Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical Activity and Cognitive Function in Mild Cognitive Impairment

Physical exercise has been associated with enhanced memory formation and consolidation in patients with mild cognitive impairment (MCI). This study aimed to investigate the relationship between objective neuropsychological performances and continuously recorded physical activity. A cut-off value of measured physical activity was used to differentiate early-stage and late-stage MCI. Fifty-four patients with MCI were included. The relationship between cognitive function and measures of daily activity measured by continuous three-axis accelerometers in Xiaomi Mi Band, including subject-level average step counts, average distance (kilometers), and average calorie expenditure per day of 7-day activity, was determined. The slope of the receiver operating characteristic curve was used to determine the measures of activity to draw comparisons between early-stage MCI and late-stage MCI. The patients were assessed by using several cognitive tests such as Cognitive Abilities Screening Instrument and Chinese Version Verbal Learning Test. The multivariate linear regression model indicated a significant correlation of higher average step counts per day of 7-day activity (aver-step-counts) with higher score in visual construction (β = 0.355; p = .015). To differentiate patients with late-stage MCI from those with early-stage MCI, the cut-off value of 6,284 steps on aver-step-counts showed an optimal sensitivity and specificity (Youden index = 0.36, area under the curve = 0.651, p = .042). The aver-step-counts showed a significantly better differentiating rate between patients with early-stage and late-stage MCI than average calorie expenditure per day of 7-day activity did (p = .046). Accelerometer-determined measures of activity patterns show as potential measurement to reflect cognitive function.
Exploration of Food-Seeking Behaviour, Food Preparation, and Restrictions to Sufficient Food among the Jahai Sub-Tribe (Indigenous People) in Gerik, Malaysia

Access to food has been determined to be a factor that strongly influences the dietary intake and eating habit of indigenous people (Orang Asli, OA). This study explored food acquisition and the barriers in obtaining traditional and market foods among the Jahai subtribe (within the Negrito ethnic group) via a qualitative approach. Twenty-eight OA (14 males and 14 females) from Gerik, Perak, Malaysia participated in four focus group discussions (FGDs; two male-only and two female-only groups; seven informants in each FGD). Thematic analysis was adopted to analyse the gathered data. The results found that the Jahai applied both modern (buying and receiving food assistance) and traditional (gathering, farming, fishing, and hunting) methods in obtaining food. The barriers they faced in gathering sufficient food supply included low purchasing power, high demand for food, high transport costs, depletion of food supplies in their surroundings, threats from wild animals on the farm and during food searching activities, and food sharing. Food preparation methods practiced by the OA include roasting and grilling, frying, simmering (gulai), and boiling. In conclusion, this study enhances our understanding of the dietary behaviour of the Jahai subtribe and highlights restricted food access among the OA, which demands urgent attention.
Cloud Detection from FY-4A's Geostationary Interferometric Infrared Sounder Using Machine Learning Approaches

FengYun-4A (FY-4A)’s Geostationary Interferometric Infrared Sounder (GIIRS) is the first hyperspectral infrared sounder on board a geostationary satellite, enabling the collection of infrared detection data with high temporal and spectral resolution. As clouds have complex spectral characteristics, and the retrieval of atmospheric profiles incorporating clouds is a significant problem, it is often necessary to undertake cloud detection before further processing procedures for cloud pixels when infrared hyperspectral data is entered into assimilation system. In this study, we proposed machine-learning-based cloud detection models using two kinds of GIIRS channel observation sets (689 channels and 38 channels) as features. Due to differences in surface cover and meteorological elements between land and sea, we chose logistic regression (lr) model for the land and extremely randomized tree (et) model for the sea respectively. Six hundred and eighty-nine channels models produced slightly higher performance (Heidke skill score (HSS) of 0.780 and false alarm rate (FAR) of 16.6% on land, HSS of 0.945 and FAR of 4.7% at sea) than 38 channels models (HSSof 0.741 and FAR of 17.7% on land, HSS of 0.912 and FAR of 7.1% at sea). By comparing visualized cloud detection results with the Himawari-8 Advanced Himawari Imager (AHI) cloud images, the proposed method has a good ability to identify clouds under circumstances such as typhoons, snow covered land, and bright broken clouds. In addition, compared with the collocated Advanced Geosynchronous Radiation Imager (AGRI)-GIIRS cloud detection method, the machine learning cloud detection method has a significant advantage in time cost. This method is not effective for the detection of partially cloudy GIIRS’s field of views, and there are limitations in the scope of spatial application.
Silica gel solid nanocomposite electrolytes with interfacial conductivity promotion exceeding the bulk Li-ion conductivity of the ionic liquid electrolyte filler

