The Antagonistic Gene Paralogs Upf3a and Upf3b Govern Nonsense-Mediated RNA Decay.

Gene duplication is a major evolutionary force driving adaptation and speciation, as it allows for the acquisition of new functions and can augment or diversify existing functions. Here, we report a gene duplication event that yielded another outcome--the generation of antagonistic functions. One product of this duplication event--UPF3B--is critical for the nonsense-mediated RNA decay (NMD) pathway, while its autosomal counterpart--UPF3A--encodes an enigmatic protein previously shown to have trace NMD activity. Using loss-of-function approaches in vitro and in vivo, we discovered that UPF3A acts primarily as a potent NMD inhibitor that stabilizes hundreds of transcripts. Evidence suggests that UPF3A acquired repressor activity through simple impairment of a critical domain, a rapid mechanism that may have been widely used in evolution. Mice conditionally lacking UPF3A exhibit "hyper" NMD and display defects in embryogenesis and gametogenesis. Our results support a model in which UPF3A serves as a molecular rheostat that directs developmental events.

In vivo stabilization of a less toxic asparaginase variant leads to a durable antitumor response in acute leukemia.

Asparagine is a non-essential amino acid since it can either be taken up via the diet or synthesized by asparagine synthetase. Acute lymphoblastic leukemia (ALL) cells do not express asparagine synthetase or express it only minimally, which makes them completely dependent on extracellular asparagine for their growth and survival. This dependency makes ALL cells vulnerable to treatment with L-asparaginase, an enzyme that hydrolyzes asparagine. To date, all clinically approved L-asparaginases have significant L-glutaminase co-activity, associated with non-immune related toxic side effects observed during therapy. Therefore, reduction of L-glutaminase co-activity with concomitant maintenance of its anticancer L-asparaginase effect may effectively improve the tolerability of this unique drug. Previously, we designed a new alternative variant of Erwinia chrysanthemi (ErA; Erwinaze) with decreased L-glutaminase co-activity, while maintaining its L-asparaginase activity, by the introduction of three key mutations around the active site (ErA-TM). However, Erwinaze and our ErA-TM variant have very short half-lives in vivo. Here, we show that the fusion of ErA-TM with an albumin binding domain (ABD)-tag significantly increases its in vivo persistence. In addition, we evaluated the in vivo therapeutic efficacy of ABD-ErA-TM in a B-ALL xenograft model of SUP-B15. Our results show a comparable long-lasting durable antileukemic effect between the standard-of-care pegylated-asparaginase and ABD-ErA-TM L-asparaginase, but with fewer co-glutaminase-related acute side effects. Since the toxic side effects of current L-asparaginases often result in treatment discontinuation in ALL patients, this novel ErA-TM variant with ultra-low L-glutaminase co-activity and long in vivo persistence may have great clinical potential.

Neural Causal Information Extractor for Unobserved Causes.

Causal inference aims to faithfully depict the causal relationships between given variables. However, in many practical systems, variables are often partially observed, and some unobserved variables could carry significant information and induce causal effects on a target. Identifying these unobserved causes remains a challenge, and existing works have not considered extracting the unobserved causes while retaining the causes that have already been observed and included. In this work, we aim to construct the implicit variables with a generator-discriminator framework named the Neural Causal Information Extractor (NCIE), which can complement the information of unobserved causes and thus provide a complete set of causes with both observed causes and the representations of unobserved causes. By maximizing the mutual information between the targets and the union of observed causes and implicit variables, the implicit variables we generate could complement the information that the unobserved causes should have provided. The synthetic experiments show that the implicit variables preserve the information and dynamics of the unobserved causes. In addition, extensive real-world time series prediction tasks show improved precision after introducing implicit variables, thus indicating their causality to the targets.

Neural Network Structure Optimization by Simulated Annealing.

A critical problem in large neural networks is over parameterization with a large number of weight parameters, which limits their use on edge devices due to prohibitive computational power and memory/storage requirements. To make neural networks more practical on edge devices and real-time industrial applications, they need to be compressed in advance. Since edge devices cannot train or access trained networks when internet resources are scarce, the preloading of smaller networks is essential. Various works in the literature have shown that the redundant branches can be pruned strategically in a fully connected network without sacrificing the performance significantly. However, majority of these methodologies need high computational resources to integrate weight training via the back-propagation algorithm during the process of network compression. In this work, we draw attention to the optimization of the network structure for preserving performance despite compression by pruning aggressively. The structure optimization is performed using the simulated annealing algorithm only, without utilizing back-propagation for branch weight training. Being a heuristic-based, non-convex optimization method, simulated annealing provides a globally near-optimal solution to this NP-hard problem for a given percentage of branch pruning. Our simulation results have shown that simulated annealing can significantly reduce the complexity of a fully connected network while maintaining the performance without the help of back-propagation.

Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network.

This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.

Mechanism of Catalysis by l-Asparaginase.

Two bacterial type II l-asparaginases, from Escherichia coli and Dickeya chrysanthemi, have played a critical role for more than 40 years as therapeutic agents against juvenile leukemias and lymphomas. Despite a long history of successful pharmacological applications and the apparent simplicity of the catalytic reaction, controversies still exist regarding major steps of the mechanism. In this report, we provide a detailed description of the reaction catalyzed by E. coli type II l-asparaginase (EcAII). Our model was developed on the basis of new structural and biochemical experiments combined with previously published data. The proposed mechanism is supported by quantum chemistry calculations based on density functional theory. We provide strong evidence that EcAII catalyzes the reaction according to the double-displacement (ping-pong) mechanism, with formation of a covalent intermediate. Several steps of catalysis by EcAII are unique when compared to reactions catalyzed by other known hydrolytic enzymes. Here, the reaction is initiated by a weak nucleophile, threonine, without direct assistance of a general base, although a distant general base is identified. Furthermore, tetrahedral intermediates formed during the catalytic process are stabilized by a never previously described motif. Although the scheme of the catalytic mechanism was developed only on the basis of data obtained from EcAII and its variants, this novel mechanism of enzymatic hydrolysis could potentially apply to most (and possibly all) l-asparaginases.

Response envelope analysis for quantitative evaluation of drug combinations.

MOTIVATION: The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. RESULTS: We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independence models of interaction to evaluate drug combination effects. Because the two models imply two extreme limits of drug interaction (mutually exclusive and mutually non-exclusive), a response envelope defined by them provides a quantitatively stringent additivity model for identifying combination effects without knowing the inhibition mechanism. As a demonstration, we apply REA to representative published data from large screens of anticancer and antibiotic combinations. We show that REA is more accurate than existing methods and provides more consistent results in the context of cross-experiment evaluation. AVAILABILITY AND IMPLEMENTATION: The open-source software package associated with REA is available at: https://github.com/4dsoftware/rea. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Synthesis of Pyrazinopyrazine-Fused Azaacenes through Direct Condensation Reactions between Quinoxalinediamine and Diketones.

We report the synthesis of a new type of pyrazinopyrazine-fused azaacene molecules by a simple and versatile procedure. 6,9-Dihexyldithieno[3,2-f:2',3'-h]quinoxaline-2,3-diamine was synthesized through the condensation between 2,7-dihexylbenzo[1,2-b:6,5-b']dithiophene-4,5-diamine and bis(2,2,2-trifluoroethyl) oximidate. A series of derivatized molecules with extended two-dimensional aromatic fused-ring structures could be obtained by simple condensation reactions between the quinoxalinediamine intermediate and various diketones. The reaction was proved to be effective for the construction of tetrazaacene derivatives with extended heterocyclic aromatic ring systems. The molecules obtained exhibit low-lying LUMO levels that can be fine-tuned by modifying the molecular structure. Crystallographic results showed that in a solid state, the molecules form "brick wall" structures with a close π-π stacking mode. The stacking between the π-ring systems in the molecules could be further enhanced by expanding the large 2D planar-conjugated structure.

The glutaminase activity of L-asparaginase is not required for anticancer activity against ASNS-negative cells.

L-Asparaginase (L-ASP) is a key component of therapy for acute lymphoblastic leukemia. Its mechanism of action, however, is still poorly understood, in part because of its dual asparaginase and glutaminase activities. Here, we show that L-ASP's glutaminase activity is not always required for the enzyme's anticancer effect. We first used molecular dynamics simulations of the clinically standard Escherichia coli L-ASP to predict what mutated forms could be engineered to retain activity against asparagine but not glutamine. Dynamic mapping of enzyme substrate contacts identified Q59 as a promising mutagenesis target for that purpose. Saturation mutagenesis followed by enzymatic screening identified Q59L as a variant that retains asparaginase activity but shows undetectable glutaminase activity. Unlike wild-type L-ASP, Q59L is inactive against cancer cells that express measurable asparagine synthetase (ASNS). Q59L is potently active, however, against ASNS-negative cells. Those observations indicate that the glutaminase activity of L-ASP is necessary for anticancer activity against ASNS-positive cell types but not ASNS-negative cell types. Because the clinical toxicity of L-ASP is thought to stem from its glutaminase activity, these findings suggest the hypothesis that glutaminase-negative variants of L-ASP would provide larger therapeutic indices than wild-type L-ASP for ASNS-negative cancers.

