Miracle-dz/UniFAR-FTUs huggingface dataset:
Vincristine, Doxorubicin, and Dexamethasone Induction before Autologous Stem Cell Transplantation in Patients with AL Amyloidosis: A Retrospective Comparison with Frontline Stem Cell Transplantation

Objective High-dose melphalan and autologous stem cell transplantation (ASCT) therapy for AL amyloidosis are now associated with reduced mortality based on the application of strict criteria. However, there is no long-term evidence concerning the performance of induction therapy with newer agents, such as bortezomib or daratumumab. Concerns regarding long-term relapse despite treatment with ASCT exist, and missing the opportunity to perform ASCT might occur if induction proves to not be efficacious and cardiac amyloidosis progression deprives the patients of a chance to receive ASCT. We herein report good amyloid control by vincristine, doxorubicin, and dexamethasone (VAD) induction therapy and argue the importance of induction therapy before ASCT. Methods We compared patients who underwent VAD induction and ASCT (VAD+ASCT) with patients who underwent frontline ASCT in our hospital. Patients A total of 26 patients with histologically proven AL amyloidosis were included (18 in the VAD+ASCT group and 8 in the frontline ASCT). Results In the VAD+ASCT group, the 10-year overall survival and renal response rates were 82% and 43%, respectively. The renal response rate at two years in the VAD+ASCT group was significantly better than that in the frontline ASCT group. Although there was no significant difference in the survival rates between the two groups, the time to next treatment or death was significantly better in the VAD+ASCT group than in the the frontline ASCT group. Acute kidney injury was the most frequent reason for failure to receive two courses of VAD, and early mortality was mainly due to gastrointestinal complications. Conclusion Considering that only those who underwent 2 courses of VAD experienced a 10-year renal response, induction therapy was deemed to be directly related to the long-term control of AL amyloidosis.
The Efficacy and Safety of Anlotinib Alone and in Combination with Other Drugs in Advanced Lung Cancer: A Retrospective Cohort Study

Objective Lung cancer is a disease associated with high levels of morbidity and mortality, with approximately 2.1 million new cases every year. Anlotinib is a new small-molecule multitarget tyrosine kinase inhibitor independently developed in China that can inhibit the formation of tumor blood vessels and has a therapeutic effect on various cancers. However, the application of anlotinib in lung cancer needs further investigation. Methods We collected the progress notes of 43 patients with advanced lung cancer treated at the Oncology Department of Guangzhou Chest Hospital from March 2019 to March 2021. Additionally, we assessed the differences between drug combination therapy and single-drug therapy among patients treated with anlotinib. Results Patients in both the anlotinib-combination and anlotinib-monotherapy groups experienced remission; however, the overall disease control rate in the anlotinib-combination group was higher than that in the anlotinib-monotherapy group. Reexamination via computed tomography showed that patients in the anlotinib-combination group had better recovery than those in the anlotinib-monotherapy group. Although the overall incidence of adverse reactions in the anlotinib-combination group was higher than that in the monotherapy group, most of the adverse reactions were I–II levels and improved after symptomatic treatment. Conclusion Anlotinib combined with other therapies is better than anlotinib alone for the management of patients with advanced lung cancer.
Effects of inter-reflections on the chromatic structure of the light field

Chromatic properties of the effective light in a space are hard to predict, measure and visualise. This is due to complex interactions between materials and illuminants. Here, we describe, measure and visualise the effects of inter-reflections on the structure of the physical light field for diffusely scattering scenes. The spectral properties of inter-reflections vary as a function of the number of bounces they went through. Via a computational model, these spectral variations were found to be systematic and correspond with brightness, saturation and hue shifts. We extended our light-field methods to measure and understand these spectral effects on the first-order properties of light fields, the light density and light vector. We tested the model via a set of computer renderings and cubic spectral illuminance measurements in mock-up rooms under different furnishing scenarios for two types of illuminants. The predicted spectral variations were confirmed and indeed varied systematically within the resulting light field, spatially and directionally. Inter-reflections predominantly affect the light density spectrum and have less impact on the light vector spectrum. It is important to consider these differential effects for their consequences on the colour rendering of 3-dimensional objects and people.
The static single information form

The Static Single Information (SSI) form is a compiler intermediate representation that allows efficient sparse implementations of predicated analysis and backward dataflow algorithms. It possesses several attractive graph-theoretic properties which aid in program analysis. An extension to SSI form, SSI+, is also presented, along with a complete executable abstract semantics for the representation. Applications to abstract interpretation and hardware compilation are discussed. The SSI form has been implemented on the FLEX compiler infrastructure, and it has been used to implement several analyses and optimizations. Details on these predicated analysis techniques are presented, as well as data from the practical implementation. Thesis Supervisor: Martin Rinard Title: Professor, Laboratory for Computer Science
Performance Analysis for Channel-Weighted Federated Learning in OMA Wireless Networks

