Miracle-dz/UniFAR-FTUs huggingface dataset:
An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer

Purpose: Although tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. Experimental Design: Using the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and “other” cells on hematoxylin–eosin (H&E)–stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets. Results: We found that all five machine TIL variables had significant prognostic association with outcomes (P ≤ 0.01 for all comparisons) but showed cell-specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathologic factors including stage, age, and histologic grade (P ≤ 0.0003 for all analyses). Conclusions: Neural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility. See related commentary by Symmans, p. 5446
Time Series Smoothing Improving Forecasting

Abstract Both statistical and neural network methods may fail in forecasting time series even operating on a great amount of data. It is an open question of which amount fits best to make sufficiently accurate forecasts on it. This implies that the length or time series might be optimised. Hence, the objective is to improve the quality of forecasting by an assumption that parameters are set nearly at their optimal values. To achieve objective, the two types of the benchmark time series are considered: sine-shaped series and random-like series with repeatability. Trend, seasonality, and decay properties embedded into each type. Based on the benchmark of 24 time series models, it is ascertained that, for improving the forecasting, the time series should be smoothed and then downsampled. These operations can be fulfilled successively until the improvement fails. If preliminary smoothing worsens forecasts, the raw time series is straightforwardly downsampled until the forecasting accuracy starts dropping. However, if time series has a visible property of being noised, the preliminary smoothing is strongly recommended.
Networks in the mind – what communities reveal about the structure of the lexicon

Abstract The mental lexicon stores words and information about words. The lexicon is seen by many researchers as a network, where lexical units are nodes and the different links between the units are connections. Based on the analysis of a word association network, in this article we show that different kinds of associative connections exist in the mental lexicon. Our analysis is based on a word association database from the agglutinative language Hungarian. We use communities – closely knit groups – of the lexicon to provide evidence for the existence and coexistence of different connections. We search for communities in the database using two different algorithms, enabling us to see the overlapping (a word belongs to multiple communities) and non-overlapping (a word belongs to only one community) community structures. Our results show that the network of the lexicon is organized by semantic, phonetic, syntactic and grammatical connections, but encyclopedic knowledge and individual experiences are also shaping the associative structure. We also show that words may be connected not just by one, but more types of connections at the same time.
Gender Disparity in Students’ Choices of Information Technology Majors

Abstract Background: The gender disparity in the Information Technology (IT) field has persisted over the years. In 2018, only 27.2% of IT workers were women. Once hired, women face more challenges, and they are leaving the field twice as fast as men are. The misconception that women are weak in tech is one of the root causes of gender disparity issues in IT. Objectives: We examine the gender disparity in students’ choices of IT majors, as well as the decision process of Computer Information Systems (CIS) graduates. Methods/Approach: We use the United States public universities’ student data from 2010 to 2018. Both the Pooled and the Satterthwaite t-test are used to investigate the gender disparity issue among the students. Results: Our results support our hypothesis that female students are statistically less likely to choose CIS than their male peers are. An additional analysis of students’ grades in CIS courses shows that female students perform equally well as male students do. We did not find any evidence that it takes longer for female students to get the CIS degree; however, female students did change their majors more often. Conclusions: Female students tend to avoid IT majors; they often think they may not do well in the courses; however, such an assumption is not true. Our findings provide strategies for university and high school administration to be more proactive in developing recruiting strategies to attract and retain female CIS students.
On the sub–diffusion fractional initial value problem with time variable order

Abstract We consider a fractional derivative with order varying in time. Then, we derive for it a Leibniz' inequality and an integration by parts formula. We also study an initial value problem with our time variable order fractional derivative and present a regularity result for it, and a study on the asymptotic behavior.
Fine-grained control of concurrency within KV-SSDs

The development of KV-SSDs allows simplifying the I/O stack compared to the traditional block-based SSDs. We propose a novel Key-Value-based Storage infrastructure for Parallel Computing(KV-SiPC)-a framework for multi-thread OpenMP applications to use NVMe-based KV-SSDs. We design a new capability to execute workloads with multiple parallel data threads along with traditional parallel compute threads, that allow us to improve the overall throughput of applications, utilizing the maximum possible storage bandwidth. We implement our KV-SiPC infrastructure in a real system by extending various processing layers (e.g., program, OS, and device layers) and evaluate the performance of KV-SiPC by using block-based NVMe SSDs in the traditional I/O stack as a baseline for comparisons. The experimental results show that KV-SiPC can better utilize the available device bandwidth and significantly increases application I/O throughput.
Bregman-style Online Convex Optimization with EnergyHarvesting Constraints

