Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Approach for Modelling the Complex Relationship between Environmental Factors and Biological Features

Environmental factors play a pivotal role in shaping the genetic and phenotypic diversity among organisms. Understanding the influence of the environment on a biological phenomenon is essential for deciphering the mechanisms resulting in trait differences among organisms. In this study, we present a novel approach utilizing an Artificial Neural Network (ANN) model to investigate the impact of environmental factors on a wide range of biological phenomena. Our proposed workflow includes hyperparameter optimization using model-based methods such as Bayesian and direct-search methods such as Random Search, and a new approach combining random search and linear models (RandomSearch+lm) to ensure a robust ANN architecture. Moreover, we employed a generalized version of the variable importance method to generate the feature importance metric using estimated weights from ANN. By applying this comprehensive ANN-based approach to functional genomics, we can gain valuable insights into the mechanisms underlying trait differentiation in organisms, while simultaneously enabling prediction and feature selection tasks. This methodology provides a robust and efficient framework for studying the complex relationships between environmental factors and biological features in biological systems.
Green kernels associated with Riesz kernels

We study properties of the $\alpha$-Green kernel of order $0<\alpha\leqslant2$ for a domain $D\subset\mathbb R^n$, $n\geqslant3$. This kernel is associated with the M. Riesz kernel $|x-y|^{\alpha-n}$, $x,y\in\mathbb R^n$, in a manner well known in the case $\alpha=2$. The usual principles in potential theory are established for the $\alpha$-Green kernel, as well as the property of consistency which allows us to prove the existence of the $\alpha$-Green equilibrium measure for a relatively closed set in $D$ of finite $\alpha$-Green capacity. The main tool is a generalization of H. Cartan's theory of balayage (sweeping) for the Newtonian kernel to the $\alpha$-Riesz kernels with $0<\alpha<2$.
Cryo-electron microscopy of cytoskeletal ‘fibril’ involved in Spiroplasma swimming

Spiroplasma, parasitic or commensal bacteria, can swim by switching the handedness of its helical cell body. A helical cell body is formed by an internal ribbon of MreB, an actin superfamily, and Spiroplasma-specific fibril proteins. Here we have solved the structure of the fibril filament by single-particle cryo-electron microscopy at 3.6 Å resolution and built its atomic structure. The structure is composed of repeated rings and cylinders. The N-terminal cylinder of the fibril protein shows a structure similar to that of S-adenosylhomocysteine nucleosidase, while the C-terminal ring does not show similarity to other proteins. The filament is nonpolar and flexible, possessing a helical pitch of 700 nm, consistent with cell helicity. Cryo-electron tomography revealed aligned several MreB filaments in the center of the ribbon, flanked by membrane-binding fibril filaments through electrostatic interactions. This study discusses the evolution and roles of the fibril filament.
Transfemoral limb loss modestly increases the metabolic cost of optimal control simulations of walking

Unlike the case of transtibial limb loss, high-functioning military service members with transfemoral limb loss have a high metabolic cost of walking compared to able-bodied peers. In transtibial limb loss, computer simulations suggest that the maintenance of muscle strength between pre- and post-limb loss can maintain the pre-limb loss metabolic cost. Here we performed optimal control simulations of walking with and without transfemoral limb loss to determine if transfemoral limb loss per se increases the metabolic cost of walking. OpenSim Moco was used to generate optimal control simulations of walking in 15 virtual subjects that minimized the weighted sum of (i) deviations from average able-bodied gait mechanics and (ii) the gross metabolic cost of walking, pre-limb loss in models with two intact biological limbs, and post-limb loss with one of the limbs replaced by a transfemoral prosthesis. No other changes were made to the model. Metabolic cost post-limb loss increased by 0.7-9.3% (p < 0.01) depending on whether cost was scaled by total body mass or biological body mass and on whether the prosthetic knee was passive or non-passive. The post-limb loss model had numerous features that predisposed it to low metabolic cost. These results suggest that transfemoral limb loss per se increases the metabolic cost of walking, but that the large differences above able-bodied peers of ∼20% or more in most gait analysis experiments may be avoidable, even when minimizing deviations from able-bodied gait mechanics.
Assessment of the Steady State Voltage Stability of the Ghanaian Transmission System with the Integration of Renewable Energy Sources

