Miracle-dz/UniFAR-FTUs huggingface dataset:
In situ modeling of acquired resistance to RTK/RAS pathway targeted therapies

Intrinsic and acquired resistance limit the window of effectiveness for oncogene-targeted cancer therapies. Preclinical studies that identify synergistic combinations enhance therapeutic efficacy to target intrinsic resistance, however, methods to study acquired resistance in cell culture are lacking. Here, we describe an in situ resistance assay (ISRA), performed in a 96-well culture format, that models acquired resistance to RTK/RAS pathway targeted therapies. Using osimertinib resistance in EGFR-mutated lung adenocarcinoma (LUAD) as a model system, we show acquired resistance can be reliably modeled across cell lines with objectively defined osimertinib doses. We further show that acquired osimertinib resistance can be significantly delayed by inhibition of proximal RTK signaling using two distinct SHP2 inhibitors. Similar to patient populations, isolated osimertinib-resistant populations showed resistance via enhanced activation of multiple parallel RTKs so that individual RTK inhibitors did not re-sensitize cells to osimertinib. In contrast, inhibition of proximal RTK signaling using the SHP2 inhibitor RMC-4550 both re-sensitized resistant populations to osimertinib. Similar, objectively defined drug doses were used to model resistance to additional RTK/RAS pathway targeted therapies including the KRASG12C inhibitors adagrasib and sotorasib, the MEK inhibitor trametinib, and the farnesyl transferase inhibitor tipifarnib. These studies highlight the tractability of in situ resistance assays to model acquired resistance to targeted therapies and provide a framework for assessing the extent to which synergistic drug combinations can target acquired drug resistance. Highlights Acquired resistance to RTK/RAS pathway members can be modeled in situ SHP2 inhibitors reduce the development of acquired osimertinib resistance Isolated osimertinib-resistant populations show hyperactivation of multiple RTKs SHP2 inhibitors re-sensitize resistant populations to osimertinib treatment
Computed tomography for visible haematuria - a single nephrographic phase is sufficient for detecting renal cell carcinoma.

OBJECTIVES No previous studies have compared two computed tomography (CT) protocols in patients presenting with visible haematuria, and most patients undergo a multiphase CT in order to detect upper tract malignancies. We aimed to prospectively compare the diagnostic performance of single- and four-phase CT for detecting renal cell carcinoma (RCC) in patients with visible haematuria. MATERIALS & METHODS 'A Prospective Trial for Examining Hematuria using Computed Tomography' (PROTEHCT) was a single-centre prospective paired diagnostic study in patients referred for CT due to painless visible haematuria between September 2019 and June 2021. All patients underwent four-phase CT (control) from which a single nephrographic phase dual energy CT (experimental) was extracted. Both were independently assessed for RCC by randomised radiologists. Histologically verified RCC defined a positive reference standard. Follow-up ascertainment of RCC diagnosis was completed in May 2022. Descriptive statistics were used to calculate the accuracies. Inter-reader agreement was assessed by kappa statistics. RESULTS A total of 308 patients (median age, 68 years [interquartile range 53-77, range 18-96], 250 males) were included for analysis. RCC was diagnosed in seven (2.3%) patients during a median follow-up time of 19 months (interquartile range: 15-25). For the control and experimental CT, sensitivity was 100% versus 100%, specificity was 97% versus 98% and accuracy 97% versus 97%. The positive predictive value was 44% versus 50%, and the negative predictive value was 100% versus 100%. The agreement between the control and experimental CT was 98% (k = 0.79). CONCLUSION A single nephrographic phase dual energy CT is sufficient for detecting RCC in patients with visible haematuria.
The Composition of the Bulge Globular Cluster NGC 6273

Abstract Observations of red giants in the Bulge globular cluster NGC 6273 with the Michigan/Magellan Fiber System (M2FS) mounted on the Nasmuth-East port of the Magellan-Clay 6.5-m telescope at the Las Campanas Observatory reveal a spread in metallicity. Members have been confirmed with radial velocity. NGC 6273 has at least two populations separated by 0.2-0.3 dex in [Fe/H]. The sodium and aluminum abundances are correlated while the magnesium and aluminum abundances are anti-correlated. The cluster also shows a rise in the abundance of the s-process element lanthanum with [Fe/H] similar to other massive clusters. The cluster contains a possible third population depleted in most elements by 0.3 dex.
Contrasting clinical outcomes and socio‐economic impact of young versus elderly‐onset oral squamous cell carcinoma, a novel health economic analysis