This paper considers online convex optimization (OCO) problems where decisions are constrained by available energy resources. A key scenario is optimal power control for an energy harvesting device with a finite capacity battery. The goal is to minimize a time-average loss function while keeping the used energy less than what is available. In this setup, the distribution of the randomly arriving harvestable energy (which is assumed to be i.i.d.) is unknown, the current loss function is unknown, and the controller is only informed by the history of past observations. A prior algorithm is known to achieve $O(\sqrtT )$ regret by using a battery with an $O(\sqrtT )$ capacity. This paper develops a new algorithm that maintains this asymptotic trade-off with the number of time steps T while improving dependency on the dimension of the decision vector from $O(\sqrtn )$ to $O(\sqrtłog(n) )$. The proposed algorithm introduces a separation of the decision vector into amplitude and direction components. It uses two distinct types of Bregman divergence, together with energy queue information, to make decisions for each component.
Online Virtual Machine Allocation with Lifetime and Load Predictions

The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information about future (total) demand. We show that the competitive factor can be dramatically improved with this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem.
Length preserving compression: marrying encryption with compression

This work tackles an inherent conflict between two important trends. The first is the integration of data compression capabilities into many storage systems supporting random I/O on the compressed data. The second is encrypting data at the host, before data is written to the storage, in order to address regulatory and enterprise requirements. This provides end-to-end protection for the data, but since the data arrives encrypted, it prevents the storage from compressing the data. Can compression savings be achieved together with host side encryption without changing the storage protocols or storage backend? In this paper we show that they can. We present Length Preserving Compression (LPC), which combines compression with encryption, to provide the benefits of both. The challenge is to achieve compression savings without overloading the host side with complex data management tasks which come with the fact that compression changes the data layout. We do this by keeping the data layout management capabilities on the storage while compressing and encrypting on the host. Equally important is that LPC works without changes to the compressing storage system or to the standard storage protocols involved. We implemented LPC in Linux dm-crypt and in the Xen blktap encryption and extensively evaluated its performance and impact on security and compression ratios.
Interactive Programmatic Modeling

Modeling and computational analyses are fundamental activities within science and engineering. Analysis activities can take various forms, such as simulation of executable models, formal verification of model properties, or inference of hidden model variables. Traditionally, tools for modeling and analysis have similar workflows: (i) a user designs a textual or graphical model or the model is inferred from data, (ii) a tool performs computational analyses on the model, and (iii) a visualization tool displays the resulting data. This article identifies three inherent problems with the traditional approach: the recomputation problem, the variable inspection problem, and the model expressiveness problem. As a solution, we propose a conceptual framework called Interactive Programmatic Modeling. We formalize the interface of the framework and illustrate how it can be used in two different domains: equation-based modeling and probabilistic programming.
Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint

Monotone submodular maximization with a knapsack constraint is NP-hard. Various approximation algorithms have been devised to address this optimization problem. In this paper, we revisit the widely known modified greedy algorithm. First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factors of 0.357 given by Wolsey and (1-1/e)/2-0.316 given by Khuller et al. More importantly, our analysis closes a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/√e)≈0.393 in the literature to clarify a long-standing misconception on this issue. Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum, which enables us to obtain a data-dependent ratio typically much higher than 0.405 between the solution value of the modified greedy algorithm and the optimum. It can also be used to significantly improve the efficiency of algorithms such as branch and bound.
Impact Analysis of Short Video on Users Behavior: Users Behavior Factors of Short VideoEvidence from Users Data of Tik Tok

