Miracle-dz/UniFAR-FTUs huggingface dataset:
Fat and Lean Mass Predict Bone Mass During Energy Restriction in Sedentary and Exercising Rodents

Energy restriction (ER) causes bone loss, but the impact of exercise during ER is less understood. In this study, we examined the impact of metabolic hormones and body composition on both total body bone mineral content (BMC) and local (proximal tibia) volumetric bone mineral density (vBMD) during short- (4 weeks) and long-term (12 weeks) ER with and without exercise in adult female rats. Our first goal was to balance energy between sedentary and exercising groups to determine the impact of exercise during ER. Second, we aimed to determine the strongest predictors of bone outcomes during ER with energy-matched exercising groups. Methods: Female Sprague–Dawley rats were divided into three sedentary groups (ad libitum, –20% ER, and –40% ER) and three exercising groups (ad libitum, –10% ER, and –30% ER). Approximately a 10% increase in energy expenditure was achieved via moderate treadmill running (∼60–100 min 4 days/week) in EX groups. n per group = 25–35. Data were analyzed as a 2 × 3 ANOVA with multiple linear regression to predict bone mass outcomes. Results: At 4 weeks, fat and lean mass and serum insulin-like growth factor-I (IGF-I) predicted total body BMC (R2 = 0.538). Fat mass decreased with ER at all levels, while lean mass was not altered. Serum IGF-I declined in the most severe ER groups (–40 and –30%). At 12 weeks, only fat and lean mass predicted total body BMC (R2 = 0.718). Fat mass declined with ER level regardless of exercise status and lean mass increased due to exercise (+5.6–6.7% vs. energy-matched sedentary groups). At the same time point, BMC declined with ER, but increased with exercise (+7.0–12.5% vs. energy-matched sedentary groups). None of our models predicted vBMD at the proximal tibia at either time point. Conclusion: Both fat and lean mass statistically predicted total body BMC during both short- and long-term ER. Fat and lean mass decreased with ER, while lean mass increased with EX at each energy level. Measures that predicted total body skeletal changes did not predict site-specific changes. These data highlight the importance of maintaining lean mass through exercise during periods of ER.
Insufficient properties of image encryption algorithms

We analyze the security of recently proposed image encryption scheme [1]. We show that the scheme is insecure and the methods used to evaluate its security are insufficient. By designing the Deliberately Weak Cipher, a completely vulnerable cipher with good statistical properties, we illustrate our main point -- a solid analysis cannot be replaced by some selected set of statistical properties.
Association between the Dietary Inflammatory Index and Risk of Frailty in Older Individuals with Poor Nutritional Status

Both inflammation and poor nutritional status are major risk factors of frailty, and the dietary inflammatory index (DII) has been suggested as being associated with the risk of frailty. The present study aimed to investigate whether DII scores were positively associated with the risk of frailty in older individuals, particularly those with poor nutritional status. In total, 321 community-dwelling older individuals aged 70–85 years were recruited and categorized as non-frail, pre-frail, and frail according to the Cardiovascular Health Study index. DII scores were calculated based on 24-h dietary recall, and nutritional status was assessed using the Mini Nutritional Assessment. Multinomial logistic regression analysis showed that DII scores were positively associated with the risk of frailty in older individuals (odds ratio, OR 1.64, 95% confidence interval, 95% CI 1.25–2.17), particularly those with poor nutritional status (OR 1.68, 95% CI 1.21–2.34). Among the frailty criteria, weight loss (OR 1.29, 95% CI 1.03–1.60), low walking speed (OR 1.33, 95% CI 1.10–1.61), and low grip strength (OR 1.34, 95% CI 1.13–1.60) were associated with DII scores. In addition, the optimal DII cut-off score for frailty was ≥0.93 (sensitivity 71%; specificity: 72%; area under the receiver operating characteristic curve, AUC = 0.792). The present study showed that a pro-inflammatory diet was associated with increased risk of frailty, particularly in older individuals with poor nutritional status. Future randomized controlled trials with a low DII diet for the prevention of frailty are needed to confirm our finding.
A Learning Framework for Robust Bin Picking by Customized Grippers

