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Recently, screens for mediators of resistance to FLT3 and ABL kinase inhibitors in leukemia resulted in the discovery of LZTR1 as an adapter of a Cullin-3 RING E3 ubiquitin ligase complex responsible for the degradation of RAS GTPases. In parallel, dysregulated LZTR1 expression via aberrant splicing and mutations was identified in clonal hematopoietic conditions. Here we identify that loss of LZTR1, or leukemia-associated mutants in the LZTR1 substrate and RAS GTPase RIT1 that escape degradation, drives hematopoietic stem cell (HSC) expansion and leukemia in vivo. Although RIT1 stabilization was sufficient to drive hematopoietic transformation, transformation mediated by LZTR1 loss required MRAS. Proteolysis targeting chimeras (PROTAC) against RAS or reduction of GTP-loaded RAS overcomes LZTR1 loss-mediated resistance to FLT3 inhibitors. These data reveal proteolysis of noncanonical RAS proteins as novel regulators of HSC self-renewal, define the function of RIT1 and LZTR1 mutations in leukemia, and identify means to overcome drug resistance due to LZTR1 downregulation.Significance:Here we identify that impairing proteolysis of the noncanonical RAS GTPases RIT1 and MRAS via LZTR1 downregulation or leukemia-associated mutations stabilizing RIT1 enhances MAP kinase activation and drives leukemogenesis. Reducing the abundance of GTP-bound KRAS and NRAS overcomes the resistance to FLT3 kinase inhibitors associated with LZTR1 downregulation in leukemia.This article is highlighted in the In This Issue feature, p. 2221

Supplementary Figure from Impaired Proteolysis of Noncanonical RAS Proteins Drives Clonal Hematopoietic Transformation

Copy number variation (CNV) of samples in TCGA

DEDD and MCL1 overexpression are not necessary for initiation of tumorigenesis.

Focal amplification of chromosome 1q23.3 in patients with advanced primary or relapsed urothelial carcinomas is associated with poor survival. We interrogated chromosome 1q23.3 and the nearby focal amplicon 1q21.3, as both are associated with increased lymph node disease in patients with urothelial carcinoma. Specifically, we assessed whether the oncogene MCL1 that resides in 1q21.3 and the genes that reside in the 1q23.3 amplicon were required for the proliferation or survival of urothelial carcinoma. We observed that suppressing MCL1 or the death effector domain–containing protein (DEDD) in the cells that harbor amplifications of 1q21.3 or 1q23.3, respectively, inhibited cell proliferation. We also found that overexpression of MCL1 or DEDD increased anchorage independence growth in vitro and increased experimental metastasis in vivo in the nonamplified urothelial carcinoma cell line, RT112. The expression of MCL1 confers resistance to a range of apoptosis inducers, while the expression of DEDD led to resistance to TNFα-induced apoptosis. These observations identify MCL1 and DEDD as genes that contribute to aggressive urothelial carcinoma.Implications:These studies identify MCL1 and DEDD as genes that contribute to aggressive urothelial carcinomas.

With a great success of the generative model via deep neural networks, monocular depth estimation has been actively studied by exploiting various encoder-decoder architectures. However, the decoding process in most previous methods, which repeats simple up-sampling operations, probably fails to fully utilize underlying properties of well-encoded features for monocular depth estimation. To resolve this problem, we propose a simple but effective scheme by incorporating the Laplacian pyramid into the decoder architecture. Specifically, encoded features are fed into different streams for decoding depth residuals, which are defined by decomposition of the Laplacian pyramid, and corresponding outputs are progressively combined to reconstruct the final depth map from coarse to fine scales. This is fairly desirable to precisely estimate the depth boundary as well as the global layout. We also propose to apply weight standardization to pre-activation convolution blocks of the decoder architecture, which gives a great help to improve the flow of gradients and thus makes optimization easier. Experimental results on benchmark datasets constructed under various indoor and outdoor environments demonstrate that the proposed method is effective for monocular depth estimation compared to state-of-the-art models. The code and model are publicly available at: | https://github.com/tjqansthd/LapDepth-release |.