Abstract: Exploring the users' behavioral factors of using short videos in depth is of great significance to the fine operation of short video manufacturers, government control on Internet public opinion, and even promotion of the development of the short video economy. Based on relevant research at home and abroad, and the integration and optimization of Technology Acceptance Model (TAM) model variables, this analysis takes Tik Tok, the No.1 product of short video, as an example to construct the structural equation model of users' behavioral factors from the perspective of Maslow's hierarchy of needs, attempting to explore the external factors and internal psychological factors that affect users' behavior. The results show that: Firstlyly, Interesting Content is the main influencing factor affecting users’ decision to use short video products. Secondlyly, the social function of short video products plays a crucial role in increasing user activity. Thirdlyly, the function of short video products largely determines users’ Perceived Ease of Use.
First-person Cinematographic Videogames

We present and explore the fruitfulness of “first-person cinematographic videogames,” a game model we have devised for the promotion of cultural, environmental, and territorial heritage. To support and foster the development of these type of games, we have developed a Web-based user-friendly authoring environment, extensively presented in the article. While employing standard first-person point-and-click game mechanics, the game model's distinctive feature is that the game environment is not based on a digital reconstruction (3D model) of the real-world settings but on cinematographic techniques combining videos and photos of existing places, integrating videoclips of mostly practical effects to obtain the interactivity typical of the first-person point-and-click adventure games. Our goal with such a game model is to mobilise mechanisms of engendering affection for real-world places when they become settings of the game world, arousing in the player forms of affection, attachment, and desire to visit them.
Display of the self-sufficient CYP102A1 on the surface of E. coli-derived Outer Membrane Vesicles

The cytochrome P450 (CYP) monooxygenase superfamily offers the unique ability to catalyze regio-and stereospecifical oxidation of a non-activated C-H bond. CYPs found applications in the synthesis of pharmaceuticals and drug metabolites as well as in bioremediation. They are typically used in whole-cell bioconversion processes, due to their low stability and the need for a redox partner and cofactor. Unfortunately, substrate uptake and/or product transport limitations are frequently encountered and side reactions occur due to other enzymes in the cellular environment. Here, we present a proof-of-principle of a novel cell-free cytochrome P-450 nanocatalyst based on surface display on bacterial outer membrane vesicles. The self-sufficient CYP 102A1 from Bacillus megaterium was engineered to be translocated on the outer membrane vesicles of Escherichia coli. The resulting vesicles can simply be isolated from the culture supernatant. Moreover, no expensive and elaborate enzyme purification is required. This approach shows great promise as an alternative strategy to recombinantly produce CYP enzymes for a variety of applications, such as in fine chemical production and in the development of biosensors.
Diagnostic primers to distinguish between wMel and wAlbB Wolbachia infection-A simplified approach for use in field-collected Aedes aegypti in dengue reduction trials

Detection of the Wolbachia endosymbiont in Aedes aegypti mosquitoes through real-time PCR assays is widely used during and after Wolbachia releases in dengue reduction trials involving the wMel and wAlbB strains. However, primers applied in current successful Wolbachia releases cannot be used in a single assay to distinguish between these strains. Here, we developed a new diagnostic primer pair wMA which can detect wMel or wAlbB infection in the same assay. We also tested current Wolbachia primers and show that there is variation in their performance when they are used to assess the relative density of Wolbachia. The new wMA primers provide an accurate and efficient estimate of the presence and density of both Wolbachia infections.
Investigating the concept of accessibility for predicting novel RNA-RNA interactions

State-of-the-art methods for predicting novel trans RNA-RNA interactions use the so-called accessibility as key concept. It estimates whether a region in a given RNA sequence is accessible for forming trans interactions, using a thermodynamic model which quantifies its secondary structure features. RNA-RNA interactions are then predicted by finding the minimum free energy base-pairing between the two transcripts, taking into account the accessibility as energy penalty. We investigated the underlying assumptions of this approach using the two methods RNAplex and IntaRNA on two datasets, containing sRNA-mRNA and snoRNA-rRNA interactions, respectively. We find that (1) known trans RNA-RNA interactions frequently overlap regions containing RNA structure features, (2) the estimated accessibility reflects sRNA structures fairly well, but often disagrees with structure annotations of longer transcripts, (3) the prediction performance of RNA-RNA interaction prediction methods is independent of the quality of the estimated accessibility profiles, and (4) one important overall effect of accessibility profiles is to prevent the thermodynamic model from predicting too long interactions. Based on our findings, we conclude that the accessibility concept to the minimum free energy approach to predicting novel RNA-RNA interactions has conceptual limitations and discuss potential ways of improving the field in the future.
Reverse-Complement Equivariant Networks for DNA Sequences

