Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bone-Forming Properties of Periosteum-Derived Cells Differ Between Harvest Sites

The development of alternatives for autologous bone grafts is a major focus of bone tissue engineering. To produce living bone-forming implants, skeletal stem and progenitor cells (SSPCs) are envisioned as key ingredients. SSPCs can be obtained from different tissues including bone marrow, adipose tissue, dental pulp, and periosteum. Human periosteum-derived cells (hPDCs) exhibit progenitor cell characteristics and have well-documented in vivo bone formation potency. Here, we have characterized and compared hPDCs derived from tibia with craniofacial hPDCs, from maxilla and mandible, respectively, each representing a potential source for cell-based tissue engineered implants for craniofacial applications. Maxilla and mandible-derived hPDCs display similar growth curves as tibial hPDCs, with equal trilineage differentiation potential toward chondrogenic, osteogenic, and adipogenic cells. These craniofacial hPDCs are positive for SSPC-markers CD73, CD164, and Podoplanin (PDPN), and negative for CD146, hematopoietic and endothelial lineage markers. Bulk RNA-sequencing identified genes that are differentially expressed between the three sources of hPDC. In particular, differential expression was found for genes of the HOX and DLX family, for SOX9 and genes involved in skeletal system development. The in vivo bone formation, 8 weeks after ectopic implantation in nude mice, was observed in constructs seeded with tibial and mandibular hPDCs. Taken together, we provide evidence that hPDCs show different profiles and properties according to their anatomical origin, and that craniofacial hPDCs are potential sources for cell-based bone tissue engineering strategies. The mandible-derived hPDCs display - both in vitro and in vivo - chondrogenic and osteogenic differentiation potential, which supports their future testing for use in craniofacial bone regeneration applications.
Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.
Towards recognizing perceived level of understanding for online lectures using earables: poster abstract

We envision that our earbuds recognize how much we understand learning materials while taking online lectures for effective learning and teaching, e.g., to pinpoint the part for which we need to put more effort to learn. To this end, we explore the feasibility of recognizing the perceived level of understanding of online learners based on IMU sensor data from earbuds. We present an exploratory study to identify head-related behaviors that can be detected by in-ear IMU data, which are associated with the perceived level of understanding for online lectures.
Effect of irrigation regime and nitrogen fertilization on bulb yield and water use of onion (Allium cepa)

A field experiment was conducted during the spring seasons of 1991 and 1992 at New Delhi to study the effect of irrigation regime and nitrogen on water use and yield of 'Pusa Madhavi' onion (Allium cepa L.). Irrigations at 1.25 irrigation water: cumulative pan-evaporation (IW : CPE) ratio (14-16 irrigations) and I.75IW: CPE ratio (19-21 irrigations) were at par, giving 69.6 and 72.8% higher bulb yield respectively than 0.75 IW : CPE ratio (9-11 irrigations). The seasonal consumptive use was 500, 636 and 784 mm with irrigation at 0.75, 1.25 and 1.75IW: CPE ratio respectively. The water-use efficiency was maximum (50.3 kg/ha/mm) at 1.25 IW : CPE ratio. N application up to 120 kg/ha gave significantly higher dry matter/bulb. 100-bulb weight, bulb diameter and bulb yield than 60kg/ha. The net return/Re investment on N was Rs 12.4 at the optimum level of 131.4 kg N/ha. Increase in N level from 0 to 120 kg/ha increased the consumptive use and water-use efficiency from 560 to 628 mm and 38.1 to 52.1 kg/ha/mm respectively.
How robust are malware detection models for Android smartphones against adversarial attacks?: poster abstract

Android-based smartphones and IoT devices have grown at an exponential rate in the last decade. Meanwhile, malicious applications have also increased dramatically, which threaten the Android ecosystem. The anti-malware community has proposed data mining based malware detection models which have shown encouraging results. However, these detection models are vulnerable to adversarial attacks. In this work, we first acted as an adversary and performed adversarial attacks on eight different malware detection models. We found all the eight detection models vulnerable to adversarial attacks and fooling rate of more than 90% was achieved against each of them. We also propose defence against these attacks by adversarial retraining and accomplish encouraging results to improve the overall robustness of malware detection models.
MDLdroidLite: A Release-and-Inhibit Control Approach to Resource-Efficient Deep Neural Networks on Mobile Devices