Aims The aim of this study was to investigate the causal relationship and evidence of an association between increased adiposity and the risk of incident cardiovascular disease (CVD) events or mortality. Methods and results Observational (informing association) and Mendelian randomization (MR) (informing causality) studies were assessed to gather mutually complementary insights and elucidate perplexing epidemiological relationships. Systematic reviews and meta-analyses of observational and MR studies that were published until January 2021 and evaluated the association between obesity-related indices and CVD risk were searched. Twelve systematic reviews with 53 meta-analyses results (including over 501 cohort studies) and 12 MR studies were included in the analysis. A body mass index (BMI) increase was associated with higher risks of coronary heart disease, heart failure, atrial fibrillation, all-cause stroke, haemorrhagic stroke, ischaemic stroke, hypertension, aortic valve stenosis, pulmonary embolism, and venous thrombo-embolism. The MR study results demonstrated a causal effect of obesity on all indices but stroke. The CVD risk increase for every 5 kg/m2 increase in BMI varied from 10% [relative risk (RR) 1.10; 95% confidence interval (CI) 1.01–1.21; certainty of evidence, low] for haemorrhagic stroke to 49% (RR 1.49; 95% CI 1.40–1.60; certainty of evidence, high) for hypertension. The all-cause and CVD-specific mortality risks increased with adiposity in cohorts, but the MR studies demonstrated no causal effect of adiposity on all-cause mortality. Conclusion High adiposity is associated with increased CVD risk despite divergent evidence gradients. Adiposity was a causal risk factor for CVD except all-cause mortality and stroke. Half (49%; 26/53) of the associations were supported by high-level evidence. The associations were consistent between sexes and across global regions. This study provides guidance on how to integrate evidence from observational (association) and genetics-driven (causation) studies accumulated to date, to enable a more reliable interpretation of epidemiological relationships.

Background: There are few reports that evaluated the association between various types of dementia and dual oral therapy with antihyperglycemic medication. Objective: The goal of this study was to investigate the association between treatment of dual antihyperglycemic medication and dementia subclass in type 2 diabetes mellitus using the Korean National Health Insurance System. Methods: This study included 701,193 individuals with diabetes prescribed dual oral therapy between 2009 and 2012 from the Korean National Health Insurance Service Database, which were tracked until 2017. All-cause, Alzheimer’s (AD) and vascular dementia (VaD) were investigated by dual oral therapy. Adjustments were made for age, sex, income, diabetes duration, hypertension, dyslipidemia, smoking, drinking, exercise, body mass index, glucose level, and estimated glomerular filtration rate. Results: Dual therapy with metformin (Met) + dipeptidyl peptidase-4 inhibitor (DPP-4i), Met + thiazolidinedione (TZD), and sulfonylurea (SU) + thiazolidinediones (TZD) were significantly associated with all-cause dementia (HR = 0.904, 0.804, and 0.962, respectively) and VaD (HR = 0.865, 0.725, and 0.911, respectively), compared with Met + SU. Met + DPP-4i and Met + TZD were associated with significantly lower risk of AD (HR = 0.922 and 0.812), compared with Met + SU. Dual therapy with TZD was associated with a significantly lower risk of all-cause dementia, AD, and VaD than nonusers of TZD (HR = 0.918, 0.925 and 0.859, respectively). Conclusion: Adding TZD or DPP-4i instead of SU as second-line anti-diabetic treatment may be considered for delaying or preventing dementia. Also, TZD users relative to TZD non-users on dual oral therapy were significantly associated with lower risk of various types of dementia.

This study was to assess the possibility of using competitive and slow binding experiments with affinity-based ultrafiltration UPLC-QTof-MS analysis to identify potent bacterial neuraminidase (bNA) inhibitors from the Broussonetia papyrifera roots extract. To isolate unbound compounds from the enzyme-binding complex, the root bark extracts were either incubated in the absence of bNA, in the presence of bNA, or with the time-dependent bNA before the ultrafiltration was performed. Thirteen flavonoids were separated from the target extract, and their inhibitory activities were tested against bNA. The isolated flavonoids exhibited potent inhibition against NA (IC50 = 0.7–54.0 μM). Our kinetic analysis of representative active flavonoids (1, 2, and 6) showed slow and time-dependent reversible inhibition. Additionally, chalcones exhibited noncompetitive inhibition characteristics, whereas flavonols and flavans showed mixed-type behavior. The computational results supported the experimental behaviors of flavonoids 2, 6, 10, and 12, indicating that bounded to the active site, but flavonoids 6 and 10 binds near but not accurately at the active site. Although this is mixed-type inhibition, their binding can be considered competitive.

