Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial complex I as a diagnostic and therapeutic target in a mouse model of tauopathy

Dysfunction of the energy producing organelle of the cell, mitochondria, plays a pivotal role in Alzheimer’s disease (AD). We have recently used a novel positron emission tomographic (PET) imaging tracer targeting mitochondrial complex I (MC-I) to visualize mitochondrial abnormalities in the brains of living tau transgenic (TauTg) mice. MC-I mediates the first and limiting step in oxidative phosphorylation, the primary source of neuronal energy production. Here we used MC-I-PET to test if inhibition of mutant tau expression through transgene suppression with doxycycline could reverse mitochondrial defects in a mouse model of tauopathy and evaluate the efficacy of a MC-I-targeted candidate therapeutic, Mdivi-1. We found that late-stage suppression of mutant tau did not rescue mitochondrial deficits measured in vivo by MC-I-PET, despite reduced burden of tauopathy and neuroinflammation. These findings demonstrate that mitochondrial dysfunction may continue even if tauopathy is halted, particularly if initiated at late-stage disease. Further, we demonstrate the potential application of MC-I-PET for monitoring therapeutic efficacy, surprisingly finding detrimental effects of the mitochondrial-targeted candidate therapeutic, Mdivi-1, in TauTg mice. These findings directly contrast with the beneficial effects of Mdivi-1 observed in other models of neurodegeneration. Together, our findings highlight the need for clinical endpoints measuring mitochondrial damage in addition to markers of tauopathy in the assessment of disease prognosis and efficacy of candidate therapeutics and demonstrates the potential application of MC-I-PET to meet this need.
Non-Stationary Dynamic Mode Decomposition

Many physical processes display complex high-dimensional time-varying behavior, from global weather patterns to brain activity. An outstanding challenge is to express high dimensional data in terms of a dynamical model that reveals their spatiotemporal structure. Dynamic Mode Decomposition is a means to achieve this goal, allowing the identification of key spatiotemporal modes through the diagonalization of a finite dimensional approximation of the Koopman operator. However, DMD methods apply best to time-translationally invariant or stationary data, while in many typical cases, dynamics vary across time and conditions. To capture this temporal evolution, we developed a method, Non-Stationary Dynamic Mode Decomposition (NS-DMD), that generalizes DMD by fitting global modulations of drifting spatiotemporal modes. This method accurately predicts the temporal evolution of modes in simulations and recovers previously known results from simpler methods. To demonstrate its properties, the method is applied to multi-channel recordings from an awake behaving non-human primate performing a cognitive task.
Overexpression of tomato SlBBX16 and SlBBX17 impacts fruit 1 development and GA biosynthesis

26 BBX proteins are B-Box zinc finger proteins that can act as transcription factors and 27 regulators of protein complexes. Several BBX proteins play important roles in plant 28 development. Two Arabidopsis thaliana microProteins belonging to the BBX family, named 29 miP1a and miP1b, homotypically interact with and modulate the activity of other BBX 30 proteins, including CONSTANS ( At CO), which transcriptionally activates the florigen gene, 31 FLOWERING LOCUS T ( FT ). In tomato, the closest homologs of miP1a and miP1b are the 32 microProteins Sl BBX16 and Sl BBX17. To deepen our understanding of the role of tomato 33 microProteins in flowering, we constitutively expressed Sl BBX16/17 in Arabidopsis and 34 tomato (MicroTom) and examined possible interacting partners. Overexpression of the two 35 tomato microProteins in Arabidopsis caused a delay in the flowering transition; however, the 36 effect was weaker than that observed in Arabidopsis plants overexpressing the native 37 miP1a/b microProteins. In tomato, overexpression of SlBBX17 prolonged the flowering 38 period; this effect was accompanied by downregulation of the flowering inhibitor Self 39 Pruning . Yeast-Two Hybrid analysis did not show a direct interaction between Sl BBX16 and 40 Sl BBX17 and At CO and its closest homolog in tomato, Sl COL1. On the other hand, Sl BBX16 41 and Sl BBX17 were able to hetero-oligomerize with TCMP-2, a cystine-knot peptide involved 42 in flowering pattern and fruit development in tomato. Increasing the expression of both 43 tomato microProteins also caused alterations in fruit development: overexpression of 44 SlBBX17 resulted in a diminished number and size of ripe fruits as compared to WT plants, 45 while overexpression of SlBBX16 caused delayed fruit production up to the breaker stage.
LESSON: Learning to Integrate Exploration Strategies for Reinforcement Learning via an Option Framework

