Miracle-dz/UniFAR-FTUs huggingface dataset:
High single‐cell diversity in carbon and nitrogen assimilations by a chain‐forming diatom across a century

Summary Almost a century ago Redfield discovered a relatively constant ratio between carbon, nitrogen and phosphorus in particulate organic matter and nitrogen and phosphorus of dissolved nutrients in seawater. Since then, the riverine export of nitrogen to the ocean has increased 20 fold. High abundance of resting stages in sediment layers dated more than a century back indicate that the common planktonic diatom Skeletonema marinoi has endured this eutrophication. We germinated unique genotypes from resting stages originating from isotope‐dated sediment layers (15 and 80 years old) in a eutrophied fjord. Using secondary ion mass spectrometry (SIMS) combined with stable isotopic tracers, we show that the cell‐specific carbon and nitrogen assimilation rates vary by an order of magnitude on a single‐cell level but are significantly correlated during the exponential growth phase, resulting in constant assimilation quota in cells with identical genotypes. The assimilation quota varies largely between different clones independent of age. We hypothesize that the success of S. marinoi in coastal waters may be explained by its high diversity of nutrient demand not only at a clone‐specific level but also at the single‐cell level, whereby the population can sustain and adapt to dynamic nutrient conditions in the environment.
Column Scan Acceleration in Hybrid CPU-FPGA Systems

Nowadays, in-memory column store database systems are state-of-the-art for analytical workloads. In these column stores, a full column scan is a fundamental key operation and thus, the optimization of this primitive is very crucial from a performance perspective. For this optimization, advances in hardware are always an interesting opportunity, but represent also a major challenge. At the moment, hardware systems are more and more changing from homogeneous CPU systems towards hybrid systems with diﬀerent computing units. Based on that, we focus on column scan acceleration for hybrid hardware systems incorporating a Field Programmable Gate Array (FPGA) and a CPU into a single system in this paper. The advantage of those hybrid systems is that the FPGA has usually direct access to the main memory of the CPU avoiding data copy which is a necessary procedure in other hybrid systems like CPU-GPU architectures. Thus, we present several FPGA designs for a recent column scan technique to fully oﬄoad the scan operation to the FPGA. In detail, we present our basic FPGA de-sign and diﬀerent optimization techniques. Then, we present selective results of our exhaustive evaluation showing the beneﬁt of our FPGA acceleration. As we are going to show, we achieve a maximum speedup of factor 7 compared to a single-threaded CPU scan execution.
Neural correlates of time distortion in a preaction period

An intention to move distorts the perception of time. For example, a visual stimulus presented during the preparation of manual movements is perceived longer than actual. Although neural mechanisms underlying this action‐induced time distortion have been unclear, here we propose a new model in which the distortion is caused by a sensory–motor interaction mediated by alpha rhythm. It is generally known that viewing a stimulus induces a reduction in amplitude of occipital 10‐Hz wave (“alpha‐blocking”). Preparing manual movements are also known to reduce alpha power in the motor cortex (“mu‐suppression”). When human participants prepared movements while viewing a stimulus, we found that those two types of classical alpha suppression interacted in the third (time‐processing) region in the brain, inducing a prominent decrease in alpha power in the supplementary motor cortex (SMA). Interestingly, this alpha suppression in the SMA occurred in an asymmetric manner (such that troughs of alpha rhythm was more strongly suppressed than peaks), which can produce a gradual increase (slow shift of baseline) in neural activity. Since the neural processing in the SMA encodes a subjective time length for a sensory event, the increased activity in this region (by the asymmetric alpha suppression) would cause an overestimation of elapsed time, resulting in the action‐induced time distortion. Those results showed a unique role of alpha wave enabling communications across distant (visual, motor, and time‐processing) regions in the brain and further suggested a new type of sensory–motor interaction based on neural desynchronization (rather than synchronization).
Paths for Improving Bevacizumab Available in 2018: The ADZT Regimen for Better Glioblastoma Treatment

During glioblastoma treatment, the pharmaceutical monoclonal antibody to vascular endothelial growth factor A, bevacizumab, has improved the quality of life and delayed progression for several months, but has not (or only marginally) prolonged overall survival. In 2017, several dramatic research papers appeared that are crucial to our understanding of glioblastoma vis-a-vis the mode of action of bevacizumab. As a consequence of these papers, a new, potentially more effective treatment protocol can be built around bevacizumab. This is the ADZT regimen, where four old drugs are added to bevacizumab. These four drugs are apremilast, marketed to treat psoriasis, dapsone, marketed to treat Hansen’s disease, zonisamide, marketed to treat seizures, and telmisartan, marketed to treat hypertension. The ancillary attributes of each of these drugs have been shown to augment bevacizumab. This paper details the research data supporting this contention. Phase three testing of AZDT addition to bevacizumab is required to establish safety and effectiveness before general use.
Helmholtz Principle on word embeddings for automatic document segmentation

