Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic review and meta-analysis of psychological and activity-based interventions for cancer-related fatigue.

CONTEXT Fatigue is among the most common and distressing symptoms experienced by cancer patients. OBJECTIVE This systematic review and meta-analysis evaluates the efficacy of psychological and activity-based interventions against cancer-related fatigue in cancer patients. DATA SOURCES MEDLINE, PsycINFO, and CINAHL. STUDY SELECTION Randomized controlled trials of psychological and activity-based interventions involving adult cancer patients in which fatigue was an outcome were reviewed. EXTRACTION Forty-one trials were reviewed and 30 were included in a meta-analysis. DATA SYNTHESIS Fifty percent of psychological trials and 44% of activity-based trials rated fair or better in quality yielded significant findings favoring the intervention condition. Meta-analysis yielded an overall effect size of 0.09 (95% CI = .02- .16) favoring nonpharmacological conditions. Further analysis indicated that effect sizes were significant for psychological interventions (d-sub(w) = . 10, 95% CI = . 02-. 18) but not activity-based interventions (d-sub(w) = . 05, 95% CI = -. 08 - . 19). CONCLUSIONS Findings provide limited support for use of nonpharmacological interventions to manage cancer-related fatigue. The lack of research with heightened fatigue as an eligibility criterion is a notable weakness of the existing evidence base.
Energy Efficiency for Airtightness and Exterior Wall Insulation of Passive Houses in Hot Summer and Cold Winter Zone of China

There exists huge differences in climatic conditions and occupant energy-consuming behavior between Berlin and the “hot summer and cold winter zone of China” (HSCW), which implies that the building energy consumption compositions of two regions are completely different. In this paper, dynamic energy consumption simulation for passive houses with Design Builder software was performed. The results indicated that the improvement of building airtightness and exterior wall insulation can reduce annual heating and cooling load effectively, and more importantly the effect is much stronger on heating load than cooling load. Because of this, the paper proposed that the ratio of heating and cooling electricity consumption (r) should be adopted as an important index in energy efficiency evaluation of passive houses. It would contribute to the reasonable selection of ventilation modes and the exterior wall heat transfer coefficient, and benefit the healthy development of passive houses in HSCW.
Loss of the SWI/SNF ATPase subunits BRM and BRG1 drives lung cancer development

Inactivation of Brg1 and Brm accelerated lung tumor development, shortened tumor latency, and caused a loss of differentiation. Tumors with Brg1 and/or Brm loss recapitulated the evolution of human lung cancer as observed by the development of local tumor invasion as well as distal tumor metastasis, thereby making this model useful in lung cancer studies. Brg1 loss contributed to metastasis in part by driving E-cadherin loss and Vimentin up-regulation. By changing more than 6% of the murine genome with the down-regulation of tumor suppressors, DNA repair, differentiation and cell adhesion genes, and the concomitant up-regulation of oncogenes, angiogenesis, metastasis and antiapoptosis genes, caused by the dual loss of Brg1/Brm further accelerated tumor development. Additionally, this Brg1/Brm-driven change in gene expression resulted in a nearly two-fold increase in tumorigenicity in Brg1/Brm knockout mice compared with wild type mice. Most importantly, Brg1/Brm-driven lung cancer development histologically and clinically reflects human lung cancer development thereby making this GEMM model potentially useful.
Topes

Programmers often omit input validation when inputs can appear in many different formats or when validation criteria cannot be precisely specified. To enable validation in these situations, we present a new technique that puts valid inputs into a consistent format and that identifies "questionable" inputs which might be valid or invalid, so that these values can be double-checked by a person or a program. Our technique relies on the concept of a "tope", which is an application-independent abstraction describing how to recognize and transform values in a category of data. We present our definition of topes and describe a development environment that supports the implementation and use of topes. Experiments with web application and spreadsheet data indicate that using our technique improves the accuracy and reusability of validation code and also improves the effectiveness of subsequent data cleaning such as duplicate identification.
Leaky Noise: New Side-Channel Attack Vectors in Mixed-Signal IoT Devices