Therapy resistance is a major challenge in the treatment of cancer. Here, we performed CRISPR/Cas9 screens across a broad range of therapies used in acute myeloid leukemia to identify genomic determinants of drug response. Our screens uncovered a selective dependency on RNA splicing factors whose loss preferentially enhanced response to the BCL2 inhibitor venetoclax. Loss of the splicing factor RBM10 augmented response to venetoclax in leukemia yet was completely dispensable for normal hematopoiesis. Combined RBM10 and BCL2 inhibition led to mis-splicing and inactivation of the inhibitor of apoptosis XIAP and downregulation of BCL2A1, an anti-apoptotic protein implicated in venetoclax resistance. A novel inhibitor of splicing kinase families CLKs and DYRKs led to aberrant splicing of key splicing and apoptotic factors that synergized with venetoclax and overcame resistance to BCL2 inhibition. Our findings underscore the importance of splicing in modulating response to therapies and provide a strategy to improve venetoclax-based treatments. Citation Format: Eric Wang, Omar Abdel-Wahab, Robert K Bradley, Jose Mario Bello, Won Jun Kim, Carine Bossard. Modulation of RNA splicing enhances response to BCL2 inhibition in leukemia [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A04.

Various images captured in complicated lighting conditions often suffer from deterioration of the image quality. Such poor quality not only dissatisfies the user expectation but also may lead to a significant performance drop in many applications. In this paper, anovel method for low-light image enhancement is proposed by leveraging useful propertiesof the Laplacian pyramid both in image and feature spaces. Specifically, the proposed method, so-called a deep stacked Laplacian restorer (DSLR), is capable of separately recovering the global illumination and local details from the original input, and progressively combining them in the image space. Moreover, the Laplacian pyramid defined in the feature space makes such recovering processes more efficient based on abundant connectionsof higher-order residuals in a multiscale structure. This decomposition-based scheme is fairly desirable for learning the highly nonlinear relation between degraded images and their enhanced results. Experimental results on various datasets demonstrate that the proposed DSLR outperforms state-of-the-art methods. The code and model are publicly available at: https://github.com/SeokjaeLIM/DSLR-release .

Janus kinases (JAKs) mediate cytokine signaling, cell growth and hematopoietic differentiation.1 Gain-of-function mutations activating JAK2 signaling are seen in the majority of myeloproliferative neoplasm (MPN) patients, most commonly due to the JAK2V617F driver allele.2 While clinically-approved JAK inhibitors improve symptoms and outcomes in MPNs, remissions are rare, and mutant allele burden does not substantively change with chronic JAK inhibitor therapy in most patients.3, 4 This has been postulated to be due to incomplete dependence on constitutive JAK/STAT signaling, alternative signaling pathways, and/or the presence of cooperating disease alleles;5 however we hypothesize this is due to the inability of current JAK inhibitors to potently and specifically abrogate mutant JAK2 signaling. We therefore developed a conditionally inducible mouse model allowing for sequential activation, and then inactivation, of Jak2V617F from its endogenous locus using a Dre-rox/Cre-lox dual orthogonal recombinase system. Deletion of oncogenic Jak2V617Fabrogates the MPN disease phenotype, induces mutant-specific cell loss including in hematopoietic stem/progenitor cells, and extends overall survival to an extent not observed with pharmacologic JAK inhibition. Furthermore, reversal of Jak2V617F in MPN cells with antecedent loss of Tet26, 7 abrogates the MPN phenotype and inhibits mutant stem cell persistence suggesting cooperating epigenetic-modifying alleles do not alter dependence on mutant JAK/STAT signaling. Our results suggest that mutant-specific inhibition of JAK2V617F represents the best therapeutic approach for JAK2V617F-mutant MPN and demonstrate the therapeutic relevance of a dual-recombinase system to assess mutant-specific oncogenic dependencies in vivo.

