Miracle-dz/UniFAR-FTUs huggingface dataset:
Family quarrels in seeds and rapid adaptive evolution in Arabidopsis

Significance Evolutionary conflict, such as between pathogens and hosts, can lead to arms races in which each party evolves rapidly in response to the harm inflicted by the other. Kin selection makes relatives much more cooperative, but some conflict is usually still expected. We show that even this reduced conflict appears to drive arms races in seeds of the plant Arabidopsis, which contain three genetic relatives: maternal tissue, the embryo, and the triploid endosperm. As expected from potential conflict over how much nutrition the embryo should receive from the mother, genes expressed in seed tissues evolve rapidly, particularly the parts directly involved in nutrient transfer. Moreover, the endosperm appears to have largely taken over the embryo’s role in this parent−offspring conflict. Evolutionary conflict can drive rapid adaptive evolution, sometimes called an arms race, because each party needs to respond continually to the adaptations of the other. Evidence for such arms races can sometimes be seen in morphology, in behavior, or in the genes underlying sexual interactions of host−pathogen interactions, but is rarely predicted a priori. Kin selection theory predicts that conflicts of interest should usually be reduced but not eliminated among genetic relatives, but there is little evidence as to whether conflict within families can drive rapid adaptation. Here we test multiple predictions about how conflict over the amount of resources an offspring receives from its parent would drive rapid molecular evolution in seed tissues of the flowering plant Arabidopsis. As predicted, there is more adaptive evolution in genes expressed in Arabidopsis seeds than in other specialized organs, more in endosperms and maternal tissues than in embryos, and more in the specific subtissues involved in nutrient transfer. In the absence of credible alternative hypotheses, these results suggest that kin selection and conflict are important in plants, that the conflict includes not just the mother and offspring but also the triploid endosperm, and that, despite the conflict-reducing role of kinship, family members can engage in slow but steady tortoise-like arms races.
Two-Stream Federated Learning: Reduce the Communication Costs

Federated learning algorithm solves the problem of training machine learning models over distributed networks that consist of a massive amount of modern smart devices. It overcomes the challenge of privacy preservation, unbalanced and Non-IID data distributions, and does its best to reduce the required communication rounds. However, communication costs are still the principle constraint compared to other factors, such as computation costs. In this paper, we adopt a two-stream model with MMD (Maximum Mean Discrepancy) constraint instead of the single model to be trained on devices in standard federated learning settings. Following experiments show that the proposed model outperforms baseline methods, especially in Non-IID data distributions, and achieves a reduction of more than 20% in required communication rounds.
Elevated levels of circulating microvesicles in coronary artery disease patients with type 2 diabetes and albuminuria: Effects of exercise training

Objective: Circulating microvesicles, released from activated/apoptotic cells, are involved in vascular complications and may be looked upon as biomarkers. Albuminuria is characteristic of disease progression in type 2 diabetes mellitus. We aimed to investigate quantitative and qualitative differences of circulating microvesicles in type 2 diabetes mellitus with and without albuminuria and whether 12-month exercise training influenced expression of circulating microvesicles. Methods: Coronary artery disease patients with type 2 diabetes mellitus (n = 75), of which 25 had albuminuria, were included. Annexin V+ (AV+) circulating microvesicles were analysed by flow cytometry in citrated plasma. The exercise volume was 150 min per week. Results: In albuminuria patients, circulating microvesicles from endothelial-(CD146+/CD62E+/AV+) and endothelial-progenitor-(CD309+/CD34+/AV+) cells were significantly higher compared to those without (p ⩽ 0.01, both). Receiver operating characteristic curve analysis of the endothelial circulating microvesicles shows an area under the curve of 0.704 (95% confidence interval: 0.57–0.84; p = 0.004). Albuminuria patients had more circulating microvesicles derived from activated leukocytes and monocytes and monocytes carrying tissue factor (CD11b+/AV+, CD11b+/CD14+/AV+, CD142+/CD14+/AV+, respectively, p ⩽ 0.05, all) and higher number of circulating microvesicles from activated platelets (CD62P+/AV+). Within exercising patients, circulating microvesicles from progenitor cells increased (p = 0.023), however, not significantly different from controls. Conclusion: Coronary artery disease patients with type 2 diabetes mellitus and albuminuria had elevated number of circulating microvesicles from activated blood and vascular cells, rendering them as potential predictors of disease severity. The circulating microvesicles were limitedly affected by long-term exercise training in our population.
Multicore MIMO-OFDM LTE Optimizing

