Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrolyte Disorders Induced by Antineoplastic Drugs

The use of antineoplastic drugs has a central role in treatment of patients affected by cancer but is often associated with numerous electrolyte derangements which, in many cases, could represent life-threatening conditions. In fact, while several anti-cancer agents can interfere with kidney function leading to acute kidney injury, proteinuria, and hypertension, in many cases alterations of electrolyte tubular handling and water balance occur. This review summarizes the mechanisms underlying the disturbances of sodium, potassium, magnesium, calcium, and phosphate metabolism during anti-cancer treatment. Platinum compounds are associated with sodium, potassium, and magnesium derangements while alkylating agents and Vinca alkaloids with hyponatremia due to syndrome of inappropriate antidiuretic hormone secretion (SIADH). Novel anti-neoplastic agents, such as targeted therapies (monoclonal antibodies, tyrosine kinase inhibitors, immunomodulators, mammalian target of rapamycin), can induce SIADH-related hyponatremia and, less frequently, urinary sodium loss. The blockade of epidermal growth factor receptor (EGFR) by anti-EGFR antibodies can result in clinically significant magnesium and potassium losses. Finally, the tumor lysis syndrome is associated with hyperphosphatemia, hypocalcemia and hyperkalemia, all of which represent serious complications of chemotherapy. Thus, clinicians should be aware of these side effects of antineoplastic drugs, in order to set out preventive measures and start appropriate treatments.
Diverse Bacteriocins Produced by Strains From the Human Milk Microbiota

Microbial colonization of the infant gut is a convoluted process dependent on numerous contributing factors, including age, mode of delivery and diet among others that has lifelong implication for human health. Breast milk also contains a microbiome which acts as a source of colonizing bacteria for the infant. Here, we demonstrate that human milk harbors a wide diversity of bacteriocin-producing strains with the potential to compete among the developing gut microbiota of the infant. We screened 37 human milk samples and found isolates with antimicrobial activity and distinct cross-immunity profiles. From these isolates, we detected 73 putative gene clusters for bacteriocins of all known sub-classes, including 16 novel prepeptides. More specifically, we detected two novel lantibiotics, four sactibiotics and three class IIa bacteriocins with an unusual modification of the pediocin box that is composed of YDNGI instead of the highly conserved motif YGNGV. Moreover, we identified a novel class IIb bacteriocin, four novel class IIc and two class IId bacteriocins. In conclusion, human milk contains a variety of bacteriocin-producing strains which may provide them a competitive advantage in the colonization of the infant gut and suggests that the milk microbiota is a source of antimicrobial potential.
Predicting Current and Future Invasion of Solidago canadensis: a Study from China

ABSTRACT: Solidago canadensis, which is native to North America, is considered to be the most widespread invasive alien plant. The invasion of Solidago canadensis in China has resulted in serious environmental problems. Therefore, understanding the relationship between the geographical distribution of S. canadensis and bioclimatic variables, and then predicting the potential distribution of this species is essential for management actions and practices. Although several studies have delineated the potential distribution of S. canadensis in China, how this species would respond to variations in future climatic conditions remains unclear. In the present study, we predicted the potential distribution of S. canadensis under current and future climatic conditions using species distribution models. We also analyzed range shifting of this species under current and future climatic conditions. We arrived at several conclusions. First, the potential distribution of S. canadensis may expand 40% under future climatic condition compare with that of under current condition. Second, mean diurnal range, isothermality, mean temperature of the wettest quarter, mean temperature of the warmest quarter, precipitation of the driest month, and precipitation seasonality (coefficient of variation) are key bioclimatic variables in determine the potential distribution of S. canadensis. Third, expansion of S. canadensis can be partly attributed to the relatively warmer and wetter future bioclimatic condition than current one.
A comparative study of metric and non‐metric traits in a series of modern crania

The purpose of this study is to question the utility of non-metric traits as predictors of age, race, and sex by comparing a group of non-metric variables with a group of metric variables. Twelve metric variables and 15 nonmetric variables were scored on 317 crania from the Terry Collection at the U. S. National Museum, and later statistically evaluated with a number of multivariate analysis programs and a stepwise discriminant program. The metric variables were found to be significant sex and race discriminators, where-as the non-metric variables were non-significant. The non-metric variables were found to be better age discriminators than the metric variables, even though neither could be classified as highly significant. It was concluded that non-metric traits by themselves have very little discriminatory value and should be used instead to supplement other osteological measurements and observations.
Flexible Joints of Picking Manipulator Based on Current Feedback

