Miracle-dz/UniFAR-FTUs huggingface dataset:
Action complexity modulates corticospinal excitability during action observation

Observing an action engages the same brain regions as executing an action. Activity in the motor areas of the brain can be measured indirectly using non-invasive techniques, such as single-pulse transcranial magnetic stimulation (TMS). The current study aimed to investigate the time-course of motor cortex activity during action observation of a simple and complex action. In Experiment 1 single-pulse TMS was used to investigate whether the time-course, and magnitude of change in motor cortex excitability was mediated by complexity of an observed action. Measures of motor cortex excitability acting on two intrinsic hand muscles and two forearm muscles were obtained during observation of a simple and a complex action. Experiment 1 found excitability in an intrinsic hand muscle was greater during observation of a complex action compared to a simple action, at the time-point at which the actions were kinematically different. Paired-pulse TMS can be used to measure excitability of motor cortex inhibitory processes. In Experiment 2, single and paired-pulse TMS was used to determine whether greater excitability during observation of the complex action compared to the simple action was associated with reduced motor cortical inhibition. Experiment 2 showed no significant difference in motor cortical inhibition between the complex and simple action. Findings suggest action complexity modulates changes in motor cortex excitability during action observation.
Integrating Tumor Sequencing Into Clinical Practice for Patients With Mismatch Repair-Deficient Lynch Syndrome Spectrum Cancers

INTRODUCTION: Uninformative germline genetic testing presents a challenge to clinical management for patients suspected to have Lynch syndrome, a cancer predisposition syndrome caused by germline variants in the mismatch repair (MMR) genes or EPCAM. METHODS: Among a consecutive series of MMR-deficient Lynch syndrome spectrum cancers identified through immunohistochemistry-based tumor screening, we investigated the clinical utility of tumor sequencing for the molecular diagnosis and management of suspected Lynch syndrome families. MLH1-deficient colorectal cancers were prescreened for BRAF V600E before referral for genetic counseling. Microsatellite instability, MLH1 promoter hypermethylation, and somatic and germline genetic variants in the MMR genes were assessed according to an established clinical protocol. RESULTS: Eighty-four individuals with primarily colorectal (62%) and endometrial (31%) cancers received tumor-normal sequencing as part of routine clinical genetic assessment. Overall, 27% received a molecular diagnosis of Lynch syndrome. Most of the MLH1-deficient tumors were more likely of sporadic origin, mediated by MLH1 promoter hypermethylation in 54% and double somatic genetic alterations in MLH1 (17%). MSH2-deficient, MSH6-deficient, and/or PMS2-deficient tumors could be attributed to pathogenic germline variants in 37% and double somatic events in 28%. Notably, tumor sequencing could explain 49% of cases without causal germline variants, somatic MLH1 promoter hypermethylation, or somatic variants in BRAF. DISCUSSION: Our findings support the integration of tumor sequencing into current Lynch syndrome screening programs to improve clinical management for individuals whose germline testing is uninformative.
Redundancy and analogue slicing for precise in-memory machine learning - Part I: Programming techniques

—In-memory computing (IMC) is receiving considerable interest for accelerating artiﬁcial intelligence (AI) tasks, such as neural network training and inference. However, IMC can also accelerate other machine learning (ML) and scien-tiﬁc computing problems, such as recommendation systems, regression and PageRank, which are ubiquitous in datacenters. These applications typically have higher precision requirements than neural networks, which can challenge analogue-based IMC and sacriﬁce some of the expected energy efﬁciency beneﬁts. Here we address these challenges experimentally, presenting new techniques improving the accuracy of the solution of linear algebra problems, such as eigenvector extraction for PageRank, in a fully integrated circuit (IC) with analogue resistive switching memory (RRAM) devices. Our custom redundancy algorithm can improve the programming accuracy by using multiple memory devices for representing a single matrix entry. Accuracy is further improved by error compensation with analogue slicing, which allows an ever more precise value representation.
Investigating the performance of mesostructured based solar cells under indoor artificial lighting