Automatic document segmentation gets more and more attention in the natural language processing field. The problem is defined as text division into lexically coherent fragments. In fact, most of realistic documents are not homogeneous, so extracting underlying structure might increase performance of various algorithms in problems like topic recognition, document summarization, or document categorization. At the same time recent advances in word embedding procedures accelerated development of various text mining methods. Models such as word2vec, or GloVe allow for efficient learning a representation of large textual datasets and thus introduce more robust measures of word similarities. This study proposes a new document segmentation algorithm combining the idea of embedding-based measure of relation between words with Helmholtz Principle for text mining. We compare two of the most common word embedding models and show improvement of our approach on a benchmark dataset.
NMR structure of a G-quadruplex formed by four d(G4C2) repeats: insights into structural polymorphism

Abstract Most frequent genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), is a largely increased number of d(G4C2)n•(G2C4)n repeats located in the non-coding region of C9orf72 gene. Non-canonical structures, including G-quadruplexes, formed within expanded repeats have been proposed to drive repeat expansion and pathogenesis of ALS and FTD. Oligonucleotide d[(G4C2)3G4], which represents the shortest oligonucleotide model of d(G4C2) repeats with the ability to form a unimolecular G-quadruplex, forms two major G-quadruplex structures in addition to several minor species which coexist in solution with K+ ions. Herein, we used solution-state NMR to determine the high-resolution structure of one of the major G-quadruplex species adopted by d[(G4C2)3G4]. Structural characterization of the G-quadruplex named AQU was facilitated by a single substitution of dG with 8Br-dG at position 21 and revealed an antiparallel fold composed of four G-quartets and three lateral C–C loops. The G-quadruplex exhibits high thermal stability and is favored kinetically and under slightly acidic conditions. An unusual structural element distinct from a C-quartet is observed in the structure. Two C•C base pairs are stacked on the nearby G-quartet and are involved in a dynamic equilibrium between symmetric N3-amino and carbonyl-amino geometries and protonated C+•C state.
Distributed Model Predictive Secondary Voltage Control of Islanded Microgrids With Feedback Linearization

This paper presents a novel distributed secondary control method for both voltage and frequency regulation in islanded microgrids. Firstly, the large-signal dynamic model of inverter-interfaced distributed generation (DG) is formulated in the form of a multi-input multi-output nonlinear system, which can be converted to a partly linear one using input–output feedback linearization. Then, the linear-distributed model predictive controller is designated in each DG to realize the secondary voltage control by incorporating the forecasted behaviors of the local and neighboring DG units. Through the receding optimization index of every update process, the implementation of optimal control action accelerates the convergence rate for voltage magnitudes to the reference value. Following, after transforming the nonlinear DG dynamics into a first-order linear system, a distributed proportional integral algorithm is introduced in the frequency restoration while maintaining the accurate active power sharing. Our approach utilizes the distributed architecture, which indicates superior reliability and flexibility compared to the centralized manner; moreover, it can accommodate diverse uncertainties in communication links, model parameters, and time delays. Simulation results are provided to verify the effectiveness of the proposed control methodology.
A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images

In recent years, unsupervised feature learning based on a neural network architecture has become a hot new topic for research [1]-[4]. The revival of interest in such deep networks can be attributed to the development of efficient optimization skills, by which the model parameters can be optimally estimated [5]. The milestone work done by Hinton and Salakhutdinov [6] proposes to initialize the weights that allow deep autoencoder networks to learn lowdimensional codes. The encoding trick introduced works much better than principal component analysis (PCA) in terms of dimension reduction.
Interactive recommendation via deep neural memory augmented contextual bandits

Personalized recommendation with user interactions has become increasingly popular nowadays in many applications with dynamic change of contents (news, media, etc.). Existing approaches model user interactive recommendation as a contextual bandit problem to balance the trade-off between exploration and exploitation. However, these solutions require a large number of interactions with each user to provide high quality personalized recommendations. To mitigate this limitation, we design a novel deep neural memory augmented mechanism to model and track the history state for each user based on his previous interactions. As such, the user's preferences on new items can be quickly learned within a small number of interactions. Moreover, we develop new algorithms to leverage large amount of all users' history data for offline model training and online model fine tuning for each user with the focus of policy evaluation. Extensive experiments on different synthetic and real-world datasets validate that our proposed approach consistently outperforms a variety of state-of-the-art approaches.
Improving Error Correction Codes for Multiple-Cell Upsets in Space Applications