-The increasing demand for electric power and the resulting need for new generating stations and grid reinforcement have led to an increased complexity in the Ghanaian power system. Power generation in Ghana is dominated by hydro and thermal generation with only 0.6% solar PV generation. Albeit, plans are underway to increase the share of renewable power generation (solar PV and wind) to 10% by the year 2020. The location of the proposed PV plants in the north, where there is less load demand with a likelihood of leading to an imbalance in the system during periods of high PV generation. Long transmission lines are therefore needed to transport power from PV units in the north to load centers in the south. This situation translates into high reactive power demand and very high voltages in the north, which eventually leads to the operation of the Ghanaian power system near its voltage stability limits. In view of the ongoing renewable energy integration, assessing the voltage stability limit becomes necessary in the planning of the Ghanaian power system. This paper thus analyzes the steady state voltage stability of the Ghanaian transmission system using simple power flow algorithms based on the Newton-Raphson method. The continuation power flow method is specifically employed to develop the nose curves of critical buses in an attempt to determine the steady state stability limit of the system. To investigate the effect of increased PV generation on the voltage stability, different simulation scenarios, which represent different percentages of PV generation, are developed for the simulations.
An Optimization Ranking Approach Based on Weighted Citation Networks and P-Rank Algorithm

Evaluating scientific articles has always been a challenging task, made even more difficult by the constantly evolving citation networks. Despite numerous attempts at solving this problem, most existing approaches fail to consider the link relationships within the citation network, which can often result in biased evaluation results. To overcome this limitation, we present an optimization ranking algorithm that leverages the P-Rank algorithm and weighted citation networks to provide a more accurate article ranking. The proposed approach employs two hyperbolic tangent functions to calculate the corresponding age of articles and the number of citations, while also updating the link relationships of each paper node in the citation network. We validate the effectiveness of the proposed approach using three evaluation indicators and conduct experiments on three public datasets. The obtained experimental results demonstrate that the optimization article ranking method can achieve competitive performance when compared to other unweighted ranking algorithms. In addition, we note that the optimal Spearman’s rank correlation and robustness can all be achieved by using a combination of the following parameters: α = 10 , β = 5 , and γ = 2 .
Paradigmatic Revolutions in Computer Vision

Kuhn’s groundbreaking Structure divides scientific progress into four phases, the pre-paradigm period, normal science, scientific crisis and revolution. Most of the time a field advances incrementally, constrained and guided by a currently agreed upon paradigm. Creative phases emerge when phenomena occur which lack satisfactory explanation within the current paradigm (the crisis) until a new one replaces it (the revolution). This model of science was mainly laid out by exemplars from natural science, while we want to show that Kuhn’s work is also applicable for information sciences. We analyze the state of one field in particular, computer vision, using Kuhn’s vocabulary. Following significant technology-driven advances of machine learning methods in the age of deep learning, researchers in computer vision were eager to accept the models that now dominate the state of the art. We discuss the current state of the field especially in light of the deep learning revolution and conclude that current deep learning methods cannot fully constitute a paradigm for computer vision in the Kuhnian sense.
Multi-dimensional Data Aggregation Scheme Supporting Fault-Tolerant Mechanism in Smart Grid