Self-powered sensors for IoT and IoT2 applications have dominated a recordable market share in the current electronics industry. In such applications, a sustainable energy harvester is considered as a key design parameter towards a life-long sensor. One of the hidden sources of energy is the indoor lighting, where a very limited efficiency is utilized by user. Most of the indoor lighting sources emits photons in the off bands spectra while turned to be a wasted energy. Herein, we evaluate the performance of three potential indoor light harvesters using Si, mesoporous TiO2 with dye and perovskites materials. The work pretends to simulate the optoelectronic behavior in the form of the characteristic J-V curves and estimate the harvested energy under various indoor lighting. Perovskite solar cells showed minimum deviation in the current density, around 5% (from 22.18 mA/cm2 to 21.18 mA/cm2), with respect to other cells while evaluating the indoor characteristic against AM1.5G spectrum for one Sun.
Reinforcement learning approach for deterministic SOT-MRAM switching

We employ a reinforcement learning strategy for finding switching schemes for deterministic switching of a spin-orbit torque magnetoresistive random access memory cell. The free layer of the memory cell is perpendicularly magnetized, and the spin-orbit torques are generated by currents through two orthogonal heavy metal wires. A rewarding scheme for the reinforcement learning approach is defined such that the objective of the algorithm is to find a pulse sequence that leads to fast deterministic field-free switching of the memory cell. The reliability of the found switching scheme is tested by performing micromagnetic simulations. The results show that a neural network model trained on fixed material parameters is able to reverse the memory cell magnetization for a wide range of material parameters and can be used to derive a writing pulse sequence for fast and deterministic spin-orbit torque switching of a perpendicular free layer.
The digital divide at school and at home: A comparison between schools by socioeconomic level across 47 countries

Despite efforts to improve digital access in schools, a persistent digital divide is identified worldwide. Drawing on data from the 2018 Organisation for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA) for 15-year-olds, I examine how students’ digital use for educational purposes (at school and at home) and their perceived digital competence differ between schools by socioeconomic status (SES) and vary across 47 countries. Using multilevel modeling, I find that the second-level digital divide between schools exists even among more developed societies. Students attending high-SES schools are more likely to use computers for schoolwork within and outside of schools, and have more digital competence than those attending low-SES schools. These differences remain substantial and statistically significant even when controlling for school-level resources. Moreover, the between-school digital divide in students’ digital competence is negatively associated with economic development and educational expenditures, and positively associated with income inequality. In conclusion, I discuss implications of the findings and highlight the importance of examining how schools with varying socioeconomic profiles provide different e-learning experiences for individual students, explained by the different institutional settings and cultural features of schools.
Cyclin-G-associated kinase GAK/dAux regulates autophagy initiation via ULK1/Atg1 in glia

Autophagy is a major means for the elimination of protein inclusions in neurons in neurodegenerative diseases such as Parkinson’s disease (PD). Yet, the mechanism of autophagy in the other brain cell type, glia, is less well characterized and remains largely unknown. Here we present evidence that the PD risk factor Cyclin G-associated kinase (GAK)/dAuxilin (dAux) is a new component in glial autophagy. GAK/dAux directly interacts with ULK1/Atg1 via its uncoating domain. Lack of GAK/dAux increases the autophagosome number and size in adult fly glia and mouse microglia, and generally upregulates levels of components in the initiation and PI3K class III complexes including ULK1/Atg1, demonstrating that GAK/dAux regulates the onset of glial autophagy. Consistently, lack of GAK/dAux enhances Atg1 and Atg9 trafficking to autophagosomes, promoting autophagy initiation. On the other hand, lack of GAK/dAux impairs the autophagic flux and blocks substrate degradation, suggesting that GAK/dAux might play additional roles in glial autophagy. Our findings identify a new autophagy factor in glia; considering the pivotal role of glia under pathological conditions, targeting glial autophagy is potentially a therapeutic strategy for PD.
The interferon-rich skin environment regulates Langerhans cell ADAM17 to promote photosensitivity in lupus

