Human Micro-Expressions in Multimodal Social Behavioral Biometrics.

The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems.

Prevalence of Dental Caries in Children in Mymensingh and Its Associated Risk Factors: A Cross-Sectional Study.

BACKGROUND: Children suffer from various oral and periodontal diseases. Dental caries is one of the most prevalent oral diseases among children in the world. This study was conducted to identify the prevalence and risk factors of dental caries in children in Mymensingh, Bangladesh. METHODS: A cross-sectional study was conducted on 362 pediatric patients who attended the Dental Unit of Mymensingh Medical College from March to September 2019. The sample size was calculated using a statistical formula and the children were selected using a systematic random sampling technique. Children and their guardians were interviewed and data were recorded using a structured questionnaire. Risk factors were analyzed using multivariate logistic regression. RESULTS: The overall prevalence of dental caries was 82.7%. The prevalence of caries was significantly higher in aged children (8-10 years) and also in rural, low-income, and illiterate families. Seven significant risk factors were identified that included residence in the rural area (OR: 7.31 [1.73-30.83]), a parental income of BDT ≤ 20,000 per month (OR: 4.75 [1.49-15.05]), reduced duration (≤1 min) of teeth cleaning (OR: 18.54 [2.05-168.17]), teeth cleaning before breakfast (OR: 93.30 [10.95-795.32]), the spoon-feeding method (OR: 12.57 [2.09-75.61]), long-term (37-48 months) breastfeeding (OR: 212.53 [8.69-5195.25]), and family oral problem (OR: 8.20 [2.57-26.16]). CONCLUSIONS: The prevalence of dental caries among the children in Mymensingh is very high and was associated with residence in rural areas, parental income, reduced duration of teeth cleaning, teeth cleaning before breakfast, the spoon-feeding method, long-term breastfeeding, and family oral problems.

KinectGaitNet: Kinect-Based Gait Recognition Using Deep Convolutional Neural Network.

Over the past decade, gait recognition had gained a lot of attention in various research and industrial domains. These include remote surveillance, border control, medical rehabilitation, emotion detection from posture, fall detection, and sports training. The main advantages of identifying a person by their gait include unobtrusiveness, acceptance, and low costs. This paper proposes a convolutional neural network KinectGaitNet for Kinect-based gait recognition. The 3D coordinates of each of the body joints over the gait cycle are transformed to create a unique input representation. The proposed KinectGaitNet is trained directly using the 3D input representation without the necessity of the handcrafted features. The KinectGaitNet design allows avoiding gait cycle resampling, and the residual learning method ensures high accuracy without the degradation problem. The proposed deep learning architecture surpasses the recognition performance of all state-of-the-art methods for Kinect-based gait recognition by achieving 96.91% accuracy on UPCV and 99.33% accuracy on the KGB dataset. The method is the first, to the best of our knowledge, deep learning-based architecture that is based on a unique 3D input representation of joint coordinates. It achieves performance higher than previous traditional and deep learning methods, with fewer parameters and shorter inference time.

Soil washing of arsenic from mixed contaminated abandoned mine soils and fate of arsenic after washing.

Arsenic contamination in abandoned soils is a global concern which warrants an effective method of remediation. In this study, two organic acids and one biodegradable chelating agent were used to treat arsenic (As) contaminated abandoned mine soils. The concentration of As was 19,100 and 75,350 (mg/kg) for Webbs Consols (WC) and Mole River (MR) samples, respectively. X-ray diffraction and scanning electron microscopy confirmed that tooeleite, arsenopyrite, scorodite and quartz were the major minerals in these soils. A major portion of the As was composed of amorphous and crystalline oxides of Fe and Al determined by sequential extraction. Among the three washing reagents (oxalic acid, citric acid and EDDS) oxalic acid showed the best performance for extracting As. Based on the batch experiment, 0.5 M oxalic acid and 3 h of washing was the most efficient treatment to extract As and other trace elements. Extraction of As, Fe, and Pb was 70, 55, and 48% respectively for WC, while 68, 45 and 63% respectively for MR soil. Oxalic acid extracted 75 and 83% of As and Fe, respectively from tooeleite. Leachability and bioaccessibility of As and Fe in the treated soil was reduced due to washing. However, bioaccessibility and leachability of Pb in soil and Fe and As in tooeleite increased in washed samples. Though the leachability and bioaccessibility of As and Fe in soil was reduced in the treated soil, As still exceeded the USEPA criteria (5 mg/L) which is needed to successfully remediate soil by washing. Soil washing and subsequent solidification/stabilization could be an alternative option to remediate extremely contaminated abandoned mine soil.