During the process of automatic picking, malfunctions caused by collisions between the picking manipulator and obstacles can damage plants and/or injure staff. To solve this problem, a flexible joint based on an elastic actuator is designed. Using a collision detection algorithm based on monitoring the current, a feedback control system with a flexible joint moment is set up, and a protection mechanism for the staff and plants against collisions with the manipulator during the automatic picking process is established. The results of the experiments on a prototype show that the proposed feedback control system with flexible joint moment can detect collisions of the manipulator with high accuracy. Based on the jump value of the current during collision detection, the impact moment on the flexible joint is estimated to have a minimum resolution of $2.444\times 10^{-{3}}\,\,\text{N}\cdot \text{m}$ . The frequency of collision detection is 37 Hz, and the minimum estimation error in the impact moment of the flexible joint is 2.25%.
Deep Collaborative Filtering Based on Outer Product

Recently, deep neural networks are widely used in recommendation systems, but most of them are used to process auxiliary information of recommendation systems such as items’ descriptions and images. When it comes to how to learn a better interaction function to model the relation between user latent features and item latent features, which is the most critical step in a recommendation task, most works employ matrix factorization together with the inner product. However, it is sub-optimal because of ignoring many correlations between latent factors. As deep neural networks perform well in building more complex non-linear models, we employ deep neural networks to improve the collaborative filtering algorithm, solving the problem of implicit feedback which is the most common scene in real applications. Some recent work has contributed to finding better interaction function, but these functions are not exact enough to model comprehensive correlations among latent features. In this work, we propose the Convolutional Neural Networks based Deep Collaborative Filtering model (CNN-DCF) to solve the key problem in the recommendation system. Based on the outer product and deep neural networks, we develop a correlation extraction module that can learn high-order correlations between item latent features and user latent features. Extensive experiments on the public implicit feedback dataset Yelp show that the proposed CNN-DCF model brings significant improvements over the state-of-the-art methods.
Reducing Wrong Labels for Distantly Supervised Relation Extraction With Reinforcement Learning

Relation extraction (RE) aims to mine semantic relations between entity pairs from plain texts, which plays an important role in various natural language processing (NLP) tasks. However, the existing methods in distant supervision (DS) are sensitive to bags and fail to handle sentence-level relation prediction. In particular, few methods focus on the sentence-level label denoising. In this paper, the sentence-level label denoising model based on reinforcement learning (RL) and the express-only-one assumption is proposed for distantly supervised RE. First, unlike removing the noisy sentences in previous studies, this paper designs Deep Q Network (DQN), a value-based RL algorithm, as a label denoiser to select the most reliable labels from the multiple relations that sentences are labeled. Second, the relation extractor applies the typical neural network model to predict relations between the data before and after the label denoiser cleans. The rewards in label denoiser are measured by the differences of prediction scores. Finally, the two modules between label denoiser and relation extractor are trained jointly to obtain correct labels and improve the extraction performance at the sentence level. The experimental results show that the proposed denoiser can deal with the noise labels of data effectively and the proposed model outperforms previous state-of-the-art baselines on both the Riedel dataset and human-annotated dataset.
Multi-Scale Aerial Target Detection Based on Densely Connected Inception ResNet

With the rapid development of unmanned aerial vehicles (UAVs), aerial targets detection has attracted extensive attention from researchers. The difficulty of aerial image detection lies in the small proportion of ground targets in aerial images and the wide variety of target sizes. After multiple down-sampling, the features of small targets are almost not available on the feature maps. To address these drawbacks, a densely connected Inception ResNet (RIDNet) is proposed. RIDNet is a lightweight multi-scale fusion detection network constructed with two residual inception units (RI): the RI-Dense model and the RI-Deconv model. The RI-Dense model consists of densely connected layers and shortcut connections. Each convolutional layer in RI-Dense has access to all the subsequent layers and passes on the information that needs to be preserved. The RI-Deconv fuses the global feature in a residual and hierarchical way, which continuously deconvolutes the output of RI-Dense and concatenates the result with the original output to get fusion layers. The fused layers absorb semantic information and detailed information from deep layers and shallow layers, respectively. Extensive experiments show the effectiveness of the proposed RIDNet. Ablation experiments also demonstrate that the RI-Dense model and RI-Deconv model can improve the mAP by 7.8% and 6.8%, respectively.
Maintaining Application Context of Smartphones by Selectively Supporting Swap and Kill