Background The autoimmune disease lupus erythematosus (lupus) is characterized by photosensitivity, where even ambient ultraviolet radiation (UVR) exposure inflames skin. Beneficial effects of anifrolumab (anti-interferon α/breceptor (anti-IFNAR)) on lupus skin disease support a pathogenic role for IFN-I, but mechanistic understanding is limited. We have shown that Langerhans cell (LC) dysfunction contributes to photosensitivity. Healthy LCs act via a disintegrin and metalloprotease 17 (ADAM17) to release epidermal growth factor receptor (EGFR) ligands that limit UVR-induced keratinocyte apoptosis and photosensitivity. However, LC ADAM17 activity is reduced in non-lesional lupus model skin, and data point to reduced LC-mediated protection in human lupus. Here, we asked about the role of the IFN-rich lupus skin environment in LC dysfunction and the implications of this regulation for photosensitivity. Methods Gene expression patterns in non-lesional skin from human lupus and multiple murine models were examined. We used MRL/lpr, B6.Sle1yaa, and imiquimod models of lupus in in vivo studies to assess the role of IFN-I in LC ADAM17 dysfunction and photosensitivity. Results We show a shared IFN-rich environment in non-lesional skin across human and murine model systems, that IFN-I inhibits LC ADAM17 activity, and that anti-IFNAR in lupus models restores LC ADAM17 function and reduces photosensitivity in EGFR and LC ADAM17-dependent manners. Reactive oxygen species (ROS) can mediate ADAM17 activity, and we show reduced LC ROS expression in lupus models that is restored by anti-IFNAR. Conclusions Our findings suggest that IFN-I promotes photosensitivity by causing LC ADAM17 dysfunction and that anifrolumab ameliorates lupus skin disease at least in part by restoring LC function. This work provides insight into IFN-I-mediated disease mechanisms, LC regulation, and a mechanism of action for anifrolumab in lupus.
Fermentation dynamics of Lactobacillus helveticus H9 revealed by ultra‐performance liquid chromatography quadrupole time‐of‐flight mass spectrometry

Lactobacillus (L.) helveticus H9 is a probiotic strain that can produce antihypertensive peptides during milk fermentation. This study analysed the dynamics of skim milk fermentation by L. helveticus H9 by ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). A total of 1992 metabolites were detected from all of the fermented samples in the LC-MS analysis by multivariate statistical analysis. Metabolites with variable importance in projection (VIP) values ≥2 were considered differentially abundant among samples and were responsible for the unique taste and nutritional and functional qualities of fermented milk. Valine, threonine, L-methionine, tyrosine, asparagine and leucine were the predominant amino acids produced during fermentation, and their quantities changed remarkably during the fermentation process. Citric acid and uric acid were the major, and only detectable, organic acids. Some intermediate metabolites, such as N-acryloylglycine and nicotinamide-N-oxide, were also detected. Moreover, certain oligopeptides such as Val-Leu, Lys-Gly, AlaGlu, Asp-Ser, Leu-Pro and Val-Phe-Ala were not detected until the middle and late fermentation periods. This study demonstrated dynamic metabolic changes, providing a strong foundation for deciphering the physiological and biochemical mechanisms of the fermentation process.
Hobit identifies tissue-resident memory T cell precursors that are regulated by Eomes

