Miracle-dz/UniFAR-FTUs huggingface dataset:
TSD: Transformers for Seizure Detection

Epilepsy is a common neurological disorder that sub-stantially deteriorates patients’ safety and quality of life. Electroencephalogram (EEG) has been the golden-standard technique for diagnosing this brain disorder and has played an essential role in epilepsy monitoring and disease management. It is extremely laborious and challenging, if not practical, for physicians and expert humans to annotate all recorded signals, particularly in long-term monitoring. The annotation process often involves identifying signal segments with suspected epileptic seizure features or other abnormalities and/or known healthy features. Therefore, automated epilepsy detection becomes a key clinical need because it can greatly improve clinical practice’s efficiency and free up human expert time to attend to other important tasks. Current automated seizure detection algorithms generally face two challenges: (1) models trained for specific patients, but such models are patient-specific, hence fail to generalize to other patients and real-world situations; (2) seizure detection models trained on large EEG datasets have low sensitivity and/or high false positive rates, often with an area under the receiver operating characteristic (AUROC) that is not high enough for potential clinical applicability. This paper proposes Transformers for Seizure Detection, which we refer to as TSD in this manuscript. A Transformer is a deep learning architecture based on an encoder-decoder structure and on attention mechanisms, which we apply to recorded brain signals. The AUROC of our proposed model has achieved 92.1%, tested with Temple University’s publically available electroencephalogram (EEG) seizure corpus dataset (TUH). Additionally, we highlight the impact of input domains on the model’s performance. Specifically, TSD performs best in identifying epileptic seizures when the input domain is a time-frequency. Finally, our proposed model for seizure detection in inference-only mode with EEG recordings shows outstanding performance in classifying seizure types and superior model initialization.
The channel mobility degradation in a nanoscale metal–oxide–semiconductor field effect transistor due to injection from the ballistic contacts

The ballistic mobility degradation is shown to originate from nonstationary (transient) transport in response to the ohmic electric field. The source and drain reservoirs launch electrons into the channel with injection velocity transiting the channel with finite ballisticity defined as the probability of a collision-free flight. The distinction is made between the ballistic mean free path and that present in a long channel. The results are in excellent agreement with those obtained from Monte Carlo procedures and experiments.
The Sin3B chromatin modifier restricts cell cycle progression to dictate hematopoietic stem cell differentiation

To maintain blood homeostasis, millions of terminally differentiated effector cells are produced every day. At the apex of this massive and constant blood production lie hematopoietic stem cells (HSCs), a rare cell type harboring unique self-renewal and multipotent properties. A key feature of HSCs is their ability to temporarily exit the cell cycle in a state termed quiescence. Defective control of cell cycle progression can eventually lead to bone marrow failure or malignant transformation. It is thought that HSCs must re-enter the cell cycle in order to commit to terminal differentiation. However, the molecular mechanisms tying cell cycle re-entry to cell fate commitment in HSCs remain elusive. Here, we identify the chromatin-associated Sin3B protein as a molecular link between cell cycle progression and differentiation in HSCs. We demonstrate that Sin3B is necessary for HSCs’ commitment to differentiation, but dispensable for their self-renewal or survival. Single cell transcriptional profiling of hematopoietic stem and progenitor cells (HSPCs) inactivated for Sin3B reveals aberrant cell cycle gene expression, consistent with the observed aberrant progression through the G1 phase of the cell cycle. The defective cell cycle control elicited upon Sin3B inactivation correlates with the engagement of discrete signaling programs, including aberrant expression of cell adhesion molecules and essential components of the interferon signaling cascade in LT-HSCs. Additionally, chromatin accessibility profiling in LT-HSCs reveals the Sin3B-dependent accessibility of genomic elements controlling HSC differentiation, suggesting a functional link between cell cycle progression, and priming of hematopoietic stem cells for differentiation. Together, these results point to controlled progression through the G1 phase of the cell cycle as a likely regulator of HSC lineage commitment through the modulation of chromatin features.
3D in vitro modeling of the exocrine pancreatic unit using tomographic volumetric bioprinting

