Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and evaluation of an intraoperative beta imaging probe for radio-guided solid tumor surgery

Extent and accuracy of surgical resection is a crucial step in the therapy of operable solid tumors. The recent availability of specific tumor-seeking agents, positron labeled, renewed the interest for radioguided surgery. The detection of beta particles, due to their short range, allows a more sensitive and accurate tumor localization. Since no mechanical collimation is necessary, it is possible to design probes with a sensitivity increased by one to three orders of magnitude compared to gamma detectors. The beta particle short range also reduces the contamination from distal non-specific radiotracers uptake region, which results in a increased signal-to-noise-ratio. Conversely, beta detection requires sensors to be extremely compact in order to operate in contact with the surveyed tissues in narrow surgical cavities. This thesis takes place in that context. Its aim was to develop an intraoperative positron imaging probe based on the silicon photomultiplier technology (SiPM) and to evaluate its ability to perform in real time tumor localization and post-operative control of the surgical cavity. During this work, two prototypes of intraoperative positron imaging probe were developed. The first detector design is based on the use of a single organic scintillator coupled to an array of SiPMs. This configuration uses a small sensitive volume to reduce the contamination noise coming from the annihilation gamma rays. The second version of the probe implements a subtraction method allowing to improve gamma rejection efficiency. This configuration uses a stack of two scintillators separated by a light guide. The events interacting in the top and the bottom scintillator are discriminated by the analysis of the different light distributions on the SiPM array. Different designs of the positron imaging probes, including scintillator material and thickness, light spreading window and optical reflector, were investigated with Montecarlo simulations and measurements. Their impact on the probes performances were optimized in terms of positron sensitivity, gamma ray rejection efficiency, spatial resolution and bias and uniformity of response. The effect of different reconstruction algorithm on spatial performances was also studied. Finally, the objective of developing an intraoperative probe fully operational in the operating room has been achieved by the design of dedicated miniaturized electronic readouts and mechanical housing. In the last part of my thesis, the evaluation of the single scintillator configuration in a realistic clinical environment was performed with 18F-FDG phantoms. We showed that the low intrinsic sensitivity of this probe to gamma radiations allows to detect tumor volumes as small as 14 mg for uptake properties corresponding to currently available radiotracers and acquisition times compatible with the surgery duration.
Geometric Group Theory

Geometry and topology Metric spaces Differential geometry Hyperbolic space Groups and their actions Median spaces and spaces with measured walls Finitely generated and finitely presented groups Coarse geometry Coarse topology Ultralimits of metric spaces Gromov-hyperbolic spaces and groups Lattices in Lie groups Solvable groups Geometric aspects of solvable groups The Tits alternative Gromov's theorem The Banach-Tarski paradox Amenability and paradoxical decomposition Ultralimits, fixed point properties, proper actions Stallings's theorem and accessibility Proof of Stallings's theorem using harmonic functions Quasiconformal mappings Groups quasiisometric to $\mathbb{H}^n$ Quasiisometries of nonuniform lattices in $\mathbb{H}^n$ A survey of quasiisometric rigidity Appendix: Three theorems on linear groups Bibliography Index
Exploration of epithelial-mesenchymal transition-related lncRNA signature and drug sensitivity in breast cancer

Background Breast cancer (BRCA) has become the most diagnosed cancer worldwide for female and seriously endanger female health. The epithelial-mesenchymal transition (EMT) process is associated with metastasis and drug resistance in BRCA patients. However, the prognostic value of EMT-related lncRNA in BRCA still needs to be revealed. The aim of this study is to construct an EMT-related lncRNA (ERL) signature with accuracy predictive ability for the prognosis of BRCA patients. Methods RNA-seq expression data and Clinical characteristics obtained from the TCGA (The Cancer Genome Atlas) were used in the study. First, we identified the EMT-related lncRNA by the Pearson correlation analysis. An EMT-related lncRNAs prognostic risk signature was constructed using univariate Cox regression and Lasso-penalized Cox regression analyses. The model’s performance was validated using Kaplan-Meier (KM) survival analysis, ROC curve and C-index. Finally, a nomogram was constructed for clinical practice in evaluating the patients with BRCA and validated by calibration curve and decision curve analysis (DCA). We also evaluated the drug sensitivity of signature lncRNA and the tumor immune cell infiltration in breast cancer. Results We constructed a 10-lncRNA risk score signature based on the lncRNAs associated with the EMT process. We could assign BRCA patients to the high- and low-risk group according to the median risk score. The prognostic risk signature showed excellent accuracy and demonstrated sufficient independence from other clinical characteristics. The immune cell infiltration analysis showed that the prognostic risk signature was related to the infiltration of the immune cell subtype. Drug sensitivity analysis proved ERLs signature could effectively predict the sensitivity of patients to common chemotherapy drugs in BRCA and provide guidance for chemotherapy drugs for high-risk and low-risk patients. Conclusion Our ERL signature and nomogram have excellent prognostic value and could become reliable tools for clinical guidance.
Care, labour force participation and health: the case of Italy