To alleviate the negative impact of noise on wireless federated learning (FL), we propose a channel-weighted aggregation scheme of FL (CWA-FL), in which the parameter server (PS) makes aggregation of the gradients according to the channel conditions of devices. In the proposed scheme, the gradients are transmitted to the PS in an uncoded way through an orthogonal multiple access (OMA) channel, which can avoid the synchronization issue among devices faced by over-the-air FL. The convergence analysis of CWA-FL is conducted and the theoretical results show that the scheme can converge with the rate of $\mathcal {O} (\frac{1}{T})$. Simulation results show that the proposed scheme performs better than the equal-weighted aggregation scheme of FL (EWA-FL) and is more robust to noise.
Comparison of Image-Based and Manual Field Survey Methods for Indoor As- Built Documentation Assessment

As-built models and drawings are essential documents used during the operations and maintenance of buildings for managing facility spaces, equipment, and energy systems. Inefficiencies in processing, communicating, and revising as-built documents therefore result in high costs imposed on building owners. Facility managers still rely heavily upon manual surveying procedures for developing and verifying as-built drawings and models. To streamline this often time consuming process, this paper addresses the advantages and limitations of photogrammetry for remote sensing and verification of interior as-built conditions. Two classrooms are captured using photogrammetric image processing software and image-based dimensions are compared to dimensions gathered through a traditional manual survey yielding an average percent error of approximately 2%. Both image-based and manual dimensions are then compared to dimensions extracted from an existing asbuilt BIM model of the interior spaces, and the proposed image-based verification method successfully identifies the same gross errors in the as-built BIM model.
Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study

Background Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be more readily useful. This study developed a CT-based visual forecasting system that can visualize and quantify a nodule in three dimensions (3D) in any future time point using follow-up CT scans. Methods We retrospectively included 246 patients with 313 lung nodules with at least 1 follow-up CT scan. For the manually segmented nodules, we calculated geometric properties including CT value, diameter, volume, and mass, as well as growth properties including volume doubling time (VDT), and consolidation-to-tumor ratio (CTR) at follow-ups. These nodules were divided into growth and non-growth groups by thresholding their VDTs. We then developed a convolutional neural network (CNN) to model the imagery change of the nodules from baseline CT image (combined with the nodule mask) to follow-up CT image with a particular time interval. The model was evaluated on the geometric and radiological properties using either logistic regression or receiver operating characteristic (ROC) curve. Results The lung nodules consisted of 115 ground glass nodules (GGN) and 198 solid nodules and were followed up for an average of 354 days with 2 to 11 scans. The 2 groups differed significantly in most properties. The prediction of our forecasting system was highly correlated with the ground truth with small relative errors regarding the four geometric properties. The prediction-derived VDTs had an area under the curve (AUC) of 0.857 and 0.843 in differentiating growth and non-growth nodules for GGN and solid nodules, respectively. The prediction-derived CTRs had an AUC of 0.892 in classifying high- and low-risk nodules. Conclusions This proof-of-concept study demonstrated that the deep learning-based model can accurately forecast the imagery of a nodule in a given future for both GGNs and solid nodules and is worthy of further investigation. With a larger dataset and more validation, such a system has the potential to become a prognostication tool for assessing lung nodules.
Description of a new species of Spiralothelphusa Bott, 1968, and redescription of Spiralothelphusa wuellerstorfi (Heller, 1862) (Decapoda: Brachyura: Gecarcinucidae)

A new species of the gecarcinucid freshwater crab genus, Spiralothelphusa Bott, 1968, is described from the Andhra Pradesh State of South India, with supporting data from mitochondrial cytochrome c oxidase subunit I. This brings the number of known species of Spiralothelphusa to seven, five from India and two from Sri Lanka. The identity of S. wuellerstorfi (Heller, 1862) has remained uncertain because the former lectotype designation was not clear. Bott (1968), while designating a male lectotype of S. wuellerstorfi from the Naturhistorisches Museum Wien, Vienna, Austria, had neither stated the catalogue number nor the size of the specimen. It is now difficult to trace the particular lectotype among several paralectotypes of the depository. A new lectotype is, therefore, designated herein for S. wuellerstorfi, and the species is redescribed. The new species, S. andhra sp. nov., has morphological affinities with S. wuellerstorfi, but can be distinguished from the latter species by the shape of the male telson and outer margin of the non-twisted portion of the male first gonopod. Phylogenetic analyses of the molecular data also corroborate their separation. An illustrated identification key is provided for the species of Spiralothelphusa.
Competitive strategies in the agri-food industry in Italy during the COVID-19 pandemic: an application of K-means cluster analysis

