Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-lead arrhythmia detection through machine learning: cross-sectional evaluation of a novel algorithm using real-world data

Background Computer-assisted interpretation of single-lead ECG is the preliminary method for clinicians to flag and further evaluate an arrhythmia of clinical importance for acutely ill patients. Critical scrutiny of novel detection algorithms is lacking, particularly in external real-world data sets. This study’s objective was to evaluate a hybrid machine learning model’s ability to classify eight arrhythmias from a single-lead ECG signal from acutely ill patients. Methods This cross-sectional external retrospective evaluation of a previously trained hybrid machine learning model against an ECG reading team in the setting of home hospital care (acute care delivered at home substituting for traditional hospital care) draws from patients admitted at two hospitals in Boston, Massachusetts, USA between 12 June 2017 and 23 November 2019. We calculated classifier statistics for each arrhythmia, all arrhythmias and strips where the model identified normal sinus rhythm. Results The model analysed 2 680 162 min of single-lead ECG data from 423 patients and identified 691 478 arrhythmias. Patients had a mean age of 70 years (SD, 18), 60% were female and 45% were white. For any arrhythmia, the model had a sensitivity of 98%, a specificity of 100%, an accuracy of 98%, a positive predictive value of 100%, a negative predictive value of 93% and an F1 Score of 99%. Performance was best for pause (F1 Score, 99%) and worst for paroxysmal supraventricular tachycardia (F1 Score, 92%). The model’s false positive rate for any arrhythmia was 0.2%, ranging from 0.4% for pause to 7.2% for paroxysmal supraventricular tachycardia. The false negative rate for any arrhythmia was 1.9%. Conclusions A hybrid machine learning model was effective at classifying common cardiac arrhythmias from a single-lead ECG in real-world data.
Three-dimensional printing of cell-laden bioink for blood vessel tissue engineering: influence of process parameters and components on cell viability

Abstract Three-dimensional (3D) bioprinting is a potential therapeutic method for tissue engineering owing to its ability to prepare cell-laden tissue constructs. The properties of bioink are crucial to accurately control the printing structure. Meanwhile, the effect of process parameters on the precise structure is not nonsignificant. We investigated the correlation between process parameters of 3D bioprinting and the structural response of κ-carrageenan-based hydrogels to explore the controllable structure, printing resolution, and cell survival rate. Small-diameter (<6 mm) gel filaments with different structures were printed by varying the shear stress of the extrusion bioprinter to simulate the natural blood vessel structure. The cell viability of the scaffold was evaluated. The in vitro culture of human umbilical vein endothelium cells (HUVECs) on the κ-carrageenan (kc) and composite gels (carrageenan/carbon nanotube and carrageenan/sodium alginate) demonstrated that the cell attachment and proliferation on composite gels were better than those on pure kc. Our results revealed that the carrageenan-based composite bioinks offer better printability, sufficient mechanical stiffness, interconnectivity, and biocompatibility. This process can facilitate precise adjustment of the pore size, porosity, and pore distribution of the hydrogel structure by optimising the printing parameters as well as realise the precise preparation of the internal structure of the 3D hydrogel-based tissue engineering scaffold. Moreover, we obtained perfused tubular filament by 3D printing at optimal process parameters.
Regional Vessel Density Reduction in the Macula and Optic Nerve Head of Patients With Pre-Perimetric Primary Open Angle Glaucoma