Abstract Objectives The incidence of young‐onset oral squamous cell carcinoma (OSCC) is growing, even among non‐smokers/drinkers. The effects of adverse histopathological features on long‐term oncologic outcomes between the young and old are controversial and confounded by significant heterogeneity. Few studies have evaluated the socio‐economic impact of premature mortality from OSCC. Our study seeks to quantify these differences and their economic impact on society. Materials and Methods Four hundred and seventy‐eight young (<45 years) and 1660 old patients (≥45 years) with OSCC were studied. Logistic regression determined predictors of recurrence and death. Survival analysis was calculated via the Kaplan–Meier method. A separate health economic analysis was conducted for India and Singapore. Years of Potential Productive Life Lost (YPPLL) were estimated with the Human Capital Approach, and premature mortality cost was derived using population‐level data. Results Adverse histopathological features were seen more frequently in young OSCC: PNI (42.9% vs. 35%, p = 0.002), LVI (22.4% vs. 17.3%, p = 0.013) and ENE (36% vs. 24.5%, p < 0.001). Although 5‐year OS/DSS were similar, the young cohort had received more intensive adjuvant therapy (CCRT 26.9% vs. 16.6%, p < 0.001). Among Singaporean males, the premature mortality cost per death was US $396,528, and per YPPLL was US $45,486. This was US $397,402 and US $38,458 for females. Among Indian males, the premature mortality cost per death was US $30,641, and per YPPLL was US $595. This was US $ 21,038 and US $305 for females. Conclusion Young‐onset OSCC is an aggressive disease, mitigated by the ability to receive intensive adjuvant treatment. From our loss of productivity analysis, the socio‐economic costs from premature mortality are substantial. Early cancer screening and educational outreach campaigns should be tailored to this cohort. Alongside, more funding should be diverted to genetic research, developing novel biomarkers and improving the efficacy of adjuvant treatment in OSCC.
Cold-formed Steel Pitched-roof Portal Frames of Back-to-back Plain Channel Sections and Bolted Joints

The paper summarises the results of an experimental program carried out in order to evaluate performance of pitched roof cold-formed steel portal frames of back-to-back plain channel sections and bolted joints. Three different configurations of apex and knee joints were tested. The behaviour and failure mechanisms of joints were observed in order to evaluate their stiffness, strength and ductility. Distribution of bolt forces showed to differ significantly from the classical assumption of forces proportional to the bolt centre of gravity. Joints between cold-formed members with bolts in the web only result in reduction of joint moment capacity and premature web buckling. The component method was applied in order to characterise joint stiffness and moment capacity on the purpose of frame analysis and design. To check the design procedure, full-scale tests on frames were performed. Fair agreement between analytical and experimental results was obtained. experimental failure mode local buckling of the cold-formed
Distributions and controllability problems (I)

We consider a non-linear real analytic control system of first order $\dot q^i = f^i(t, q, w)$, with controls $w = (w^\alpha)$ in a connected open set $\mathcal{K} \subset \mathbb{R}^m$ and configurations $q = (q^i)$ in $\mathcal{Q} := \mathbb{R}^n$. The set of points in the extended space-time $\mathcal{M} = \mathbb{R} \times \mathcal{Q} \times \mathcal{K}$, which can be reached from a triple $x_o = (t_o , q_o, w_o) \in \mathcal{M}$ through a continuous graph completion $\gamma(s) = \big(t(s), q(t(s)), w(t(s))\big)$ of the graph of a solution $t \to (q(t), w(t))$, $t \in [t_o ,t_o + T]$, with piecewise real analytic controls, is called the {\it $\mathcal{M}$-attainable set of $x_o$ in time $T$}. We prove that if $y_o$ is an $\mathcal{M}$-attainable point of $x_o$, a large set of other nearby $\mathcal{M}$-attainable points of $x_o$ can be determined starting directly from $y_o$ and applying an appropriate ordered composition of flows of vector fields in a distinguished distribution $\mathcal{D}^{II} \subset T \mathcal{M}$, canonically associated with the control system. We then determine sufficient conditions for such neighbouring points to constitute an orbit of the pseudogroup of local diffeomorphisms generated by the vector fields in $\mathcal{D}^{II}$. If such conditions are satisfied and if the tangent spaces of these orbits have maximal rank projections onto $\mathcal{Q}$, the control system is locally accessible and has the small time local controllability property near the state points of equilibrium. These results lead to new proofs of classical local controllability criterions and yield new methods to establish the accessibility and the small time local controllability of non-linear control systems.
Counting in Lattice Orbits