PurposeThis paper aims at understanding how companies in the agri-food are reconfiguring their competitive strategies to face the coronavirus disease 2019 (COVID-19) pandemic. Literature has focused mainly on the effect of the pandemic on the supply chain or in its value chain but little has been said on the competitive strategies adopted to overcome the challenges posed by COVID-19 pandemic.Design/methodology/approachThe study was conducted on Italian companies operating in the agri-food industry from May to September 2020, following a mixed method approach. First, a focus group with experts in competitive strategy and agri-food industry has been organized to identify the most important challenges that companies are facing. Second, through a structured questionnaire, data from 123 companies operating in the agri-food industry were gathered and analyzed with K-means clustering method.FindingsFour clusters of companies were identified, each implementing different strategies to face the COVID-19 pandemic: “the cost fanatics”, “the brand focused”, “the sales centered” and “the strategists”. Furthermore, it was possible to identify new trends in competitive strategies to increase the added value that the Italian agri-food industry can provide to the customer.Originality/valueThe paper answers to the need for an investigation of the competitive strategies that agri-food companies have put in place to face the COVID-19 pandemic; moreover, it provides insights on how companies are adapting themselves to this rapidly changing environment. From a managerial point of view, it gives practitioners and managers useful insights on the different strategies that companies are undertaking to maintain a competitive advantage.
ABM-SpConv-SIMD: Accelerating Convolutional Neural Network Inference for Industrial IoT Applications on Edge Devices

Convolutional Neural Networks (CNNs) have been widely deployed, while traditional cloud data-centers based applications suffer from the bandwidth and latency network demand when applying to Industrial-Internet-of-Things (IIoT) fields. It is critical to migrate the CNNs inference to edge devices for efficiency and security concerns. However, it is challenging to deploy complex CNNs on resource-constraint IIoT edge devices due to a large number of parameters and intensive floating-point computations. In this paper, we propose ABM-SpConv-SIMD, an on-device inference optimization framework, aiming at accelerating the network inference by fully utilizing the low-cost and common CPU resource. ABM-SpConv-SIMD first adopts a model optimizer with pruning and quantization, which produces <italic><underline>Sp</underline></italic>arse <italic><underline>Conv</underline></italic>olutional models. And then, the <italic><underline>A</underline></italic>ccumulation-<italic><underline>B</underline></italic>efore-<italic><underline>M</underline></italic>ultiplication mechanism is proposed to reduce multiplication operations. Additionally, the <italic><underline>SIMD</underline></italic> instructions, which are commonly available on cost-effective edge devices, are employed to improve the performance of convolutions. We have implemented <italic>ABM-SpConv-SIMD</italic> base on the ARM Compute Library software framework and evaluated on Hikey970 and Raspberry Pi devices with two representative models AlexNet and ResNet50. The results show that the ABM-SpConv-SIMD can significantly improve the performance, and achieve on average of 1.96x and 1.73x speedup respectively over the baseline implementation with negligible loss of accuracy.
The Digital Shift in the Media and Content Industries: Policy Brief

This policy brief offers a structured review of the major transformations the MCI industries have been going through. It provides some insights into the policy questions these transformations are raising. New ways of implementing policies are suggested and their effectiveness in a digital world is discussed. The first section gives some figures showing the significance of the MCI industries. The second section follows the digital shifts and its impact. The third section reviews the policies.
Design of security primitives for trustworthy integrated circuits

The horizontal business model in semiconductor industry has brought economic benefits but relinquished the control that integrated circuit (IC) designer had in chip design and manufacturing, thus making chips expose to greater security threats and vulnerable to various kinds of hardware attacks. As electronic devices becoming increasingly ubiquitous and permeated into our daily lives, critical basic infrastructures and national defense systems, hardware security features that can assure the trustworthiness of an integrated system in a reliable, efficient and inexpensive way are highly desirable. In the dissertation, two embedded hardware security primitives, namely active current sensors and Physical Unclonable Functions (PUFs) are investigated for hardware Trojan (HT) detection, device identification and authentication. Hardware Trojans are the deliberate and malicious alterations to original IC designs that can jeopardize the design integrity, stealing the confidential information or paralyzing the system connected to the subverted chip upon their activation. In this thesis, a transient power supply current sensor to facilitate the screening of an IC for HT infection is proposed. Based on the power gating scheme, it converts the current activity on local power grid into a timing pulse from which the timing and power related side channel signals can be externally monitored by the existing scan test architecture. Its current comparator threshold can be adjusted for calibration against the quiescent current noise floor to reduce the impacts of process variations. Post-layout statistical simulations of process variations are performed on the ISCAS’85 benchmark circuits to demonstrate the effectiveness of the proposed technique for the detection of delay-invariant and rarely switched
Comorbidity is not associated with dialysis modality choice in patients with end‐stage kidney disease