Mobile deep learning (MDL) has emerged as a privacy-preserving learning paradigm for mobile devices. This paradigm offers unique features such as privacy preservation, continual learning and low-latency inference to the building of personal mobile sensing applications. However, squeezing Deep Learning to mobile devices is extremely challenging due to resource constraint. Traditional Deep Neural Networks (DNNs) are usually over-parametered, hence incurring huge resource overhead for on-device learning. In this paper, we present a novel on-device deep learning framework named MDLdroidLite that transforms traditional DNNs into resource-efficient model structures for on-device learning. To minimize resource overhead, we propose a novel release-and-inhibit control (RIC) approach based on Model Predictive Control theory to efficiently grow DNNs from tiny to backbone. We also design a gate-based fast adaptation mechanism for channel-level knowledge transformation to quickly adapt new-born neurons with existing neurons, enabling safe parameter adaptation and fast convergence for on-device training. Our evaluations show that MDLdroidLite boosts on-device training on various PMS datasets with 28× to 50× less model parameters, 4× to 10× less floating number operations than the state-of-the-art model structures while keeping the same accuracy level.
Detecting replay attacks against industrial robots via power fingerprinting

Industrial robots have been shown to suffer from replay attacks, via which adversaries not only manipulate the robot operation by downloading malicious code, but also prevent the detection of this manipulation by replaying recorded (and normal) movement data to the monitoring system. To protect industrial robots from replay attacks, we design a novel intrusion detection system using the power fingerprint of robots, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant electrical power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information --- a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluated PIDS using three models of robots from different vendors --- i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots --- with over 2, 000 operation cycles. The experimental results show that PIDS detects replay attacks with an average rate of 96.5% (up to 99.9%) and a 0.1s latency.
Structure-Function of Neuronal Nicotinic Acetylcholine Receptor Inhibitors Derived From Natural Toxins

Neuronal nicotinic acetylcholine receptors (nAChRs) are prototypical cation-selective, ligand-gated ion channels that mediate fast neurotransmission in the central and peripheral nervous systems. nAChRs are involved in a range of physiological and pathological functions and hence are important therapeutic targets. Their subunit homology and diverse pentameric assembly contribute to their challenging pharmacology and limit their drug development potential. Toxins produced by an extensive range of algae, plants and animals target nAChRs, with many proving pivotal in elucidating receptor pharmacology and biochemistry, as well as providing templates for structure-based drug design. The crystal structures of these toxins with diverse chemical profiles in complex with acetylcholine binding protein (AChBP), a soluble homolog of the extracellular ligand-binding domain of the nAChRs and more recently the extracellular domain of human α9 nAChRs, have been reported. These studies have shed light on the diverse molecular mechanisms of ligand-binding at neuronal nAChR subtypes and uncovered critical insights useful for rational drug design. This review provides a comprehensive overview and perspectives obtained from structure and function studies of diverse plant and animal toxins and their associated inhibitory mechanisms at neuronal nAChRs.
Software design as story telling: reflecting on the work of Italo Calvino

Are we really writing software? What do software writers have in common with other professional writers? What can we software developers learn from professional writers? This paper proposes a reflection on such topics using as a reference the book “Six Memos for the Next Millennium”, a posthumous essay by the Italian novelist, editor, and literary critic Italo Calvino. A comparison is drawn between such work and the current principles ruling how software should be written and developed, and a claim is made that this is an area worth further exploration.
Zero-wire: a deterministic and low-latency wireless bus through symbol-synchronous transmission of optical signals

The performance dichotomy between wired and wireless networks for the Internet of Things primarily arises from the inherent complexity and inefficiency of networking abstractions such as routing, medium access control and store-and-forward packet switching. This paper aims to enable a new class of latency-sensitive applications by breaking all three of these abstractions to deliver a performance envelope that resembles that of a wired bus in terms of deterministic latency and throughput. The essence of this approach is a novel networking paradigm for optical wireless communication, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmit LED-based signals. This paper realises the paradigm within a platform called Zero-Wire and evaluates it on a 25-node testbed under laboratory conditions. Key end-to-end performance measurements on this physical prototype include 19 kbps of contention-agnostic goodput, interface-level latency under 1 ms for two-byte frames across four hops, jitter on the order of 10s of μs, and a base reliability of 99%. These first results indicate a bright future for the under-explored area of optical wireless mesh networks in delivering ubiquitous connectivity through a simple and low-cost physical layer.
TrustICT: an efficient trusted interaction interface between isolated execution domains on ARM multi-core processors