Given a discrete lattice, $\Gamma<\text{SL}_m(\mathbb{R})$, and a base point $o\in \mathbb{R}^m$, let $N_\Gamma(T)$ denote the number of points in the orbit $o\cdot \Gamma$ whose (Euclidean) length is bounded by a growing parameter, $T$. We demonstrate an abstract spectral method capable of obtaining strong asymptotic estimates for $N_\Gamma(T)$ without relying on the meromorphic continuation of higher rank Langlands Eisenstein series.
Spatial Distributions of Larval, Newly-Settled, and Benthic Stages of Bivalves in Subtidal Areas Adjacent to Tidal Flats

TSUTSUMI, YASUO and HIDEO SEKIGUCHI (Faculty of Bioresources, Mie University). 1996. Spatial Distributions of Larval, Newly-Settled, and Benthic Stages of Bivalves in Subtidal Areas Adjacent to Tidal Flats. Benthos Research, 50:29-37. Spatial distributions of planktonic larvae and newly-settled and benthic stages of three species of bivalves (Ruditapes philippinarum, Nut tallia oliuacea, Musculista senhousia) were investigated in the subtidal area adjacent to the tidal flats of the Ano and Shitomo Rivers. In all three species the benthic juveniles and adults were very scarce in the subtidal area, while the larvae occurred abundantly there. The newly-settled and benthic bivalves tended to occur at sites near the low water of tidal level water. This suggests that larvae avoid settling on the bottom in the subtidal area but are transported to the lower margin of the tidal flats.
3QFP: Efficient neural implicit surface reconstruction using Tri-Quadtrees and Fourier feature Positional encoding

Neural implicit surface representations are currently receiving a lot of interest as a means to achieve high-fidelity surface reconstruction at a low memory cost, compared to traditional explicit representations.However, state-of-the-art methods still struggle with excessive memory usage and non-smooth surfaces. This is particularly problematic in large-scale applications with sparse inputs, as is common in robotics use cases. To address these issues, we first introduce a sparse structure, \emph{tri-quadtrees}, which represents the environment using learnable features stored in three planar quadtree projections. Secondly, we concatenate the learnable features with a Fourier feature positional encoding. The combined features are then decoded into signed distance values through a small multi-layer perceptron. We demonstrate that this approach facilitates smoother reconstruction with a higher completion ratio with fewer holes. Compared to two recent baselines, one implicit and one explicit, our approach requires only 10\%--50\% as much memory, while achieving competitive quality.
Lower Bounds for Unitary Property Testing with Proofs and Advice

In unitary property testing a quantum algorithm, also known as a tester, is given query access to a black-box unitary and has to decide whether it satisfies some property. We propose a new technique for proving lower bounds on the quantum query complexity of unitary property testing and related problems, which utilises the connection between unitary property testing and unitary channel discrimination. The main advantage of this technique is that all obtained lower bounds hold for any $\mathsf{C}$-tester with $\mathsf{C} \subseteq \mathsf{QMA}(\text{poly(n)} / \mathsf{qpoly}$, showing that even having access to both (unentangled) quantum proofs and advice does not help for many unitary problems. We apply our technique to prove lower bounds for problems like quantum phase estimation, the entanglement entropy problem, quantum Gibbs sampling and more, removing all logarithmic factors in the lower bounds obtained by the sample-to-query lifting theorem of Wang and Zhang (2023). As a direct corollary, we show that there exists a quantum oracle relative to which $\mathsf{QMA}(\text{poly(n)} / \mathsf{qpoly} \not\supset \mathsf{SBQP}$.
Combining Machine Learning and Ontology: A Systematic Literature Review