Pancreatic ductal adenocarcinoma (PDAC) is the most frequent type of pancreatic cancer, one of the leading causes of cancer-related deaths worldwide. The first lesions associated with PDAC occur within the functional units of exocrine pancreas. The crosstalk between PDAC cells and stromal cells plays a key role in tumor progression. Thus, in vitro, fully human models of the pancreatic cancer microenvironment are needed to foster the development of new, more effective therapies. However, it is challenging to make these models anatomically and functionally relevant. Here, we used tomographic volumetric bioprinting, a novel method to fabricate three-dimensional cell-laden constructs, to produce a portion of the complex convoluted exocrine pancreas in vitro. Human fibroblast-laden gelatin methacrylate-based pancreatic models were processed to reassemble the tubuloacinar structures of the exocrine pancreas and, then human pancreatic ductal epithelial (HPDE) cells overexpressing the KRAS oncogene (HPDE-KRAS) were seeded in the acinar lumen to reproduce the pathological exocrine pancreatic tissue. The growth and organization of HPDE cells within the structure was evaluated and the formation of a thin epithelium which covered the acini inner surfaces in a physiological way inside the 3D model was successfully demonstrated. Interestingly, immunofluorescence assays revealed a significantly higher expressions of alpha smooth muscle actin (α-SMA) vs. actin in the fibroblasts co-cultured with cancerous than with wild-type HPDE cells. Moreover, α-SMA expression increased with time, and it was found to be higher in fibroblasts that laid closer to HPDE cells than in those laying deeper into the model. Increased levels of interleukin (IL)-6 were also quantified in supernatants from co-cultures of stromal and HPDE-KRAS cells. These findings correlate with inflamed tumor-associated fibroblast behavior, thus being relevant biomarkers to monitor the early progression of the disease and to target drug efficacy. To our knowledge, this is the first demonstration of a 3D bioprinted portion of pancreas that recapitulates its true 3-dimensional microanatomy, and which shows tumor triggered inflammation.
The landscape of m1A modification and its posttranscriptional regulatory functions in primary neurons

Cerebral ischaemia‒reperfusion injury, during which neurons undergo oxygen-glucose deprivation/reoxygenation (OGD/R), is a notable pathological process in many neurological diseases. N1-methyladenosine (m1A) is an RNA modification that can affect gene expression and RNA stability. The m1A landscape and potential functions of m1A modification in neurons remain poorly understood. We explored RNA (mRNA, lncRNA, and circRNA) m1A modification in normal and OGD/R-treated neurons and the effect of m1A on diverse RNAs. We investigated the m1A landscape in primary neurons, identified m1A-modified RNAs, and found that OGD/R increased the number of m1A RNAs. m1A modification might also affect the regulatory mechanisms of noncoding RNAs, e.g., lncRNA–RBP interactions and circRNA translation. We showed that m1A modification mediates the circRNA/lncRNA‒miRNA–mRNA ceRNA mechanism and that 3’UTR methylation of mRNAs can hinder miRNA–mRNA binding. Three methylation patterns were identified, and genes with different patterns had intrinsic mechanisms with potential m1A-regulatory specificity. Systematic analysis of the m1A landscape in normal and OGD/R neurons lays a critical foundation for understanding RNA methylation and provides new perspectives and a theoretical basis for treating and developing drugs for OGD/R pathology-related diseases.
In silico Antibody-Peptide Epitope prediction for Personalized cancer therapy