Beryllium in contaminated soils: Implication of beryllium bioaccessibility by different exposure pathways.

Inhalation exposure and beryllium (Be) toxicity are well-known, but research on bioaccessibility from soils via different exposure pathways is limited. This study examined soils from a legacy radioactive waste disposal site using in vitro ingestion (Solubility Bioaccessibility Research Consortium [SBRC], physiologically based extraction test [PBET], in vitro gastrointestinal [IVG]), inhalation (simulated epithelial lung fluid [SELF]) and dynamic two-stage bioaccessibility (TBAc) methods, as well as 0.43 M HNO3 extraction. The results showed, 70 ±â€¯4.8%, 56 ±â€¯16.8% and 58 ±â€¯5.7% of total Be were extracted (gastric phase [GP] + intestinal phase [IP]) in the SBRC, PBET, and IVG methods, respectively. Similar bioaccessibility of Be (~18%) in PBET-IP and SELF was due to chelating agents in the extractant. Moreover, TBAc-IP showed higher extraction (20.8 ±â€¯2.0%) in comparison with the single-phase (SBRC-IP) result (4.8 ±â€¯0.23%), suggesting increased Be bioaccessibility and toxicity in the gastrointestinal tract when the contamination derives from the inhalation route. The results suggested Be bioaccessibility depends on solution pH; time of extraction; soil reactive fractions (organic-inorganic); particle size, and the presence of chelating agents in the fluid. This study has significance for understanding Be bioaccessibility via different exposure routes and the application of risk-based management of Be-contaminated sites.

Arsenic bioaccessibility and fractionation in abandoned mine soils from selected sites in New South Wales, Australia and human health risk assessment.

Understanding the transport behaviour of arsenic (As) from soils to humans is critical when undertaking human health risk assessment and contamination control. This research examined As bioaccessibility in different As fractions and particle size fractions of As-enriched mine soils using different extractions. Bioaccessibility of As ranged from 0.24% to 32% for Solubility Bioaccessibility Research Consortium (SBRC) and Physiologically Based Extraction Test (PBET) methods, with extractable As (using 0.43 M HNO3) being 1.3-24.9%. The highest As bioaccessibility (19-32%) was consistently observed in the fine particle size fraction (< 53 µm) of all three extractions. Sequential extractions revealed that As fractions were mostly associated with crystalline (30-73%) and amorphous (9-59%) Fe/Al oxyhydroxides. The bioaccessibility of As in the gastric phase of SBRC and PBET methods highlighted a positive correlation (R2 = 0.83-0.88, p < 0.01) with exchangeable, surface and amorphous- bound As fractions, while the intestinal phase showed a strong positive correlation (R2 = 0.85-0.89, p < 0.01) with exchangeable and surface bound fractions. The study revealed that As bioaccessibility in soils can potentially be determined using the 0.43 M HNO3 extraction procedure. Health risk assessment confirmed that there was a strong increase in chronic daily intake, hazard quotient and cancer risk, with a reduction in particle size.

A multimethod approach to the differentiation of enthesis bone microstructure based on soft tissue type.

Whereas there is a wealth of research studying the nature of various soft tissues that attach to bone, comparatively little research focuses on the bone's microscopic properties in the area where these tissues attach. Using scanning electron microscopy to generate a dataset of 1600 images of soft tissue attachment sites, an image classification program with novel convolutional neural network architecture can categorize images of attachment areas by soft tissue type based on observed patterns in microstructure morphology. Using stained histological thin section and liquid crystal cross-polarized microscopy, it is determined that soft tissue type can be quantitatively determined from the microstructure. The primary diagnostic characters are the orientation of collagen fibers and heterogeneity of collagen density throughout the attachment area thickness. These determinations are made across broad taxonomic sampling and multiple skeletal elements.