Précis: Capillary and neuronal tissue loss occur both globally and with regional specificity in pre-perimetric glaucoma patients at the level of the optic nerve and macula, with perifovea regions affected earlier than parafovea areas. Purpose: To investigate optic nerve head (ONH) and macular vessel densities (VD) and structural parameters assessed by optical coherence tomography angiography in pre-perimetric open angle glaucoma (ppOAG) patients and healthy controls. Materials and Methods: In all, 113 healthy and 79 ppOAG patients underwent global and regional (hemispheric/quadrants) assessments of retinal, ONH, and macular vascularity and structure, including ONH parameters, retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) thickness. Comparisons between outcomes in ppOAG and controls were adjusted for age, sex, race, BMI, diabetes, and hypertension, with P<0.05 considered statistically significant. Results: In ppOAG compared with healthy controls: RNFL thicknesses were statistically significantly lower for all hemispheres, quadrants, and sectors (P<0.001–0.041); whole image peripapillary all and small blood vessels VD were statistically significantly lower for all the quadrants (P<0.001–0.002), except for the peripapillary small vessels in the temporal quadrant (ppOAG: 49.66 (8.40), healthy: 53.45 (4.04); P=0.843); GCC and inner and full macular thicknesses in the parafoveal and perifoveal regions were significantly lower in all the quadrants (P=0.000–P=0.033); several macular VD were significantly lower (P=0.006–0.034), with the exceptions of macular center, parafoveal superior and inferior quadrant, and perifoveal superior quadrant (P>0.05). Conclusions: In ppOAG patients, VD biomarkers in both the macula and ONH, alongside RNFL, GCC, and macular thickness, were significantly reduced before detectable visual field loss with regional specificity. The most significant VD reduction detected was in the peripheric (perifovea) regions. Macular and ONH decrease in VD may serve as early biomarkers of glaucomatous disease.
SM-RNet: A Scale-Aware-Based Multiattention-Guided Reverse Network for Pulmonary Nodules Segmentation

Lung cancer is a leading cause of cancer death, but its mortality continues to decline substantially, benefiting from early screening and/or treatment of lung nodules. Therefore, the accurate and robust segmentation of pulmonary nodules from lung computed tomography (CT) is an essential task. However, automated solution is still challenging due to the high variability, blurred edges, and complex background of pulmonary nodules in CT scans. In this article, we propose a novel scale-aware-based multiattention-guided reverse network (SM-RNet) for pulmonary nodules segmentation, which makes full use of the complementary information from different attention dimensions and scales and progressively obtains the complete object by reverse erasure (RE) manner, consisting of two stages. Stage I performs weighted cross-scale fusion for features with different scale attentions to extract the intermediate multiscale features (MFs) with channel and scale awareness, and stage II adaptively selects features from the most appropriate scale for the target and sequentially mines the details required for the segmentation task by a new scale-guided spatial attention (SSA) block and a new RE block. In addition, the model is equipped with two-stage deep supervision. We evaluate the segmentation accuracy of our method by calculating the dice similarity coefficient (DI), Jaccard index (JI), Hausdorff distance (HD), recall, specificity (SPE), and precision (PRE) on an internal institution dataset from the Fudan University Shanghai Cancer Center (FUSCC) and a public dataset LUng Nodule Analysis 2016 (LUNA16). Our proposed SM-RNet achieves the Dice scores of 89.290% ± 0.040% and 86.496% ± 0.076% and an HD of 5.532 ± 0.186 and 6.131 ± 0.288 for FUSCC and LUNA, respectively. SM-RNet significantly outperforms other state-of-the-art (SOTA) networks. In addition, the visualization results of qualitative analysis show that the network has strong robustness.
Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation

Affordance detection presents intricate challenges and has a wide range of robotic applications. Previous works have faced limitations such as the complexities of 3D object shapes, the wide range of potential affordances on real-world objects, and the lack of open-vocabulary support for affordance understanding. In this paper, we introduce a new open-vocabulary affordance detection method in 3D point clouds, leveraging knowledge distillation and text-point correlation. Our approach employs pre-trained 3D models through knowledge distillation to enhance feature extraction and semantic understanding in 3D point clouds. We further introduce a new text-point correlation method to learn the semantic links between point cloud features and open-vocabulary labels. The intensive experiments show that our approach outperforms previous works and adapts to new affordance labels and unseen objects. Notably, our method achieves the improvement of 7.96% mIOU score compared to the baselines. Furthermore, it offers real-time inference which is well-suitable for robotic manipulation applications.
Spread-Spectrum Modulated Multi-Channel Biosignal Acquisition Using a Shared Analog CMOS Front-End