Hobit-expressing effector CD8+ T cells are TRM precursors, whose formation is regulated by the T-box transcription factor Eomes. Tracing TRM roots Tissue-resident memory cells (TRM) established in peripheral tissues after initial antigen exposure are poised to rapidly respond upon reinfection. Parga-Vidal et al. used Hobit reporter/deleter mice to map the origin and differentiation of TRM cells during LCMV infection. They found that a subset of effector phase CD8+ T cells expressing the TRM-specific transcription factor Hobit selectively gave rise to TRM cells in nonlymphoid tissues and had already initiated a tissue residency transcriptional program early after infection. While the T-box transcription factor Eomes was down-regulated in Hobit+ effector cells, Eomes was found to limit TRM formation and suppress Hobit expression. These results demonstrate that TRM commitment can occur early after initial antigen exposure and provide insight into the transcription factor networks regulating TRM differentiation. Tissue-resident memory CD8+ T cells (TRM) constitute a noncirculating memory T cell subset that provides early protection against reinfection. However, how TRM arise from antigen-triggered T cells has remained unclear. Exploiting the TRM-restricted expression of Hobit, we used TRM reporter/deleter mice to study TRM differentiation. We found that Hobit was up-regulated in a subset of LCMV-specific CD8+ T cells located within peripheral tissues during the effector phase of the immune response. These Hobit+ effector T cells were identified as TRM precursors, given that their depletion substantially decreased TRM development but not the formation of circulating memory T cells. Adoptive transfer experiments of Hobit+ effector T cells corroborated their biased contribution to the TRM lineage. Transcriptional profiling of Hobit+ effector T cells underlined the early establishment of TRM properties including down-regulation of tissue exit receptors and up-regulation of TRM-associated molecules. We identified Eomes as a key factor instructing the early bifurcation of circulating and resident lineages. These findings establish that commitment of TRM occurs early in antigen-driven T cell differentiation and reveal the molecular mechanisms underlying this differentiation pathway.
Robotics for Occupational Therapy: Learning Upper-Limb Exercises From Demonstrations

We describe a learning-from-demonstration technique that enables a general-purpose humanoid robot to lead a user through object-mediated upper-limb exercises. It needs only tens of seconds of training data from a therapist teleoperating the robot to do the task with the user. We model the robot behavior as a regression problem, inferring the desired robot effort using the end-effector's state (position and velocity). Compared to the conventional approach of learning time-based trajectories, our strategy produces customized robot behavior and eliminates the need to tune gains to adapt to the user's motor ability. In our study, one occupational therapist and six people with stroke trained a Willow Garage PR2 on three example tasks (periodic 1D and 2D motions plus episodic pick-and-place). They then repeatedly did the tasks with the robot and blindly compared the state- and time-based controllers learned from the training data. Our results show that working models were reliably obtained to allow the robot to do the exercise with the user; that our state-based approach enabled users to be more actively involved, allowed larger excursion, and generated power outputs more similar to the therapist demonstrations; and that the therapist found our strategy more agreeable than the traditional time-based approach.
FxlogRLP: The Filtered-X Logarithmic Recursive Least P-Power Algorithm

For active impulsive noise control, a filtered-x recursive least p-power (FxRLP) algorithm is proposed by minimizing the weighted summation of the p-power of the a posteriori errors. Since the characteristic of the target noise is investigated, the FxRLP algorithm achieves good performance and robustness. To obtain a better performance, we develop a filtered-x logarithmic recursive least p-power (FxlogRLP) algorithm which integrates the p-order moment with the logarithmic-order moment. Simulation results demonstrate that the FxlogRLP algorithm is superior to the existing algorithms in terms of convergence rate and noise reduction.
Micro and macro-level risk factors for extremism and terrorism: Toward a criminology of extremist violence

Abstract Over the past twenty years, research on political extremism and terrorism has become one of the fastest growing sub-fields within criminology. This rapid growth is reminiscent of the early years of criminologyitself, characterized by energy, imagination and creativity but at the same time a specialization struggling to collect and analyze valid data, apply appropriate research methods and develop coherent theoretical frameworks. In this paper, we take stock of these developments by considering a basket of micro- and macro-level risk factors that have been frequently linked to the decision to engage in violent extremism. Following a review of risk factors, we consider major definitional, theoretical, data and methodological challenges and also progress made. Prior criminological research on violent extremism has focused especially on micro-level characteristics and few studies to date have integrated micro and macro determinants to explain extremist outcomes. However, with the growing availability of data, including open-source databases, paired with the application of more sophisticated statistical methods, we expect to see more robust results in the years ahead.
Divergent transcriptional and transforming properties of PAX3-FOXO1 and PAX7-FOXO1 paralogs