PurposeThis paper investigates the relationship between health and labour market participation considering the potential role played by the presence of children and elderly persons (with/without disabilities) in Italian households.Design/methodology/approachThe authors use longitudinal data from the European Union Statistics on Income and Living Conditions and full-information maximum likelihood to estimate a two-equation model (one equation for labour force participation and one for health status) with instruments to address the endogeneity of the labour force participation choice. The model is estimated separately by gender.FindingsThe authors find that while the presence of children, elderly persons or both is positively associated with the health status of both genders, the presence of disabled elderly persons exerts a negative role. As for participation, interesting differences emerge. The presence of children discourages women's participation but is positively associated with men's labour force participation. Interestingly, a caring role for elderly persons without disability emerges for both genders when the presence of children is combined with that of elderly people. Gender differences are also at work for the role of childcare services and elderly and/or disabled home care/assistance.Originality/valueThe findings indicate a possible caring role for elderly persons without disabilities, neutralizing the effect of the presence of children on the labour force participation of both genders. The results also suggest that greater coverage of care services should increase the active participation of women in the labour market.
Lightweight Human-Friendly Robotic Arm Based on Transparent Hydrostatic Transmissions

We present theoretical and experimental results regarding the development and the control of a two-link robotic arm with remotized actuation via rolling diaphragm hydrostatic transmissions. We propose a dynamical model capturing the essential dynamics of the developed transmission/robot ensemble and implement a control strategy consisting of two nested loops, the inner one performing high-bandwidth joint torque regulation and the outer one producing various types of compliance responses for effective human–robot interactions. Extensive sets of experiments, testing both the low-level torque controller and the high-level compliance controller, confirm the effectiveness of the proposed hardware-software remotization architecture.
Influence of number and depth of magnetic mirror on Alfvénic gap eigenmode

Alfvenic gap eigenmode (AGE) can eject energetic particles from confinement and thereby threaten the success of magnetically controlled fusion. A low-temperature plasma cylinder is a promising candidate to study this eigenmode, due to easy diagnostic access and simple geometry, and the idea is to arrange a periodic array of magnetic mirrors along the plasma cylinder and introduce a local defect to break the field periodicity. The present work validates this idea by reproducing a clear AGE inside a spectral gap, and more importantly details the influence of the number and depth (or modulation factor) of magnetic mirror on the characteristics of AGE. Results show that AGE is suppressed by other modes inside the spectral gap when the number of magnetic mirrors is below a certain value, which leads to a weakened Bragg's effect. The structure and frequency of AGE remain unchanged for a decreased number of magnetic mirrors, as long as this number is enough for the AGE formation. The width of spectral gap and decay constant (inverse of decay length) of AGE are linearly proportional to the depth of magnetic mirror, implying easier observation of AGE through a bigger mirror depth. The frequency of AGE shifts to a lower range with the depth increased, possibly due to the unfrozen plasma with field line and the invalidity of small-perturbation analysis. Nevertheless, it is exciting to find that the depth of field modulation can be increased to form AGE for a very limited number of magnetic mirrors. This is of particular interest for the experimental implementation of AGE on a low-temperature plasma cylinder with limited length.
Application of Choquet Calculus to Statistics With Distorted Probabilities

This paper deals with distorted statistics as statistics based on distorted probabilities by applying Choquet calculus. First, elementary distorted statistics on the nonnegative line is explored for exponential and Gaussian distributions. Next, by introducing Choquet calculus for real-valued functions on the real line, distorted statistics is explored for uniform and normal distributions. Finally, the relations of distorted statistics with conventional skew statistics are clarified.
Diagnosticando Tuberculose com Redes Neurais Artificiais e Recursos BPPC

Tuberculose é uma doença grave e contagiosa que mata milhões de pessoas, sendo um problema de saúde pública global. Enquanto isso, o uso da Inteligência Artificial na radiologia tem despertado crescente interesse de pesquisadores e da indústria. Soluções para auxiliar no diagnóstico já são uma realidade, mas ainda distante de populações vulneráveis e regiões subdesenvolvidas. Logo, soluções acessíveis são essenciais para populações altamente dependentes de ações e serviços públicos. Assim, propomos um método de baixo custo computacional e alta eficiência para auxiliar no diagnóstico de tuberculose. Utilizamos imagens de radiografia torácicas e construímos um modelo de rede neural artificial com recursos BPPC com e sem a geração de dados sintéticos. Os resultados equivalentes à literatura relacionada mostram o desempenho excepcional e de baixo custo da solução, colocando-a como uma solução alternativa viável.
Lévy Processes in Cones of Banach Spaces