Microcontrollers and SoC devices have widely been used in Internet of Things applications. This also brings the question whether they lead to new security threats unseen in traditional computing systems. In fact, almost all modern SoC chips, particularly in the IoT domain, contain both analog and digital components, for various sensing and transmission tasks. Traditional remote-accessible online systems do not have this property, which can potentially become a security vulnerability. In this paper we demonstrate that such mixed-signal components, namely ADCs, expose a new security threat that allows attackers with ADC access to deduce the activity of a CPU in the system. To prove the leakage, we perform leakage assessment on three individual microcontrollers from two different vendors with various ADC settings. After showing a correlation of CPU activity with ADC noise, we continue with a leakage assessment of modular exponentiation and AES. It is shown that for all of these devices, leakage occurs for at least one algorithm and configuration of the ADC. Finally, we show a full key recovery attack on AES that works despite of the limited ADC sampling rate. These results imply that even remotely accessible microcontroller systems should be equipped with proper countermeasures against power analysis attacks, or restrict access to ADC data.
Towards a meta-model for data ecosystems

Data Ecosystems are socio-technical networks that enable collaboration between autonomous actors such as enterprises, institutions, and individuals. While Data Ecosystems are thus gaining importance, research into Data Ecosystems is still in its infancy stages. The terminology and definitions for Data Ecosystem vary greatly. This diversity imposes a pressing problem for the development of a clear understanding of the new opportunities and emergent challenges in exploiting Data Ecosystems. Accurate definitions are required to get a mutual understanding of what Data Ecosystems involve. Moreover, to the best of our knowledge, a model for describing a Data Ecosystem and its essential concepts has not been proposed yet. In this work, we aim to fill these gaps by reviewing the Data Ecosystem literature, and based on the field literature, we propose a meta-model for describing Data Ecosystems. In particular, the proposed meta-model describes the Data Ecosystem fundamental concepts and their inter-relationships for enabling analysis and description of ecosystems. Especially, the meta-model declares explicitly how all these concepts are related to each other in such holistic view, hence facilitating knowledge creation and management in the ecosystem.
All SMILES VAE

Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries. However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions. To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, pooling hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation. By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior. Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.
DS3L: Deep Self-Semi-Supervised Learning for Image Recognition

Despite the recent progress in deep semi-supervised learning (Semi-SL), the amount of labels still plays a dominant role. The success in self-supervised learning (Self-SL) hints a promising direction to exploit the vast unlabeled data by leveraging an additional set of deterministic labels. In this paper, we propose Deep Self-Semi-Supervised learning (D$S^3$L), a flexible multi-task framework with shared parameters that integrates the rotation task in Self-SL with the consistency-based methods in deep Semi-SL. Our method is easy to implement and is complementary to all consistency-based approaches. The experiments demonstrate that our method significantly improves over the published state-of-the-art methods on several standard benchmarks, especially when fewer labels are presented.
Foundations of Digital Archæoludology

Digital Archaeoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines – computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. – to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project.
Automated MRI-Based Quantification of the Cerebral Atrophy Providing Diagnostic Information on Mild Cognitive Impairment and Alzheimer&#146;s Disease

Alzheimer's disease (AD) is a major public health challenge as the median age of the industrialized world's population is increasing gradually. No cure for this disease has yet been found and the development of new treatments has become a topic of major research interest. This paper aims to propose a sequence of fully automated MRI-based image analysis steps to measure the development stage of atrophy in the brain. The results have been validated on a mixed group of 68 subjects by distinguishing between AD patients, MCIs and health controls using linear classifiers and ANNs. The best classifier identified unseen AD patients correctly in 80% of the cases and control subjects in 85%. Recognizing more than 8 out of 10 MCI subjects, the method also yields an early indication of AD. This simple yet powerful analysis can compete with other more time-consuming and semi-automatic methodologies. It could abet an AD diagnosis and provide a tool for measuring the success of therapies
Bacteriophage Production Models: An Overview

The use of bacteriophages has been proposed as an alternative method to control pathogenic bacteria. During recent years several reports have been published about the successful use of bacteriophages in different fields such as food safety, agriculture, aquaculture, and even human health. Several companies are now commercializing bacteriophages or bacteriophage-based products for therapeutic purposes. However, this technology is still in development and there are challenges to overcome before bacteriophages can be widely used to control pathogenic bacteria. One big hurdle is the development of efficient methods for bacteriophage production. To date, several models for bacteriophage production have been reported, some of them evaluated experimentally. This mini-review offers an overview of different models and methods for bacteriophage production, contrasting their principal differences.
Heterodera avenae GLAND5 Effector Interacts With Pyruvate Dehydrogenase Subunit of Plant to Promote Nematode Parasitism