The Trusted Execution Environment (TEE) has been widely used to protect the security-sensitive sensing systems on Internet-of-Thing (IoT) devices. In the TEE systems, the execution environment is securely divided into a normal domain and a higher privileged secure domain which executing sensing systems through hardware. One common way to achieve the protection is implementing the sensitive functions of the sensing systems as trusted applications (TAs) in the well-isolated secure domain. Users in rich OS have to call TAs through the client applications (CAs), and the invocations must pass through the rich OS kernel. However, an untrusted rich OS may launch man-in-the-middle attacks on the communication between the CAs and TAs, and the misuse of cross-domain communication channel is becoming one severe threat on the TEE systems. In this paper, we develop a defense system named TrustICT to construct a lightweight trusted interaction channel between CAs and TAs without modifying existing TEE architecture. The main idea is to block attacks on the cross-domain interactions via dynamically setting the access permission of domain-shared memory, locking it from kernel mode and unlocking it only to legal CAs in the user mode. Particularly, we propose a multi-core scheduling strategy to defeat potential attacks from all privileged cores. Compared to existing cryptography-based methods, TrustICT dramatically reduces the system overhead since it does not require time-consuming cryptographic computation or sophisticated real-time kernel protection. We implement a prototype of TrustICT on a Freescale i.MX6Quad platform with the OP-TEE software system and evaluate its impacts on rich OS and the cross-domain transactions.
Toll-Like Receptor 4 Signaling and Drug Addiction

The emphasis of neuronal alterations and adaptations have long been the main focus of the studies of the mechanistic underpinnings of drug addiction. Recent studies have begun to appreciate the role of innate immune system, especially toll-like receptor 4 (TLR4) signaling in drug reward-associated behaviors and physiology. Drugs like opioids, alcohol and psychostimulants activate TLR4 signaling and subsequently induce proinflammatory responses, which in turn contributes to the development of drug addiction. Inhibition of TLR4 or its downstream effectors attenuated the reinforcing effects of opioids, alcohol and psychostimulants, and this effect is also involved in the withdrawal and relapse-like behaviors of different drug classes. However, conflicting results also argue that TLR4-related immune response may play a minimal part in drug addiction. This review discussed the preclinical evidence that whether TLR4 signaling is involved in multiple drug classes action and the possible mechanisms underlying this effect. Moreover, clinical studies which examined the potential efficacy of immune-base pharmacotherapies in treating drug addiction are also discussed.
Neuroplex: learning to detect complex events in sensor networks through knowledge injection

Despite the remarkable success in a broad set of sensing applications, state-of-the-art deep learning techniques struggle with complex reasoning tasks across a distributed set of sensors. Unlike recognizing transient complex activities (e.g., human activities such as walking or running) from a single sensor, detecting more complex events with larger spatial and temporal dependencies across multiple sensors is extremely difficult, e.g., utilizing a hospital's sensor network to detect whether a nurse is following a sanitary protocol as they traverse from patient to patient. Training a more complicated model requires a larger amount of data-which is unrealistic considering complex events rarely happen in nature. Moreover, neural networks struggle with reasoning about serial, aperiodic events separated by large quantities in the spatial-temporal dimensions. We propose Neuroplex, a neural-symbolic framework that learns to perform complex reasoning on raw sensory data with the help of high-level, injected human knowledge. Neuroplex decomposes the entire complex learning space into explicit perception and reasoning layers, i.e., by maintaining neural networks to perform low-level perception tasks and neurally reconstructed reasoning models to perform high-level, explainable reasoning. After training the neurally reconstructed reasoning model using human knowledge, Neuroplex allows effective end-to-end training of perception models with an additional semantic loss using only sparse, high-level annotations. Our experiments and evaluation show that Neuroplex is capable of learning to efficiently and effectively detect complex events-which cannot be handled by state-of-the-art neural network models. During the training, Neuroplex not only reduces data annotation requirements by 100x, but also significantly speeds up the learning process for complex event detection by 4x.
DynamIPs: analyzing address assignment practices in IPv4 and IPv6

IP addresses are commonly used to identify hosts or properties of hosts. The address assigned to a host may change, however, and the extent to which these changes occur in time as well as in the address space is currently unknown, especially in IPv6. In this work, we take a first step towards understanding the dynamics of IPv6 address assignments in various networks around the world and how they relate to IPv4 dynamics. We present fine-grained observations of dynamics using data collected from over 3,000 RIPE Atlas probes in dual-stack networks. RIPE Atlas probes in these networks report both their IPv4 and their IPv6 address, allowing us to track changes over time and in the address space. To corroborate and extend our findings, we also use a dataset containing 32.7 billion IPv4 and IPv6 address associations observed by a major CDN. Our investigation of temporal dynamics with these datasets shows that IPv6 assignments have longer durations than IPv4 assignments---often remaining stable for months---thereby allowing the possibility of long-term fingerprinting of IPv6 subscribers. Our analysis of spatial dynamics reveals IPv6 address-assignment patterns that shed light on the size of the address pools network operators use in domestic networks, and provides preliminary results on the size of the prefixes delegated to home networks. Our observations can benefit many applications, including host reputation systems, active probing methods, and mechanisms for privacy preservation.
Young Patients With Persistent and Complex Care Needs Require an Integrated Care Approach: Baseline Findings From the Multicenter Youth Flexible ACT Study