Over the past years the proportion of home dialysis patients has decreased in the Netherlands. In addition, the home dialysis use varies significantly among centres. It is unclear whether this is the result of differences in comorbidity, or other factors. Our aim was to investigate the association between comorbidity and dialysis modality choice.
Predictive Launcher Guidance using Second Order Sliding Mode Control

A new approach to the design of a simplified predictive launcher-guidance is presented. The first stage open-loop prescribed launcher trajectory is simply defined by a controlled pitch tip-off maneuver. Second and third stage closed loop in-plane guidance is based on the continuous calculation, at each thrusting-time of commanded velocity required to achieve the mission objective; function of the current position. Second and third stage out of plane guidance is based on the dot product of velocity and position errors with the unity vector orthogonal to the desired orbital plane. Corresponding values of second and third stage in-plane terminal semi major axis, eccentricity required to achieve desired circular orbit altitude objectives are calculated off-line for given launch latitude and azimuth angle. The autopilot tracking commanded velocity is based on second order sliding mode control technique that yields continuous controls, and achieves desired error response in finite time. Robust high accuracy launcher guidance is achieved in presence of wind shear at the beginning of the trajectory and considerable uncertainties in the thrust magnitude model. The algorithm was tested on Ariane IV type launcher model. Results of the computer simulation demonstrate excellent robust high accuracy tracking performance of the proposed design. Nomenclature
Characterization of an Optical Moiré Wall Shear Stress Sensor for Harsh Environments

The ability to make time-resolved, direct wall shear stress measurements in harsh environments is an important measurement capability for improving understanding of complex fluid flow phenomena. This work presents the design, packaging, and characterization of a third-generation silicon-on-Pyrex moire optical wall shear stress sensor designed for use in environments up to 450◦C. Optical transduction of the floating element microelectromechanical systems (MEMS) sensor is accomplished by interrogation of the geometric moire fringe via a four-channel fiber optic array. Device performance is improved over previous generations by patterning discrete sections of the moire fringe and the use of a silicon nitride anti-reflective coating. Calibration of the sensor system yields a dynamic shear stress sensitivity of 12.6 mrad/Pa at 1.128 kHz, sensor resonance of 4.1 kHz, pressure rejection ratio of 87 dB, minimum detectable shear stress of 0.53 mPa at 1.128 kHz for a 1 Hz bin, and an experimentally verified dynamic range of 79 dB.
Tipping the scales: how fire controls the balance among functional groups in Angolan grasslands

Fire plays an important role in the ecology of tropical grasslands globally. However, detailed studies comparing the response of plant functional groups to fire seasonality, and thus phenological timing, are scarce from humid Afrotropical grasslands. Such information, however, is crucial for understanding what keeps the grasslands open and for enabling evidence-based management, and conservation. We initiated an experiment in frost- and fire-prone Angolan geoxyle grasslands in 2017, investigating the effect of different timing of fires on cover and diversity of trees, grasses, geoxyles and herbs. We installed permanent, randomised plots with fire exclusion, annual early and late dry-season burning, representing the local burning practices. Functional plant groups reacted differently to the treatments, becoming apparent at species level and their performance metrics. After three years, grass species benefitted from late and no burning, and herbs from late burning, whereas geoxyles thrived under early burning. Tree growth was suppressed under late burning, and did not increase with fire exclusion. Species richness and diversity was higher in burnt than in unburnt plots. Seasonal timing of fire tips the scales between geoxyles and grasses. The extraordinary plant diversity of Angolan geoxyle grasslands is a result of pyrodiversity and frost, keeping the grasslands open even in absence of fire.
Sirolimus Release from Biodegradable Polymers for Coronary Stent Application: A Review

Drug-eluting stents (DESs) are commonly used for the treatment of coronary artery disease. The evolution of the drug-eluting layer on the surface of the metal stent plays an important role in DES functionality. Here, the use of biodegradable polymers has emerged as an attractive strategy because it minimizes the occurrence of late thrombosis after stent implantation. Furthermore, understanding the drug-release behavior of DESs is also important for improving the safety and efficacy of stent treatments. Drug release from biodegradable polymers has attracted extensive research attention because biodegradable polymers with different properties show different drug-release behaviors. Molecular weight, composition, glass transition temperature, crystallinity, and the degradation rate are important properties affecting the behavior of polymers. Sirolimus is a conventional anti-proliferation drug and is the most widely used drug in DESs. Sirolimus-release behavior affects endothelialization and thrombosis formation after DES implantation. In this review, we focus on sirolimus release from biodegradable polymers, including synthetic and natural polymers widely used in the medical field. We hope this review will provide valuable up-to-date information on this subject and contribute to the further development of safe and efficient DESs.