—With the large-scale deployment of smart grids, the scheme of smart grid data aggregation has gradually enriched in recent years. Based on the principle of protecting user privacy, existing schemes usually choose to introduce a trusted third party (TTP) to participate in the collaboration. However, this also increases the risk of privacy exposure as the attacker can target the TTP which provides services to smart grid operators. In addition, many existing schemes do not take into account the operational requirements of smart meters in case of failure. Furthermore, some schemes ignore the control center’s demand for analyzing multi-dimensional data, which causes a lot of inconvenience in actual operation. Therefore, a fault-tolerant multi-dimensional data aggregation scheme is proposed in this paper. We have constructed a scheme without TTP participation in collaboration, and also meet the following two requirements. The scheme not only ensures the normal operation of the system when the smart meter fails but also meets the requirements of the control center for multi-dimensional data analysis. Security analysis shows that the proposed scheme can resist external attack, internal attack, and collusion attack. The experimental results show that the proposed scheme improves the fault tolerance and reduces the computational cost compared with the existing schemes.
S CROOGE VM : B OOSTING C LOUD R ESOURCE U TILIZATION WITH G REEDY O VERSUBSCRIPTION

Despite continuous improvements, cloud physical resources remain underused, hence severely impacting the efficiency of these infrastructures at large. To overcome this inefficiency, Infrastructure-as-a-Service (IaaS) providers usually compensate for oversized Virtual Machine s (VMs) by offering more virtual resources than are physically available on a host. However, this technique—known as oversubscription (or overcommitment or overbooking )—may hinder performances when a statically-defined oversubscription ratio results in resource contention of hosted VMs. Therefore, instead of setting a static and cluster-wide ratio, this article proposes to dynamically estimate the optimal oversubscription ratio per Physical Machine (PM) and resources type, while preserving performance goals. Keeping performance unchanged allows our contribution to be more realistically adopted by production-scale IaaS infrastructures. In particular, to avoid penalties due to live migrations of VMs, the amount of exposed virtual resources and, therefore, the associated resource oversubscription ratios, should only be increased. We address this challenge by introducing S CROOGE VM, a greedy approach that leverages the detection of PM stability to carefully increase the associated oversubscription ra-tios. Based on measurements made available by public cloud providers, we first study the impact of resource oversubscrip-tion for different aggregation windows. Then, we propose a methodology to estimate confidence in a chosen percentile value, using its previously observed evolution. We show that appropriate combinations of window duration and percentile improve commonly adopted oversubscription strategies, without imposing live migration penalties nor performance impacts for stakeholders.
Biogeochemical Processes Are Altered by Non‐Conservative Mixing at Stream Confluences

Stream confluences are ubiquitous interfaces in freshwater networks and serve as junctions of previously independent landscapes. However, few studies have investigated how confluences influence the transport, mixing, and fate of organic matter (OM) and inorganic nutrients at the scale of river networks. To understand how network biogeochemical fluxes may be altered by confluences, we conducted two sampling campaigns at five confluences in summer and fall 2021 spanning the extent of a mixed land use stream network. We sampled the confluence mainstem and tributary reaches as well as throughout the mixing zone downstream. We predicted that biologically reactive solutes would mix non‐conservatively downstream of confluences and that alterations to downstream biogeochemistry would be driven by differences in chemistry and size of the tributary and upstream reaches. In our study, confluences were geomorphically distinct (e.g., wider, deeper, unique erosional, and depositional features) downstream compared to reaches upstream of the confluence. Dissolved OM and nutrients mixed non‐conservatively downstream of the five confluences. Biogeochemical patterns downstream of confluences were only partially explained by contributing reach chemistry and drainage area. We found that the relationship between geomorphic variability, water residence time, and microbial respiration differed between reaches upstream and downstream of confluences. The lack of explanatory power from network‐scale drivers suggests that non‐conservative mixing downstream of confluences may be driven by biogeochemical processes within the confluence mixing zone. The unique geomorphology, non‐conservative biogeochemistry, and ubiquity of confluences highlights a need to account for the distinct functional role of confluences in water resource management in freshwater networks.
Impacts of hydrofacies geometry designed from seismic refraction tomography on estimated hydrogeophysical variables