Arsenic geochemistry and mineralogy as a function of particle-size in naturally arsenic-enriched soils.

Naturally arsenic (As) enriched agricultural soils represent a significant global human health risk. In this study, As fractionation and mineralogy were investigated in naturally As-enriched agricultural soils and their corresponding sand, silt and clay fractions. Median As increased generally in the order (mg/kg)∶ silt (280) < bulk (314) < sand (323)

Arsenic exposure from food exceeds that from drinking water in endemic area of Bihar, India.

Extensive evidence of elevated arsenic (As) in the food-chain, mainly rice, wheat and vegetables exists. Nevertheless, the importance of exposure from food towards total As exposure and associated health risks in areas with natural occurring As in drinking water is still often neglected, and accordingly mitigations are largely focused on drinking water only. In this study, the contribution of food over drinking water to overall As exposure was estimated for As exposed populations in Bihar, India. Increased lifetime cancer risk was predicted using probabilistic methods with input parameters based on detailed dietary assessment and estimation of As in drinking water, cooked rice, wheat flour and potato collected from 91 households covering 19 villages. Median total exposure was 0.83 µg/kgBW/day (5th and 95th percentiles were 0.21 and 11.1 µg/kgBW/day) and contribution of food (median = 49%) to overall exposure was almost equal to that from drinking water (median = 51%). More importantly and contrary to previous studies, food was found to contribute more than drinking water to As exposure, even when drinking water As was above the WHO provisional guide value of 10 µg/L. Median and 95th percentile excess lifetime cancer risks from food intake were 1.89 × 10-4 and 7.32 × 10-4 respectively when drinking water As was below 10 µg/L and 4.00 × 10-4 and 1.83 × 10-3 respectively when drinking water As was above 10 µg/L. Our results emphasise the importance of food related exposure in As-endemic areas, and, perhaps surprisingly, particularly in areas with high As concentrations in drinking water - this being partly ascribed to increases in food As due to cooking in high As water. These findings are timely to stress the importance of removing As from the food chain and not just drinking water in endemic areas.

Removal of arsenate from contaminated waters by novel zirconium and zirconium-iron modified biochar.

A novel biochar metal oxide composite was synthesized for effective removal of arsenate (As(V)) from aqueous solution. The materials synthesized for As(V) removal was based on a biosolid-derived biochar (BSBC) impregnated with zirconium (Zr) and zirconium-iron (Zr-Fe). The synthesized materials were comprehensively characterized with a range of techniques including Brunauer-Emmett-Teller (BET-N2) surface area, zeta potential, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The results confirmed that loading of Zr and Zr-Fe onto the biochar surface was successful. The influence of pH, biochar density, ionic strength, As(V) dose rate, major anions and cations on As(V) removal was also investigated. Under all pH and reaction conditions the Zr-Fe composite biochar removed the greatest As(V) from solution of the materials tested. The maximum sorption capacity reached 15.2 mg/g for pristine BSBC (pH 4.0), while modified Zr-BSBC and Zr-FeBSBC composites achieved 33.1 and 62.5 mg/g (pH 6), respectively. The thermodynamic parameters (Gibbs free energy, enthalpy, and entropy) suggested that the adsorption process is spontaneous and endothermic. The ZrBSBC and Zr-FeBSBC showed excellent reusability and stability over four cycles. Unmodified biochar resulted in partial reduction of As(V) under oxic conditions, whilst modified biochars did not influence the oxidation state of As. All results demonstrated that the Zr and Zr-Fe BSBC composites could perform as promising adsorbents for efficient arsenate removal from natural waters.

Geochemical fractionation and mineralogy of metal(loid)s in abandoned mine soils: Insights into arsenic behaviour and implications to remediation.