With the rapid diffusion of smartphones as well as the technical advances in mobile platform technologies, application domains supported by smartphones have dramatically increased. Unlike traditional computer systems, however, the context of applications may be lost in a smartphone as mobile OS usually kills applications without user’s approval when free memory space is exhausted. This was not a serious issue when a smartphone was a personal entertainment device, but it is now significant as a lot of official works are also performed by a smartphone. Instead of killing a process, the context of an application can be backed up to swap storage, but smartphones do not accept it as swap incurs excessively heavy I/O traffic. However, our findings show that the overhead of swap is not serious in recent smartphone devices and it can also be eliminated by judicious software management. This article proposes a selective swap scheme that classifies applications based on their context-saving characteristics, and controls the number of processes involved in swap by monitoring system situations and application characteristics. That is, we maintain the context of applications selectively by swap or application’s own state-saving. We implement and measure the effectiveness of our scheme on a real Android device, showing that it provides the context-savings of applications without additional overhead.
Think Cities: the accelerator for sustainable planning

The Think-Cities® project aims to develop a digital tool for smart city to evaluate urban projects. Think-Cities® transforms the planning sector by offering a smart application that values the territories and connects its actors. A proof of concept (URBAN THINK®) has been developed in SaaS mode in partnership with Rennes Métropole. Think-Cities® aims to bring major evolutions to the URBAN THINK® functionalities and will thus offer a global tool for observing territories and evaluating development projects. Always focused on a Web mapping platform, based on benchmark indicators, to evaluate and simulate the impacts of development projects and to recommend optimal hypotheses about these impacts, Think-Cities® also opens up to dynamic data (IOT) and therefore to new types of indicators complementing its catalog of decision support criteria. Grafted onto a collaborative 3D mapping platform in SAAS mode, Think-Cities® transforms the spatial planning sector by offering national (HQE, ADEME,…), international (LEED,…) and userspecific reference indicators. to evaluate and simulate the impacts of development projects.
spiNNlink: FPGA-Based Interconnect for the Million-Core SpiNNaker System

SpiNNaker is a massively-parallel computer system optimized for the simulation, in real time, of very large networks of spiking neurons. The system consists of over 1 million, energy-efficient ARM cores distributed over 57,600 SpiNNaker chips, each of which contains 18 cores interconnected by a neurobiologically-inspired, asynchronous (clock-less) Network-on-Chip. The NoC is extended to the chip boundary for chip-to-chip communication. To construct the massively-parallel system, SpiNNaker boards, housing 48 SpiNNaker chips, are connected together using FPGA-based, high-speed serial links. This paper presents some of the novel aspects of the design and implementation of the bespoke interconnect, including a credit-based, reliable frame transport protocol that allows the multiplexing of asynchronous SpiNNaker channels over the serial links, and an efficient FPGA-to-SpiNNaker chip interface that provides twice the throughput of traditional asynchronous interfaces. SpiNNaker houses 3,600 Xilinx Spartan-6 FPGAs, provides a bisection bandwidth of 480 Gbit/s, and ran the first-ever, true real-time brain cortical simulation [1] – a feat not currently achievable using conventional HPCs or GPUs.
RS-CapsNet: An Advanced Capsule Network

Capsule Network is a novel and promising neural network in the field of deep learning, which has shown good performance in image classification by encoding features into capsules and constructing the part-whole relationships. However, the original Capsule Network is not suitable for the images with complex background due to its weak ability of feature extraction, a large number of training parameters, and the characteristic of explaining everything in the image. To address the above issues, we propose an advanced Capsule Network named RS-CapsNet, which uses Res2Net block to extract multi-scale features, and Squeeze-and-Excitation (SE) block to highlight useful features and suppress useless features. Meanwhile, we adopt the method of linear combination between capsules, which enhances the representation ability of capsules for detected object and reduces the number of capsules. Moreover, we propose the method of firstly constructing intermediate capsules that can represent most of the detected object, and then using the intermediate capsules and primary capsules to construct the classification capsules together. The experimental results show that proposed RS-CapsNet has better performance on CIFAR10, CIFAR100, SVHN, FashionMNIST, and AffNIST datasets, it can also provide better translation equivariance, and the number of training parameters is reduced by 65.11%.
On the necessity of explicit cross-layer data formats in near-data processing systems

Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-Data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become feasible. The shift towards NDP system architectures calls for revision of established principles. Abstractions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under RocksDB and the COSMOS hardware platform.
An Optimal Locality-Aware Task Scheduling Algorithm Based on Bipartite Graph Modelling for Spark Applications