The human leukocyte antigen (HLA) system is a complex of genes on chromosome 6 in humans that encodes cell-surface proteins responsible for regulating the immune system. Viral peptides presented to cancer cell surfaces by the HLA trigger the immune system to kill the cells, creating Antibody-peptide epitopes (APE). This study proposes an in-silico approach to identify patient-specific APEs by applying complex networks diagnostics on a novel multiplex data structure as input for a deep learning model. The proposed analytical model identifies patient and tumor-specific APEs with as few as 20 labeled data points. Additionally, the proposed data structure employs complex network theory and other statistical approaches that can better explain and reduce the black box effect of deep learning. The proposed approach achieves an F1-score of 80% and 93% on patients one and two respectively and above 90% on tumor-specific tasks. Additionally, it minimizes the required training time and the number of parameters.
The biological activity of bacterial rhamnolipids is linked to their molecular structure

Biosurfactants are amphiphilic compounds of microbial origin with a wide range of industrial applications. Rhamnolipids (RLs) offer a broad range of potential applications as biosurfactants in industry and agriculture. Several studies report a high capacity for controlling different plant pests and pathogens and consider RLs as promising candidates for bio-based plant protection agents. RLs are a class of glycolipids consisting of one (mRLs) or two (dRLs) rhamnose moieties linked to a single or branched β-fatty acid. Due to the diversity of RLs, so far little is known about the relation between molecular structure and biological activity. Engineering the synthesis pathway allowed us to differentiate between the activities of mixtures of pure mRL and of pure dRL congeners and elaborated HPLC techniques further enabled to analyse the activity of single congeners. In a model system with the plant Arabidopsis thaliana and the plant-parasitic nematode Heterodera schachtii we demonstrate that RLs can significantly reduce infection, whereas their impact on the host plant varies depending on their molecular structure. While mRLs reduced plant growth even at low concentration, dRLs showed no or a beneficial impact on plant development. In addition, the effect of RLs on plant H2O2 production was measured as an indicator of plant defense activity. Treatment with mRLs or dRLs at a concentration of 50 ppm increased H2O2 production. Lower concentrations of up to 10 ppm were used to stimulate plants prior to a treatment with water or flagellin (flg22), a bacterial inducer of plant defense responses. At 10 ppm both mRLs and dRLs fostered an increased response to flg22. However, mRLs also led to an increased response to water, the non-inducing negative control, indicating a generally elevated stress level. Neither mRLs nor dRLs induced expression of plant defense marker genes of salicylic acid, jasmonic acid and ethylene pathways within a 1 hour and 48 hours treatment. Due to the negative effect of mRLs on plants further studies were concentrated on dRLs. Treatment of pre-parasitic infective juveniles of H. schachtii revealed that dRLs did not increase mortality even at a very high concentration of 755 ppm. In order to analyse the effect of single dRL congeners nematode infection assays were performed. While dRL congeners with a C10-C8 acyl chain increased nematode infection, dRLs with C10-C12 and C10-C12:1 acyl chains reduced nematode infection even at concentrations below 2 ppm. Plant growth was not reduced by C10-C8 dRLs, but by C10-C12 and C10-C12:1 dRLs at concentrations of 8.3 ppm. H2O2 production was increased compared to the water control upon treatment with C10-C8 dRLs at a concentration of 200 ppm, while C10-C12 and C10-C12:1 dRLs triggered the same effect already at 50 ppm. Our experiments show a clear structure-effect relation. In conclusion, functional assessment and analysis of mode of action of RLs in plants require careful consideration of their molecular structure and composition.
A Comparison of Lossless Compression Methods in Microscopy Data Storage Applications

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy are capable of producing petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions. CCS CONCEPTS • Applied computing → Bioinformatics; Imaging.
The spectral slope as a marker of excitation/inhibition ratio and cognitive functioning in multiple sclerosis