Understanding the critical zone processes related to groundwater ﬂows relies on subsurface structure knowledge and its associated parameters. We propose a methodology to draw the patterns of the subsurface critical zone at the catchment scale from seismic refraction data and show its interest for hydrological modelling. The designed patterns deﬁne the structure of a physically based distributed hydrological model applied to a mountainous catchment. With that goal, we acquired 10 seismic proﬁles covering the different geomorphology zones of the studied catchment. We develop a methodology to analyse the geostatistical characteristics of the seismic data and interpolate them over the whole catchment. The applied geostatistical model considers the scale variability of the subsurface structures observed from the seismic data analysis. We use compressional seismic wave velocity thresholds to identify the depth of the soil and sapro-lite bottom boundaries. Assuming that such porous compartments host the main part of the active aquifer, their patterns are embedded in a distributed hydrological model. We examine the sensitivity of classical hydrological data (piezometric heads) and geophysical data (magnetic resonance soundings) to the applied velocity thresholds used to deﬁne the soil and saprolite boundaries. Different sets of hydrogeological parameters are used in order to distinguish general trends or speciﬁcities related to the choice of parameter values. The application of the methodology to an actual catchment illustrates the interest of seismic refraction in constraining the structure of the critical zone subsurface compartments. The sensitivity tests highlight the complementarity of the analysed hydrogeophysical data sets.
Virtual staining overlay enabled combined morphological and spatial transcriptomic analysis of individual malignant B cells and local tumor microenvironments

B-cell lymphomas are complex entities consisting of a component of malignant B-cells admixed in a local tumor microenvironment (TME) inflammatory milieu. Discrete characterization of both compartments can drive deeper understanding of pathophysiology, allowing more accurate diagnoses and prognostic predictions. However, limitations in both pathologist time and input tissue to generate multiple stains can greatly limit accurate identification of minute, cellular-level regions of interest necessary to achieve the full potential of spatial biology. Here, we present a novel method to perform precise sampling of cells for transcriptomic analysis using virtual staining of autofluorescence images via deep learning algorithms. We validated the performance of the model on regions of interest (ROIs) identified on chemically stained images by board certified pathologists against virtually stained images. The results confirmed the usability and accuracy of the workflow and identified distinct transcriptomic profiles across a range of virtually identified ROIs, raising the possibility of our workflow’s applicability across a broader range of pathologies and tissue types.
A Fuzzy Reward and Punishment Scheme for Vehicular Ad Hoc Networks

—Trust management is an important security approach for the successful implementation of Vehicular Ad Hoc Networks (VANETs). Trust models evaluate messages to assign reward or punishment. This can be used to influence a driver’s future behaviour. In the author’s previous work, a sender-side based trust management framework is developed which avoids the receiver evaluation of messages. However, this does not guarantee that a trusted driver will not lie. These “untrue attacks” are resolved by the RSUs using collaboration to rule on a dispute, providing a fixed amount of reward and punishment. The lack of sophistication is addressed in this paper with a novel fuzzy RSU controller considering the severity of incident, driver past behaviour, and RSU confidence to determine the reward or punishment for the conflicted drivers. Although any driver can lie in any situation, it is expected that trustworthy drivers are more likely to remain so, and vice versa. This behaviour is captured in a Markov chain model for sender and reporter drivers where their lying characteristics depend on trust score and trust state. Each trust state defines the driver’s likelihood of lying using different probability distribution. An extensive simulation is performed to evaluate the performance of the fuzzy assessment and examine the Markov chain driver behaviour model with changing the initial trust score of all or some drivers in Veins simulator. The fuzzy and the fixed RSU assessment schemes are compared, and the result shows that the fuzzy scheme can encourage drivers to improve their behaviour.
Predictors of Speed and Agility in Youth Male Basketball Players