The key challenges in designing a multi-channel biosignal acquisition system for an ambulatory or invasive medical application with a high channel count are reducing the power consumption, area consumption and the outgoing wire count. This article proposes a spread-spectrum modulated biosignal acquisition system using a shared amplifier and an analog-to-digital converter (ADC). We propose a design method to optimize a recording system for a given application based on the required SNR performance, number of inputs, and area. The proposed method is tested and validated on real pre-recorded atrial electrograms and achieves an average percentage root-mean-square difference (PRD) performance of 2.65% and 3.02% for sinus rhythm (SR) and atrial fibrillation (AF), respectively by using pseudo-random binary-sequence (PRBS) codes with a code-length of 511, for 16 inputs. We implement a 4-input spread-spectrum analog front-end in a <inline-formula><tex-math notation="LaTeX">$0.18 \;\mu \mathrm{m}$</tex-math></inline-formula> CMOS process to demonstrate the proposed approach. The analog front-end consists of a shared amplifier, a 2nd order <inline-formula><tex-math notation="LaTeX">$\Sigma \Delta$</tex-math></inline-formula> ADC sampled at <inline-formula><tex-math notation="LaTeX">$7.8 \;\mathrm{MHz}$</tex-math></inline-formula>, used for digitization, and an on-chip 7-bit PRBS generator. It achieves a number-of-inputs to outgoing-wire ratio of 4:1 while consuming <inline-formula><tex-math notation="LaTeX">$23 \;\mu \mathrm{A}$</tex-math></inline-formula>/input including biasing from a <inline-formula><tex-math notation="LaTeX">$1.8 \;\mathrm{V}$</tex-math></inline-formula> power supply and <inline-formula><tex-math notation="LaTeX">$0.067 \;\mathrm{mm}^{2}$</tex-math></inline-formula> in area.
Quel futur pour la biodiversité en milieu agricole dans un contexte de changements climatiques ? : de l'évaluation des mesures de conservation aux scénarios d'usage des sols

Les changements climatiques et d’usages des sols sont des facteurs cles affectant la biodiversite. Par ailleurs, la necessite de concilier production agricole et biodiversite, en ameliorant les mesures de conservation existantes et en integrant les considerations environnementales dans les futures politiques agricoles, est devenue une preoccupation majeure. L’objectif de la these etait d’etudier comment les changements environnementaux (climatiques et d’usages des sols agricoles) et les mesures de conservation mises en œuvre en reponse a ces pressions environnementales, sont susceptibles d’affecter les communautes d’oiseaux nicheurs. Une premiere partie de mes travaux a porte sur l’evaluation des effets des mesures agro-environnementales (MAE) recemment mises en œuvre en France. Ces travaux mettent en evidence l’efficacite mitigee des MAE dans leur ensemble, bien que certaines semblent pouvoir ameliorer la dynamique des populations en declin. Dans une seconde partie, j’ai cherche a predire les impacts potentiels des changements environnementaux futurs sur les communautes d’oiseaux. Les predictions des impacts potentiels de differents scenarios PAC a l’horizon 2020 m’ont tout d’abord permis de mettre en evidence la necessite de developper des scenarios a fine echelle spatiale, tenant compte de la diversite des systemes agricoles en France. J’ai donc developpe differents scenarios de changements d’assolements a l’echelle des petites regions agricoles, certains etant declines a deux echelles de prise de decision politique : nationale et regionale. Les predictions des impacts de ces scenarios combines a differents scenarios de changements climatiques et d’usage des sols, nous permettent notamment de mettre en evidence que la regionalisation de politiques d’extensification en fonction des agrosystemes, pourrait permettre de maximiser la conservation des communautes d’oiseaux communs en milieu agricole. En conclusion, le developpement de telles approches predictives est utile pour predire les impacts des changements potentiels du climat et de l'utilisation des terres, et est par consequent un reel outil d'aide a la decision pour elaborer les futures politiques agricoles
Optimal map-making with singularities