Cancer is caused by uncontrolled cell division and is a leading cause of mortality worldwide. Oenothera odorata (O. odorata) extract is used in herbal medicine to inhibit inflammation, but its potential anti-tumor properties have not been fully evaluated. Here, we demonstrated that O. odorata extract inhibits the proliferation of lung adenocarcinoma and melanoma cell lines In Vitro, and also inhibits the growth of melanoma cells In Vivo. After partitioning the extract with n-hexane, chloroform, ethyl acetate, and n-butanol, it was found that the butanol-soluble (OOB) and water-soluble (OOW) fractions of O. odorata extract are effective at inhibiting tumor cell growth In Vivo although OOW is more effective than OOB. Interestingly, these fractions did not inhibit the growth of non-cancerous cells. The anti-proliferative effects of the OOW fraction were found to be mediated by inhibition of glycolysis and cellular respiration. UPLC of both fractions showed two major common peaks, which were predicted to be hydrolyzable tannin-related compounds. Taken together, these data suggest that O. odorata extract has anti-tumor properties, and the molecular mechanism involves metabolic alterations and inhibition of cell proliferation. O. odorata extract therefore holds promise as a novel natural product for the treatment of cancer.

Thermal sensors bring a lot of benefits for object tracking due to the robustness to lighting changes over time, which still give a great difficulty to traditional tracking methods. Despite this powerful advantage, the ambiguity due to background clutters and occlusions makes the problem of thermal sensor-based tracking intractable. In this paper, we propose a novel framework for multiple object tracking in livestock breeding spaces using a single thermal sensor. The key idea of the proposed method is to define a new rotatable elliptical motion model, which is suitable for describing complicated motions of animals. Moreover, the proposed local constraint guides our tracker to robustly chase targets even with severe occlusions. Experimental results on various thermal video sequences show the significant improvement for tracking animals compared to other approaches proposed in the literature.

Objective To determine whether the bioimpedance analysis (BIA) ratios of upper to lower extremities could predict treatment outcomes after complex decongestive therapy (CDT) for gynecological cancer related lymphedema (GCRL).Methods A retrospective study, from March 2015 to December 2018, was conducted. The study sample comprised patients receiving CDT, 30 minutes per day, for 10 days. Bioimpedance was measured pre- and post-CDT. Circumference measurements were obtained at 20 and 10 cm above the knee (AK) and 10 cm below the knee (BK). We calculated the expected impedance at 0 Hz (R0) of extremities and upper/lower extremity R0 ratios (R0U/L). We evaluated the relationship between R0U/L and changes in R0U/L and circumferences, pre- and post-CDT.Results Overall, 59 patients were included in this study. Thirty-one lower extremities in 26 patients comprised the acute group, and 38 lower extremities in 33 patients comprised the chronic group. Pre-treatment R0U/L was significantly correlated with R0U/L change after adjusting for age and BMI (acute: R=0.513, pU/L showed a tendency to be correlated with circumference change (AK 20 cm: R=0.427, p=0.02; AK 10 cm: R=0.399, p=0.03).Conclusion Our study results suggested that pre-treatment BIA could predict volume reductions after CDT in the early stages of GCRL. These findings implied that BIA value could be one possible parameter to apply in treatment outcomes prediction, during the early stage of GCRL. Therefore, further large-scale prospective studies will be beneficial.