Player performance in an intense sport such as basketball is known to be related to attributes such as speed, agility, and power. This study presents a comparative analysis of associations between anthropometric assessment and physical performance in different age groups of elite youth basketball players, while simultaneously identifying the predictors for speed and agility in these players. U14 (n = 44), U15 (n = 45), and U16 (n = 51) players were tested for anthropometry, lower-body power, speed, and agility. U16 players were found to be taller, heavier, more muscular than U14 and U15 players. In addition, the U16 group showed better performance in all performance tests. Age had a significant positive correlation with countermovement (CMJ) and drop jump (DJ) performance in U14 players, and a significant negative correlation with 15m and 20m sprint times in the U15 group. CMJ and DJ emerged as the most significant predictors for sprint and agility variables, respectively. Body fat percentage was found to be a significant predictor for the speed and agility tests in all age groups, but a negative lower-body power predictor. Therefore, besides all sport-specific and fitness tests, it is essential to place emphasis on the percentage of body fat when designing players’ individualized training programs, and during team selection.
Cylindrocladiella hahajimaensis, a new species of Cylindrocladiella transferred from Verticillium

We used phylogenetic analyses based on ribosomal internal transcribed spacer (ITS) and beta-tubulin (TUB) sequences and determined that the correct name of ‘Verticillium hahajimaense’ was Cylindrocladiella hahajimaensis. A closest relative could not be determined, since C. hahajimaensis clustered within the C. infestans species complex, a poorly resolved group of taxa. Cylindrocladiella hahajimaensis differed from all other members of the C. infestans species complex by at least 18 substitutions at the two loci. Morphological characters supported the placement within Cylindrocladiella. In addition to the verticillate conidiophores mentioned in the type description, we found evidence for the presence of penicillate conidiophores. Other differences to the type description included the presence of yellow to brown-pigmented hyphae, and the hyaline instead of pigmented chlamydospores.
Supporting Information for “Hierarchical planning with deep reinforcement learning for three-dimensional navigation of microrobots in blood vessels”

This supporting information includes supplemental figures, movies, additional results, and the key steps in the neural network training algorithm. Corresponding author Email: yyang60@jhu.edu (Y.Y.) libome@tsinghua.edu (B.L.) 1 List of Supplemental Figures • Fig. S1. The Actor-Critic architecture used to learn optimal control policies for the microrobot. • Fig. S2. The approximate RBC shape we used to facilitate a fast collision check in the simulation. • Fig. S3. Scheme of training and evaluation workflow. Efficient training is achieved by selecting increasingly challenging tasks as motivated by curriculum learning. • Fig. S4. Navigation and localization trajectories of microrobots in free space with different propulsion rotation ratios. • Fig. S5. Navigation and localization trajectories of microrobots in free space with different external flow fields. • Fig. S6. Navigation of microrobots in blood vessels with different external flow fields. 2 Supplemental Movies • Movie S1. Navigation and localization of microrobots in free space with different external flow fields. • Movie S2. Navigation of microrobots in blood vessels with different vessel diameter D and RBC volume fraction f . Specifically, from left to right, D = 12um, f ̃ 10%; D = 25um, f ̃10%; D = 50um, f ̃5%; D = 50um, f ̃10%. • Movie S3. Navigation of microrobots in curved blood vessels with varying cross-section diameter. • Movie S4. Exhaustive spatial survey in a blood vessel. From left to right, the blood vessel has zero RBCs, ̃5% RBCs, and ̃10% RBCs.
Unusual Human Behavior Detection System in Real-Time Video Systems

—Abnormal behavior detection, in terms of importance, has become a necessity in real-time visual systems. The main problem is the ambiguity in the difference between the characteristics of abnormal and normal behavior, which its definition is usually different according to the previous context of images. In this research, three approaches are used. In the first approach, a standard Convolutional Automatic Encoder (CAE) is used. After evaluation, it was found that the standard CAE problem is that it does not take into account the temporal aspect of the image frames sequence. The second method involves automatic encoding to learn the dataset's spatio-temporal structures. In the third approach, the complex LSTM cells are used for further improvement. The outcomes of the test display that the proposed methods have better performance compared to many of the previous conventional methods, and their efficiency in identifying abnormal behavior is very competitive compared to previous methods.