Customized grippers have specifically designed fingers to increase the contact area with the workpieces and improve the grasp robustness. However, grasp planning for customized grippers is challenging due to the object variations, surface contacts and structural constraints of the grippers. In this paper, we propose a learning framework to plan robust grasps for customized grippers in real-time. The learning framework contains a low-level optimization-based planner to search for optimal grasps locally under object shape variations, and a high-level learning-based explorer to learn the grasp exploration based on previous grasp experience. The optimization-based planner uses an iterative surface fitting (ISF) to simultaneously search for optimal gripper transformation and finger displacement by minimizing the surface fitting error. The high-level learning-based explorer trains a region-based convolutional neural network (R-CNN) to propose good optimization regions, which avoids ISF getting stuck in bad local optima and improves the collision avoidance performance. The proposed learning framework with RCNN-ISF is able to consider the structural constraints of the gripper, learn grasp exploration strategy from previous experience, and plan optimal grasps in clutter environment in real-time. The effectiveness of the algorithm is verified by experiments.
Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China

Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status within a catchment and develop a targeted planning strategy for river restoration. To address this need, coupling hydrological and machine learning models were constructed to identify priority zones for river restoration based on a dataset of aquatic organisms (i.e., algae, macroinvertebrates, and fish) and physicochemical indicators that were collected from 130 sites in September 2014 in the Taizi River, northern China. A process-based model soil and water assessment tool (SWAT) was developed to model the temporal-spatial variations in environmental indicators. A support vector machine (SVM) model was applied to explore the relationships between aquatic organisms and environmental indicators. Biological indices among different hydrological periods were simulated by coupling SWAT and SVM models. Results indicated that aquatic biological indices and physicochemical indicators exhibited apparent temporal and spatial patterns, and those patterns were more evident in the upper reaches compared to the lower reaches. The ecological status of the Taizi River was better in the flood season than that in the dry season. Priority zones were identified for different hydrological seasons by setting the target values for ecological restoration based on biota organisms, and the results suggest that hydrological conditions significantly influenced restoration prioritization over other environmental parameters. Our approach could be applied in other seasonal river ecosystems to provide important preferences for river restoration.
A Polarization-Independent Fiber-Optic SPR Sensor

Fiber-optic surface plasmon resonance (SPR) sensors possess the advantages of small size, flexible, allowing for a smaller sample volume, easy to be integrated, and high sensitivity. They have been intensively developed in recent decades. However, the polarizing nature of the surface plasmon waves (SPWs) always hinders the acquisition of SPR spectrum with high signal-noise ratio in wavelength modulation unless a polarizer is employed. The addition of polarizer complicates the system and reduces the degree of compactness. In this work, we propose and demonstrate a novel, polarization-independent fiber-optic SPR sensor based on a BK7 bi-prism with two incident planes orthogonal to each other. In the bi-prism, TM-polarized components of non-polarized incident lights excite SPWs on the first sensing channel, meanwhile the TE components and the remaining TM components are reflected, then the reflected TE components serve as TM components of incident lights for the second sensing channel to excite SPWs. Simulations show the proposed SPR structure permit us to completely eliminate the polarization dependence of the plasmon excitation. Experimental results agree well with the simulations. This kind of devices can be considered an excellent option for development of simple and compact SPR chemical sensors.
Statin Use and Cancer Incidence in Patients with Type 2 Diabetes Mellitus: A Network Meta-Analysis

Background Type 2 diabetes mellitus (T2DM) patients are involved closely with cancer. This work aims to conduct a systematic review and network meta-analysis (NMA) to examine the effect of different types of statins on cancer incidence in patients with T2DM. Methods We systematically searched the Cochrane Library, PubMed, Embase, and Wanfang databases from January 1999 to March 2017. We performed a pairwise meta-analysis to estimate the pooled ratios (ORs) and 95% confidence intervals (CIs). A NMA was performed to compare different types of statins. Results Seven publications were included. In pairwise meta-analysis, the incidence of cancer in T2DM patients was reduced when simvastatin, atorvastatin, pravastatin, fluvastatin, lovastatin, rosuvastatin, and pitavastatin were used. In the result of NMA, the usage of simvastatin (RR 0.30 and 95% CI 0.16–0.56), atorvastatin (RR 0.29 and 95% CI 0.09–0.88), pravastatin (RR 0.34 and 95% CI 0.12–0.93), fluvastatin (RR 0.27 and 95% CI 0.09–0.83), rosuvastatin (RR 0.22 and 95% CI 0.10–0.49), and pitavastatin (RR 0.33 and 95% CI 0.20–0.57) was superior to the nonstatin groups. When compared with six other statins, rosuvastatin appeared to be the best one. Conclusions Different statins can reduce the risk of cancer in patients with T2DM. Our analyses suggest that rosuvastatin may be more effective than others.
Neural Classifiers with Limited Connectivity and Recurrent Readouts