Mechanisms of antibacterial activity of MgO: non-ROS mediated toxicity of MgO nanoparticles towards Escherichia coli.

The toxicity of metal oxide nanomaterials and their antimicrobial activity is attracting increasing attention. Among these materials, MgO is particularly interesting as a low cost, environmentally-friendly material. The toxicity of MgO, similar to other metal oxide nanomaterials, is commonly attributed to the production of reactive oxygen species (ROS). We investigated the toxicity of three different MgO nanoparticle samples, and clearly demonstrated robust toxicity towards Escherichia coli bacterial cells in the absence of ROS production for two MgO nanoparticle samples. Proteomics data also clearly demonstrate the absence of oxidative stress and indicate that the primary mechanism of cell death is related to the cell membrane damage, which does not appear to be due to lipid peroxidation.

A tabular data generation framework guided by downstream tasks optimization.

Recently, generative models have been gradually emerging into the extended dataset field, showcasing their advantages. However, when it comes to generating tabular data, these models often fail to satisfy the constraints of numerical columns, which cannot generate high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. Responding to the challenge, we propose a tabular data generation framework guided by downstream task optimization (TDGGD). It incorporates three indicators into each time step of diffusion generation, using gradient optimization to align the generated fake data. Unlike the traditional strategy of separating the downstream task model from the upstream data synthesis model, TDGGD ensures that the generated data has highly focused columns feasibility in upstream real tabular data. For downstream task, TDGGD strikes the utility of tabular data over solely pursuing statistical fidelity. Through extensive experiments conducted on real-world tables with explicit column constraints and tables without explicit column constraints, we have demonstrated that TDGGD ensures increasing data volume while enhancing prediction accuracy. To the best of our knowledge, this is the first instance of deploying downstream information into a diffusion model framework.

Novel and Advanced Ultrasound Techniques for Thyroid Thermal Ablation.

Thyroid radiofrequency ablation and microwave ablation are widely adopted minimally invasive treatments for diverse thyroid conditions worldwide. Fundamental skills such as the trans-isthmic approach and the moving shot technique are crucial for performing thyroid ablation, and advanced techniques, including hydrodissection and vascular ablation, improve safety and efficacy and reduce complications. Given the learning curve associated with ultrasound-guided therapeutic procedures, operators need training and experience. While training models exist, limited attention has been given to ultrasound maneuvers in ablation needle manipulation. This article introduces two essential maneuvers, the zigzag moving technique and the alienate maneuver, while also reviewing the latest ultrasound techniques in thyroid ablation, contributing valuable insights into this evolving field.

Mitochondrial defects and metabolic vulnerabilities in Lynch syndrome-associated MSH2-deficient endometrial cancer.

Lynch syndrome (LS) is defined by inherited mutations in DNA mismatch repair genes, including MSH2, and carries 60% lifetime risk of developing endometrial cancer (EC). Beyond hypermutability, specific mechanisms for LS-associated endometrial carcinogenesis are not well understood. Here, we assessed the effects of MSH2 loss on EC pathogenesis using a novel mouse model (PR-Cre Msh2 flox/flox , abbreviated Msh2KO), primary cell lines established from this model, human tissues, and human EC cell lines with isogenic MSH2 knockdown. Beginning at eight months of age, 30% of Msh2KO mice exhibited endometrial atypical hyperplasia (AH), a precancerous lesion. At 12 to 16 months of age, 47% of Msh2KO mice exhibited either AH or ECs with histologic features similar to human LS-related ECs. Transcriptomic profiling of EC from Msh2KO mice revealed a transcriptomic signature for mitochondrial dysfunction. Studies in vitro and in vivo revealed mitochondrial dysfunction based upon two mechanisms: marked mitochondrial content reduction, along with pronounced disruptions to the integrity of retained mitochondria. Human LS-related ECs also exhibited mitochondrial content reduction compared with non-LS-related ECs. Functional studies revealed metabolic reprogramming of MSH2-deficient EC cells in vitro , including reduced oxidative phosphorylation and increased susceptibility to glycolysis suppression. We are the first to identify mitochondrial dysfunction and metabolic disruption as a consequence of MSH2 deficiency-related EC. Mitochondrial and metabolic aberrations should be evaluated as novel biomarkers for endometrial carcinogenesis or risk stratification and could serve as targets for cancer interception in women with LS. Significance: This is the first study to report mitochondrial dysfunction contributing to MSH2-deficient endometrial cancer development, identifying a noncanonical pathway for MSH2 deficient carcinogenesis, which also imparts vulnerability to metabolic targeting.