In this work, we investigate the optimal map-making technique for the linear system $d=Ax+n$ while carefully taking into account singularities that may come from either the covariance matrix $C = \langle nn^t \rangle$ or the main matrix $A$. We first describe the general optimal solution, which is quite complex, and then use the modified pseudo inverse to create a near-optimal solution, which is simple, robust, and can significantly alleviate the unwanted noise amplification during map-making. The effectiveness of the nearly optimal solution is then compared to that of the naive co-adding solution and the standard pseudo inverse solution, showing noticeable improvements. Interestingly, all one needs to get the near-optimal solution with singularity is just a tiny change to the classical solution, which is designed for the case without singularity.
Stimulation of fat oxidation in rat muscle by unacylated ghrelin persists for 2–3 hours, but is independent of fatty acid transporter translocation

While a definitive mechanism‐of‐action remains to be identified, recent findings indicate that ghrelin, particularly the unacylated form (UnAG), stimulates skeletal muscle fatty acid oxidation. The biological importance of UnAG‐mediated increases in fat oxidation remains unclear, as UnAG peaks in the circulation before mealtimes, and decreases rapidly during the postprandial situation before increases in postabsorptive circulating lipids. Therefore, we aimed to determine if the UnAG‐mediated stimulation of fat oxidation would persist long enough to affect the oxidation of meal‐derived fatty acids, and if UnAG stimulated the translocation of fatty acid transporters to the sarcolemma as a mechanism‐of‐action. In isolated soleus muscle strips from male rats, short‐term pre‐treatment with UnAG elicited a persisting stimulus on fatty acid oxidation 2 h after the removal of UnAG. UnAG also caused an immediate phosphorylation of AMPK, but not an increase in plasma membrane FAT/CD36 or FABPpm. There was also no increase in AMPK signaling or increased FAT/CD36 or FABPpm content at the plasma membrane at 2 h which might explain the sustained increase in fatty acid oxidation. These findings confirm UnAG as a stimulator of fatty acid oxidation and provide evidence that UnAG may influence the handling of postprandial lipids. The underlying mechanisms are not known.
Hybrid Refresh: Improving DRAM Performance by Handling Weak Rows Smartly

Modern memory systems use DRAM as their basic building block due to its low cost per bit property. DRAM cells are leaky and they need to be refreshed periodically to retain their data. Refresh operation degrades memory throughput by adding extra latency and eats up significant power. The refresh rate of the overall DRAM is decided according to the retention time of the weakest row. Although there are very few rows that need refresh at a high rate, it is kept same for all the rows as per the standard DRAM refresh technique. Many existing papers work on reducing refresh overhead, but they are compromising with space either by discarding weak rows or populating them with duplicate data. We propose a novel refresh approach Hybrid Refresh, to handle weak rows smartly and not let them become a bottleneck for the overall refresh rate. Hybrid Refresh is reducing up to 75.16% of the total DRAM refreshes. It is able to reduce up to 71.41% of the refresh energy. The total energy consumption reduced by Hybrid Refresh is 14.99% on average. Our model improves speedup up to 8.5% for single core and up to 9.8% for multi-core systems. We show that Hybrid Refresh outperforms Auto-Refresh, Smart Refresh, and CROW-ref refresh techniques. We have evaluated the performance of our model for SPEC-2006, TPC, and MediaBench benchmarks. Hybrid Refresh imposes very less hardware overhead and does not require any major architectural change in traditional DRAM.
Large-Eddy Simulation of Turbulence Decayin an Urban Area