Background Multiple sclerosis (MS) is a neurodegenerative disease characterized by neuronal and synaptic loss, resulting in an imbalance of excitatory and inhibitory synaptic transmission and potentially cognitive impairment. Current methods for measuring the excitation/inhibition (E/I) ratio are mostly invasive, but recent research combining neurocomputational modeling with measurements of local field potentials has indicated that the slope with which the power spectrum of neuronal activity captured by electro- and/or magnetoencephalography rolls off, is a non-invasive biomarker of the E/I ratio. A steeper roll- off is associated with a stronger inhibition. This novel method can be applied to assess the E/I ratio in people with multiple sclerosis (pwMS), detect the effect of medication such as benzodiazepines, and explore its utility as a biomarker for cognition. Methods We recruited 44 healthy control subjects and 95 people with multiple sclerosis (pwMS) who underwent resting-state magnetoencephalographic recordings. The 1/f spectral slope of the neural power spectra was calculated for each subject and for each brain region. Results As expected, the spectral slope was significantly steeper in pwMS treated with benzodiazepines (BZDs) compared to pwMS not receiving BZDs (p = 0.01). In the sub-cohort of pwMS not treated with BZDs, we observed a steeper slope in cognitively impaired pwMS compared to cognitively preserved pwMS (p = 0.01) and healthy subjects (p = 0.02). Furthermore, we observed a significant correlation between 1/f spectral slope and verbal and spatial working memory functioning in the brain regions located in the prefrontal and parietal cortex. Conclusions In this study, we highlighted the value of the spectral slope in MS by quantifying the effect of benzodiazepines and by putting it forward as a potential biomarker of cognitive deficits in pwMS.
IRF1 regulates self-renewal and stress-responsiveness to support hematopoietic stem cell maintenance

Inflammatory mediators induce emergency myelopoiesis and cycling of adult hematopoietic stem cells (HSCs) through incompletely understood mechanisms. To suppress the unwanted effects of inflammation and preserve its beneficial outcomes, the mechanisms by which inflammation affects hematopoiesis need to be fully elucidated. Rather than focusing on specific inflammatory stimuli, we here investigated the role of transcription factor Interferon (IFN) regulatory factor 1 (IRF1), which receives input from several inflammatory signaling pathways. We identify IRF1 as a master HSC regulator. IRF1 loss impairs HSC self-renewal, increases stress-induced cell cycle activation, and confers apoptosis resistance. Transcriptomic analysis revealed an aged, inflammatory signature devoid of IFN signaling with reduced megakaryocytic/erythroid priming and antigen presentation in IRF1-deficient HSCs. Finally, we conducted IRF1-based AML patient stratification to identify groups with distinct proliferative, survival and differentiation features, overlapping with our murine HSC results. Our findings position IRF1 as a pivotal regulator of HSC preservation and stress-induced responses.
Identifying the engagement of a brain network during a targeted tDCS-fMRI experiment using a machine learning approach

Transcranial direct current stimulation (tDCS) can noninvasively modulate behavior, cognition, and physiologic brain functions depending on polarity and dose of stimulation as well as montage of electrodes. Concurrent tDCS-fMRI presents a novel way to explore the parameter space of non-invasive brain stimulation and to inform the experimenter as well as the participant if a targeted brain region or a network of spatially separate brain regions has been engaged and modulated. We compared a multi-electrode (ME) with a single electrode (SE) montage and both active conditions with a no-stimulation (NS) control condition to assess the engagement of a brain network and the ability of different electrode montages to modulate network activity. The multi-electrode montage targeted nodal regions of the right Arcuate Fasciculus Network (AFN) with anodal electrodes placed over the skull position of the posterior superior temporal/middle temporal gyrus (STG/MTG), supramarginal gyrus (SMG), posterior inferior frontal gyrus (IFG) and a return cathodal electrode over the left supraorbital region. In comparison, the single electrode montage used only one anodal electrode over a nodal brain region of the AFN, but varied the location between STG/MTG, SMG, and posterior IFG for different participants. Whole-brain rs-fMRI was obtained every three seconds. The tDCS-stimulator was turned on at 3 minutes after the scanning started. A 4D rs-fMRI data set was converted to dynamic functional connectivity (DFC) matrices using a set of ROI pairs belonging to the AFN as well as other unrelated brain networks. In this study, we evaluated the performance of five algorithms to classify the DFC matrices from the three conditions (ME, SE, NS) into three different categories. The highest accuracy of 0.92 was obtained for the classification of the ME condition using the K Nearest Neighbor (KNN) algorithm. In other words, applying the classification algorithm allowed us to identify the engagement of the AFN and the ME condition was the best montage to achieve such an engagement. The top 5 ROI pairs that made a major contribution to the classification of participant’s rs-fMRI data were identified using model performance parameters; ROI pairs were mainly located within the right AFN. This proof-of-concept study using a classification algorithm approach can be expanded to create a real-time feedback system at a participant level to detect the engagement and modulation of a brain network that spans multiple brain lobes.
Photon-Radiative Recombination of Electrons and Holes in Germanium