Motivated by the desire to explore the process of combining inductive and deductive reasoning, we conducted a systematic literature review of articles that investigate the integration of machine learning and ontologies. The objective was to identify diverse techniques that incorporate both inductive reasoning (performed by machine learning) and deductive reasoning (performed by ontologies) into artificial intelligence systems. Our review, which included the analysis of 128 studies, allowed us to identify three main categories of hybridization between machine learning and ontologies: learning-enhanced ontologies, semantic data mining, and learning and reasoning systems. We provide a comprehensive examination of all these categories, emphasizing the various machine learning algorithms utilized in the studies. Furthermore, we compared our classification with similar recent work in the field of hybrid AI and neuro-symbolic approaches.
Confounded Budgeted Causal Bandits

We study the problem of learning 'good' interventions in a stochastic environment modeled by its underlying causal graph. Good interventions refer to interventions that maximize rewards. Specifically, we consider the setting of a pre-specified budget constraint, where interventions can have non-uniform costs. We show that this problem can be formulated as maximizing the expected reward for a stochastic multi-armed bandit with side information. We propose an algorithm to minimize the cumulative regret in general causal graphs. This algorithm trades off observations and interventions based on their costs to achieve the optimal reward. This algorithm generalizes the state-of-the-art methods by allowing non-uniform costs and hidden confounders in the causal graph. Furthermore, we develop an algorithm to minimize the simple regret in the budgeted setting with non-uniform costs and also general causal graphs. We provide theoretical guarantees, including both upper and lower bounds, as well as empirical evaluations of our algorithms. Our empirical results showcase that our algorithms outperform the state of the art.
Lightweight Modality Adaptation to Sequential Recommendation via Correlation Supervision

In Sequential Recommenders (SR), encoding and utilizing modalities in an end-to-end manner is costly in terms of modality encoder sizes. Two-stage approaches can mitigate such concerns, but they suffer from poor performance due to modality forgetting, where the sequential objective overshadows modality representation. We propose a lightweight knowledge distillation solution that preserves both merits: retaining modality information and maintaining high efficiency. Specifically, we introduce a novel method that enhances the learning of embeddings in SR through the supervision of modality correlations. The supervision signals are distilled from the original modality representations, including both (1) holistic correlations, which quantify their overall associations, and (2) dissected correlation types, which refine their relationship facets (honing in on specific aspects like color or shape consistency). To further address the issue of modality forgetting, we propose an asynchronous learning step, allowing the original information to be retained longer for training the representation learning module. Our approach is compatible with various backbone architectures and outperforms the top baselines by 6.8% on average. We empirically demonstrate that preserving original feature associations from modality encoders significantly boosts task-specific recommendation adaptation. Additionally, we find that larger modality encoders (e.g., Large Language Models) contain richer feature sets which necessitate more fine-grained modeling to reach their full performance potential.
FiGCLIP: Fine-Grained CLIP Adaptation via Densely Annotated Videos

While contrastive language image pretraining (CLIP) have exhibited impressive performance by learning highly semantic and generalized representations, recent works have exposed a fundamental drawback in its syntactic properties, that includes interpreting fine-grained attributes, actions, spatial relations, states, and details that require compositional reasoning. One reason for this is that natural captions often do not capture all the visual details of a scene. This leads to unaddressed visual concepts being misattributed to the wrong words. And the pooled image and text features, ends up acting as a bag of words, hence losing the syntactic information. In this work, we ask: Is it possible to enhance CLIP's fine-grained and syntactic abilities without compromising its semantic properties? We show that this is possible by adapting CLIP efficiently on a high-quality, comprehensive, and relatively small dataset. We demonstrate our adaptation strategy on VidSitu, a video situation recognition dataset annotated with verbs and rich semantic role labels (SRL). We use the SRL and verb information to create rule-based detailed captions, making sure they capture most of the visual concepts. Combined with hard negatives and hierarchical losses, these annotations allow us to learn a powerful visual representation, dubbed Fine-Grained CLIP (FiGCLIP), that preserves semantic understanding while being detail-oriented. We evaluate on five diverse vision-language tasks in both fine-tuning and zero-shot settings, achieving consistent improvements over the base CLIP model.
On free boundary problems shaped by oscillatory singularities