Li-ion conductivity increases with introduction of an interfacial ice layer in nano-SCE. The transition to solid-state Li-ion batteries will enable progress toward energy densities of 1000 W·hour/liter and beyond. Composites of a mesoporous oxide matrix filled with nonvolatile ionic liquid electrolyte fillers have been explored as a solid electrolyte option. However, the simple confinement of electrolyte solutions inside nanometer-sized pores leads to lower ion conductivity as viscosity increases. Here, we demonstrate that the Li-ion conductivity of nanocomposites consisting of a mesoporous silica monolith with an ionic liquid electrolyte filler can be several times higher than that of the pure ionic liquid electrolyte through the introduction of an interfacial ice layer. Strong adsorption and ordering of the ionic liquid molecules render them immobile and solid-like as for the interfacial ice layer itself. The dipole over the adsorbate mesophase layer results in solvation of the Li+ ions for enhanced conduction. The demonstrated principle of ion conduction enhancement can be applied to different ion systems.
A Pair of CoII Supramolecular Isomers Based on Flexible Bis-Pyridyl-Bis-Amide and Angular Dicarboxylate Ligands

Thermal reactions of cobalt(II) salts with flexible N,N′-bis(pyrid-3-ylmethyl)adipoamide (L) and angular 4,4′-sulfonyldibenzoic acid (H2SDA) in H2O and CH3OH afforded a pair of supramolecular isomers: [Co2(L)(SDA)2], 1, and [Co2(L)(SDA)2]⋅CH3OH⋅H2O, 2. The structure of complex 1 can be simplified as a one-dimensional (1D) looped chain with L ligands penetrating into the middles of squares, forming a new type of self-catenated net with the (42⋅54)(4)2(5)2 topology, whereas complex 2 displays a 2-fold interpenetrated 2D net with the rare (42⋅68⋅8⋅104)(4)2-2,6L1 topology. While both complexes 1 and 2 display antiferromagnetism with strong spin-orbital coupling, the antiferromagnetism of 2 is accompanied by a cross-over behavior and probably a spin canting phenomenon.
Obese Women Experience Fewer Complications after Oncoplastic Breast Reconstruction Following Segmental Mastectomy Versus Immediate Breast Reconstruction Following Skin-Sparing Mastectomy:

78 METHODS: The Tracking Operations and Outcomes for Plastic Surgeons (TOPS) database from 2008-2011 was queried for patients undergoing breast reconstruction. Demographic variables were noted and binary regression models constructed for the following complications: seroma, dehiscence, surgical site infection (SSI), explantation, fl ap failure, reoperation, and overall complications. Model performance was assessed via Hosmer-Lemeshow (H-L) p-value, c-statistic, and Brier score.
S2k guidelines for the diagnosis and treatment of herpes zoster and postherpetic neuralgia

The present guidelines are aimed at residents and board‐certified specialists in the fields of dermatology, ophthalmology, ENT, pediatrics, neurology, virology, infectious diseases, anesthesiology, general medicine and any other medical specialties involved in the management of patients with herpes zoster. They are also intended as a guide for policymakers and health insurance funds. The guidelines were developed by dermatologists, virologists, ophthalmologists, ENT physicians, neurologists, pediatricians and anesthesiologists/pain specialists using a formal consensus process (S2k).
Transcriptional adaptation in Caenorhabditis elegans

Transcriptional adaptation is a recently described phenomenon by which a mutation in one gene leads to the transcriptional modulation of related genes, termed adapting genes. At the molecular level, it has been proposed that the mutant mRNA, rather than the loss of protein function, activates this response. While several examples of transcriptional adaptation have been reported in zebrafish embryos and in mouse cell lines, it is not known whether this phenomenon is observed across metazoans. Here we report transcriptional adaptation in C. elegans, and find that this process requires factors involved in mutant mRNA decay, as in zebrafish and mouse. We further uncover a requirement for Argonaute proteins and Dicer, factors involved in small RNA maturation and transport into the nucleus. Altogether, these results provide evidence for transcriptional adaptation in C. elegans, a powerful model to further investigate underlying molecular mechanisms.
Predicting Target Feature Configuration of Non-stationary Objects for Grasping with Image-Based Visual Servoing

—In this paper we consider the problem of the ﬁnal approach stage of closed-loop grasping where RGB-D cameras are no longer able to provide valid depth information. This is essential for grasping non-stationary objects; a situation where current robotic grasping controllers fail. We predict the image-plane coordinates of observed image features at the ﬁnal grasp pose and use image-based visual servoing to guide the robot to that pose. Image-based visual servoing is a well established control technique that moves a camera in 3D space so as to drive the image-plane feature conﬁguration to some goal state. In previous works the goal feature conﬁguration is assumed to be known but for some applications this may not be feasible, if for example the motion is being performed for the ﬁrst time with respect to a scene. Our proposed method provides robustness with respect to scene motion during the ﬁnal phase of grasping as well as to errors in the robot kinematic control. We provide experimental results in the context of dynamic closed-loop grasping.
Performance of Wireless Optical Communication With Reconfigurable Intelligent Surfaces and Random Obstacles