Heterodera avenae mainly infects cereal crops and causes severe economic losses. Many studies have shown that parasitic nematodes can secrete effector proteins to suppress plant immune responses and then promote parasitism. In this study, we showed that HaGland5, a novel effector of H. avenae, was exclusively expressed in dorsal esophageal gland cell of nematode, and up-regulated in the early parasitic stage. Transgenic Arabidopsis thaliana lines expressing HaGland5 were significantly more susceptible to H. schachtii than wild-type control plants. Conversely, silencing of HaGland5 through barley stripe mosaic virus-medicated host-induced gene silencing technique substantially reduced the infection of H. avenae in wheat. Moreover, HaGland5 could suppress the plant defense responses, including the repression of plant defense-related genes, reducing deposition of cell wall callose and the burst of reactive oxygen species. Mass spectrometry, co-immunoprecipitation, and firefly luciferase complementation imaging assays confirmed that HaGland5 interacted specifically with Arabidopsis pyruvate dehydrogenase subunit (AtEMB3003).
Pumped Up by the Cold: Elemental Quotas and Stoichiometry of Cold-Water Diatoms

Phytoplankton cellular elemental quotas are important for evaluating the coupling of nutrient cycles and assessing nutrient limitation in the ocean. While cell element quotas have been shown to vary in response to growth conditions (e.g., light, nutrient supply), an overriding trait is cell size and biovolume. Allometric relationships are lacking data for phytoplankton grown under polar temperature conditions (e.g., <5oC); limited field data using single-cell stoichiometry measurements have shown polar diatoms may have significantly different allometry versus low-latitude counterparts. In this study, 11 strains of diatoms isolated from sub/polar waters (both Arctic and Antarctic) were grown in batch growth mode. Cellular carbon (C), nitrogen (N), phosphorus (P), silica (Si), cell number and biovolume were measured during mid-exponential and early stationary growth phases. An allometric log-log relationship was observed between cell element quotas and biovolume, although the strain Odontella aurita was consistently an outlier. Including O. aurita resulted in regression slopes for C, N, P and Si that were lower than values from the reviewed literature of temperate organisms; excluding O. aurita resulted in regression slopes that were more similar to published values for each element. However, the intercepts of the elemental quotas in the allometric relationships ranged from ~5-fold to ~100-fold greater than published values depending upon the element and the growth phase, meaning that the elemental density is significantly higher for diatoms grown at cold temperatures, although the physiological mechanism(s) cannot be resolved in this study. Cellular ratios of C, N, and P were consistent with prior research, but clearly showed the importance of taxonomic variability as all strains of the genus Thalassiosira behaved more similarly to each other than to the other diatom strains tested. Si:C ratios, in contrast, for non-Thalassiosira strains were greater and approached values commonly observed for iron-limited cells. These results show that a different set of allometric scaling equations is needed when considering sub/polar diatoms relative to temperate and tropical diatoms, which will impact regional and global model parameterization. In addition, these results highlight the role of phytoplankton diversity on biogeochemistry, even within the closely aligned families of diatoms.
Secure HMAC Authentication using DSDV in VANET

Vehicular Adhoc Network (VANET) is a part of MANET where the nodes pass on to vehicles. HMAC authentication scheme based on secretcryptography key technique for VANETs. Even though secretkey is extensively used in VANETs to realize unidentified verification, the presented schemes based on public key suffer from long computation delay in the certificate revocation list (CRL) checking and in the key verification method, significant to high message thrashing(lose). As a result, they cannot meet the requirement of verifying hundreds of messages per second in VANETs. In this scheme, first divide the precinct into several domains, in which roadside units (RSUs) are responsible for distributing group private keys and managing vehicles in a localized manner. Then, use a hash message authentication code (HMAC) to avoid time consuming CRL checking and to ensure the integrity of messages before batch group certification. HMAC may be used to simultaneously verify both the data integrity and the authentication of a message. As a final point, adopt cooperative message authentication among entities, in which each vehicle only needs to verify a small number of messages, thus greatly alleviating the authentication burden. The security and performance analysis show that scheme is more efficient in terms of authentication speed, while keeping conditional privacy in VANETs.
Clinical Presentation and Diagnosis of Tuberous Sclerosis Complex in Infancy