Subordinators in Banach spaces are studied. The existence of the special LévyKhintchine representation is related to the geometry of the space and a Pettis integral with respecto to the underlying Lévy measure. Rates of growth of subordinators in a special type of Banach spaces are studied, including laws of iterated logarithm which give new results in finite dimensions bigger than one.
A 3 dB Compact UWB Hybrid Coupler for 5G Millimeter-wave Applications

|This paper presents a 3 dB compact Ultra-Wide-Band (UWB) Substrate Integrated Gap Waveguide (SIGW) based hybrid coupler suitable for 5G mm-wave applications. It is a key component in signal processing for wireless communication. It provides a way to control the power distribution of the signal along diﬀerent ports. It could be used to achieve beamforming and adaptive antenna system. The design steps started with implementing a unit cell of the gap waveguide structure satisfying the required bandwidth of the coupler. A supercell is then implemented. A network of complete ridges is constructed. A further step is to design a coupling section which achieves the required power distribution along the coupling and isolated ports. This coupling section is implemented using a novel approach of inserting an elliptical slot with variable major and minor axes with a certain orientation that achieves the standard performance of a 3 dB directional coupler with 90 ◦ (cid:6) 5% phase shift. For precise adjustment of this amplitude and phase, vias are further added perpendicular to the major axis of the slot. Its dimension and location have to be optimized. The Finite-Integral-Time-Domain (FDTD) analysis method is adopted (CST Microwave Studio). In addition, another novel approach is developed on this coupler such that the transition and gap layer is implemented on the same PCB layer, which saves the number of layers to only two layers compared to the usual three layers used in literature. Also, using SIGW technology saves the collapse of the top ground layer on the ridge structure, and only plastic pins are used to (cid:12)x the two layers. The proposed coupler is fabricated and tested, and the results show that it serves the majority of frequency bands employed in 5G systems in the USA and Canada.
Predictive Learning on Sign-Valued Hidden Markov Trees

We provide high-probability sample complexity guarantees for exact structure recovery and accurate Predictive Learning using noise-corrupted samples from an acyclic (tree-shaped) graphical model. The hidden variables follow a tree-structured Ising model distribution, whereas the observable variables are generated by a binary symmetric channel, taking the hidden variables as its input. This model arises naturally in a variety of applications, such as in physics, biology, computer science, and finance. The noiseless structure learning problem has been studied earlier by Bresler and Karzand (2018); this paper quantifies how noise in the hidden model impacts the sample complexity of structure learning and predictive distributional inference by proving upper and lower bounds on the sample complexity. Quite remarkably, for any tree with p vertices and probability of incorrect recovery δ > 0, the order of necessary number of samples remains logarithmic as in the noiseless case, i.e., O(log(p/δ)), for both aforementioned tasks. We also present a new equivalent of Isserlis’ Theorem for sign-valued tree-structured distributions, yielding a new low-complexity algorithm for higher order moment estimation.
Outer midplane scrape-off layer profiles and turbulence in simulations of Alcator C-Mod inner-wall limited discharges

A region of steep plasma gradients, the so-called “narrow-feature,” has been found in the near scrape-off layer (SOL) of inner-wall limited (IWL) discharges. Dedicated IWL discharges were carried out in Alcator C-Mod [Marmar et al., Nucl. Fusion 55, 104020 (2015)] to study this phenomenon, allowing detailed observations of the plasma profiles and fluctuations. Langmuir probe (LP) measurements show a clear two-decay length ne and Te profile structure at the outer midplane. The Gas-Puff Imaging (GPI) diagnostic shows large turbulent fluctuations across the last closed flux-surface, hence supporting the hypothesis that turbulent phenomena play a role in setting the profile steepness. Flux-driven non-linear turbulence simulations of two C-Mod discharges have been carried out, allowing a three-way comparison between LP, GPI, and simulation data. Observations and simulations correlate the steep gradient region characterizing the narrow feature with sheared poloidal flows and a deviation of the plasma potential .
Effectiveness of vonoprazan-based regimens compared with proton pump inhibitor-based regimens as first-line Helicobacter pylori agents