Nineteen compounds were isolated from the stems of Maackia amurensis by activity-guided screening for new human monoamine oxidase-B (hMAO-B) inhibitors. Among the compounds isolated, flavonoids calycosin (5) and 8-O-methylretusin (6) were found to potently and selectively inhibit hMAO-B (IC50 = 0.24 and 0.23 μM, respectively) but not hMAO-A with high selectivity index (SI) values (SI = 293.8 and 81.3, respectively). In addition, 5 and 6 reversibly and competitively inhibited hMAO-B with Ki values of 0.057 and 0.054 μM, respectively. A pterocarpan (−)-medicarpin (18) was also observed to strongly inhibit hMAO-B (IC50 = 0.30 μM). Most of the compounds weakly inhibited AChE, except isolupalbigenin (13) (IC50 = 20.6 μM), which suggested 13 be considered a potential dual function inhibitor of MAO-B and AChE. Molecular docking simulation revealed that the binding affinities of 5 and 6 for hMAO-B (both −9.3 kcal/mol) were higher than those for hMAO-A (−7.4 and −7.2 kcal/mol, respectively). Compound 5 was found to interact by hydrogen bonding with hMAO-B at Cys172 residue (distance: 3.250 A); no hydrogen bonding was predicted between 5 and hMAO-A. These findings suggest that compounds 5 and 6 be considered novel potent, selective, and reversible hMAO-B inhibitors and candidates for the treatment of neurological disorders.

Succinic acid (SA), a dicarboxylic acid of industrial importance, can be efficiently produced by metabolically engineered Mannheimia succiniciproducens. Malate dehydrogenase (MDH) is one of the key enzymes for SA production, but has not been well characterized. Here we report biochemical and structural analyses of various MDHs and development of hyper-SA producing M. succiniciproducens by introducing the best MDH. Corynebacterium glutamicum MDH (CgMDH) shows the highest specific activity and least substrate inhibition, whereas M. succiniciproducens MDH (MsMDH) shows low specific activity at physiological pH and strong uncompetitive inhibition toward oxaloacetate (ki of 67.4 and 588.9 μM for MsMDH and CgMDH, respectively). Structural comparison of the two MDHs reveals a key residue influencing the specific activity and susceptibility to substrate inhibition. A high-inoculum fed-batch fermentation of the final strain expressing cgmdh produces 134.25 g L−1 of SA with the maximum productivity of 21.3 g L−1 h−1, demonstrating the importance of enzyme optimization in strain development., Malate dehydrogenase (MDH) is one of the key enzymes for succinic acid (SA) bioproduction. Here, the authors report biochemical and structural analyses of various MDHs to reveal amino acids influencing the specific activity and susceptibility to substrate inhibition, and achieve industrial-level SA production.

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under challenging illumination conditions. Even though the significant progress has been made for enhancing the poor visibility, the intrinsic noise amplified in low-light areas still remains as an obstacle for further improvement in visual quality. In this paper, a novel and simple method for low-light image enhancement is proposed. Specifically, the subspace, which has an ability to separately reveal illumination and noise, is constructed from a group of similar image patches, so-called volume, at each pixel position. Based on the principal energy analysis onto this volume-based subspace, the illumination component is accurately inferred from a given image while the unnecessary noise is simultaneously suppressed. This leads to clearly unveiling the underlying structure in low-light areas without loss of details. Experimental results show the efficiency and robustness of the proposed method for low-light image enhancement compared to state-of-the-art methods.

As biometric authentication systems are popularly used in various mobile devices, e.g., smart-phones and tablets, face anti-spoofing methods have been actively developed for the high-level security. However, most previous approaches still suffer from diverse types of spoofing attacks, which are hardly covered by the limited number of training datasets, and thus they often show the poor accuracy when unseen samples are given for the test. To address this problem, a novel method for face anti-spoofing is proposed based on one-class (i.e., live face only) learning with the live correlation loss. Specifically, encoder-decoder networks are firstly trained with only live faces to extract latent features, which have an ability to compactly represent various live facial properties in the embedding space and produce the spoofing cues, which are simply obtained by subtracting the original RGB image and the generated one. After that, such features are fed into the proposed feature correlation network (FCN) so that weights of FCN learn to compute “liveness” of given features under the guidance of the live correlation loss. It is noteworthy that the proposed method only requires live facial images for training the model, which are easier to obtain than fake ones, and thus the generality power for resolving the problem of face anti-spoofing can be expected to be improved. Experimental results on various benchmark datasets demonstrate the efficiency and robustness of the proposed method.