Background: The Multicenter Youth Flexible ACT Study is an ongoing observational prospective cohort study that examines the effects of Youth Flexible ACT (Assertive Community Treatment) on young people with complex care needs who are difficult to engage in traditional (office-based) mental health services. However, a clear and detailed description of this patient group is lacking. In the current paper, we present baseline characteristics and psychosocial outcomes of the Youth Flexible ACT target group and explore the existence of underlying specific patient subgroups. Methods: Sixteen Youth Flexible ACT teams from seven mental healthcare institutes in the Netherlands participated in the study. Research participants were monitored for 18 months and administered questionnaires measuring psychiatric- and social functioning every 6 months, yielding four measurements. Baseline data were obtained from 199 adolescents, their mental health workers, and parents/carers. Latent Class Analysis based on HoNOSCA scores (measuring psychosocial and daily functioning) was conducted to identify underlying subgroups. Results: The target group of Youth Flexible ACT mainly consisted of patients older than 15 years of age with a history of (specialized) mental healthcare. They face many complex problems, including trauma; developmental, mood, and anxiety disorders; and problems with school attendance, family life, and peer relationships. Other frequently reported difficulties were substance misuse, the involvement of the legal system or police, problems with intellectual functioning, and personal finance. Patients were classified into four distinct classes: the “internalizing,” “externalizing,” “non-specific,” and the “overly impulsive” subgroup. Each subgroup had its unique pattern of difficulties and focus, respectively, high levels of depression and anxiety, disruptive behavior, unspecific difficulties, and substance misuse. Conclusions: As expected, patients in Youth Flexible ACT experienced many severe problems, rendering them vulnerable to fragmented and, thus, ineffective care. Our findings underscore the need for an integrated care approach with a multidisciplinary team of skilled professionals that can bridge these wide-ranging psychosocial problems, as each class of participants experienced a different set of difficulties. Youth Flexible ACT teams need to adjust their care services accordingly.
Post-exercise Effects and Long-Term Training Adaptations of Hormone Sensitive Lipase Lipolysis Induced by High-Intensity Interval Training in Adipose Tissue of Mice

Although studies have proven that high-intensity interval training (HIIT) shows a comparable effect to moderate-intensity continuous training (MICT) on reducing body fat, especially visceral fat, the mechanism is still unclear. Since MICT consumes more fat during exercise, the mechanism of HIIT weight loss may be related to post-exercise effects, long-term adaptive changes, and hormone sensitive lipase (HSL). The objective of this study was to compare the post-effects of acute exercise, long-term adaptive changes on HSL activity, and catecholamine-induced lipolysis between HIIT and MICT. Following a 14-week high-fat diet (HFD), obese female C57Bl/6 mice were divided into acute exercise groups (one time training, sacrificed at rest and 0, 1, and 12 h after exercise, n = 49), -L groups (12-week long-term training, 12-h fasting, n = 21), and -C groups (12-week training, primary adipocytes were isolated and stimulated by catecholamine in vitro, n = 18). MICT or HIIT treadmill protocols (running distance matched) were carried out during training. Comparison of acute exercise effects by two-way ANOVA showed no time × group interaction effect, however, a significant increase in HSL-Ser563 (at 0 and 1 h) and Ser660 phosphorylation (at 0, 1, and 12 h) in inguinal (subcutaneous) fat was only observed in HIIT mice (p < 0.05 vs. rest), but not in MICT mice. The periuterine (visceral) fat HSL expression and phosphorylation of HIIT mice was similar to or lower than MICT mice. After long-term training, 12-h fasting significantly increased periuterine fat Ser563 phosphorylation in HIIT mice (p < 0.05), but there was no change in MICT mice. Under stimulation of catecholamine in vitro, isolated primary adipocytes from periuterine fat of long-term HIIT mice showed a higher Ser563 increase than that found in MICT mice (p < 0.05). The quantity of triglyceride (TG) lipid bonds (representing lipolysis level) was significantly lower after HIIT than MICT (p < 0.05). The results indicate that (1) acute HIIT can induce an increase of HSL phosphorylation in subcutaneous fat lasting at least 12 h, implying longer post-exercise lipolysis than MICT and (2) long-time HIIT has a better effect on improving catecholamine resistance of visceral adipocytes caused by a HFD, which allows fat to be mobilized more easily when stimulated.