Currently, faults suffered by SRAM memory systems have increased due to the aggressive CMOS integration density. Thus, the probability of occurrence of single-cell upsets (SCUs) or multiple-cell upsets (MCUs) augments. One of the main causes of MCUs in space applications is cosmic radiation. A common solution is the use of error correction codes (ECCs). Nevertheless, when using ECCs in space applications, they must achieve a good balance between error coverage and redundancy, and their encoding/decoding circuits must be efficient in terms of area, power, and delay. Different codes have been proposed to tolerate MCUs. For instance, Matrix codes use Hamming codes and parity checks in a bi-dimensional layout to correct and detect some patterns of MCUs. Recently presented, column–line–code (CLC) has been designed to tolerate MCUs in space applications. CLC is a modified Matrix code, based on extended Hamming codes and parity checks. Nevertheless, a common property of these codes is the high redundancy introduced. In this paper, we present a series of new low-redundant ECCs able to correct MCUs with reduced area, power, and delay overheads. Also, these new codes maintain, or even improve, memory error coverage with respect to Matrix and CLC codes.
Competency-Based Supervision in Motivational Interviewing for Advanced Psychology Trainees: Targeting an a Priori Benchmark

Motivational interviewing (MI) is an evidence-based intervention with considerable support for promoting behavior change across a broad range of health and mental health issues. Despite its effectiveness, challenges associated with learning the approach may limit its full implementation in many clinical settings. The aim of the present study was to evaluate a supervised MI training practicum implemented within a doctoral internship/postdoctoral fellowship training program. The goal of the practicum was to enable each trainee to achieve expert competence in MI. Participants were 29 psychology doctoral interns and 1 postdoctoral fellow who participated in the training as part of their internship or fellowship program. Training included an initial workshop followed by a supervised practicum during which progress toward an a priori established expert competence benchmark was tracked through the use of an established coding system. Results indicated that trainees were satisfied with the supervision received. Three trainees did not achieve the a priori benchmark due to schedule conflicts. The 27 trainees who achieved the benchmark required between 4 and 20 supervision sessions to do so (M = 9.22, SD = 3.77). With the exception of reflective listening skill, prior training, baseline skill, and self-reported motivation were not associated with number of supervision sessions required to achieve the benchmark. Implications for training and dissemination of MI in clinical settings are discussed.
Deep Structures for a Reformer Unit Soft Sensor

Deep Neural Network (DNN) based Soft Sensors (SSs) have been demonstrated as successful alternatives to other data-driven structures. Here, a dynamic DNN based SS is proposed for the estimation of the Research Octane Number (RON) for a Reformer Unit in a refinery. The SS is required to estimate the RON when the plant operates in two different working conditions. Nonlinear Finite Inputs Response (NFIR) models have been investigated. The regressors in the models have been selected according to a cross-correlation analysis between candidate inputs and the RON value. The performance of the proposed SSs has been compared with previously designed deep structures, based on different dynamic first level models, coupled with a fuzzy algorithm.
How and When Are Job Crafters Engaged at Work?

The importance of work engagement and the lack of engaged employees have led researchers to focus on how to enhance employees’ levels of engagement in the workplace. Although job crafting as a principal driver of work engagement has recently received much attention from academics, little is known about the processes and conditions in which employees who craft their tasks become engaged. In order to address these research gaps, we hypothesize that psychological capital (PsyCap) is likely to mediate the association between job crafting and work engagement, and that coworker support, rather than supervisor support, is likely to moderate the relationship between job crafting and PsyCap. Further, we integrated these hypotheses and tested the moderated mediation effect. Using survey data from 175 flight attendants in South Korea, we found the results to be in line with our expectations. The findings of this empirical research contribute to the understanding of how and when job crafters become engaged at work.
Shifted balance of dorsal versus ventral striatal communication with frontal reward and regulatory regions in cannabis‐dependent males

The transition from voluntary to addictive behavior is characterized by a loss of regulatory control in favor of reward driven behavior. Animal models indicate that this process is neurally underpinned by a shift in ventral–dorsal striatal control of behavior; however, this shift has not been directly examined in humans. The present resting state functional magnetic resonance imaging (fMRI) study employed a two‐step approach to: (a) precisely map striatal alterations using a novel, data‐driven network classification strategy combining intrinsic connectivity contrast with multivoxel pattern analysis and, (b) to determine whether a ventral to dorsal striatal shift in connectivity with reward and regulatory control regions can be observed in abstinent (28 days) male cannabis‐dependent individuals (n = 24) relative to matched controls (n = 28). Network classification revealed that the groups can be reliably discriminated by global connectivity profiles of two striatal regions that mapped onto the ventral (nucleus accumbens) and dorsal striatum (caudate). Subsequent functional connectivity analysis demonstrated a relative shift between ventral and dorsal striatal communication with fronto‐limbic regions that have been consistently involved in reward processing (rostral anterior cingulate cortex [ACC]) and executive/regulatory functions (dorsomedial prefrontal cortex [PFC]). Specifically, in the cannabis‐dependent subjects, connectivity between the ventral striatum with the rostral ACC increased, whereas both striatal regions were uncoupled from the regulatory dorsomedial PFC. Together, these findings suggest a shift in the balance between dorsal and ventral striatal control in cannabis dependence. Similar changes have been observed in animal models and may promote the loss of control central to addictive behavior.
Developmental trajectories of adaptive functioning following early mild traumatic brain injury