As DNA sequencing technologies keep improving in scale and cost, there is a growing need to develop machine learning models to analyze DNA sequences, e.g., to decipher regulatory signals from DNA fragments bound by a particular protein of interest. As a double helix made of two complementary strands, a DNA fragment can be sequenced as two equivalent, so-called Reverse Complement (RC) sequences of nucleotides. To take into account this inherent symmetry of the data in machine learning models can facilitate learning. In this sense, several authors have recently proposed particular RC-equivariant convolutional neural networks (CNNs). However, it remains unknown whether other RC-equivariant architectures exist, which could potentially increase the set of basic models adapted to DNA sequences for practitioners. Here, we close this gap by characterizing the set of all linear RC-equivariant layers, and show in particular that new architectures exist beyond the ones already explored. We further discuss RC-equivariant pointwise nonlinearities adapted to different architectures, as well as RC-equivariant embeddings of k-mers as an alternative to one-hot encoding of nucleotides. We show experimentally that the new architectures can outperform existing ones.
The vagus nerve is critical for regulation of hypothalamic-pituitary-adrenal axis responses to acute stress

The hypothalamic pituitary adrenal (HPA) axis is a critical regulator of physiologic and psychological responses to acute and chronic stressors. HPA axis function is control by numerous feedback inhibitory mechanisms, disruptions of which can lead to various psychiatric conditions, such as depression, posttraumatic stress disorder, and schizophrenia. Vagus nerve stimulation has been shown to be efficacious in the treatment of in these various mental health issues potentially via modulation of HPA axis function, but the mechanisms by which the vagus nerve may regulate HPA function has not been fully elucidated. In the present studies, we sought to test the hypothesis that the vagus nerve is a critical regulator of HPA function. Neuroendocrine function and neurocircuit changes in corticotropin releasing factor (CRF) neurons in the paraventricular nucleus of the hypothalamus (PVN) were examined following acute stress after subdiaphragmatic left vagotomy (VX) in adult male Sprague-Dawley rats. We found that VX mimics HPA activation seen in sham surgery animals exposed to acute restraint stress, particularly increased plasma corticosterone levels, elevated PVN CRF mRNA, and increased action potential firing of putative CRF neurons in PVN brain slices. Furthermore, VX animals exposed to acute restraint stress showed increased elevations of plasma corticosterone and PVN CRF mRNA which may be due to lack of compensatory PVN GABAergic signaling in response to acute stress. Both Sham/Stress and VX/no stress conditions increased action potential firing in putative PVN CRF neurons, but this effect was not seen in the VX/stress condition, suggesting that not all forms of stress compensation are lost following VX. Overall, these findings suggest that the vagus nerve may play a critical role in regulating HPA axis function via modulation of local PVN neurocircuit activity.
Unrestrained growth of correctly oriented microtubules instructs axonal microtubule orientation

In many eukaryotic cells, directed molecular transport occurs along microtubules. Within neuronal axons, transport over vast distances particularly relies on uniformly oriented microtubules, whose +-ends point towards the distal axon tip (+end out). However, axonal microtubules initially have mixed orientations, and how they orient during development is not yet fully understood. Using live imaging of primary Drosophila melanogaster neurons and physical modelling, we found that +end out microtubules are less likely to undergo catastrophe near the advancing axon tip, leading to their persistent long-term growth. In contrast, oppositely oriented microtubules remain short. Using chemical and physical perturbations of microtubule growth and genetic perturbations of the anti -catastrophe factor p150, which was enriched in the distal axon tip, we confirmed that the enhanced growth of +end out microtubules is critical for achieving uniform microtubule orientation. Computer simulations of axon development mimicking the enhanced +end out microtubule growth identified here along with previously proposed mechanisms correctly predicted the long-term evolution of axonal microtubule orientation as found in our experiments, highlighting the importance of the reduced catastrophe rate of +end out microtubules near the advancing axon tip in establishing uniform microtubule polarity. Our study thus leads to a holistic explanation of how axonal microtubules orient uniformly, a prerequisite for efficient long-range transport essential for neuronal functioning.