The hallmarks of the alveolar subclass of rhabdomyosarcoma are chromosomal translocations that generate chimeric PAX3-FOXO1 or PAX7-FOXO1 transcription factors. Both PAX-FOXO1s result in related cell transformation in animal models, but both mutations are associated with distinct pathological manifestations in patients. To assess the mechanisms underlying these differences, we generated isogenic fibroblast lines expressing either PAX-FOXO1 paralog. Mapping of their genomic recruitment using CUT&Tag revealed that the two chimeric proteins have distinct DNA binding preferences. In addition, PAX7-FOXO1 causes stronger de novo transactivation of its bound regions than PAX3-FOXO1, resulting in greater transcriptomic dynamics involving genes regulating cell shape and cycle. Consistently, PAX3-FOXO1 accentuates fibroblast cellular traits associated with contractility and surface adhesion and limits entry into M phase. In contrast, PAX7-FOXO1 drives cells to adopt an amoeboid shape, reduces entry into S phase, and causes more genomic instabilities. Altogether, our results argue that the diversity of rhabdomyosarcoma manifestation arises, in part, from the divergence between the transcriptional activities of PAX3-FOXO1 and PAX7-FOXO1. Furthermore, the identified pronounced deleterious effects of PAX7-FOXO1 provide an explanation for the low frequency of the translocation generating this factor in patients with rhabdomyosarcoma.
Blind Estimation of Sparse Simo Channels: Quadratic Vs. Linear Constraints

Blind multichannel estimation is classically done considering one of two constraints on the channel coefficients: (i) a quadratic constraint (i.e., unit-norm), or (ii) a linear constraint (i.e., fixed value for a particular coefficient). These constraints serve to remove the indeterminacy of the solution inherent to this estimation problem. In this paper, we investigate the adequacy of both constraints in the particular case of sparse channels. For this purpose, we first conduct a Cramér-Rao Bound (CRB)-based performance comparison, then we support the obtained results with simulation experiments using a subspace method. The obtained results indicate that, contrary to common practice, the linear constraint should be favored over the quadratic constraint for the blind estimation of sparse channels.
Radar Ghost Target Detection via Multimodal Transformers

Ghost targets caused by inter-reflections are by design unavoidable in radar measurements, and it is challenging to distinguish these artifact detections from real ones. In this letter, we propose a novel approach to detect radar ghost targets by using LiDAR data as a reference. For this, we adopt a multimodal transformer network to learn interactions between points. We employ self-attention to exchange information between radar points, and local crossmodal attention to infuse information from surrounding LiDAR points. The key idea is that a ghost target should have higher semantic affinity with the reflected real target than the other ones. Extensive experiments on nuScenes [1] show that our method outperforms the baseline method on radar ghost target detection by a large margin.
Probabilistic Tensor Train Decomposition with Automatic Rank Determination from Noisy Data

Tensor train (TT) decomposition, a powerful tool for analyzing multidimensional data, exhibits superior performance in many signal processing and machine learning tasks. However, existing TT decomposition methods either require the knowledge of the true TT ranks, or extensive fine-tuning of the balance between model complexity and representation accuracy. In this paper, a fully Bayesian treatment of TT decomposition is employed to enable automatic rank determination. Based on the proposed probabilistic model, an efficient learning algorithm is derived under the variational inference framework. Simulation results on synthetic data show the success of the proposed model and algorithm in recovering the ground-truth TT structure from noisy data.