Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences in Body Composition in Older People from Two Regions of Mexico: Implications for Diagnoses of Sarcopenia and Sarcopenic Obesity

Background Mexico is a country that is rich in ethnicity and cultural diversity, divided into three well-defined socioeconomic, ecological, and epidemiological areas. However, we do not know the influence that these factors may have on body composition. Therefore, this study was designed to assess body composition and compare appendicular skeletal muscle mass (ASM) in older people from two areas of the country. Methods This is a cross-sectional study that included 430 subjects ≥60 years of age from northwestern and central Mexico. Body composition, including ASM, was measured by dual-energy X-ray absorptiometry, while anthropometry, handgrip strength, demographic variables, health status/chronic conditions, and energy expenditure data were all included. Results Men and women from the northwestern region had 5.9 kg and 3.8 kg more body fat, respectively, and 3.9 kg more as a group than their counterparts from central Mexico (p ≤ 0.0001). While there were no significant differences across gender or region in terms of ASM, the older subjects from central Mexico had a significantly higher ASM index (ASMI) than the sample from the northwest. When ASM was adjusted for age, body weight, height, health status/chronic conditions, estimated energy expenditure, and demographic variables, the subjects from central Mexico had significantly higher adjusted mean values of ASM and ASMI than their counterparts from the northwest. Conclusion Older people from two regions of Mexico had significantly different estimates of body composition. Our findings highlight the importance of regionalizing estimates of ASM and ASMI if they are to be used for diagnostic purposes. It is also important to emphasize that appendicular skeletal muscle mass, or the ASM index, should be adjusted for other associated biological variables.
Victims of war-Psychoendocrine evidence for the impact of traumatic stress on psychological well-being of adolescents growing up during the Israeli-Palestinian conflict.

Violent conflicts are severe traumatic stressors with detrimental effects on physical and mental health, with children and adolescents being particularly at risk. For the hypothalamic-pituitary-adrenal (HPA) axis, characteristic patterns of dysregulation in trauma-exposed individuals have been shown. This study set out to investigate self-reported mental well-being in Palestinian adolescents growing up during the Israeli-Palestinian conflict. Hair cortisol concentrations (HCC) as a psychoendocrine marker for long-term HPA axis aberrations along with the potential protective factor sense of coherence (SoC; i.e., the global mindset to interpret the world and emerging stressors as comprehensible, manageable, and meaningful) were examined. Between 2014 and 2016, posttraumatic stress disorder (PTSD), depression, anxiety, HCC, and SoC were examined in 233 adolescents aged 11 to 16 from the West Bank. More than half of the participants reported trauma exposure, with 40% fulfilling the criteria of a preliminary PTSD diagnosis and a high prevalence of anxiety and depression. HCC was significantly elevated in the PTSD subgroup compared to the subgroup not exposed to any traumatic events (p = 0.046), with trauma-exposed individuals in between. HCC was further associated with typical sequelae of traumatic stress. Notably, SoC was inversely related to self-reported psychopathology, as well as to HCC in the trauma group. The results illustrate the situation of adolescents exposed to chronic traumatic stress and extend the literature on aberrant HPA axis functioning under such conditions. They also point out a central role of SoC, which may imply new strategies to aid individuals exposed to ongoing conflicts.
Prenatal exposure to an environmentally relevant mixture of Canadian Arctic contaminants decreases male reproductive function in an aging rat model

Abstract Elevated levels of organochlorines (OC) have been reported in Inuit populations in the Arctic. We hypothesized that prenatal exposure to a Canadian Arctic OC mixture adversely affects male reproductive function and health with age. Sprague–Dawley female rats (F0) were gavaged with an environmentally relevant concentration of an Arctic OC mixture or corn oil (Control) during mating with untreated males until parturition (F1 litters). After postnatal day (PND) 90, the weights of the OC F1 males differed dramatically relative to Controls (P<0.05; n=10) and they exhibited respiratory distress. Except for possible thinning of the alveolar barrier, histological observation of the lungs revealed no apparent pathology to explain the respiratory distress. At PND 365, OC F1 males had reduced relative reproductive organ weights and lower sperm quality than Controls (P<0.05). At PND 90, OC F1 males were subfertile (P<0.05), but were infertile at PND 365. In conclusion, environmentally relevant prenatal OC exposure reduced reproductive function and health in aging male rats, providing new insight into the effects of early-life exposures to these contaminants.
Global patterns of STR sequence variation: Sequencing the CEPH human genome diversity panel for 58 forensic STRs using the Illumina ForenSeq DNA Signature Prep Kit