In the distributed computing framework of Spark, cross-node/rack data transfer produced by map tasks and reduce tasks are common problems resulting in performance degradation, such as prolonging of entire execution time and network congestion. To address these problems, this article utilizes the bipartite graph modelling to propose an optimal locality-aware task scheduling algorithm. By considering global optimality, the algorithm can generate the optimal scheduling solution for both the map tasks and the reduce tasks for data locality. Because of the different communication modes, this article uses a unified graph to model the map task scheduling and the reduce task scheduling respectively. Then, by calculating the communication cost matrix of tasks, we formulate an optimal task scheduling scheme to minimize overall communication cost and transform the problem as the well-known graph problem: minimum weighted bipartite matching (MWBM), which can be resolved by Kuhn-Munkres algorithm. In addition, this article proposes a locality-aware executor allocation strategy to improve the data locality further. We implement our algorithm and strategy in Spark-2.4. 1 and evaluate its performance using several representative micro-benchmarks, macro-benchmarks, and HiBench benchmark suite. The experimental results verify that by reducing the network traffic and access latency, the proposed algorithm can improve the job performance substantially compared to some other task scheduling algorithms.
A Model for the Regulation of Mammalian Platelet Production a

The pathophysiology of cyclical thrombocytopenia and idiopathic thrombocytopenia purpura are examined in a simple model for the regulation of mammalian platelet production. We let P(t) denote the total number of platelets of all ages, T, stand for the maturation time of a megakaryocyte, T, represent the age of death (due to senescence) of a platelet, and y be the (age-independent) random destruction of platelets. Then it may be shown that
Entrainment defects

Abstract Most metals start their lives in the liquid state, and are subjected to various transfers involving pouring or other types of surface turbulence. These actions entrain (fold in) the surface film to create entrainment defects. These are principally (a) bubbles that in turn create bubble trails; (b) bifilms; doubled over surface films, that act as cracklike defects; and (c) sundry entrained debris, collectively known as exogenous inclusions. The bifilm is the subject of this perspective. It appears to be a common, but serious and almost overlooked metallurgical defect. Analysis of bifilms provides a simple, powerful and elegant concept based on an enduring legacy from turbulence during the pouring of liquid metals. Usually large populations of bifilms are introduced into metals at an early stage of their production. In general their presence has been unsuspected because although they can have large area, they can often be only nanometres thick and not easily detected by conventional non-destructive techniques. The populations of cracks in suspension in liquid metal explains many otherwise inexplicable features of cast products such as porosity, hot tearing, the morphologies of second phases, and impaired reliability of mechanical properties. The fundamental difference between such entrained defects (associated with a macroscopic unbonded interface) and defects and inclusions grown in the melt is seen to be of central significance for the failures of metals by mechanical or corrosion type mechanisms. For wrought products the continued presence of bifilms, now usually extended and elongated and mainly occupying grain boundaries, appears to offer explanations for many metallurgical phenomena. Bifilms are likely to influence the development of texture, and are the most likely source for many types of failure in the solid state. Thus the limitations to superplastic forming, cavitation in tertiary creep, pitting corrosion of various types and stress corrosion cracking are likely to be profoundly affected by bifilms. Although the effects of bifilms can be reduced by expensive post-casting operations such as hipping or working, the major future potential lies in techniques for their avoidance. Some casting operations are already taking some first steps in new technology for their avoidance, and benefiting technically and commercially.
Genetics and modern human origins

The past decade has brought considerable debate on the subject of modern human origins. The nature of the transition from Homo erectus to archaic Homo sapiens to modern H. sapiens has been examined primarily in terms of the relative contribution of archaic populations to later moderns, both within and among geographic regions. The recent African origin model proposes that modern humans appeared first in Africa between 100,000 and 200,000 years ago, and then spread through the rest of the Old World, replacing preexisting populations.1–6 This model has been referred to by a variety of names, including “replacement”, “Garden of Eden”, “Noah's Ark”, and “out of Africa”. The recent African origin model contrasts with the multiregional model, which proposes a species‐wide transition to modern humans throughout the Old World during the past million years or more.7–10 Indeed, some proponents of the multiregional model advocate placing Homo erectus and all subsequent species of Homo in the evolutionary species Homo sapiens.11 This contrasts with the view that there were multiple hominid species during the Middle Pleistocene. The debate continues.12,13 Although the multiregional model is often portrayed as proposing a simultaneous transition to anatomically modern humans in different geographic regions, it explicitly allows for varying degrees of continuity across time and space.10 This model, in the broad sense, does not rule out the possibility that modern human morphology appeared first in Africa and then spread through the rest of the Old World through gene flow. However, not all advocates of the multiregional model adhere to this specific subset of the general model.9