It is difficult for free space optical communication to be applied in mobile communication due to the obstruction of obstacles in the environment, which is expected to be solved by reconfigurable intelligent surface technology. The reconfigurable intelligent surface is a new type of digital coding meta-materials, which can reflect, compute and program electromagnetic and optical waves in real time. We purpose a controllable multi-branch wireless optical communication system based on the optical reconfigurable intelligent surface technology. By setting up multiple optical reconfigurable intelligent surface in the environment, multiple artificial channels are built to improve system performance and to reduce the outage probability. Three factors affecting channel coefficients are investigated in this paper, which are beam jitter, jitter of the reconfigurable intelligent surface and the probability of obstruction. Based on the model, we derive the closed-form probability density function of channel coefficients, the asymptotic system's average bit error rate and outage probability for systems with single and multiple branches. It is revealed that the probability density function contains an impulse function, which causes irreducible error rate and outage probability floors. Numerical results indicate that compared with free-space optical communication systems with single direct path, the performance of the multi-branch system is improved and the outage probability is reduced.
Actomyosin, vimentin and LINC complex pull on osteosarcoma nuclei to deform on micropillar topography

Cell deformation occurs in many critical biological processes, including cell extravasation during immune response and cancer metastasis. These cells deform the nucleus, its largest and stiffest organelle, while passing through narrow constrictions in vivo and the underlying mechanisms still remain elusive. It is unclear which biochemical actors are responsible and whether the nucleus is pushed or pulled (or both) during deformation. Herein we use an easily-tunable poly-L-lactic acid micropillar topography, mimicking in vivo constrictions to determine the mechanisms responsible for nucleus deformation. Using biochemical tools, we determine that actomyosin contractility, vimentin and nucleo-cytoskeletal connections play essential roles in nuclear deformation, but not A-type lamins. We chemically tune the adhesiveness of the micropillars to show that pulling forces are predominantly responsible for the deformation of the nucleus. We confirm these results using an in silico cell model and propose a comprehensive mechanism for cellular and nuclear deformation during confinement. These results indicate that microstructured biomaterials are extremely versatile tools to understand how forces are exerted in biological systems and can be useful to dissect and mimic complex in vivo behaviour.
DEF Cell-Derived Exosomal miR-148a-5p Promotes DTMUV Replication by Negative Regulating TLR3 Expression

Duck tembusu virus (DTMUV) is a single-stranded, positive-polarity RNA flavivirus that has caused considerable economic losses in China in recent years. Innate immunity represents the first line of defense against invading pathogens and serves as an important role in resisting viral infections. In this study, we found that the infection of ducks by DTMUV triggers Toll-like receptors (TLRs) and (RIG-I)-like receptors (RLRs) signaling pathways and inducing abundant of pro-inflammatory factors and type I interferons (IFNs), in which melanoma differentiation-associated gene 5 (MDA5) and Toll-like receptor 3 (TLR3) play important immunity roles, they can inhibit the replication process of DTMUV via inducing type I IFNs. Moreover, we demonstrated that type I IFNs can inhibit the DTMUV replication process in a time- and dose-dependent manner. Exosomes are small membrane vesicles that have important roles in intercellular communication. MicroRNAs (miRNAs) are small non-coding RNAs that can modulate gene expression and are common substances in exosomes. In our experiment, we successfully isolated DEF cells derived exosome for the first time and explored its function. Firstly, we found the expression of miR-148a-5p is significantly decreased following DTMUV infect. Then we found miR-148a-5p can target TLR3 and down-regulate the expression of TLR3, serving as a negative factor in innate immunity. Unfortunately, we cannot find miRNAs with different expression changes that can target MDA5. Lastly, our experimental results showed that TLR3 was one of the causes of miR-148a-5p reduction, suggesting that the high level of TLR3 after DTMUV infect can both trigger innate immunity and suppress miR-148a-5p to resist DTMUV.
Internipple Distance and Internipple Index in Prepubertal Turkish Girls