The 944 individuals of the CEPH human genome diversity panel (HGDP–CEPH), a standard sample set of 51 globally distributed populations, were sequenced using the Illumina ForenSeq™ DNA Signature Prep Kit. The ForenSeq™ system is a single multiplex for the MiSeq/FGx™ massively parallel sequencing instrument, comprising: amelogenin, 27 autosomal STRs, 24 Y‐STRs, 7 X‐STRs, and 94 SNPforID+Kiddlab autosomal ID‐SNPs (plus optionally detected ancestry and phenotyping SNP sets). We report in detail the patterns of sequence variation observed in the repeat regions of the 58 forensic STR loci typed by the ForenSeq™ system. Sequence alleles were characterized and repeat region structures annotated by aligning the ForenSeq™ sequence output to the latest GRCh38 human reference sequence, necessitating the reversal and re‐alignment of STR allele sequences reported by the Forenseq™ system in 20 of 58 STRs (plus the reverse alleles in two Y‐STRs with duplicated‐inverted repeat regions). Individual population sample sizes of the HGDP–CEPH panel do not allow reliable inferences to be made about levels of genetic variability in low frequency STR alleles‐where particular sequence variants are found in only a few individuals; but we assessed the occurrence of both population‐specific sequence variants and singleton observations; finding each of these in a sizeable proportion of HGDP–CEPH samples, with consequences for planning the co‐ordinated compilation of sequence variation on a much larger scale than was required before by forensic laboratories now adopting massively parallel sequencing.
DeepMag

Many important physical phenomena involve subtle signals that are difficult to observe with the unaided eye, yet visualizing them can be very informative. Current motion magnification techniques can reveal these small temporal variations in video, but require precise prior knowledge about the target signal, and cannot deal with interference motions at a similar frequency. We present DeepMag, an end-to-end deep neural video-processing framework based on gradient ascent that enables automated magnification of subtle color and motion signals from a specific source, even in the presence of large motions of various velocities. The advantages of DeepMag are highlighted via the task of video-based physiological visualization. Through systematic quantitative and qualitative evaluation of the approach on videos with different levels of head motion, we compare the magnification of pulse and respiration to existing state-of-the-art methods. Our method produces magnified videos with substantially fewer artifacts and blurring whilst magnifying the physiological changes by a similar degree.
Densely Connected Convolutional Networks for Speech Recognition

This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition. DenseN-ets are very deep, compact convolutional neural networks, which have demonstrated incredible improvements over the state-of-the-art results on several data sets in computer vision. Our experimental results show that DenseNet can be used for AM significantly outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore, results on Wall Street Journal revealed that with only a half of the training data DenseNet was able to outperform other models trained with the full data set by a large margin.
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning

We introduce Error Forward-Propagation, a biologically plausible mechanism to propagate error feedback forward through the network. Architectural constraints on connectivity are virtually eliminated for error feedback in the brain; systematic backward connectivity is not used or needed to deliver error feedback. Feedback as a means of assigning credit to neurons earlier in the forward pathway for their contribution to the final output is thought to be used in learning in the brain. How the brain solves the credit assignment problem is unclear. In machine learning, error backpropagation is a highly successful mechanism for credit assignment in deep multilayered networks. Backpropagation requires symmetric reciprocal connectivity for every neuron. From a biological perspective, there is no evidence of such an architectural constraint, which makes backpropagation implausible for learning in the brain. This architectural constraint is reduced with the use of random feedback weights. Models using random feedback weights require backward connectivity patterns for every neuron, but avoid symmetric weights and reciprocal connections. In this paper, we practically remove this architectural constraint, requiring only a backward loop connection for effective error feedback. We propose reusing the forward connections to deliver the error feedback by feeding the outputs into the input receiving layer. This mechanism, Error Forward-Propagation, is a plausible basis for how error feedback occurs deep in the brain independent of and yet in support of the functionality underlying intricate network architectures. We show experimentally that recurrent neural networks with two and three hidden layers can be trained using Error Forward-Propagation on the MNIST and Fashion MNIST datasets, achieving $1.90\%$ and $11\%$ generalization errors respectively.
BAMM at the court of false equivalency: A response to Meyer and Wiens

The software program BAMM has been widely used to study rates of speciation, extinction, and phenotypic evolution on phylogenetic trees. The program implements a model‐based clustering algorithm to identify clades that share common macroevolutionary rate dynamics and to estimate parameters. A recent simulation study by Meyer and Wiens (M&W) argued that (1) a simple inference framework (MS) performs much better than BAMM, and (2) evolutionary rates inferred with BAMM are poorly correlated with true rates. I address two statistical concerns with their assessment that affect the generality of their conclusions. These considerations are not specific to BAMM and apply to other methods for estimating parameters from empirical data where the true grouping structure of the data is unknown. M&W constrain roughly half of the parameters in their MS analyses to their true values, but BAMM is given no such information and must estimate all parameters from the data. This information disparity results in a substantial degrees of freedom advantage for the MS estimators. When both methods are given equivalent information, BAMM outperforms the MS estimators.
Impulse model-based differential expression analysis of time course sequencing data