A Large-Eddy Simulation study of the decay of the TKE during the evening transition in an urban boundary layer was performed. A realistic LES is employed to simulatean Urban Turbulence Project in the city of Turin (Italy). The LES simulation result have demonstrated that, during the last stage of decay the t −6 behavior persists after averaging the TKE over the entire boundary layer depth.
Anal cancer screening results from 18‐to‐34‐year‐old men who have sex with men living with HIV

Men who have sex with men living with HIV (MSM LWH) are at highest risk for human papillomavirus (HPV)‐associated anal cancer. There is no consensus on the optimal screening initiation age. This study aimed to assess the prevalence and severity of anal HPV disease among MSM LWH under the age of 35, which is a currently proposed screening age threshold. Between 2014 and 2020, 1255 18‐to‐34‐year‐old MSM LWH underwent anal cytology screening. 916 were co‐tested for high‐risk HPV (HR‐HPV). 467 underwent high‐resolution anoscopy (HRA) and biopsy. Cancer registry data were queried. Predictors of abnormal cytology (ie, ≥ASCUS) and histological high‐grade squamous intraepithelial lesions (HSIL) were evaluated using unadjusted logistic regression models. Median age was 28 years (range, 18‐34). 19% received at least one dose of HPV vaccine. Abnormal cytology rate was 65%. HR‐HPV and HPV16 prevalence were 87% and 30%. Biopsy results were benign (10%), LSIL (43%) and HSIL (47%). No cases of prevalent or incident anal cancers were detected. Findings were similar between age subgroups (18‐24, 25‐29 and 30‐34) except for a higher prevalence of AIN 3 in the 30‐34 group (19%). Abnormal cytology was significantly associated with HR‐HPV infection. Histological HSIL was associated with HR‐HPV infection and cytological LSIL or worse. The absence of anal cancer in a large cohort of MSM LWH under the age of 35, despite high prevalence of anal HR‐HPV infection and precancer, supports an age‐based anal cancer screening strategy for MSM LWH.
Retinal Layer Segmentation in OCT Images With Boundary Regression and Feature Polarization

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github. com/tyb311/TCCT.
Privacy Amplification Against Quantum Side Information Via Regular Random Binning

We consider privacy amplification against quantum side information by using regular random binning as an effective extractor. For constant-type sources, we obtain error exponent in terms of the so-called quantum Augustin information. Via type decomposition, we then recover the error exponent for independent and identically distributed sources proved by Dupuis [arXiv:2105.05342]. As an application, we obtain an achievable secrecy exponent for classical-quantum wiretap channel coding in terms of the Augustin information, which solves an open problem in [IEEE Trans. Inf. Theory, 65(12):7985, 2019]. Our approach is to establish an operational equivalence between privacy amplification and quantum soft covering; this may be of independent interest. The full version of this paper can be found in [arXiv:2309.11073]
The Insulin receptor and Insulin like growth factor receptor 5’ UTRs support translation initiation independently of eIF4G1

Protein synthesis is tightly regulated under stress conditions where energy may be scarce. Despite global repression of translation, some transcripts remain actively translated in order for the cell to be able to respond to the insult or prepare to quickly return normal cellular function after the stress ends. For the insulin receptor (Insr) and insulin-like growth factor receptor (Igf1r) transcripts this translation is mediated by an internal ribosome entry site (IRES) in their 5’UTRs that functions independently of eukaryotic initiation factor 4A (eIF4A) and eIF4E. Here we show that these cellular IRESes are also able to promote translation independently of the scaffolding protein eIF4G1 both in vitro and in the cell. Background IRES mediated translation initiation requires a different repertoire of factors than canonical cap-dependent translation. Results Treatments that inhibit the canonical translation factor eIF4G1 have little or no effect on the ability of the Insr and Igf1r cellular IRESes to promote translation. Conclusion Transcripts for two cellular receptors contain RNA elements that facilitate translation initiation without intact eIF4G1 Significance Cellular IRES mechanisms may resemble viral type III IRESes allowing them to promote translate with a limited number of initiation factors allowing them to work under stress conditions when canonical translation is repressed.