Antibacterial and photocatalytic activity of TiO2 and ZnO nanomaterials in phosphate buffer and saline solution.

We studied antibacterial and photocatalytic activity of anatase TiO2 and ZnO in phosphate buffer and saline solution. We found that the different anions in the suspension medium (chloride and phosphate) significantly affected the following suspension properties: the stability of nanoparticle suspension, the release of metal ions from the nanoparticles, and the production of the reactive oxygen species by the nanoparticles. As a result, antibacterial activity and photocatalytic dye degradation were also affected. However, the effect of the suspension medium was different for ZnO and TiO2. Obtained results are discussed.

Modeling SARS-CoV-2 nucleotide mutations as a stochastic process.

This study analyzes the SARS-CoV-2 genome sequence mutations by modeling its nucleotide mutations as a stochastic process in both the time-series and spatial domain of the gene sequence. In the time-series model, a Markov Chain embedded Poisson random process characterizes the mutation rate matrix, while the spatial gene sequence model delineates the distribution of mutation inter-occurrence distances. Our experiment focuses on five key variants of concern that had become a global concern due to their high transmissibility and virulence. The time-series results reveal distinct asymmetries in mutation rate and propensities among different nucleotides and across different strains, with a mean mutation rate of approximately 2 mutations per month. In particular, our spatial gene sequence results reveal some novel biological insights on the characteristic distribution of mutation inter-occurrence distances, which display a notable pattern similar to other natural diseases. Our findings contribute interesting insights to the underlying biological mechanism of SARS-CoV-2 mutations, bringing us one step closer to improving the accuracy of existing mutation prediction models. This research could also potentially pave the way for future work in adopting similar spatial random process models and advanced spatial pattern recognition algorithms in order to characterize mutations on other different kinds of virus families.

Heteroleptic ruthenium(II) complex-containing polymers and their bandgap tuning and photosensitizing properties.

A new synthetic route to novel ruthenium-containing polymers is reported. [Ru(L)(L')Cl(2)] type complexes (L and L' = bidentate N^N ligands) with a dibromo-substituted ligand were polymerized by Stille cross-coupling reaction. The subsequent displacement of chloride ligands by thiocyanate was highly effective and the structures of the target polymers were fully characterized. The main chain absorption showed a significantly red-shift upon metal coordination and the metal-to-ligand charge transfer (MLCT) band of the complex enhanced the photon harvesting ability of the polymer. The extent of π-electron delocalization of the ancillary ligands also showed interesting effects on the electronic properties of the polymers. The photosensitizing and bandgap tuning properties of these Ru(II) complexes demonstrated a new avenue to develop new classes of optoelectronic materials.

The degradation mechanism of methyl orange under photo-catalysis of TiO2.

The properties of photo-generated reactive species, holes and electrons in bulk TiO(2) (anatase) film and nano-sized TiO(2) were studied and their effects towards decomposing pollutant dye methyl orange (MO) were compared by transient absorption spectroscopies. The recombination of holes and electrons in nano-sized TiO(2) was found to be on the microsecond time scale consistent with previous reports in the literature. However, in bulk TiO(2) film, the holes and electrons were found to be on the order of picoseconds due to ultra fast free electrons. The time-correlated single-photon counting (TCSPC) technique combined with confocal fluorescence microscopy revealed that the fluorescence intensity of MO is at first enhanced noticeably by TiO(2) under UV excitation and soon afterwards weakened dramatically, with the lifetime prolonged. Photo-generated holes in nano-sized TiO(2) can directly oxidize MO on the time scale of nanoseconds, while free electrons photo-generated in bulk TiO(2) film can directly inject into MO on the order of picoseconds. Through cyclic voltammetry measurements, it was found that MO can be reduced at -0.28 V and oxidized at 1.4 V (vs. SCE) and this provides thermodynamic evidence for MO to be degraded by electrons and holes in TiO(2). Through comparison of the hole-scavenging effect of MO and water, it was found that in polluted water when MO is above 1.6 × 10(-4) M, the degradation is mainly due to a direct hole oxidation process, while below 1.6 × 10(-4) M, hydroxyl oxidation competes strongly and might exceed the hole oxidation.