The global gene expression trajectories of cellular systems in response to developmental or environmental stimuli often follow the prototypic single-pulse or state-transition patterns which can be modeled with the impulse model. Here we combine the continuous impulse expression model with a sequencing data noise model in ImpulseDE2, a differential expression algorithm for time course sequencing experiments such as RNA-seq, ATAC-seq and ChIP-seq. We show that ImpulseDE2 outperforms currently used differential expression algorithms on data sets with sufficiently many sampled time points. ImpulseDE2 is capable of differentiating between transiently and monotonously changing expression trajectories. This classification separates genes which are responsible for the initial and final cell state phenotypes from genes which drive or are driven by the cell state transition and identifies down-regulation of oxidative-phosphorylation as a molecular signature which can drive human embryonic stem cell differentiation.
Live Video Comment Generation Based on Surrounding Frames and Live Comments

In this paper, we propose the task of live comment generation. Live comments are a new form of comments on videos, which can be regarded as a mixture of comments and chats. A high-quality live comment should be not only relevant to the video, but also interactive with other users. In this work, we first construct a new dataset for live comment generation. Then, we propose a novel end-to-end model to generate the human-like live comments by referring to the video and the other users' comments. Finally, we evaluate our model on the constructed dataset. Experimental results show that our method can significantly outperform the baselines.
Reliability Studies of Excimer Laser-Ablated Microvias Below 5 Micron Diameter in Dry Film Polymer Dielectrics for Next Generation, Panel-Scale 2.5D Interposer RDL

This paper demonstrates the thermal cycling reliability of 4 µm diameter microvias using an ultra-thin dry film ABF, a non-photosensitive dielectric material. Such via scaling in conjunction with line scaling to achieve silicon BEOL-like RDL densities is required for the next generation of interposers. The dry film dielectric, ABF, is an epoxy-silica filler based material. This is an ideal material for a double-sided, panel-scale compatible electroless copper seed metal deposition process. The test vehicle consisting of daisy chain structures used for the reliability studies was fabricated by an excimer laser dual damascene process. The trenches for the daisy chain line and pad structures were first formed in a novel dry film ABF material. Microvias with diameter of 4 µm were then ablated in the film. The stepper system of the excimer laser allowed sub-micron alignment accuracy for the via structures. Two different capture pad structures were used to land the microvias. The 4 µm diameter microvias were landed in 4 µm width and 5 µm width capture pad structures. A panel-based electroless copper seed metal deposition process was used to form a conductive layer on the polymer film. The desmear process during the electroless deposition increased the microvia diameter to 5 µm and the capture pad widths to 5 µm and 6 µm respectively. The structures were filled by conventional electrolytic plating process and overburdened to a thickness of 5 µm. The panel-scalable Surface Planar DFS8910 tool was used to fly-cut 1 µm deep into the polymer and achieve the final circuitry. The challenges of this mechanical fly-cut process with filler based ABF materials and removal of complete electroless copper seed from the polymer anchors will be discussed. The resistance of the daisy chain structures containing an array of 400 microvias was measured after the planarization process. A yield of 88 % was achieved on a 300 mm wafer with 4 µm microvias and 5 µm capture pad structures with excellent daisy chain resistance. The samples were then exposed to: (A) 1000 liquid-to-liquid thermal shock cycles with a dwell time of 5 mins each at 125 °C and -55 °C and (B) 1000 air-to-air thermal cycles from -55 °C to 125 °C with a dwell time of 15 mins at each temperature node and a total cycle time of 1 hour. The resistances after thermal cycling tests showed an average increase of < 5 %, well within the 10 % resistance change criteria.
The Utilization of Virtual Reality as a System of Systems Research Tool

Virtual reality (VR) has become a very popular gaming platform in recent years, but has not been used as much in the research domain. Several technologies used to create virtual environments can also be used to assist in research. For example, recent developments in VR allow for easier integration of humans in the loop for control and manipulation of complex, multi-modal system of systems. In this paper we show some of the methods in which a virtually emulated system can be integrated into an artificial intelligence (AI) based system of systems. We demonstrate how this integration allows the reseacher to more easily understand and analyse the inter-system dynamics, and to improve performance using post-simulation visualization, real-time simulation interaction, hardware emulation, and physical object tracking. The results of using virtual reality as a simulation environment shows the usefulness of the tool to system of systems researchers.
Importance of Information technologies and systems (ITS) in the traceability of the supply chain

In front of numerous challenges been imperative by the globalization of the trade, the uncertainty of the environment, the new requirements of the consumers, He is essential for any company to be equipped with tools, ways and effective technologies allowing them to identify the information, to record them, to exchange them and to share them with the partners. Because today the paper traceability is not any more a reliable option according to [1]. The traceability mobilizes an outfit of systems and technological devices. Indeed, the objective of this paper is double: at first, raise a current situation of the various technologies and the information systems used in an approach of traceability. Then, identify them role in the traceability of the supply chain.
Localization of double-bond positions in lipids by tandem mass spectrometry succeeding high-performance liquid chromatography with post-column derivatization.