For many neural network models in which neurons are trained to classify inputs like perceptrons, the number of inputs that can be classified is limited by the connectivity of each neuron, even when the total number of neurons is very large. This poses the problem of how the biological brain can take advantage of its huge number of neurons given that the connectivity is sparse. One solution is to combine multiple perceptrons together, as in committee machines. The number of classifiable random patterns would then grow linearly with the number of perceptrons, even when each perceptron has limited connectivity. However, the problem is moved to the downstream readout neurons, which would need a number of connections as large as the number of perceptrons. Here we propose a different approach in which the readout is implemented by connecting multiple perceptrons in a recurrent attractor neural network. We prove analytically that the number of classifiable random patterns can grow unboundedly with the number of perceptrons, even when the connectivity of each perceptron remains finite. Most importantly, both the recurrent connectivity and the connectivity of downstream readouts also remain finite. Our study shows that feedforward neural classifiers with numerous long-range afferent connections can be replaced by recurrent networks with sparse long-range connectivity without sacrificing the classification performance. Our strategy could be used to design more general scalable network architectures with limited connectivity, which resemble more closely the brain neural circuits that are dominated by recurrent connectivity. SIGNIFICANCE STATEMENT The mammalian brain has a huge number of neurons, but the connectivity is rather sparse. This observation seems to contrast with the theoretical studies showing that for many neural network models the performance scales with the number of connections per neuron and not with the total number of neurons. To solve this dilemma, we propose a model in which a recurrent network reads out multiple neural classifiers. Its performance scales with the total number of neurons even when each neuron of the network has limited connectivity. Our study reveals an important role of recurrent connections in neural systems like the hippocampus, in which the computational limitations due to sparse long-range feedforward connectivity might be compensated by local recurrent connections.
Complete Chloroplast Genome Sequence and Phylogenetic Analysis of Aster tataricus

We sequenced and analyzed the complete chloroplast genome of Aster tataricus (family Asteraceae), a Chinese herb used medicinally to relieve coughs and reduce sputum. The A. tataricus chloroplast genome was 152,992 bp in size, and harbored a pair of inverted repeat regions (IRa and IRb, each 24,850 bp) divided into a large single-copy (LSC, 84,698 bp) and a small single-copy (SSC, 18,250 bp) region. Our annotation revealed that the A. tataricus chloroplast genome contained 115 genes, including 81 protein-coding genes, 4 ribosomal RNA genes, and 30 transfer RNA genes. In addition, 70 simple sequence repeats (SSRs) were detected in the A. tataricus chloroplast genome, including mononucleotides (36), dinucleotides (1), trinucleotides (23), tetranucleotides (1), pentanucleotides (8), and hexanucleotides (1). Comparative chloroplast genome analysis of three Aster species indicated that a higher similarity was preserved in the IR regions than in the LSC and SSC regions, and that the differences in the degree of preservation were slighter between A. tataricus and A. altaicus than between A. tataricus and A. spathulifolius. Phylogenetic analysis revealed that A. tataricus was more closely related to A. altaicus than to A. spathulifolius. Our findings offer valuable information for future research on Aster species identification and selective breeding.
The Rise of Certificate Transparency and Its Implications on the Internet Ecosystem

In this paper, we analyze the evolution of Certificate Transparency (CT) over time and explore the implications of exposing certificate DNS names from the perspective of security and privacy. We find that certificates in CT logs have seen exponential growth. Website support for CT has also constantly increased, with now 33% of established connections supporting CT. With the increasing deployment of CT, there are also concerns of information leakage due to all certificates being visible in CT logs. To understand this threat, we introduce a CT honeypot and show that data from CT logs is being used to identify targets for scanning campaigns only minutes after certificate issuance. We present and evaluate a methodology to learn and validate new subdomains from the vast number of domains extracted from CT logged certificates.
A type system equivalent to flow analysis

Flow-based safety analysis of higher-order languages has been studied by Shivers, and Palsberg and Schwartzbach. Open until now is the problem of finding a type system that accepts exactly the same programs as safety analysis. In this article we prove that Amadio and Cardelli's type system with subtyping and recursive types accepts the same programs as a certain safety analysis. The proof involves mappings from types to flow information and back. As a result, we obtain an inference algorithm for the type system, thereby solving an open problem.
Cortical and subcortical areas involved in the regulation of reach movement speed in the human brain: An fMRI study