Shadow detection is one of the most challenging issues in computer vision. Inspired by the great success of the convolutional neural network (CNN) for the problem of image restoration, learned features have been widely adopted for shadow detection. However, most existing methods still suffer from ambiguities driven by black-colored objects, which are not actually shaded, as well as the background clutter. In this letter, we propose the attentive feedback feature pyramid network (AFFPN) for shadow detection in a single image. The key idea of the proposed method is to extract shadow-relevant features based on multiple feedback modules, which are defined in the feature pyramid network. Specifically, attentive features extracted from each level of the encoder are progressively refined via connections between feedback modules from high-level to low-level layers for learning properties of shadow more accurately. Experimental results on benchmark datasets show that the proposed method is effective for shadow detection under complicated real-world environments. The code and model are publicly available at: https://github.com/JinheeKIM94/AFFPN_release .

Inspired by the great success of the deep neural networks in various fields of computer vision, studies for image deblurring have begun to become more active in recent days. However, most previous approaches often fail to accurately remove the blur artifacts, e.g., ghosting effects at the object boundaries and degradation of local details, in restored results. In this paper, we propose a deep spectral-spatial network (DSSN) for resolving the problem of single image deblurring. Specifically, the proposed method is able to efficiently recover scene characteristics in a global manner by minimizing differences of the frequency magnitude between the blurred input and corresponding sharp image via the spectral restorer, and the spatial restorer fine-tunes local details of the intermediate result, which is estimated by the spectral one, based on the intensity similarity. This cascaded scheme of deblurring processes is fairly desirable for clearly restoring edge-like structures as well as the textural information in a coarse-to-fine manner. Experimental results on benchmark datasets demonstrate that the proposed DSSN outperforms state-of-the-art methods. The code and model are publicly available at: https://github.com/SeokjaeLIM/DSSN_release .

Studies on morbid obesity have shown remarkable improvement of diabetes in patients who have undergone bariatric operations. It was subsequently shown that these operations induce diabetes remission independent of the resultant weight loss; as a result, surgeons began to investigate whether operations for gastric cancer (GC) could have the same beneficial effect on diabetes as bariatric operations. It was then shown in multiple reports that followed that certain operations for GC were able to improve or even cure type 2 diabetes mellitus (T2DM) in GC patients. This finding gave rise to the concept of "oncometabolic surgery", in which a patient diagnosed with both GC and T2DM undergo a single operation with the purpose of treating both diseases. With the increasing incidence of T2DM, oncometabolic surgery has the potential to improve the quality of life and even extend survival of many GC patients. However, because the GC patient population and the bariatric patient population are wildly different and because different GC operations have different properties, the effect of oncometabolic surgery must be carefully assessed and engineered in order to maximize benefit and avoid harm. This manuscript aims to summarize the findings made so far in the field of oncometabolic surgery and to provide an outlook regarding the possibility of oncometabolic surgery being incorporated into standard clinical practice.

Wilms tumor (WT) is the most common renal malignancy of childhood. Despite improvements in the overall survival, relapse occurs in ~15% of patients with favorable histology WT (FHWT). Half of these patients will succumb to their disease. Identifying novel targeted therapies in a systematic manner remains challenging in part due to the lack of faithful preclinical in vitro models. We established ten short-term patient-derived WT cell lines and characterized these models using low-coverage whole genome sequencing, whole exome sequencing and RNA-sequencing, which demonstrated that these ex-vivo models faithfully recapitulate WT biology. We then performed targeted RNAi and CRISPR-Cas9 loss-of-function screens and identified the nuclear export genes (XPO1 and KPNB1) as strong vulnerabilities. We observed that these models are sensitive to nuclear export inhibition using the FDA approved therapeutic agent, selinexor (KPT-330). Selinexor treatment of FHWT suppressed TRIP13 expression, which was required for survival. We further identified in vitro and in vivo synergy between selinexor and doxorubicin, a chemotherapy used in high risk FHWT. Taken together, we identified XPO1 inhibition with selinexor as a potential therapeutic option to treat FHWTs and in combination with doxorubicin, leads to durable remissions in vivo.