RATIONALE Lipids are a group of biomolecules known for their importance in a broad variety of biological tasks, which is attributed to their diversity of molecular structures. The exact position of a lipids' double bond is affecting its biological properties. Therefore, the determination of its position is highly important. Nevertheless, the unambiguous assignment is still a challenging task and requires the development of new approaches. METHODS A novel method for pinpointing double bond positions in lipids is presented, based on the photochemical Paternὸ Büchi reaction. Therefore, the reaction was performed using a post-column derivatization by UV light irradiation (254 nm) and an additional constant acetone flow. The acetone adduct was formed due to the reaction of acetone with the double bond resulting in an oxetane. Subsequent fragmentation experiments generated diagnostic fragments allowing the localization of double bond positions. RESULTS Intending online hyphenation with HPLC-tandem mass spectrometry, the reaction was optimized regarding parameters such as reaction time and the influence of trace oxygen as well as solvents. An additional separation of the reaction products by means of HPLC enabled further characterization of the chemical structures formed by UV irradiation. To demonstrate the applicability of the developed method, a mixture of the constitutional phospholipid isomers bis (monooleoylglycero) phosphate and dioleyl-phosphatidylglycerol was analysed regarding their double bond positions, and lipids from an extract of the green alga Chlamydomonas reinhardtii were investigated. CONCLUSIONS The successful hyphenation of HPLC-tandem mass spectrometry with the online reaction was demonstrated for localization of double bonds in single lipids, lipid mixtures, and complex biological samples. Matrix effects can be reduced and further structural information, for example in case of constitutional isomers, can be obtained. This opens up new avenues for pinpointing lipid double bond positions even in complex samples.
An investigation of a deep learning based malware detection system

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and an False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.
Meropenem Combined with Ciprofloxacin Combats Hypermutable Pseudomonas aeruginosa from Respiratory Infections of Cystic Fibrosis Patients

Hypermutable Pseudomonas aeruginosa organisms are prevalent in chronic respiratory infections and have been associated with reduced lung function in cystic fibrosis (CF); these isolates can become resistant to all antibiotics in monotherapy. This study aimed to evaluate the time course of bacterial killing and resistance of meropenem and ciprofloxacin in combination against hypermutable and nonhypermutable P. aeruginosa. ABSTRACT Hypermutable Pseudomonas aeruginosa organisms are prevalent in chronic respiratory infections and have been associated with reduced lung function in cystic fibrosis (CF); these isolates can become resistant to all antibiotics in monotherapy. This study aimed to evaluate the time course of bacterial killing and resistance of meropenem and ciprofloxacin in combination against hypermutable and nonhypermutable P. aeruginosa. Static concentration time-kill experiments over 72 h assessed meropenem and ciprofloxacin in mono- and combination therapies against PAO1 (nonhypermutable), PAOΔmutS (hypermutable), and hypermutable isolates CW8, CW35, and CW44 obtained from CF patients with chronic respiratory infections. Meropenem (1 or 2 g every 8 h [q8h] as 3-h infusions and 3 g/day as a continuous infusion) and ciprofloxacin (400 mg q8h as 1-h infusions) in monotherapies and combinations were further evaluated in an 8-day hollow-fiber infection model study (HFIM) against CW44. Concentration-time profiles in lung epithelial lining fluid reflecting the pharmacokinetics in CF patients were simulated and counts of total and resistant bacteria determined. All data were analyzed by mechanism-based modeling (MBM). In the HFIM, all monotherapies resulted in rapid regrowth with resistance at 48 h. The maximum daily doses of 6 g meropenem (T>MIC of 80% to 88%) and 1.2 g ciprofloxacin (area under the concentration-time curve over 24 h in the steady state divided by the MIC [AUC/MIC], 176), both given intermittently, in monotherapy failed to suppress regrowth and resulted in substantial emergence of resistance (≥7.6 log10 CFU/ml resistant populations). The combination of these regimens achieved synergistic killing and suppressed resistance. MBM with subpopulation and mechanistic synergy yielded unbiased and precise curve fits. Thus, the combination of 6 g/day meropenem plus ciprofloxacin holds promise for future clinical evaluation against infections by susceptible hypermutable P. aeruginosa.