Objective: To determine internipple distance and internipple index in prepubertal Turkish girls. Methods: The internipple distance and chest circumference of 667 healthy prepubertal Turkish girls aged 6 to 11 years were measured in a school screening program in Düzce. Measurements were performed at the end of expiration with a standard non-stretch tape measure graduated in millimeters with the arms hanging in a relaxed position on the sides of the body. The internipple distance was measured between the centers of both nipples, and chest circumference was measured across the internipple line. The internipple index was calculated by dividing the internipple distance (cm) x100 by the chest circumference (cm). Age specific internipple index reference curves were constructed and smoothed with the Lambda-Mu-Sigma method. Mean and standard deviations of internipple distance and internipple index were calculated according to decimal ages. Results: Age was found to be positively correlated with internipple distance and chest circumference, while it was negatively correlated with internipple index. The reference values of internipple index, including 3rd, 10th, 25th, 50th, 75th, 90th, and 97th percentiles, and standard deviations were calculated for prepubertal girls. Conclusion: The reference ranges provided by this study might be helpful for the evaluation of syndromic cases by serving as normative data for internipple index in prepubertal girls aged 6-11 years in Turkey although ethnic differences may affect applicability to other countries.
Self-Reported General Health, Overall and Work-Related Stress, Loneliness, and Sleeping Problems in 335,625 Swedish Adults from 2000 to 2016

The prevalence of poor health, in particular stress-related mental ill-health, is increasing over time and birth cohorts. As rapid societal changes have occurred in the last decade and still are occurring, there is an interest in investigating the trends in health-related factors. The aim of the present study was to investigate trends in self-reported general health, overall stress, work-related stress, feelings of loneliness, and sleeping problems in 335,625 Swedish adults across categories of gender, geographic regions, length of education, and age from 2000 to 2016. On population level, sleeping problems and poor general health have increased markedly and significantly, while experiences of work stress decreased between 2000 and 2016 (p < 0.05). Overall stress and level of loneliness were unchanged (p > 0.05). The risk of having ≥3 symptoms (any of poor or very poor general health, often or very often perceived overall stress, loneliness, or sleeping problems) has increased significantly from 2000 to 2016 (ß = 1034 (1027–1040)). This increase was significantly higher in young (ß = 1052 (1038–1065)) and individuals with lower education (ß = 1056 (1037–1076)) compared to older and high length of education.
A Probability-Based Spectral Unmixing Analysis for Mapping Percentage Vegetation Cover of Arid and Semi-Arid Areas

China has been facing serious land degradation and desertification in its north and northwest arid and semi-arid areas. Monitoring the dynamics of percentage vegetation cover (PVC) using remote sensing imagery in these areas has become critical. However, because these areas are large, remote, and sparsely populated, and also because of the existence of mixed pixels, there have been no accurate and cost-effective methods available for this purpose. Spectral unmixing methods are a good alternative as they do not need field data and are low cost. However, traditional linear spectral unmixing (LSU) methods lack the ability to capture the characteristics of spectral reflectance and scattering from endmembers and their interactions within mixed pixels. Moreover, existing nonlinear spectral unmixing methods, such as random forest (RF) and radial basis function neural network (RBFNN), are often costly because they require field measurements of PVC from a large number of training samples. In this study, a cost-effective approach to mapping PVC in arid and semi-arid areas was proposed. A method for selection and purification of endmembers mainly based on Landsat imagery was first presented. A probability-based spectral unmixing analysis (PBSUA) and a probability-based optimized k nearest-neighbors (PBOkNN) approach were then developed to improve the mapping of PVC in Duolun County in Inner Mongolia, China, using Landsat 8 images and field data from 920 sample plots. The proposed PBSUA and PBOkNN methods were further validated in terms of accuracy and cost-effectiveness by comparison with two LSU methods, with and without purification of endmembers, and two nonlinear approaches, RF and RBFNN. The cost-effectiveness was defined as the reciprocal of cost timing relative root mean square error (RRMSE). The results showed that (1) Probability-based spectral unmixing analysis (PBSUA) was most cost-effective and increased the cost-effectiveness by 29.3% 29.3%, 33.5%, 50.8%, and 53.0% compared with two LSU methods, PBOkNN, RF, and RBFNN, respectively; (2) PBSUA, RF, and RBFNN gave RRMSE values of 22.9%, 21.8%, and 22.8%, respectively, which were not significantly different from each other at the significance level of 0.05. Compatibly, PBOkNN and LSU methods with and without purification of endmembers resulted in significantly greater RRMSE values of 27.5%, 32.4%, and 43.3%, respectively; (3) the average estimates of the sample plots and predicted maps from PBSUA, PBOkNN, RF, and RBFNN fell in the confidence interval of the test plot data, but those from two LSU methods did not, although the LSU with purification of endmembers improved the PVC estimation accuracy by 25.2% compared with the LSU without purification of endmembers. Thus, this study indicated that the proposed PBSUA had great potential for cost-effectively mapping PVC in arid and semi-arid areas.
Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).