The MIMO-OFDM is the prevailing candidate for 4G and 5G broadband wireless communications. This technology is widely adopted in a variety of communication standards like LTE and LTE advanced. It combines multiple inputs and multiple outputs which improves the capacity by transmitting different signals over multiple antennas and orthogonal frequency division multiplexing which divides a radio channel into a large number of closely spaced sub channels to provide more reliable communications at high speed. The most important blocks in LTE 4G are OFDM symbols generation and OFDM symbols reception based on IFFT and FFT. In this paper, we attempt to optimize these blocks to ensure the multicore aspect of MIMO-OFDM LTE.
Developing a Flood Risk Assessment Using Support Vector Machine and Convolutional Neural Network: A Conceptual Framework

Flooding is one of the most devastating natural hazards that affect not only to infrastructures and agriculture but also to human lives. The prominent effect of global warming boasted its danger and impact in a wider range. In order to address and provide more effective measures to lessen the impact of flood hazards, it would be better to identify first the areas with such flood vulnerability. The proposed study aims to exploit the data available from the Geographical Information System (GIS) and the technology advancement in the modern world in producing a reliable flood susceptibility and probability map. Fusing ConvNet, a feedforward neural networks that specialize in image processing and prediction with SVM, a supervised machine learning for classification and regression analysis for a better image map results. Distinct image prediction output from dilated convolution and deconvolution network will be used as an input to SVM in producing its final output.
Work Exposures and Musculoskeletal Disorders Among Railroad Maintenance-of-Way Workers.

OBJECTIVE Measure musculoskeletal disorders and occupational risk factors among railroad maintenance-of-way (MOW) workers. METHODS 4,816 active, retired and disabled members of the Brotherhood of Maintenance of Way Employes Division (BMWED) completed a survey. RESULTS Compared to U.S. employed men, adjusting for age, race and region, active male MOW workers were more likely to report "repeated lifting, pushing, pulling, or bending" at work (74.6% vs. 46.9%), not enough staff (88.1% vs. 65.2%) and a diagnosis of carpal tunnel syndrome (7.9% vs. 3.6%). They were less likely to report management priority on workplace health and safety (59.37% vs. 94.8%), ability to make job decisions on their own (68.4% vs. 87.7%), and supervisor support (60.3% vs. 90.8%) (all comparisons, p < 0.001). CONCLUSIONS Prevention programs should address risk of musculoskeletal disorders and occupational hazards faced by MOW workers.
Treatment of classical Hodgkin’s lymphoma in children and adolescents

ABSTRACT Introduction: The treatment of classical Hodgkin lymphoma (cHL) in children is a story of success. Nowadays, more than 90% of patients are cured and overall survival is nearly 100% at 5 years. Efforts have been made to avoid related effects of therapies; therefore, children are treated using different chemotherapy schemes in comparison with adults. Areas covered: This review includes a view of the clinical classification and risk assessment in children suffering from HL. The chemotherapy more commonly employed is revisited. The use of PET/CT to evaluate the disease in order to guide therapy is analyzed. New options of chemotherapy and emerging immunotherapy are also included. Expert opinion: In order to make the right treatment choice, a proper initial assessment of risk is mandatory. The choice of therapy in these kinds of patients must be done according to the experience of the team, and also, the cost and logistics related to the eligible scheme are very important. If possible, efforts must be made to include PET/CT in guiding therapy and avoiding overtreatment and long-term adverse effects in children. New options in immunotherapy are emerging and must be considered with caution in selected patients.
A Communication Efficient Collaborative Learning Framework for Distributed Features

We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters. In particular, we propose a Federated Stochastic Block Coordinate Descent (FedBCD) algorithm, in which each party conducts multiple local updates before each communication to effectively reduce the number of communication rounds among parties, a principal bottleneck for collaborative learning problems. We analyze theoretically the impact of the number of local updates and show that when the batch size, sample size, and the local iterations are selected appropriately, within $T$ iterations, the algorithm performs $\mathcal{O}(\sqrt{T})$ communication rounds and achieves some $\mathcal{O}(1/\sqrt{T})$ accuracy (measured by the average of the gradient norm squared). The approach is supported by our empirical evaluations on a variety of tasks and datasets, demonstrating advantages over stochastic gradient descent (SGD) approaches.
Neuromuscular and Muscle Metabolic Functions in MELAS Before and After Resistance Training: A Case Study