The spectral distribution of the rate of photon generation for the photon-radiative recombination of electrons and holes in germanium is determined from known optical properties by application of the principle of detailed balance. Quantities characterizing the process are evaluated: The thermal equilibrium recombination rate at 300\ifmmode^\circ\else\textdegree\fi{}K is 1.57\ifmmode\times\else\texttimes\fi{}${10}^{13}$ ${\mathrm{cm}}^{\ensuremath{-}3}$ ${\mathrm{sec}}^{\ensuremath{-}1}$, which corresponds to a recombination cross section of 2.9\ifmmode\times\else\texttimes\fi{}${10}^{\ensuremath{-}21}$ ${\mathrm{cm}}^{2}$ and a decay time for a small disturbance in carrier concentration in intrinsic material of 0.75 sec. The extension to the steady-state case of added current carriers is given, and estimates are included of the dependence of the quantities on temperature.
Convection in pulsating stars I. Nonlinear hydrodynamics

A nonlinear, nonlocal, time-dependent treatment of convection suitable for use in models of cool giant stars is presented. Local conservation equations plus a diffusive transport equation are used to derive the convective hydrodynamic equations for the case in which turbulent pressure energy, and viscosity cannot be ignored. The effects of convective overshooting, superadiabatic gradients, convection/pulsation interaction, and time dependence enter this treatment in a natural way. Methods of treating turbulent viscosity and acoustic losses are discussed. Also, an efficient computational scheme for computing the derivatives needed for an implicit hydrodynamic code is outlined. Application to RR Lyrae star envelopes will be presented in a companion paper.
Fast Multipole Method for the Biharmonic Equation

The evaluation of sums (matrix-vector products) of the solutions of the three-dimensional biharmonic equation can be accelerated using the fast multipole method, while memory requirements can also be significantly reduced. We develop a complete translation theory for these equations. It is shown that translations of elementary solutions of the biharmonic equation can be achieved by considering the translation of a pair of elementary solutions of the Laplace equations. The extension of the theory to the case of polyharmonic equations in R is also discussed. An efficient way of performing the FMM for biharmonic equations using the solution of a complex valued FMM for the Laplace equation is presented. Compared to previous methods presented for the biharmonic equation our method appears more efficient. The theory is implemented and numerical tests presented that demonstrate the performance of the method for varying problem sizes and accuracy requirements. In our implementation, the FMM for the biharmonic equation is faster than direct matrix vector product for a matrix size of 550 for a relative L2 accuracy 22 = 10−4, and N = 3550 for 22 = 10−12.
Computation of unsteady flows using nonmatching blocks of structured grid

Abstract This article presents application of a finite-volume method for computing unsteady flow problems using nonmatching blocks of structured grid. The discretization is of second order in both space and time, and the solution algorithm is based on the SIMPLE method. Results of computations for three unsteady-flow problems are presented to demonstrate the capabilities of the method: flow around a free cylinder, flow around a cylinder confined by two parallel walls, and flow between co-rotating square cylinders. The emphasis is on accuracy and efficiency of the method, so computations are done on systematically refined grids and using several time steps.