In this study, we compared the success rate of eradicating Helicobacter pylori (H. pylori) in adults and children using vonoprazan (VPZ)-based H. pylori regimens to that of proton pump inhibitors (PPIs). In Japan, the success rate of a VPZ-based regimen as first-line therapy was lower in children than in adults. Compared with adults, children around puberty have higher CYP2C19 and CYP3A4 enzymatic activity to metabolize PPIs and VPZ. Further, children generally have shorter intestinal transit times than adults and may absorb antibiotics to a lesser extent. When comparing success rates of pediatric and adult eradication therapy using VPZ, it is very important to maintain a higher intragastric pH with sufficient gastric acid suppression to maintain H. pylori in a replicating state and amoxicillin and clarithromycin in the intestinal tract for as long as possible by reducing diarrhea as a side effect. Based on the above, it is reasonable that VPZ, which can suppress stomach acids more strongly than PPI, is a more relevant H. pylori eradication therapy.
Estudio de algoritmos de inteligencia artificial más utilizados para el diagnóstico de diabetes mellitus tipo 2

La diabetes es la segunda causa de muerte a nivel mundial sobre todo en países de bajos recursos. En el Ecuador uno de cada diez personas es diagnosticado con diabetes mellitus tipo 2, esto es debido a factores de riesgo como: antecedentes familiares con diabetes, medicación, sedentarismo o mala alimentación. Por lo tanto, es indispensable hacer una Revisión Sistemática de Literatura sobre el estado del uso de técnicas o algorítmos de Inteligencia Artificial para el diagnóstico de diabetes mellitus tipo 2; con este fin de responder la pregunta: ¿Cuáles son las técnicas de inteligencia artificial aplicadas al diagnóstico de diabetes mellitus tipo 2?
On the Optimization of Magneto‐Volume Coupling for Practical Applied Field Magnetic Refrigeration

Materials with strong magneto‐volume coupling can present a coupled first‐order magnetic and structural transition and a giant magnetocaloric effect. The trade‐off of the resulting increased entropy change is hysteresis, and incomplete transition when the applied field is <2 T, implying that the full cooling capacity of the refrigerant is not harnessed. In this work, the behavior of the magnetic entropy change as a function of temperature is simulated, considering the Bean–Rodbell model, for various spin systems with Curie temperatures close to room temperature. Increasing magneto‐volume coupling parameter (η) results in higher entropy change peak value (ΔSMmax), together with a narrowing of entropy change function ΔSM(T), limiting the working temperature range. Accordingly, cooling capacity behaves non‐monotonously, reaching a peak value at ηMAX, which departs from the tri‐critical point as H increases up to 2 T. By tuning η, the cooling capacity can be considerably larger than the uncoupled system (up to 15–40% for low applied fields). Such tuning can be accomplished experimentally through proper chemical substitution. A universal behavior of cooling capacity as a function of ηMAX underlines that the performance of magnetic refrigerant is optimized, by tuning magneto‐volume coupling within the second order range, rather than the tri‐critical region.
On the Convergence Properties of a Heterogeneous Multiagent System on the Unit Sphere

We establish the convergence of a heterogeneous multiagent system on the unit sphere in even-dimensional Euclidean space. In this work, we show that all relative distances converge to some definite values, when the natural frequencies are completely determined diagonal matrices with half-dimension and the coupling strength is sufficiently larger than the maximal differences of the natural frequencies. Our result extends the previous results about practical aggregation where the maximal distance is merely bounded by the inverse of the coupling strength without convergence.
Robust Thermal Management of Electric Vehicles Using Model Predictive Control With Adaptive Optimization Horizon and Location-Dependent Constraint Handling Strategies

The thermal management system (TMS) in electric vehicles (EVs) consumes a considerable amount of energy for maintaining the battery and cabin temperatures within the desired range. This energy consumption can significantly impact the vehicle’s driving range. In this article, a model predictive control (MPC) is applied to minimize the energy consumption of the TMS while enforcing power and thermal constraints. The MPC-based thermal management strategy relies on a control-oriented model that captures the dynamics of the powertrain and thermal subsystems of an EV, as well as the coupling between these subsystems at different timescales. The relatively slow dynamics of the thermal systems call for a long prediction horizon to achieve the best performance. However, large uncertainties associated with speed prediction and preview information significantly impact the performance and robustness. In this study, an extensive sensitivity analysis is conducted to: 1) determine the key traffic and speed features over a long prediction horizon with a significant influence on the EV optimal performance and 2) study the impact of uncertainties associated with predicting these key traffic and speed features on EV performance in terms of energy efficiency and constraint enforcement. The MPC-based thermal management strategy is evaluated using real-world traffic data. To improve the robustness of the algorithm in the presence of uncertainties, a location-dependent constraint is proposed and integrated into the MPC-based thermal management strategy. The simulation results demonstrate that the location-dependent constraint enhances the capacity to enforce the battery temperature constraint, resulting in improved algorithmic robustness against uncertainties in preview information.