We start the investigation of free boundary variational models featuring oscillatory singularities. The theory varies widely depending upon the nature of the singular power $\gamma(x)$ and how it oscillates. Under a mild continuity assumption on $\gamma(x)$, we prove the optimal regularity of minimizers. Such estimates vary point-by-point, leading to a continuum of free boundary geometries. We also conduct an extensive analysis of the free boundary shaped by the singularities. Utilizing a new monotonicity formula, we show that if the singular power $\gamma(x)$ varies in a $W^{1,n^{+}}$ fashion, then the free boundary is locally a $C^{1,\delta}$ surface, up to a negligible singular set of Hausdorff co-dimension at least $2$.
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition

Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on face recognition datasets for feature extraction. However, due to the vast scale of face recognition datasets and the high cost associated with collecting facial labels, this pre-training paradigm incurs significant expenses. Towards this end, we propose to pre-train vision Transformers (ViTs) through a self-supervised approach on a mid-scale general image dataset. In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced. Therefore, we propose a contrastive fine-tuning approach to effectively mitigate this domain disparity. Specifically, we introduce a novel FER training paradigm named Mask Image pre-training with MIx Contrastive fine-tuning (MIMIC). In the initial phase, we pre-train the ViT via masked image reconstruction on general images. Subsequently, in the fine-tuning stage, we introduce a mix-supervised contrastive learning process, which enhances the model with a more extensive range of positive samples by the mixing strategy. Through extensive experiments conducted on three benchmark datasets, we demonstrate that our MIMIC outperforms the previous training paradigm, showing its capability to learn better representations. Remarkably, the results indicate that the vanilla ViT can achieve impressive performance without the need for intricate, auxiliary-designed modules. Moreover, when scaling up the model size, MIMIC exhibits no performance saturation and is superior to the current state-of-the-art methods.
Determinants and Dynamics of Schooling and Child Labor in Bolivia

This paper investigates the determinants of primary school enrolment, attendance, and child labour in Bolivia from 1999 to 2007, and attempts to analyse the interactions among these decisions over time. Although enrolment rates show a significant improvement, a high proportion of children do not attend school. The empirical results reveal that the increase in enrolment is led by indigenous children and those living in urban areas. Moreover, contrary to common belief, being extremely poor and indigenous are the main determinants of school attendance. Finally, although extremely poor children increased their school attendance, there was no reduction in child labour, which remains a relevant issue in Bolivia.
The Chronicles of RAG: The Retriever, the Chunk and the Generator

Retrieval Augmented Generation (RAG) has become one of the most popular paradigms for enabling LLMs to access external data, and also as a mechanism for grounding to mitigate against hallucinations. When implementing RAG you can face several challenges like effective integration of retrieval models, efficient representation learning, data diversity, computational efficiency optimization, evaluation, and quality of text generation. Given all these challenges, every day a new technique to improve RAG appears, making it unfeasible to experiment with all combinations for your problem. In this context, this paper presents good practices to implement, optimize, and evaluate RAG for the Brazilian Portuguese language, focusing on the establishment of a simple pipeline for inference and experiments. We explored a diverse set of methods to answer questions about the first Harry Potter book. To generate the answers we used the OpenAI's gpt-4, gpt-4-1106-preview, gpt-3.5-turbo-1106, and Google's Gemini Pro. Focusing on the quality of the retriever, our approach achieved an improvement of MRR@10 by 35.4% compared to the baseline. When optimizing the input size in the application, we observed that it is possible to further enhance it by 2.4%. Finally, we present the complete architecture of the RAG with our recommendations. As result, we moved from a baseline of 57.88% to a maximum relative score of 98.61%.