Mitochondrial encephalomyopathy, lactic acidosis, and recurrent stroke-like episodes syndrome (MELAS) is a rare degenerative disease. Recent studies have shown that resistant training (RT) can ameliorate muscular force in mitochondrial diseases. However, the effects of RT in MELAS are unknown. The aim of this case report was to investigate the effects of RT on skeletal muscle and mitochondrial function in a 21-years old patient with MELAS. RT included 12 weeks of RT at 85% of 1 repetition maximum. Body composition (DXA), in vivo mitochondrial respiration capacity (mVO2) utilizing Near-infrared spectroscopy on the right plantar-flexor muscles, maximal voluntary torque (MVC), electrically evoked resting twitch (EET) and maximal voluntary activation (VMA) of the right leg extensors (LE) muscles were measured with the interpolated twitch technique. The participant with MELAS exhibited a marked increase in body mass (1.4 kg) and thigh muscle mass (0.3 kg). After the training period MVC (+5.5 Nm), EET (+2.1 N⋅m) and VMA (+13.1%) were ameliorated. Data of mVO2 revealed negligible changes in the end-exercise mVO2 (0.02 mM min-1), Δ mVO2 (0.09 mM min-1), while there was a marked amelioration in the kinetics of mVO2 (τ mVO2; Δ70.2 s). This is the first report of RT-induced ameliorations on skeletal muscle and mitochondrial function in MELAS. This case study suggests a preserved plasticity in the skeletal muscle of a patient with MELAS. RT appears to be an effective method to increase skeletal muscle function, and this effect is mediated by both neuromuscular and mitochondrial adaptations.
Regtech Adoption and the Cost of Capital

This paper studies the cost of capital effect of a major regulatory technology, or RegTech, event: the staggered implementation of the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system of the Securities and Exchange Commission in the period from 1993 to 1996. This event represents a largely exogenous shock to corporate information dissemination technologies, resulting in a considerable reduction in information acquisition costs for investors. Using a difference-in-differences research design, we show that the cost of equity capital declines substantially after a firm switches from paper filing to mandatory electronic filing in EDGAR. The effect is stronger for small firms and firms with low analyst coverage and low institutional ownership. We identify three channels through which EDGAR affects a firm’s cost of capital: the liquidity, risk-taking, and corporate governance channels. EDGAR implementation also improves a firm’s investment efficiency significantly. We find evidence that the marginal value of a firm’s capital investment and cash is higher during the post-EDGAR period. This paper was accepted by Suraj Srinivasan, accounting. Funding: C. Lin acknowledges financial support from the National Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project HKU R7054-18 and T35/710/20R]. S. Lai acknowledges financial support from the Ministry of Science and Technology, Taiwan [Grants 107-2410-H-002-038-MY3 and 110-2410-H-002-079-MY2] and the E. Sun Academic Award. X. Ma acknowledges research support from the University of Macau [Grants SRG2019-00158-FBA and MYRG2020-00259-FBA]. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022. 4660 .
A Review of Machine Learning and Deep Learning Applications

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well. There are various techniques devised for the same. Traditional machine learning algorithms have been applied in many application areas. Researchers have put many efforts to improve the accuracy of that machinelearning algorithms. Another dimension was given thought which leads to deep learning concept. Deep learning is a subset of machine learning. So far few applications of deep learning have been explored. This is definitely going to cater to solving issues in several new application domains, sub-domains using deep learning. A review of these past and future application domains, sub-domains, and applications of machine learning and deep learning are illustrated in this paper.
Opportunities for Self-Evaluation Increase Student Calibration in an Introductory Biology Course

Accurate self-evaluation is critical for learning. Calibration describes the relationship between learners’ perception of their performance and their actual performance on a task. Here, we describe two studies aimed at assessing and improving student calibration in a first-semester introductory biology course at a 4-year public institution. Study 1 investigated students’ (n = 310) calibration (the difference between estimated and actual exam performance) across one semester. Students were significantly miscalibrated for the first exam: their predicted scores were, on average, significantly higher than their actual scores. The lowest-performing students had the most inaccurate estimates. Calibration improved with each exam. By the final exam, students underestimated their scores. We initiated a second study in the following semester to examine whether explicitly teaching students about self-evaluation strategies would improve their calibration and performance. Instruction in the experimental section (n = 290) focused on students’ tendency to overestimate their abilities and provided retrieval-practice opportunities. Students in the experimental section showed better calibration and performance on the first exam compared with students in a control section taught by a different instructor during the same semester (n = 251). These findings suggest that simple instructional strategies can increase students’ metacognitive awareness and improve their performance.
Muscle precursor cell movements in zebrafish are dynamic and require Six family genes

ABSTRACT Muscle precursors need to be correctly positioned during embryonic development for proper body movement. In zebrafish, a subset of hypaxial muscle precursors from the anterior somites undergo long-range migration, moving away from the trunk in three streams to form muscles in distal locations such as the fin. We mapped long-distance muscle precursor migrations with unprecedented resolution using live imaging. We identified conserved genes necessary for normal precursor motility (six1a, six1b, six4a, six4b and met). These genes are required for movement away from somites and later to partition two muscles within the fin bud. During normal development, the middle muscle precursor stream initially populates the fin bud, then the remainder of this stream contributes to the posterior hypaxial muscle. When we block fin bud development by impairing retinoic acid synthesis or Fgfr function, the entire stream contributes to the posterior hypaxial muscle indicating that muscle precursors are not committed to the fin during migration. Our findings demonstrate a conserved muscle precursor motility pathway, identify dynamic cell movements that generate posterior hypaxial and fin muscles, and demonstrate flexibility in muscle precursor fates. Summary: In vivo examination of long-distance migrations of zebrafish muscle precursors reveals how these cells are specified prior to migration and organized during migration to generate four hypaxial muscles.
Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram

Algorithms are said to affect social realities, often in unseen ways. This article explores conscious, instrumental interactions with algorithms, as a window into the complexities and extent of algorithmic power. Through a thematic analysis of online discussions among Instagram influencers, I observed that influencers’ pursuit of influence resembles a game constructed around “rules” encoded in algorithms. Within the “visibility game,” influencers’ interpretations of Instagram’s algorithmic architecture—and the “game” more broadly—act as a lens through which to view and mechanize the rules of the game. Illustrating this point, this article describes two prominent interpretations, which combine information influencers glean about Instagram’s algorithms with preexisting discourses within influencer communities on authenticity and entrepreneurship. This article shows how directing inquiries toward the visibility game makes present the interdependency between users, algorithms, and platform owners and demonstrates how algorithms structure, but do not unilaterally determine user behavior.
Protein restriction in early life increases intracellular calcium and insulin secretion, but does not alter expression of SNARE proteins during pregnancy

What is the central question of this study? Does protein restriction in early life modify glucose‐induced insulin secretion by altering [Ca2+]i and the expression of SNARE proteins in pancreatic islets from pregnant rats? What is the main finding and its importance? Protein restriction in early life increased the first phase of glucose‐induced insulin secretion and [Ca2+]i without altering the expression of SNARE proteins during pregnancy. This finding contributes to our understanding of the mechanisms of altered insulin secretion and might provide new perspectives for the development of therapeutic tools for gestational diabetes.
Factors Influencing Users Acceptance of Online Consultation Feature on the SOBAT-UKM Portal

The emergence of digital economy has motivated PT Telkom Indonesia to launch SOBAT-UKM portal. It aims to support Small and Medium Enterprises (SMEs) in Indonesia. The portal has an e-consultation feature, which provides online business discussion between business experts and the SMEs. Despite its significant benefits, the utilization of e-consultation feature only reached 10.54% of the yearly target set by PT Telkom. To address this problem, a study was conducted to investigate factors affecting the low users’ acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used to construct the research model and a questionnaire for data collection. The data were analyzed with Partial Least Square-Structural Equation Modeling (PLS-SEM). The results indicate that the influencing factors are Social Influence, Effort Expectancy, Facilitating Conditions, User Behavior, and Behavioral Intention.
Big Data Analytics and Organizational Culture as Complements to Swift Trust and Collaborative Performance in the Humanitarian Supply Chain

The main objective of the study is to understand how big data analytics capability (BDAC) as an organizational culture can enhance trust and collaborative performance between civil and military organizations engaged in disaster relief operations. The theoretical framework is grounded in organizational information processing theory (OIPT). We have conceptualized an original theoretical model to show, using the competing value model (CVM), how BDAC, under a moderating influence of organizational culture, affects swift trust (ST) and collaborative performance (CP). We used WarpPLS 6.0 to test the proposed research hypotheses using multirespondent data gathered through an email questionnaire sent to managers working in 373 organizations, including the military forces of different countries, government aid agencies, UN specialized agencies, international non-government organizations (NGOs), service providers, and contractors. The results offer four important implications. First, BDAC has a positive, significant effect on ST and CP. Second, flexible orientation (FO) and controlled orientation (CO) have no significant influence on building ST. Third, FO has a positive and significant moderating effect on the path joining BDAC and CP. Finally, CO has negative and significant moderating effect on the path joining BDAC and CP. The control variables: temporal orientation (TO) and interdependency (I) have significant effects on ST and CP. These results extend OIPT to create a better understanding of the application of information processing capabilities to build swift trust and improve collaborative performance. Furthermore, managers can derive multiple insights from this theoretically-grounded study to understand how BDAC can be exploited to gain insights in contexts of different management styles and cultures. We have also outlined the study limitations and provided numerous future research directions. Keywordsvisibility, big data and predictive analytics, trust, collaborative performance, organizational culture, multi-level confirmatory factor analysis
Obiect-Oriented Analysis and Top-Down Software Development

oo-analysis; top-down; ensemble In this paper, we address the issue of how to provide an analyst that uses the object-oriented paradigm with a top-down approach. An analyst gets this approach for free when working within the structured paradigm. Ensembles are introduced that differ from objects in that they connote entities with internal parallelism. Preliminary experimentation suggests that ensembles allow for information hiding.