In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for each given task. The effectiveness of the proposed exploration framework is demonstrated by various experiments in the MiniGrid and Atari environments.
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences

A treatment policy defines when and what treatments are applied to affect some outcome of interest. Data-driven decision-making requires the ability to predict what happens if a policy is changed . Existing methods that predict how the outcome evolves under different scenarios assume that the tentative sequences of future treatments are fixed in advance, while in practice the treatments are determined stochastically by a policy and may depend, for example, on the efficiency of previous treatments. Therefore, the current meth-ods are not applicable if the treatment policy is unknown or a counterfactual analysis is needed. To handle these limitations, we model the treatments and outcomes jointly in continuous time, by combining Gaussian processes and point processes. Our model enables the estimation of a treatment policy from observational sequences of treatments and outcomes, and it can predict the interventional and counterfactual progression of the outcome after an intervention on the treat-ment policy (in contrast with the causal effect of a single treatment). We show with real-world and semi-synthetic data on blood glucose progression that our method can answer causal queries more accurately than existing alternatives.
Scalable Safe Policy Improvement via Monte Carlo Tree Search

Algorithms for safely improving policies are important to deploy reinforcement learning approaches in real-world scenarios. In this work, we propose an algorithm, called MCTS-SPIBB, that computes safe policy improvement online using a Monte Carlo Tree Search based strategy. We theoretically prove that the policy generated by MCTS-SPIBB converges, as the number of simulations grows, to the optimal safely improved policy generated by Safe Policy Improvement with Baseline Bootstrapping (SPIBB), a popular algo-rithm based on policy iteration. Moreover, our empirical analysis performed on three standard benchmark domains shows that MCTS-SPIBB scales to signiﬁcantly larger problems than SPIBB because it computes the policy online and locally, i.e., only in the states actually visited by the agent.
Role of Adenosine Deaminase in Prostate Cancer Progression

Prostate cancer (PCa) is the second most common cancer and constitutes about 14.7% of total cancer cases. PCa is highly prevalent and more aggressive in African American (AA) men when compared to European-American (EA) men. PCa tends to be a highly heterogeneous malignancy with a complex biology that is not fully understood. We use metabolomics as a tool to understand the mechanisms behind PCa progression and disparities in its clinical outcome. A key enzyme in the purine metabolic pathway, Adenosine deaminase (ADA) was found upregulated in PCa. ADA was also associated with higher-grade PCa and poor disease-free survival. The inosine-to-adenosine ratio which is a surrogate for ADA activity was high in the urine of PCa patients and higher in AA PCa compared to EA PCa. To understand the significance of high ADA in PCa, we established ADA overexpression models and performed various in vitro and in vivo studies. Our studies have revealed that an acute increase in the expression of ADA during later stages of tumor development enhances in vivo growth in multiple pre-clinical models. Further analysis reveals that this tumor growth could be driven by the activation of mTOR signaling. Chronic ADA overexpression shows alterations in the cells’ adhesion machinery and a decrease in the adhesion potential of the cells to the extracellular matrix in vitro. Loss of cell-matrix interaction is critical for metastatic dissemination, suggestive of ADA’s role in promoting metastasis. This is consistent with the association of higher ADA expression with higher-grade tumors and poor patient survival. Overall, our findings suggest that increased ADA expression may promote PCa progression, specifically tumor growth and metastatic dissemination.
DeGenPrime provides robust primer design and optimization unlocking the biosphere

Polymerase chain reaction (PCR) is the world’s most important molecular diagnostic with applications ranging from medicine to ecology. PCR can fail because of poor primer design. The nearest-neighbor thermodynamic properties, picking conserved regions, and filtration via penalty of oligonucleotides form the basis for good primer design. DeGenPrime is a console-based high quality PCR primer design tool that can utilize MSA formats and degenerate bases expanding target range for a single primer set. Our software utilizes thermodynamic properties, filtration metrics, penalty scoring, and conserved region finding of any proposed primer. It has degeneracy, repeated k-mers, relative GC content, and temperature range filters. Minimal penalty scoring is included according to secondary structure self-dimerization metrics, GC clamping, tri- and tetra-loop hairpins and internal repetition. We compared PrimerDesign-M, DegePrime, ConsensusPrimer, and DeGenPrime on acceptable primer yield. PrimerDesign-M, DegePrime, and ConsensusPrimer provided 0%, 11%, and 17% yield respectively for alternative iron nitrogenase (anfD) gene target. DeGenPrime successfully identified quality primers within the conserved regions of the T4-like phage major capsid protein (g23), conserved regions of molybdenum-based nitrogenase (nif), and its alternatives vanadium (vnf) and iron (anf) nitrogenase. DeGenPrime provides a universal and scalable primer design tool for the entire tree of life.
Deep Learning Based Melanoma Diagnosis Identification