The age-dependent nature of the characteristic features of tuberous sclerosis complex has historically presented challenges for the diagnosis in infancy. Although the increasing availability of neuroimaging and genetic testing has facilitated the diagnosis in neonates and infants, there are few reports describing how tuberous sclerosis complex presents in this age group. We performed a retrospective review of children diagnosed with tuberous sclerosis complex during the first year of life, compiling their clinical features at presentation and diagnosis, seizure history, and imaging findings. We identified 41 infants diagnosed with tuberous sclerosis complex before age 1 year. Their age at initial presentation ranged from antenatal to 9 months of age. Twenty-three patients (56%) initially presented with a cardiac rhabdomyoma, of which 15 were identified antenatally. Fourteen patients (34%) initially presented with seizures, and 6 (15%) initially presented with hypomelanotic macules. Five infants (12%) had a family history of tuberous sclerosis complex. A definitive diagnosis of tuberous sclerosis complex was accomplished antenatally in 4 patients, whereas the rest were diagnosed at a median age of 2 months. All 41 patients underwent neuroimaging during infancy; 36 (88%) had radiographic evidence of cortical tubers, and 38 (93%) had subependymal nodules. Neuroimaging resulted in a definitive diagnosis of tuberous sclerosis complex in 95% of patients. The diagnosis of tuberous sclerosis complex in infancy is aided by a high index of suspicion and timely access to neuroimaging. Early diagnosis of tuberous sclerosis complex may be essential to the success of future therapies by providing a window of opportunity for their use.
Exploiting Multi-Level Parallelism for Stitching Very Large Microscopy Images

Due to the limited field of view of the microscopes, acquisitions of macroscopic specimens require many parallel image stacks to cover the whole volume of interest. Overlapping regions are introduced among stacks in order to make it possible automatic alignment by means of a 3D stitching tool. Since state-of-the-art microscopes coupled with chemical clearing procedures can generate 3D images whose size exceeds the Terabyte, parallelization is required to keep stitching time within acceptable limits. In the present paper we discuss how multi-level parallelization reduces the execution times of TeraStitcher, a tool designed to deal with very large images. Two algorithms performing dataset partition for efficient parallelization in a transparent way are presented together with experimental results proving the effectiveness of the approach that achieves a speedup close to 300×, when both coarse- and fine-grained parallelism are exploited. Multi-level parallelization of TeraStitcher led to a significant reduction of processing times with no changes in the user interface, and with no additional effort required for the maintenance of code.
Plasticity of Fine-Root Traits Under Long-Term Irrigation of a Water-Limited Scots Pine Forest

Trait-based approaches are increasingly used to investigate plant strategies for resource acquisition, growth, or competition between individual organisms or across species. However, the characterization of responses to environmental stimuli by fine-root systems of trees at the trait level is rather limited, particularly regarding the timing and degree of plasticity of the traits involved. These aspects become especially relevant under current climate-driven shifts in environmental conditions. In the present study, we examined the responses of the fine roots of Scots pines to increased soil water availability from long-term irrigation starting in the year 2003. The Scots pine forest is situated in a water-limited region in the central European Alps where increased tree mortality has been observed over the last two decades. The fine-root traits investigated include root system traits, root dynamic traits, architectural traits, and morphological traits. A first survey of fine-root traits in 2005 using ingrowth cores did not reveal any trait-based responses resulting from the irrigation treatment over a three-year period. Fine-root biomass, as periodically recorded by coring the topsoil from 2003 to 2016, showed a significant increase compared to the non-irrigated controls between three and nine years after the start of treatment. Overall, a maximum biomass increase due to the irrigation treatment was recorded in 2016 with about 80% higher biomass compared to controls. The analysis of fine-root traits revealed that irrigation significantly increased biomass, length, and production, but did not alter morphological and architectural traits, such as diameter, frequency of tips, specific root length (SRL), and root tissue density (RTD). In contrast, clear significant differences were found for all traits except for length when comparing the two root sampling methods, namely, ingrowth cores and soil coring. However, there were no interactions between the irrigation treatment and the sampling methods used and, therefore, the methods used did not affect the documented patterns, just the actual measured trait values.