concept, a performance-based plastic design (PBPD) methodology for steel concentrically braced frames was presented here. This method uses pre-selected target drift and yield mechanism as key performance limit states. The designed base shear for selected hazard levels was derived based on work-energy balance equations. Plastic design was performed to design bracing members and connection nodes in order to achieve the expected yield mechanism and behavior. The method has been successively applied to design a six-storey steel concentrically braced frame. Results of inelastic dynamic analyses showed that the story drifts were well within the target values, thus to meet the desired performance requirements. The proposed method provided a basis for performance-based plastic design of steel concentrically braced frames

A Survey of Explainable Knowledge Tracing

With the long term accumulation of high quality educational data, artificial intelligence has shown excellent performance in knowledge tracing. However, due to the lack of interpretability and transparency of some algorithms, this approach will result in reduced stakeholder trust and a decreased acceptance of intelligent decisions. Therefore, algorithms need to achieve high accuracy, and users need to understand the internal operating mechanism and provide reliable explanations for decisions. This paper thoroughly analyzes the interpretability of KT algorithms. First, the concepts and common methods of explainable artificial intelligence and knowledge tracing are introduced. Next, explainable knowledge tracing models are classified into two categories: transparent models and black box models. Then, the interpretable methods used are reviewed from three stages: ante hoc interpretable methods, post hoc interpretable methods, and other dimensions. It is worth noting that current evaluation methods for explainable knowledge tracing are lacking. Hence, contrast and deletion experiments are conducted to explain the prediction results of the deep knowledge tracing model on the ASSISTment2009 by using three XAI methods. Moreover, this paper offers some insights into evaluation methods from the perspective of educational stakeholders. This paper provides a detailed and comprehensive review of the research on explainable knowledge tracing, aiming to offer some basis and inspiration for researchers interested in the interpretability of knowledge tracing

Anomaly Detection by Adapting a pre-trained Vision Language Model

Recently, large vision and language models have shown their success when adapting them to many downstream tasks. In this paper, we present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. To this end, we make two important improvements: 1) To acquire unified anomaly detection across industrial images of multiple categories, we introduce the learnable prompt and propose to associate it with abnormal patterns through self-supervised learning. 2) To fully exploit the representation power of CLIP, we introduce an anomaly region refinement strategy to refine the localization quality. During testing, the anomalies are localized by directly calculating the similarity between the representation of the learnable prompt and the image. Comprehensive experiments demonstrate the superiority of our framework, e.g., we achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and VisA for anomaly detection and localization. In addition, the proposed method also achieves encouraging performance with marginal training data, which is more challenging

Overcoming myelosuppression due to synthetic lethal toxicity for FLT3-targeted acute myeloid leukemia therapy

Activating mutations in FLT3 confer poor prognosis for individuals with acute myeloid leukemia (AML). Clinically active investigational FLT3 inhibitors can achieve complete remissions but their utility has been hampered by acquired resistance and myelosuppression attributed to a 'synthetic lethal toxicity' arising from simultaneous inhibition of FLT3 and KIT. We report a novel chemical strategy for selective FLT3 inhibition while avoiding KIT inhibition with the staurosporine analog, Star 27. Star 27 maintains potency against FLT3 in proliferation assays of FLT3-transformed cells compared with KIT-transformed cells, shows no toxicity towards normal human hematopoiesis at concentrations that inhibit primary FLT3-mutant AML blast growth, and is active against mutations that confer resistance to clinical inhibitors. As a more complete understanding of kinase networks emerges, it may be possible to define anti-targets such as KIT in the case of AML to allow improved kinase inhibitor design of clinical agents with enhanced efficacy and reduced toxicity.published_or_final_versio

A3^3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-to-text translation. However, all the above tasks are in the direction of speech understanding, but for the inverse direction, speech synthesis, the potential of representation learning is yet to be realized, due to the challenging nature of generating high-quality speech. To address this problem, we propose our framework, Alignment-Aware Acoustic-Text Pretraining (A$^3$T), which reconstructs masked acoustic signals with text input and acoustic-text alignment during training. In this way, the pretrained model can generate high quality of reconstructed spectrogram, which can be applied to the speech editing and unseen speaker TTS directly. Experiments show A$^3$T outperforms SOTA models on speech editing, and improves multi-speaker speech synthesis without the external speaker verification model.Comment: under review, 12 pages, 10 figure

Experimental investigation of peening cylindrical workpieces utilizing a transducer with ring sonotrode

In industrial applications, the shafting components with high stress are easily damaged due to cyclic loads if there is no surface treatment. With the use of ultrasonic cavitation peening, the residual compressive stress and the surface hardness of these components can be improved. While tradi-tional longitudinal vibration transducers are used to treat cylindrical workpieces, the treated areas are limited, and the treatment period is relatively long. To solve these problems, we designed a novel configuration of the piezoelectric transducer as a type of the combination of rod and ring. During ultrasonic cavitation peening, we placed the cylindrical workpieces in the ring tool to improve the limitation. However, the treated surface properties were largely influenced by the input parameters (driving voltage and rod diameters). In this investigation, the cylindrical workpieces, which were covered with aluminum foils, were first treated by ultrasonic cavitation peening to detect the intensity and distribution of the cavitation bubbles on the treated surface. Then, the sonochemiluminescence method was utilized as an additional way to find the optimal operation parameters (190 V and 16 mm). Finally, the ultrasonic cavitation process was conducted with the optimal parameters. The treatment results showed that the surface hardness increased by about 36% without significant increase of the surface roughness. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

FairBench: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models

Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied. However, the majority of existing methods focus on measuring the model's preference towards sentences containing biases and stereotypes within datasets, which lacks interpretability and cannot detect implicit biases and stereotypes in the real world. To address this gap, this paper introduces a four-stage framework to directly evaluate stereotypes and biases in the generated content of LLMs, including direct inquiry testing, serial or adapted story testing, implicit association testing, and unknown situation testing. Additionally, the paper proposes multi-dimensional evaluation metrics and explainable zero-shot prompts for automated evaluation. Using the education sector as a case study, we constructed the Edu-FairBench based on the four-stage framework, which encompasses 12,632 open-ended questions covering nine sensitive factors and 26 educational scenarios. Experimental results reveal varying degrees of stereotypes and biases in five LLMs evaluated on Edu-FairBench. Moreover, the results of our proposed automated evaluation method have shown a high correlation with human annotations

LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition

Recently, 3D shape understanding has achieved significant progress due to the advances of deep learning models on various data formats like images, voxels, and point clouds. Among them, point clouds and multi-view images are two complementary modalities of 3D objects and learning representations by fusing both of them has been proven to be fairly effective. While prior works typically focus on exploiting global features of the two modalities, herein we argue that more discriminative features can be derived by modeling ``where to fuse''. To investigate this, we propose a novel Locality-Aware Point-View Fusion Transformer (LATFormer) for 3D shape retrieval and classification. The core component of LATFormer is a module named Locality-Aware Fusion (LAF) which integrates the local features of correlated regions across the two modalities based on the co-occurrence scores. We further propose to filter out scores with low values to obtain salient local co-occurring regions, which reduces redundancy for the fusion process. In our LATFormer, we utilize the LAF module to fuse the multi-scale features of the two modalities both bidirectionally and hierarchically to obtain more informative features. Comprehensive experiments on four popular 3D shape benchmarks covering 3D object retrieval and classification validate its effectiveness