Abstract Malignant melanoma is considered to be one of the deadliest types of skin cancer, and it is responsible for the death of a large number of people worldwide. However, distinguishing whether melanoma is benign or malignant has been a challenging task. Many Computer Aided Diagnosis and Detection Systems have been developed in the past for this task. This paper presents a deep learning framework based approach for melanoma diagnosis and recognition. In the proposed method, the original skin mirror image is first preprocessed and then passed to the VGG16 convolutional neural network for tumor property classification. VGG16 uses smaller convolutional kernels instead of a larger convolutional kernel to achieve a reduction in network parameters and thus improve network performance. The system is trained using segmented RGB images generated from ground truth images of the ISIC2016 dataset, and finally a softmax classifier is used for pixel-level classification of melanoma lesions. In this study, a new method to become a lesion classifier was designed to classify melanoma lesion regions into benign and malignant tumors based on the results of pixel-level classification, and experiments were conducted on two well-established public test datasets, ISIC2016 and ISIC2017, with a final accuracy of 96.1%. The results indicate that convolutional neural networks are suitable for melanoma diagnosis identification. This study is of great relevance for advanced cancer caused by malignant melanoma.
Long duration environmental biosensing by recording analyte detection in DNA using recombinase memory

Microbial biosensors that convert environmental information into real-time visual outputs are limited in their sensing abilities in complex environments, such as soil and wastewater. Alternative reporter outputs are needed that stably record the presence of analytes. Here, we test the performance of recombinase-memory biosensors that sense a sugar (arabinose) and a microbial communication molecule (3-oxo-C12- homoserine lactone) over 8 days (∼70 generations) following analyte exposure. These biosensors use analyte sensing to trigger the expression of a recombinase which flips a segment of DNA, creating a genetic memory, and initiates fluorescent protein expression. The initial designs failed over time due to unintended DNA flipping in the absence of the analyte and loss of the flipped state after exposure to the analyte. Biosensor performance was improved by decreasing recombinase expression, removing the fluorescent protein output, and using qPCR to read out stored information. Application of memory biosensors in wastewater isolates achieved memory of analyte exposure in an uncharacterized Pseudomonas isolate. By returning these engineered isolates to their native environments, recombinase-memory systems are expected to enable longer duration and in situ investigation of microbial signaling, community shifts, and gene transfer beyond the reach of traditional environmental biosensors. IMPORTANCE Living microbial sensors can monitor chemicals and biomolecules in the environment in real-time, but they remain limited in their ability to function on the week, month, and year timescales. To determine if environmental microbes can be programmed to record the detection of analytes over longer timescales, we evaluated whether the sensing of a microbial signaling molecule could be recorded through a DNA rearrangement. We show that off-the-shelf DNA memory is suboptimal for long-duration information storage, use iterative design to enable robust functioning over more than a week, and demonstrate DNA memory in an uncharacterized wastewater Pseudomonas isolate. Memory biosensors will be useful for monitoring the role of quorum sensing in wastewater biofilm formation, and variations of this design are expected to enable studies of ecological processes in situ that are currently challenging to monitor using real-time biosensors and analytical instruments.
MC:Unsupervised Multiple Social Network Alignment

LI SUN, North China Electric Power University, China ZHONGBAO ZHANG, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China GEN LI, Beijing University of Posts and Telecommunications, China PENGXIN JI, Beijing University of Posts and Telecommunications, China SEN SU, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China PHILIP S. YU, University of Illinois at Chicago, USA
BBSome-deficient cells activate intraciliary CDC42 to trigger actin-dependent ciliary ectocytosis

Bardet-Biedl syndrome (BBS) is a pleiotropic ciliopathy caused by dysfunction of the BBSome, a cargo adaptor essential for export of transmembrane receptors from cilia. Although actin-dependent ectocytosis has been proposed as compensatory cellular process for defective cargo retrieval, the underlying molecular mechanism is poorly understood, particularly in the context of BBS pathology. In this study, we investigated how actin polymerization and ectocytosis are regulated within the cilium. Our findings reveal that ciliary CDC42, a RHO-family GTPase triggers in situ actin polymerization, ciliary ectocytosis, and cilia shortening in BBSome-deficient cells. Furthermore, activation of the Sonic Hedgehog pathway further enhances CDC42 activity in BBSome-deficient cilia, but not in healthy cells. Inhibition of CDC42 decreased the frequency and duration of ciliary actin polymerization events and lead to the accumulation of the GPR161 receptor in bulges along the axoneme in BBSome-deficient cells during Sonic Hedgehog signaling. Overall, our study identifies CDC42 as a key trigger of ciliary ectocytosis. Moreover, the hyperactivated ciliary CDC42-actin-ectocytosis axis in BBSome-deficient cells results in cilia shortening and loss of ciliary material, potentially impacting disease severity.
Resting-state EEG recorded with gel-based versus consumer dry electrodes: spectral characteristics and across-device correlations

Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic Neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes. Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the Neiry Headband, the Neiry Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications. Distinctive characteristics of signal quality were found, with the Neiry Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The Neiry Headphones were more prone to low-frequency artifacts compared to the Neiry Headband, yet recorded modulations in alpha power and had a strong alignment with the NVX at higher frequencies. The Muse Headband had several limitations in signal quality. We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighted against the capacity of each concrete system.
A new experimental evidence-weighted signaling pathway resource in FlyBase

Research in model organisms is central to the characterization of signaling pathways in multicellular organisms. Here, we present the systematic curation of 17 Drosophila signaling pathways using the Gene Ontology framework to establish a comprehensive and dynamic resource that has been incorporated into FlyBase, providing visualization and data integration tools to aid research projects. By restricting to experimental evidence reported in the research literature and quantifying the amount of such evidence for each gene in a pathway, we captured the landscape of empirical knowledge of signaling pathways in Drosophila. Summary statement Comprehensive curation of Drosophila signaling pathways and new visual displays of the pathways provides a new FlyBase resource for researchers, and new insights into signaling pathway architecture.
Joint Assortment Optimization and Customization Under a Mixture of Multinomial Logit Models: On the Value of Personalized Assortments

Assortment Personalization in E-commerce In the age of big data, online businesses have access to a tremendous amount of browsing and purchasing data from their customers. Consequently, there has been great effort in exploiting this data to offer personalized product assortments to each customer based on what is known about her or his preferences. A personalized assortment can potentially allow enhancing the customer experience as well as improving the revenue of the retailer. In “Joint Assortment Optimization And Customization Under A Mixture Of Multinomial Logit Models: On The Value Of Personalized Assortments,” Omar El Housni and Huseyin Topaloglu consider an optimization model that jointly optimizes the assortment of products carried by an online retailer as well as the personalized assortments offered to each customer. They study the value of personalization and develop algorithms with provable theoretical guarantees to solve the optimization problem.
Solving Linear Programs with Fast Online Learning Algorithms

This paper presents fast first-order methods for solving linear programs (LPs) approximately. We adapt online linear programming algorithms to offline LPs and obtain algorithms that avoid any matrix multiplication. We also introduce a variable-duplication technique that copies each variable K times and reduces the optimality gap and constraint violation by a factor of √ K . Furthermore, we show how online algorithms can be effectively integrated into sifting, a column generation scheme for large-scale LPs. Numerical experiments demonstrate that our methods can serve as either an approximate direct solver, or an initialization subroutine for exact LP solving.
User-level Private Stochastic Convex Optimization with Optimal Rates

We study the problem of differentially private (DP) stochastic convex optimization (SCO) under the notion of user-level differential privacy. In this problem, there are n users, each contributing m > 1 samples to the input dataset of the private SCO algorithm, and the notion of indistinguisha-bility embedded in DP is w.r. t. replacing the entire local dataset of any given user. Under smoothness conditions of the loss, we establish the optimal rates for user-level DP-SCO in both the central and local models of DP. In particular, we show, roughly, that the optimal rate is 1 √ nm + √ d εn √ m in the central setting and is √ d ε √ nm in the local setting, where d is the dimensionality of the problem and ε is the privacy parameter. Our algorithms combine new user-level DP mean estimation techniques with carefully designed first-order stochastic optimization methods. For the central DP setting, our optimal rate improves over the rate attained for the same setting in Levy et al. (2021) by √ d factor. One of the main ingredients that enabled such an improvement is a novel application of the generalization properties of DP in the context of multi-pass stochastic gradient methods.
A General Representation Learning Framework with Generalization Performance Guarantees

The generalization performance of machine learning methods depends heavily on the quality of data representation. However, existing researches rarely consider representation learning from the perspective of generalization error. In this paper, we prove that generalization error of representation learning function can be estimated effectively by solving two convex optimization problems. Based on it, we propose a general representation learning framework. And then, we apply the proposed framework to two most commonly used nonlinear mapping methods, i