Trace element contamination from abandoned mine sites is a major threat to the environment. The distribution of trace elements in various particle size fractions of soils from abandoned mine sites plays a critical role in designing remediation approaches. This study investigated the geochemical distribution of trace element enrichment and mineralogical composition in various particle size fractions from contrasting abandoned mine sites (Webbs Consols, Halls Peak and Mole River, Australia). Results revealed that arsenic and other element concentrations increased with decreasing particle size for samples from Webbs Consols and Halls Peak. The highest arsenic (3.05%), lead (3.23%) and zinc (1110 mg/kg) were found in the finest fraction (<0.053 mm). In Mole River, the highest concentration of arsenic (10.8%), lead (209 mg/kg) and zinc (351 mg/kg) were observed in coarse fractions. Arsenic fractionation by sequential extraction showed that arsenic was strongly associated with the amorphous and crystalline iron phases. X-ray diffraction (XRD) and scanning electron microscopy (SEM) studies revealed that tooeleite (a ferric arsenite mineral, also confirmed by Transmission electron microscopy (TEM)), arsenopyrite, scorodite and arsenolite were the dominant arsenic minerals. The study showed elevated levels of arsenic bearing minerals across particle sizes which has significant implications for remediation approaches at abandoned mine sites.

Analysis and significance of anti-nuclear antibodies in dogs.

Assays for detecting and measuring antinuclear antibodies (ANA) in dogs were compared. They included the indirect immunofluorescence test, using rat liver as substrate, and ELISAs for three nuclear antigens: double-stranded DNA, single-stranded DNA, and histone. There was no correlation between the ANA titre and antibodies to the three nuclear antigens. Analysis of ANA in different arthropathies showed no specific disease association. HEP-2 cells showed no fluorescence reaction with either ANA-positive or ANA-negative dog sera. Western blotting produced too complex a pattern to identify specific antigens. The antigens that reacted with ANA in dogs were not identified; there is either a broad range of reactivities or non-specific binding of immunoglobulins.

Anti-type II collagen antibody in naturally occurring canine joint diseases.

Autoimmunity to collagen was investigated in several naturally occurring arthropathies of the dog. Increased levels of serum anti-native collagen type II antibody, as assessed by ELISA, were shown in 72.4% of dogs with rheumatoid arthritis (RA), 88% of dogs with infective arthritis (IA) and 52% of dogs with osteoarthritis (OA) (p less than 0.001). The mean levels of antibody in cruciate disease patients (CR) were also significantly increased compared to control dogs (p less than 0.01). Serum anti-collagen antibody in OA dogs correlated with that in precipitated serum immune complexes. There was also a correlation between anti-collagen antibody level in synovial fluid and in synovial fluid complexes in dogs with rupture of the cranial cruciate ligament. In all patient groups, collagenase digestion of polyethylene glycol (PEG) precipitates from sera and synovial fluids caused a significant rise in specific antibody levels to collagen, indicating the presence of collagen-anti-collagen complexes in all arthropathies. In dogs with RA, the levels of collagen-specific antibody in synovial fluid complexes correlated with the total IgG in these complexes. These findings implicate collagen-anti-collagen complexes in the pathogenesis of naturally occurring joint diseases in the dog, but they are unlikely to be the primary aetiological mechanism.

Immune complexes and rheumatoid factors in canine arthritides.

Thirty two domestic dogs with naturally occurring polyarthritis were investigated to determine the contribution of autoimmunity in the pathological mechanisms. Comparisons were made with canine infective arthritis (12 dogs), osteoarthritis (32), and osteoarthritis secondary to rupture of the cranial cruciate ligament (19). Rheumatoid factors, immune complexes, and complement fixation (C1q binding) were measured in sera and synovial fluids. Compared with normal dogs (32), dogs with rheumatoid arthritis (RA) had increased serum and synovial fluid immune complexes and rheumatoid factors. Increases were generally also seen in dogs with other arthropathies, however. Rheumatoid factors were higher in sera than in synovial fluids. Rheumatoid factors correlated with immune complex levels and complexed rheumatoid factor only in the group of dogs with RA. Both rheumatoid factors and immune complexes may contribute to the pathogenesis of canine RA but are considered to arise as a result of non-specific inflammatory mechanisms in the non-rheumatoid groups.

Effect of Raillietina echinobothrida infection on blood values and intestinal tissues of domestic fowls of Bangladesh.

An examination of the blood profile of domestic fowls infected with Raillietina echinobothrida revealed anaemia with a significant increase of total leukocyte counts and decrease of total serum protein. The pathological changes in the intestines were characterised by villous atrophy, enteritis with cellular infiltration and formation of characteristic granulomas.