Frame-disrupting mutations elicit pre-mRNA accumulation independently of frame disruption.

The T-cell receptor (TCR) and immunoglobulin (Ig) genes are unique among vertebrate genes in that they undergo programmed rearrangement, a process that allows them to generate an enormous array of receptors with different antigen specificities. While crucial for immune function, this rearrangement mechanism is highly error prone, often generating frameshift or nonsense mutations that render the rearranged TCR and Ig genes defective. Such frame-disrupting mutations have been reported to increase the level of TCRbeta and Igmicro pre-mRNA, suggesting the hypothesis that RNA processing is blocked when frame disruption is sensed. Using a chimeric gene that contains TCRbeta sequences conferring this upregulatory response, we provide evidence that pre-mRNA upregulation is neither frame- nor translation-dependent; instead, several lines of evidence suggested that it is the result of disrupted cis elements necessary for efficient RNA splicing. In particular, we identify the rearranging VDJ(beta) exon as being uniquely densely packed with exonic-splicing enhancers (ESEs), rendering this exon hypersensitive to mutational disruption. As the chimeric gene that we developed for these studies generates unusually stable nuclear pre-mRNAs that accumulate when challenged with ESE mutations, we suggest it can be used as a sensitive in vivo system to identify and characterize ESEs.

Successful Applications of Food-Assisted and -Simulated Training Model of Thyroid Radiofrequency Ablation.

Background: Radiofrequency ablation (RFA) for benign thyroid nodules is one kind of scarless treatment for symptomatic or cosmetic benign thyroid nodules. However, how to train RFA-naive physicians to become qualified operators for thyroid RFA is an important issue. Our study aimed to introduce a successful training model of thyroid RFA. Materials and Methods: We used a food-assisted and -simulated training model of thyroid RFA. Chicken hearts were simulated into thyroid nodules, three-layer pork meats were simulated into peri-thyroid structure, and gel bottles were simulated into trachea, respectively. Successful training ablations were defined as chicken hearts that were fully cooked. After repeating training ablations of chicken hearts at least 100 times with the nearly 100% success rates for three young trainees, they served as the first assistant for the real procedures of thyroid RFA and then were qualified to perform thyroid RFA on real patients under the supervision of one experienced interventional radiologist. Results: 23 real patients who received RFA and follow-up at least 6 months after treatment were included in Linkou Chang Gung Memorial Hospital from January 1, 2020 to October 1, 2021. Three young endocrinologists performed thyroid RFA independently. The outcomes were volume reduction rate (VRR), major complications and minor complications. The median VRR at 12 months was 82.00%, two major complications were transient hoarseness, and three minor complications were wound pain. All complications were completely recovered within three days. Conclusions: For young and RFA-native physicians without any basic skills of echo-guided intervention, this food-assisted and -simulated training model of thyroid RFA was useful for medical training and education.

Tumoral microenvironment prevents de novo asparagine biosynthesis in B cell lymphoma, regardless of ASNS expression.

Depletion of circulating asparagine with l-asparaginase (ASNase) is a mainstay of leukemia treatment and is under investigation in many cancers. Expression levels of asparagine synthetase (ASNS), which catalyzes asparagine synthesis, were considered predictive of cancer cell sensitivity to ASNase treatment, a notion recently challenged. Using [U-13C5]-l-glutamine in vitro and in vivo in a mouse model of B cell lymphomas (BCLs), we demonstrated that supraphysiological or physiological concentrations of asparagine prevent de novo asparagine biosynthesis, regardless of ASNS expression levels. Overexpressing ASNS in ASNase-sensitive BCL was insufficient to confer resistance to ASNase treatment in vivo. Moreover, we showed that ASNase's glutaminase activity enables its maximal anticancer effect. Together, our results indicate that baseline ASNS expression (low or high) cannot dictate BCL dependence on de novo asparagine biosynthesis and predict BCL sensitivity to dual ASNase activity. Thus, except for ASNS-deficient cancer cells, ASNase's glutaminase activity should be considered in the clinic.