Reach movements are characterized by multiple kinematic variables that can change with age or due to medical conditions such as movement disorders. While the neural control of reach direction is well investigated, the elements of the neural network regulating speed (the nondirectional component of velocity) remain uncertain. Here, we used a custom made magnetic resonance (MR)‐compatible arm movement tracking system to capture the real kinematics of the arm movements while measuring brain activation with functional magnetic resonance imaging to reveal areas in the human brain in which BOLD‐activation covaries with the speed of arm movements. We found significant activation in multiple cortical and subcortical brain regions positively correlated with endpoint (wrist) speed (speed‐related activation), including contralateral premotor cortex (PMC), supplementary motor area (SMA), thalamus (putative VL/VA nuclei), and bilateral putamen. The hand and arm regions of primary sensorimotor cortex (SMC) and a posterior region of thalamus were significantly activated by reach movements but showed a more binary response characteristics (movement present or absent) than with continuously varying speed. Moreover, a subregion of contralateral SMA also showed binary movement activation but no speed‐related BOLD‐activation. Effect size analysis revealed bilateral putamen as the most speed‐specific region among the speed‐related clusters whereas primary SMC showed the strongest specificity for movement versus non‐movement discrimination, independent of speed variations. The results reveal a network of multiple cortical and subcortical brain regions that are involved in speed regulation among which putamen, anterior thalamus, and PMC show highest specificity to speed, suggesting a basal‐ganglia‐thalamo‐cortical loop for speed regulation.
Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Movement analysis of infants’ body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.
Music in newspapers: interdisciplinary opportunities and data-related challenges

Over the past millennia, music has actively been performed and listened to by mankind, thus also playing an important role in establishing sociocultural identities that have evolved over time. In parallel, for many centuries, newspapers played an important role in informing society on a regular and frequent basis on topics noteworthy at that time. Therefore, in retrospect, these newspapers offer windows into historic topics of sociocultural significance, including cultural and musical life. Thanks to ongoing digitization efforts, large-scale newspaper corpora now have become broadly available and accessible. Taking the digitized historical newspaper collection of the National Library of The Netherlands as an example, in this paper, we discuss how considering music-related mentionings in newspapers can enable potential new research directions and questions. We discuss open syntactic and semantic data-related technical challenges when analyzing music-related mentionings in digitized historical newspaper collections. Finally, we discuss how successful detection of music-related mentionings can also benefit engagement of non-scholarly end users, concluding with an invitation to the interdisciplinary research community to actively contribute to the given use case.
Hybrid Feature Selection Method using Fisher's Discriminate Ratio to Classify Internet Traffic Data

Identification and classification of network applications is a key area of network management and network security. This is due to exponential growth of the Internet users, which in turn increases the growth of the internet traffic. As Internet grows, different types of Internet traffic generated over the network also grow. This recommends proposing new methods to identify and classify the network traffic. In this paper, we proposed to embed Fishers's Discriminate Ratio (FDR) with Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and Plus L Minus R feature selection methods to analyze and classify the Internet traffic. To evaluate the proposed method, we used publicly available KDDcup99 dataset. Experimental results proved that the proposed embedding method will outperform compare to the existing methods.
Potential use of compounds from sea cucumbers as MDM2 and CXCR4 inhibitors to control cancer cell growth

Ineffectiveness of cancer therapy may originate in the incompatibility of the treatment with various mutations in the cancer cells. Finding novel anticancer treatments that work efficiently for varying types of cancer cells remains challenging. Previous studies have identified that compounds in sea cucumbers are capable of inhibiting the growth of cancer cells and inducing apoptosis. However, information on the underlying mechanisms controlling cancer cell growth at a molecular level remains limited. The current study analyzed the potential of colochiroside A, ds-echinoside A, philinopside E, sphingosine and stichoposide C as inhibitors for anticancer target proteins, including mouse double minute 2 homolog (MDM2) and C-X-C chemokine receptor type 4 (CXCR4). Inhibition of MDM2 triggers apoptosis through regulation of tumor protein 53 and CXCR4 inhibition may prevent cancer cell proliferation and growth by affecting the Janus kinase 2/3 signal transducer and activator of transcription signaling pathway and protein tyrosine kinase 2. The results of a binding affinity analysis using molecular docking revealed that philinopside E and ds-echinoside A may inhibit MDM2 and CXCR4. The data suggested that these active compounds may be promising inhibitors of cell growth by binding to two targets simultaneously. Furthermore, stichoposide C and colochiroside A were predicted to inhibit CXCR4. Additional research is needed to validate the in vitro activity of the aforementioned compounds.