Adaptive behavior impairments have been reported in children with severe traumatic brain injury (TBI) but are not typically found following mild TBI. It is possible that mild TBI induces subtle changes in adaptive functioning that are not captured in conventional group comparisons. This study aimed to explore time course changes in adaptive functioning following early mild TBI. Parents of 63 children with mild TBI and 53 children with orthopedic injuries aged between 1.5 and 5 years at the time of injury completed the Adaptive Behavior Assessment System-II at three time points: retrospectively to assess pre-injury functioning, then at 6 and 18 months post-injury. Developmental trajectories of adaptive functioning domains (practical, conceptual, and social) reported by parents were modeled using linear mixed-model analyses. Findings suggest that mild TBI may disrupt the expected developmental progression of children's social adaptive behavior, but does not appear to alter practical and conceptual domains.
Leader-Controlled Protocols to Accelerate Convergence in Consensus Networks

In this paper, we consider a discrete-time consensus network, and assume that one of the agents acts as a leader and injects an input signal to improve the overall system performance, in particular to increase the speed of convergence to consensus or to achieve finite-time consensus. Two possible control protocols are proposed, and the characteristic polynomials of the resulting closed-loop systems are determined. These results allow us to investigate the consensus and finite-time consensus of the overall systems. Open problems and future research directions conclude this paper.
Repetitive Transcranial Magnetic Stimulation for Treatment of Depression in a Patient With Severe Traumatic Brain Injury

Background: Traumatic brain injury (TBI) commonly presents with a constellation of findings, including physical, cognitive, and psychological disturbances. Traditionally, few options have been available for long-term management of mood symptoms for post-TBI patients who are refractory to medications. Repetitive transcranial magnetic stimulation (rTMS) is a promising alternative treatment. Case Report: A 37-year-old male with history of anxiety and bipolar depression incurred a TBI after a 60-foot fall. After his physical recovery, the patient had refractory depression. Eight months after the TBI event, he underwent a 6-week course of rTMS treatment and had a 70.8% improvement in mood symptoms by the end of the therapy as indicated by the Patient Health Questionnaire-9, transitioning from severe to mild depression according to the scoring system. Clinical correlation during the months following conclusion of rTMS therapy showed no signs of remission or adverse side effects. The patient remains stable and lives independently 1 year after treatment with mood-stabilizing medications. Conclusion: This case provides evidence for successful treatment of refractory depressive symptoms after severe TBI with the addition of rTMS to psychotherapy and mood-stabilizing medications, supporting the safety and tolerability of this novel therapeutic approach. Further studies are needed to validate the contribution of rTMS for management of mood symptoms in patients with TBI.
The Consumption of Beef Burgers Prepared with Wine Grape Pomace Flour Improves Fasting Glucose, Plasma Antioxidant Levels, and Oxidative Damage Markers in Humans: A Controlled Trial

Wine grape pomace flour (WGPF) is a fruit byproduct that is high in fiber and antioxidants. We tested whether WGPF consumption could affect blood biochemical parameters, including oxidative stress biomarkers. In a three-month intervention study, 27 male volunteers, each with some components of metabolic syndrome, consumed a beef burger supplemented with 7% WGPF containing 3.5% of fiber and 1.2 mg gallic equivalents (GE)/g of polyphenols (WGPF-burger), daily, during the first month. The volunteers consumed no burgers in the second month, and one control-burger daily in the third month. At baseline and after these periods, we evaluated the metabolic syndrome components, plasma antioxidant status (i.e., 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity (DPPH), vitamin E, vitamin C), and oxidative damage markers (i.e., advanced oxidation protein products (AOPPs), oxidized low-density lipoproteins (oxLDLs), malondialdehyde (MDA)). The WGPF-burger intake significantly reduced glycemia and homeostatic model assessment-based measurement of insulin resistance. Vitamin C increased and decreased during the consumption of the WGPF-burger and control-burger, respectively. The WGPF-burger intake significantly decreased AOPP and oxLDL levels. Vitamin E and MDA levels showed no significant changes. In conclusion, the consumption of beef burgers prepared with WGPF improved fasting glucose and insulin resistance, plasma antioxidant levels, and oxidative damage markers. Therefore, this functional ingredient has potential as a dietary supplement to manage chronic disease risk in humans.